___ ____ ____ ____ ____(R) /__ / ____/ / ____/ ___/ / /___/ / /___/ Statistics/Data Analysis 1 . set linesize 80 2 . set seed 1010101 3 . set matsize 11000 4 . 5 . 6 . ******* 7 . 8 . * This script requires pre-installation of spost2 by J. Scott Long for fits > tat command 9 . * It also requires installation of title2.ado, title3.ado 10 . * 11 . * for the fitstat command to function 12 . ****** 13 . di "$User" Robert Alan Yaffee 14 . * 16 June 2012 15 . 16 . *------- Primary objective: Hypothesis 1 tests for Part 2 of Nottingham heal > th profile 17 . 18 . ********* Hypothesis tests Robert A. Yaffee 10 June 2012 main effect > s and moderator identification approach 19 . * protocol is to test Health profile subscales against potential confounders > including socio-demog vars, 20 . * major neg life events, stresses and hassles, social supports, perceived > health, distance, 21 . * threat and dose to see whether there is a dose-psych response 22 . 23 . * We construct a full model, trimmed model (at .1 level), and then test inte > ractions with dose if there is a 24 . * significant main effect dose psych response 25 . 26 . * Trimming is performed with backward elimination to the .1 level 27 . * 28 . * This approach identifies the moderating variables for our path analysis. > We analyze our 29 . * final models for congruence with regression assumptions with the rdiag > program for a validity assessment 30 . 31 . *---------------------------------------------------------------------------- > ------------------------------ 32 . 33 . *----------- Organization of the program ------------------------------------ > -------------------------------------- 34 . * main effects regresssions are run for all part 1 health profile subscales > except that of emotional 35 . * reaction for both males and females separately 36 . * these models are trimmed with backward elimination to determine whether th > e main effect of avg cumulative dose 37 . * is significant for that wave 38 . * If the main effect of average cumulative dose for that wave is not statis > tically significant with the 39 . * covariates controlled we do not go further along that path 40 . * If the main effect of average cumulative dose for that wave is statistica > lly significant, we trim the 41 . * model to a p = .1 42 . * If average cumulative dose is not significant, we stop 43 . * If average cumulative dose is significant we perform a hierarchical regre > ssion with interactions between dose 44 . * and other significant main effects 45 . * We proceed to test for mediating effects of those other significant expla > natory variable 46 . * We evaluate the model for statistical conguency. 47 . * This concludes the test of part 1 of H1 48 . ***-------------------------------------------------------------------------- > ---------------------------------------- 49 . 50 . * Part 2 Nottingham subscales 51 . * 1: hp2work 52 . * 2: hp2hmcare 53 . * 3: hp2probsoc 54 . * 4: hp2pbfhm 55 . * 5: hp2sexlife 56 . * 6: hp2inthob 57 . * 7: hp2vactn 58 . 59 . 60 . local dvwhole HP2work-HP2vacatn 61 . local dv2 HP2work HP2hmcare HP2probsoc HP2pbfhm HP2sxlife HP2inthob HP2vacatn 62 . 63 . 64 . 65 . di "$User" Robert Alan Yaffee 66 . 67 . title "Testing part 2 of hypothesis 1 for wave two" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Testing part 2 of hypothesis 1 for wave two ***** ***** ***** ***** ***** ***** 1 Jul 2012 14:58:43 ***** ******************************************************************************* ******************************************************************************* 68 . 69 . cd /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/h1p > t2 /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/h1pt2 70 . use chwide1jul2012, clear (Zero for missing on all icdx) 71 . cd /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/h1p > t2 /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/h1pt2 72 . 73 . 74 . di "{hline}" ------------------------------------------------------------------------------- 75 . di "{hline}" ------------------------------------------------------------------------------- 76 . 77 . title "Chunk 1 Hyp 1:radiation dose and Nottingham Health profile subscales" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****Chunk 1 Hyp 1:radiation dose and Nottingham Health profile subscales ***** ***** ***** ***** ***** ***** 1 Jul 2012 14:58:43 ***** ******************************************************************************* ******************************************************************************* 78 . 79 . 80 . // there is substantial intercorrelation among the items warranting a 81 . // multivariate regression model 82 . cap dummies educ 83 . cap order educ1-educ8, after(educ) 84 . 85 . * These variables are substantially correlated 86 . pwcorr HP2work HP2hmcare HP2probsoc HP2pbfhm HP2sxlife HP2inthob HP2vacatn, / > // > obs sig | HP2work HP2hmc~e HP2pro~c HP2pbfhm HP2sxl~e HP2int~b HP2vac~n -------------+--------------------------------------------------------------- HP2work | 1.0000 | | 703 | HP2hmcare | 0.4878 1.0000 | 0.0000 | 703 703 | HP2probsoc | 0.4587 0.5420 1.0000 | 0.0000 0.0000 | 703 703 703 | HP2pbfhm | 0.2832 0.4150 0.4745 1.0000 | 0.0000 0.0000 0.0000 | 703 703 703 703 | HP2sxlife | 0.4968 0.4576 0.5589 0.4192 1.0000 | 0.0000 0.0000 0.0000 0.0000 | 703 703 703 703 703 | HP2inthob | 0.3787 0.4757 0.5956 0.5089 0.5401 1.0000 | 0.0000 0.0000 0.0000 0.0000 0.0000 | 703 703 703 703 703 703 | HP2vacatn | 0.4166 0.4757 0.5956 0.4416 0.5211 0.6840 1.0000 | 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 | 703 703 703 703 703 703 703 | 87 . 88 . cap gen havmilsq = havmil^2 89 . 90 . cap rename Havmil havmil 91 . // controlling for potential confounders 92 . // socio-demographics age gender educ income occp marstat children inc 93 . // distance from accident side 94 . // perceived Chornobyl related health threat to oneself 95 . 96 . set linesize 130 97 . 98 . est clear 99 . title "Zero order tests" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Zero order tests ***** ***** ***** ***** ***** ***** 1 Jul 2012 14:58:43 ***** ******************************************************************************* ******************************************************************************* 100 . set more off 101 . foreach var in HP2work HP2hmcare HP2probsoc HP2pbfhm HP2sxlife HP2inthob /// > HP2vacatn { 2. forvalues k=2/2 { 3. logit `var' avgcumdosew2 if gender==`k', nolog 4. eststo w2`var'dose 5. } 6. } Logistic regression Number of obs = 363 LR chi2(1) = 6.63 Prob > chi2 = 0.0100 Log likelihood = -203.24758 Pseudo R2 = 0.0160 ------------------------------------------------------------------------------ HP2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .2046594 .0826501 2.48 0.013 .0426681 .3666507 _cons | -1.260388 .1448325 -8.70 0.000 -1.544255 -.9765218 ------------------------------------------------------------------------------ Logistic regression Number of obs = 363 LR chi2(1) = 0.01 Prob > chi2 = 0.9082 Log likelihood = -233.72194 Pseudo R2 = 0.0000 ------------------------------------------------------------------------------ HP2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.0093108 .0810645 -0.11 0.909 -.1681944 .1495727 _cons | -.6356448 .13193 -4.82 0.000 -.8942228 -.3770668 ------------------------------------------------------------------------------ Logistic regression Number of obs = 363 LR chi2(1) = 25.69 Prob > chi2 = 0.0000 Log likelihood = -170.72516 Pseudo R2 = 0.0700 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .5024747 .1391198 3.61 0.000 .229805 .7751444 _cons | -1.841993 .1807682 -10.19 0.000 -2.196292 -1.487694 ------------------------------------------------------------------------------ Logistic regression Number of obs = 363 LR chi2(1) = 1.97 Prob > chi2 = 0.1602 Log likelihood = -138.91068 Pseudo R2 = 0.0070 ------------------------------------------------------------------------------ HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .1290516 .0853896 1.51 0.131 -.0383089 .2964121 _cons | -2.03386 .1824844 -11.15 0.000 -2.391523 -1.676197 ------------------------------------------------------------------------------ Logistic regression Number of obs = 363 LR chi2(1) = 13.99 Prob > chi2 = 0.0002 Log likelihood = -200.62862 Pseudo R2 = 0.0337 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .3220323 .1022608 3.15 0.002 .1216048 .5224598 _cons | -1.353229 .1520692 -8.90 0.000 -1.65128 -1.055179 ------------------------------------------------------------------------------ Logistic regression Number of obs = 363 LR chi2(1) = 5.33 Prob > chi2 = 0.0210 Log likelihood = -169.44834 Pseudo R2 = 0.0155 ------------------------------------------------------------------------------ HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .1881347 .0796521 2.36 0.018 .0320195 .34425 _cons | -1.691176 .1610367 -10.50 0.000 -2.006802 -1.37555 ------------------------------------------------------------------------------ Logistic regression Number of obs = 363 LR chi2(1) = 4.26 Prob > chi2 = 0.0389 Log likelihood = -165.38435 Pseudo R2 = 0.0127 ------------------------------------------------------------------------------ HP2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .1706012 .0795148 2.15 0.032 .0147551 .3264474 _cons | -1.730143 .1632828 -10.60 0.000 -2.050172 -1.410115 ------------------------------------------------------------------------------ 102 . 103 . 104 . *------- estout code snippt 105 . 106 . estout *, cells(b(star fmt(%9.3f)) se(par) t p) /// > stats( bic N, fmt(%9.3f %9.0g) ) /// > legend collabels(none) varlabels(_cons Constant) /// > mlabels(workw2 homecarew2 socprobsw2 pbfhmw2 sexlifew2 intho > bw2 vactnw2) /// > title("Zero-order regression coefficients" // > / > "of reconstructed dose for females in wave 2") Zero-order regression coefficients of reconstructed dose for females in wave 2 ------------------------------------------------------------------------------- --------------------------------------------- workw2 homecarew2 socprobsw2 pbfhmw2 > sexlifew2 inthobw2 vactnw2 ------------------------------------------------------------------------------- --------------------------------------------- main > avgcumdosew2 0.205* -0.009 0.502*** 0.129 > 0.322** 0.188* 0.171* (0.083) (0.081) (0.139) (0.085) > (0.102) (0.080) (0.080) 2.476 -0.115 3.612 1.511 > 3.149 2.362 2.146 0.013 0.909 0.000 0.131 > 0.002 0.018 0.032 Constant -1.260*** -0.636*** -1.842*** -2.034*** > -1.353*** -1.691*** -1.730*** (0.145) (0.132) (0.181) (0.182) > (0.152) (0.161) (0.163) -8.702 -4.818 -10.190 -11.145 > -8.899 -10.502 -10.596 0.000 0.000 0.000 0.000 > 0.000 0.000 0.000 ------------------------------------------------------------------------------- --------------------------------------------- bic 418.284 479.233 353.239 289.610 > 413.046 350.685 342.558 N 363 363 363 363 > 363 363 363 ------------------------------------------------------------------------------- --------------------------------------------- * p<0.05, ** p<0.01, *** p<0.001 107 . 108 . 109 . 110 . 111 . // signif zero order relationships in wave 2 are 112 . 113 . // female work social life sex life interest and hobbies vacation plans 114 . // no significant male zero order effects 115 . 116 . 117 . 118 . local w1bf bf1 bf4 bf9 bf10 bf11 bf4m bf15m bf20 bf22 bf30 bf40 119 . local w2bf bf1 bf4 bf6 bf7 bf14 bf15 bf40 120 . local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 bf40 121 . 122 . *---------------------------------------------------------------------------- > --- 123 . * Hypothesis 1 Part 2 wave 2 tests male and female 124 . * endogneous Nottingham pt 2 subscales: HP2work HP2hmcare HP2probsoc HP2pbfhm > /// > * HP2sxlife HP2inthob HP2vacatn 125 . * structure of models 126 . * 1. general models on all Pt 2 subscales with all potential confounders 127 . * 2. trimmed models on all Pt 2 subscales with from all potential confound > ers 128 . * 3. from trimmed models examination of possible moderator variables 129 . * 4. from trimmed models examination of possible mediator variables 130 . * 5. Summary analysis and model evaluation of final models only 131 . * program is divided into 8 chunks one a general model and 1 for each 132 . * endogenous variable 133 . *---------------------------------------------------------------------------- > --- 134 . * Chunk 1 General models for all part 2 of Nottingham Health Profile 135 . 136 . title " Hypothesis 1 part2 wave 2 H1: General models" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Hypothesis 1 part2 wave 2 H1: General models ***** ***** ***** ***** ***** ***** 1 Jul 2012 14:58:53 ***** ******************************************************************************* ******************************************************************************* 137 . forvalues j=2/2 { 2. set more off 3. 138 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 139 . foreach var in HP2work HP2hmcare HP2probsoc HP2pbfhm HP2sxlife HP2inthob // > / > HP2vacatn { 5. forvalues k=1/2 { 6. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 7. 140 . di as input "Full main model for `var' for wave= `j' " 8. di _skip(4) 9. di as input "chunk 2 H1 test:Gender= `k' model Wave = `j' for `e(depvar > )' " 10. di _skip(4) 11. 141 . xi: logistic `var' age i.educ occ1w`j'-occ8w`j' /// > marrw`j'1- marrw`j'3 marrw`j'5-marrw`j'6 inc1w`j'-inc4w`j' // > / > radhlw`j' havmil avgcumdosew`j' `w`j'bf' /// > deaw`j' dvcew`j' sepaw`j' accdw`j' movew`j' /// > illw`j' shfamw`j' shhlw`j' shjobw`j' shrelaw`j' suprtw`j' suc > hrw`j' /// > havmilsq if gender==`k', coef nolog difficult iterate(50) 12. estat class 13. estat gof 14. fitstat 15. } 16. } 17. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Full main model for HP2work for wave= 2 chunk 2 H1 test:Gender= 1 model Wave = 2 for HP2vacatn i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: _Ieduc_4 != 0 predicts failure perfectly _Ieduc_4 dropped and 13 obs not used note: _Ieduc_7 != 0 predicts failure perfectly _Ieduc_7 dropped and 4 obs not used note: _Ieduc_8 != 0 predicts failure perfectly _Ieduc_8 dropped and 2 obs not used note: bf17 != 0 predicts failure perfectly bf17 dropped and 5 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 6 obs not used note: _Ieduc_6 omitted because of collinearity Logistic regression Number of obs = 308 LR chi2(49) = 125.45 Prob > chi2 = 0.0000 Log likelihood = -101.11616 Pseudo R2 = 0.3828 ------------------------------------------------------------------------------ HP2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0178547 .02477 0.72 0.471 -.0306936 .066403 _Ieduc_2 | .9615826 1.161557 0.83 0.408 -1.315028 3.238193 _Ieduc_3 | .1912036 .4980888 0.38 0.701 -.7850324 1.16744 _Ieduc_4 | 0 (omitted) _Ieduc_5 | .2718345 .6506078 0.42 0.676 -1.003333 1.547002 _Ieduc_6 | 0 (omitted) _Ieduc_7 | 0 (omitted) _Ieduc_8 | 0 (omitted) occ1w2 | -1.858647 2.691228 -0.69 0.490 -7.133356 3.416062 occ2w2 | -.9190027 2.701749 -0.34 0.734 -6.214333 4.376327 occ3w2 | -1.367634 2.744529 -0.50 0.618 -6.746813 4.011544 occ4w2 | -1.016435 2.721494 -0.37 0.709 -6.350464 4.317595 occ5w2 | -.5999128 2.751196 -0.22 0.827 -5.992157 4.792331 occ6w2 | -.5670745 3.449094 -0.16 0.869 -7.327174 6.193025 occ7w2 | 2.144137 2.835688 0.76 0.450 -3.413709 7.701983 occ8w2 | -2.120696 2.805072 -0.76 0.450 -7.618536 3.377144 marrw21 | 14.38867 1053.846 0.01 0.989 -2051.112 2079.889 marrw22 | 16.49882 1053.847 0.02 0.988 -2049.002 2082 marrw23 | 12.83702 1053.846 0.01 0.990 -2052.664 2078.338 marrw25 | 16.98709 1053.848 0.02 0.987 -2048.516 2082.49 marrw26 | 10.7478 1053.85 0.01 0.992 -2054.761 2076.257 inc1w2 | -1.841061 2.889059 -0.64 0.524 -7.503512 3.82139 inc2w2 | -.6970371 2.730495 -0.26 0.799 -6.048709 4.654635 inc3w2 | .9384724 2.718417 0.35 0.730 -4.389527 6.266472 inc4w2 | -.858635 3.109771 -0.28 0.782 -6.953674 5.236404 radhlw2 | .0018692 .0077721 0.24 0.810 -.0133638 .0171022 havmil | .0022022 .0068548 0.32 0.748 -.0112329 .0156372 avgcumdosew2 | .070113 .0753046 0.93 0.352 -.0774813 .2177072 bf1 | -.0449847 .0685219 -0.66 0.512 -.179285 .0893157 bf4 | -.1461292 .1896969 -0.77 0.441 -.5179284 .2256699 bf2 | .0001003 .000156 0.64 0.520 -.0002054 .000406 bf4m | -.1398009 .1731245 -0.81 0.419 -.4791186 .1995169 bf5m | .0035939 .0017506 2.05 0.040 .0001629 .0070249 bf7m | .0007449 .0005463 1.36 0.173 -.0003258 .0018155 bf8 | -.0001778 .0000525 -3.39 0.001 -.0002807 -.000075 bf15m | .0003329 .0002956 1.13 0.260 -.0002465 .0009123 bf17 | 0 (omitted) bf20 | .0339605 .0629951 0.54 0.590 -.0895076 .1574286 bf22 | .0001355 .0001659 0.82 0.414 -.0001898 .0004607 bf29 | .0000158 .0000126 1.26 0.209 -8.86e-06 .0000405 bf30 | -.0008498 .0004225 -2.01 0.044 -.001678 -.0000217 bf40 | .2709577 .2293641 1.18 0.237 -.1785876 .720503 deaw2 | -.1725628 .3614849 -0.48 0.633 -.8810602 .5359345 dvcew2 | .1194741 2.327974 0.05 0.959 -4.44327 4.682218 sepaw2 | 0 (omitted) accdw2 | .5158656 .4604363 1.12 0.263 -.3865729 1.418304 movew2 | .5486063 .5239018 1.05 0.295 -.4782225 1.575435 illw2 | -.3094309 .3968427 -0.78 0.436 -1.087228 .4683664 shfamw2 | .0096065 .0075095 1.28 0.201 -.0051119 .0243249 shhlw2 | .025216 .0096203 2.62 0.009 .0063606 .0440714 shjobw2 | -.0077587 .0087746 -0.88 0.377 -.0249567 .0094392 shrelaw2 | -.0309717 .0089502 -3.46 0.001 -.0485137 -.0134296 suprtw2 | -.0064843 .0062583 -1.04 0.300 -.0187504 .0057818 suchrw2 | .0039898 .0060514 0.66 0.510 -.0078708 .0158504 havmilsq | -3.35e-06 .0000119 -0.28 0.779 -.0000267 .00002 _cons | -13.33296 1053.852 -0.01 0.990 -2078.845 2052.179 ------------------------------------------------------------------------------ Note: 1 failure and 0 successes completely determined. Logistic model for HP2work -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 39 14 | 53 - | 30 225 | 255 -----------+--------------------------+----------- Total | 69 239 | 308 Classified + if predicted Pr(D) >= .5 True D defined as HP2work != 0 -------------------------------------------------- Sensitivity Pr( +| D) 56.52% Specificity Pr( -|~D) 94.14% Positive predictive value Pr( D| +) 73.58% Negative predictive value Pr(~D| -) 88.24% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 5.86% False - rate for true D Pr( -| D) 43.48% False + rate for classified + Pr(~D| +) 26.42% False - rate for classified - Pr( D| -) 11.76% -------------------------------------------------- Correctly classified 85.71% -------------------------------------------------- Logistic model for HP2work, goodness-of-fit test number of observations = 308 number of covariate patterns = 308 Pearson chi2(258) = 291.84 Prob > chi2 = 0.0724 Measures of Fit for logistic of HP2work Log-Lik Intercept Only: -163.843 Log-Lik Full Model: -101.116 D(252): 202.232 LR(49): 125.453 Prob > LR: 0.000 McFadden's R2: 0.383 McFadden's Adj R2: 0.041 Maximum Likelihood R2: 0.335 Cragg & Uhler's R2: 0.511 McKelvey and Zavoina's R2: 0.690 Efron's R2: 0.405 Variance of y*: 10.621 Variance of error: 3.290 Count R2: 0.857 Adj Count R2: 0.362 AIC: 1.020 AIC*n: 314.232 BIC: -1241.753 BIC': 155.322 Full main model for HP2work for wave= 2 chunk 2 H1 test:Gender= 2 model Wave = 2 for HP2work i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 7 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 343 LR chi2(50) = 108.12 Prob > chi2 = 0.0000 Log likelihood = -145.38882 Pseudo R2 = 0.2710 ------------------------------------------------------------------------------ HP2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0346814 .0173019 2.00 0.045 .0007704 .0685925 _Ieduc_2 | -12.41086 627.6909 -0.02 0.984 -1242.662 1217.841 _Ieduc_3 | -12.75017 627.6907 -0.02 0.984 -1243.001 1217.501 _Ieduc_4 | -11.4477 627.6909 -0.02 0.985 -1241.699 1218.804 _Ieduc_5 | -12.87307 627.6909 -0.02 0.984 -1243.125 1217.378 _Ieduc_6 | -12.46967 627.6908 -0.02 0.984 -1242.721 1217.782 _Ieduc_7 | -13.03665 627.6932 -0.02 0.983 -1243.293 1217.219 _Ieduc_8 | 0 (omitted) occ1w2 | -1.438425 1.492752 -0.96 0.335 -4.364164 1.487314 occ2w2 | -1.219105 1.521371 -0.80 0.423 -4.200936 1.762727 occ3w2 | -.4904819 1.510651 -0.32 0.745 -3.451303 2.470339 occ4w2 | -.7947689 1.589999 -0.50 0.617 -3.911109 2.321571 occ5w2 | -1.879935 1.884007 -1.00 0.318 -5.572521 1.812651 occ6w2 | -1.41539 1.738012 -0.81 0.415 -4.82183 1.991051 occ7w2 | -.4577922 1.461285 -0.31 0.754 -3.321858 2.406274 occ8w2 | .0350208 1.713285 0.02 0.984 -3.322957 3.392998 marrw21 | -1.099109 1.06403 -1.03 0.302 -3.18457 .9863517 marrw22 | -.6975171 1.30484 -0.53 0.593 -3.254956 1.859922 marrw23 | -.3956567 .7323121 -0.54 0.589 -1.830962 1.039649 marrw25 | -.7188967 1.177006 -0.61 0.541 -3.025787 1.587994 marrw26 | 0 (omitted) inc1w2 | -.0941374 1.513476 -0.06 0.950 -3.060495 2.872221 inc2w2 | .386998 1.46305 0.26 0.791 -2.480528 3.254524 inc3w2 | 1.121465 1.474278 0.76 0.447 -1.768066 4.010997 inc4w2 | -.1060071 1.96548 -0.05 0.957 -3.958277 3.746263 radhlw2 | .0065443 .006673 0.98 0.327 -.0065345 .0196232 havmil | -.0029953 .0026107 -1.15 0.251 -.0081121 .0021215 avgcumdosew2 | .0909294 .1016445 0.89 0.371 -.1082903 .290149 bf1 | .0126789 .0317569 0.40 0.690 -.0495634 .0749212 bf4 | -.5949349 .2433538 -2.44 0.014 -1.0719 -.1179702 bf2 | .0000735 .0001079 0.68 0.496 -.000138 .0002851 bf4m | .4176215 .2303793 1.81 0.070 -.0339136 .8691567 bf5m | .0024297 .0013344 1.82 0.069 -.0001856 .0050451 bf7m | .0006992 .000527 1.33 0.185 -.0003337 .0017321 bf8 | -.0000458 .0000327 -1.40 0.161 -.0001099 .0000182 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.0183297 .0268984 -0.68 0.496 -.0710496 .0343903 bf22 | -.0000828 .0001199 -0.69 0.490 -.0003178 .0001523 bf29 | .0000183 .0000373 0.49 0.624 -.0000549 .0000915 bf30 | -.0001261 .0002961 -0.43 0.670 -.0007064 .0004542 bf40 | .1798311 .1314097 1.37 0.171 -.0777271 .4373893 deaw2 | .2386669 .2047617 1.17 0.244 -.1626586 .6399924 dvcew2 | -.6260314 1.520804 -0.41 0.681 -3.606752 2.354689 sepaw2 | 0 (omitted) accdw2 | .6391808 .5174116 1.24 0.217 -.3749273 1.653289 movew2 | -.2794343 .5405557 -0.52 0.605 -1.338904 .7800355 illw2 | .0571776 .1835422 0.31 0.755 -.3025585 .4169137 shfamw2 | -.007536 .0064071 -1.18 0.240 -.0200936 .0050216 shhlw2 | .0061348 .0064298 0.95 0.340 -.0064673 .0187369 shjobw2 | -.0013978 .005787 -0.24 0.809 -.0127401 .0099444 shrelaw2 | -.0084778 .0068136 -1.24 0.213 -.0218322 .0048765 suprtw2 | -.0040427 .0050205 -0.81 0.421 -.0138827 .0057973 suchrw2 | .0071509 .0055885 1.28 0.201 -.0038025 .0181042 havmilsq | 5.93e-07 1.54e-06 0.38 0.701 -2.43e-06 3.62e-06 _cons | 8.163054 627.6961 0.01 0.990 -1222.099 1238.425 ------------------------------------------------------------------------------ Logistic model for HP2work -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 46 16 | 62 - | 46 235 | 281 -----------+--------------------------+----------- Total | 92 251 | 343 Classified + if predicted Pr(D) >= .5 True D defined as HP2work != 0 -------------------------------------------------- Sensitivity Pr( +| D) 50.00% Specificity Pr( -|~D) 93.63% Positive predictive value Pr( D| +) 74.19% Negative predictive value Pr(~D| -) 83.63% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 6.37% False - rate for true D Pr( -| D) 50.00% False + rate for classified + Pr(~D| +) 25.81% False - rate for classified - Pr( D| -) 16.37% -------------------------------------------------- Correctly classified 81.92% -------------------------------------------------- Logistic model for HP2work, goodness-of-fit test number of observations = 343 number of covariate patterns = 343 Pearson chi2(292) = 491.86 Prob > chi2 = 0.0000 Measures of Fit for logistic of HP2work Log-Lik Intercept Only: -199.448 Log-Lik Full Model: -145.389 D(287): 290.778 LR(50): 108.119 Prob > LR: 0.000 McFadden's R2: 0.271 McFadden's Adj R2: -0.010 Maximum Likelihood R2: 0.270 Cragg & Uhler's R2: 0.393 McKelvey and Zavoina's R2: 0.502 Efron's R2: 0.313 Variance of y*: 6.601 Variance of error: 3.290 Count R2: 0.819 Adj Count R2: 0.326 AIC: 1.174 AIC*n: 402.778 BIC: -1384.651 BIC': 183.768 Full main model for HP2hmcare for wave= 2 chunk 2 H1 test:Gender= 1 model Wave = 2 for HP2work i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: _Ieduc_4 != 0 predicts failure perfectly _Ieduc_4 dropped and 13 obs not used note: _Ieduc_8 != 0 predicts failure perfectly _Ieduc_8 dropped and 2 obs not used note: occ8w2 != 0 predicts failure perfectly occ8w2 dropped and 44 obs not used note: bf17 != 0 predicts failure perfectly bf17 dropped and 3 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 6 obs not used note: _Ieduc_7 omitted because of collinearity Logistic regression Number of obs = 270 LR chi2(49) = 112.39 Prob > chi2 = 0.0000 Log likelihood = -98.319474 Pseudo R2 = 0.3637 ------------------------------------------------------------------------------ HP2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0339056 .0231493 1.46 0.143 -.0114661 .0792774 _Ieduc_2 | -4.046801 2.06481 -1.96 0.050 -8.093754 .0001521 _Ieduc_3 | -2.332961 1.654719 -1.41 0.159 -5.576151 .9102284 _Ieduc_4 | 0 (omitted) _Ieduc_5 | -2.109138 1.662617 -1.27 0.205 -5.367807 1.149531 _Ieduc_6 | -2.575578 1.598895 -1.61 0.107 -5.709355 .5581988 _Ieduc_7 | 0 (omitted) _Ieduc_8 | 0 (omitted) occ1w2 | .2791915 3.076664 0.09 0.928 -5.750959 6.309342 occ2w2 | -.2043325 3.082144 -0.07 0.947 -6.245223 5.836559 occ3w2 | .5280791 3.097215 0.17 0.865 -5.542352 6.59851 occ4w2 | .3530379 3.103258 0.11 0.909 -5.729235 6.435311 occ5w2 | 1.030726 3.128223 0.33 0.742 -5.100479 7.161931 occ6w2 | 1.834194 3.454783 0.53 0.595 -4.937056 8.605444 occ7w2 | .6310877 3.205439 0.20 0.844 -5.651457 6.913633 occ8w2 | 0 (omitted) marrw21 | -1.774375 1.681557 -1.06 0.291 -5.070167 1.521417 marrw22 | -3.603201 2.051956 -1.76 0.079 -7.624961 .4185589 marrw23 | -3.822298 1.717545 -2.23 0.026 -7.188624 -.4559723 marrw25 | -.1941937 2.536946 -0.08 0.939 -5.166517 4.77813 marrw26 | -3.213845 2.602626 -1.23 0.217 -8.314899 1.887208 inc1w2 | 2.821191 3.187221 0.89 0.376 -3.425647 9.068029 inc2w2 | 2.680447 3.109489 0.86 0.389 -3.41404 8.774935 inc3w2 | 2.9859 3.107015 0.96 0.337 -3.103737 9.075536 inc4w2 | 1.060877 3.414563 0.31 0.756 -5.631544 7.753298 radhlw2 | .0009802 .0078741 0.12 0.901 -.0144527 .0164132 havmil | .0033106 .008219 0.40 0.687 -.0127982 .0194195 avgcumdosew2 | -.0408898 .0892564 -0.46 0.647 -.2158292 .1340496 bf1 | -.0224294 .0411105 -0.55 0.585 -.1030045 .0581457 bf4 | -.1579765 .2337949 -0.68 0.499 -.616206 .300253 bf2 | .0000992 .0001626 0.61 0.542 -.0002196 .000418 bf4m | -.1739281 .2154639 -0.81 0.420 -.5962296 .2483734 bf5m | .0006314 .0017487 0.36 0.718 -.002796 .0040589 bf7m | .0005115 .000594 0.86 0.389 -.0006527 .0016757 bf8 | -.0000403 .0000473 -0.85 0.394 -.0001331 .0000525 bf15m | -.0001301 .0003714 -0.35 0.726 -.000858 .0005979 bf17 | 0 (omitted) bf20 | .0027387 .0345917 0.08 0.937 -.0650597 .0705372 bf22 | -.0000529 .0001896 -0.28 0.780 -.0004245 .0003187 bf29 | 2.63e-06 .0000221 0.12 0.905 -.0000407 .0000459 bf30 | -.0002608 .0004218 -0.62 0.536 -.0010875 .0005659 bf40 | .3296594 .2457937 1.34 0.180 -.1520874 .8114062 deaw2 | .0069355 .3395872 0.02 0.984 -.6586432 .6725142 dvcew2 | -.8346564 3.597118 -0.23 0.817 -7.884878 6.215565 sepaw2 | 0 (omitted) accdw2 | .1481124 .4606844 0.32 0.748 -.7548124 1.051037 movew2 | .2192344 .5237343 0.42 0.676 -.807266 1.245735 illw2 | -.2794365 .3922624 -0.71 0.476 -1.048257 .4893838 shfamw2 | -.0130582 .0087013 -1.50 0.133 -.0301125 .0039961 shhlw2 | -.0041104 .0095817 -0.43 0.668 -.0228901 .0146694 shjobw2 | .0000993 .009716 0.01 0.992 -.0189438 .0191423 shrelaw2 | .0041598 .0074695 0.56 0.578 -.0104802 .0187997 suprtw2 | .0129457 .0070817 1.83 0.068 -.0009342 .0268256 suchrw2 | -.0024472 .0063512 -0.39 0.700 -.0148953 .010001 havmilsq | -8.45e-06 .000015 -0.56 0.573 -.0000379 .000021 _cons | 3.897935 3.960914 0.98 0.325 -3.865314 11.66118 ------------------------------------------------------------------------------ Logistic model for HP2hmcare -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 41 17 | 58 - | 29 183 | 212 -----------+--------------------------+----------- Total | 70 200 | 270 Classified + if predicted Pr(D) >= .5 True D defined as HP2hmcare != 0 -------------------------------------------------- Sensitivity Pr( +| D) 58.57% Specificity Pr( -|~D) 91.50% Positive predictive value Pr( D| +) 70.69% Negative predictive value Pr(~D| -) 86.32% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 8.50% False - rate for true D Pr( -| D) 41.43% False + rate for classified + Pr(~D| +) 29.31% False - rate for classified - Pr( D| -) 13.68% -------------------------------------------------- Correctly classified 82.96% -------------------------------------------------- Logistic model for HP2hmcare, goodness-of-fit test number of observations = 270 number of covariate patterns = 270 Pearson chi2(220) = 227.64 Prob > chi2 = 0.3476 Measures of Fit for logistic of HP2hmcare Log-Lik Intercept Only: -154.516 Log-Lik Full Model: -98.319 D(214): 196.639 LR(49): 112.393 Prob > LR: 0.000 McFadden's R2: 0.364 McFadden's Adj R2: 0.001 Maximum Likelihood R2: 0.340 Cragg & Uhler's R2: 0.500 McKelvey and Zavoina's R2: 0.594 Efron's R2: 0.390 Variance of y*: 8.112 Variance of error: 3.290 Count R2: 0.830 Adj Count R2: 0.343 AIC: 1.143 AIC*n: 308.639 BIC: -1001.423 BIC': 161.930 Full main model for HP2hmcare for wave= 2 chunk 2 H1 test:Gender= 2 model Wave = 2 for HP2hmcare i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: bf29 != 0 predicts success perfectly bf29 dropped and 4 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity convergence not achieved Logistic regression Number of obs = 358 LR chi2(51) = 177.21 Prob > chi2 = 0.0000 Log likelihood = -139.72732 Pseudo R2 = 0.3881 ------------------------------------------------------------------------------ HP2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0689184 .0184884 3.73 0.000 .0326818 .105155 _Ieduc_2 | -17.33023 2.370203 -7.31 0.000 -21.97575 -12.68472 _Ieduc_3 | -17.85228 2.271622 -7.86 0.000 -22.30458 -13.39998 _Ieduc_4 | -16.22367 2.322199 -6.99 0.000 -20.7751 -11.67225 _Ieduc_5 | -17.51753 2.330541 -7.52 0.000 -22.0853 -12.94975 _Ieduc_6 | -18.46499 2.320848 -7.96 0.000 -23.01377 -13.91621 _Ieduc_7 | -17.72595 3.034199 -5.84 0.000 -23.67287 -11.77903 _Ieduc_8 | 0 (omitted) occ1w2 | -2.066459 1.51974 -1.36 0.174 -5.045095 .9121779 occ2w2 | -2.243078 1.553998 -1.44 0.149 -5.288858 .8027026 occ3w2 | -1.29392 1.568233 -0.83 0.409 -4.367601 1.77976 occ4w2 | -3.169244 1.685231 -1.88 0.060 -6.472236 .1337472 occ5w2 | -4.174961 1.966604 -2.12 0.034 -8.029433 -.3204881 occ6w2 | -5.016961 2.033875 -2.47 0.014 -9.003283 -1.030639 occ7w2 | -1.338521 1.557092 -0.86 0.390 -4.390365 1.713323 occ8w2 | .0102882 1.823109 0.01 0.995 -3.56294 3.583517 marrw21 | -.4364242 1.195764 -0.36 0.715 -2.780078 1.907229 marrw22 | .2779117 1.516808 0.18 0.855 -2.694977 3.2508 marrw23 | 1.982364 .8407589 2.36 0.018 .3345069 3.630221 marrw25 | .7265557 1.247315 0.58 0.560 -1.718137 3.171248 marrw26 | 0 (omitted) inc1w2 | 2.1918 1.615301 1.36 0.175 -.9741308 5.357731 inc2w2 | 3.722591 1.551917 2.40 0.016 .6808895 6.764292 inc3w2 | 3.205652 1.561296 2.05 0.040 .1455678 6.265737 inc4w2 | 3.577016 1.917029 1.87 0.062 -.1802924 7.334325 radhlw2 | -.0017814 .0069396 -0.26 0.797 -.0153829 .01182 havmil | .0004744 .0029523 0.16 0.872 -.005312 .0062609 avgcumdosew2 | -.232422 .1275125 -1.82 0.068 -.4823419 .017498 bf1 | -.0155252 .0283044 -0.55 0.583 -.0710008 .0399503 bf4 | -.6331543 .2104116 -3.01 0.003 -1.045553 -.2207551 bf2 | .0002047 .0001148 1.78 0.075 -.0000203 .0004296 bf4m | .369727 .1901433 1.94 0.052 -.0029469 .742401 bf5m | -.0009929 .001436 -0.69 0.489 -.0038073 .0018215 bf7m | .0003779 .0005084 0.74 0.457 -.0006186 .0013744 bf8 | -5.36e-07 .0000355 -0.02 0.988 -.0000702 .0000691 bf15m | -.0123361 .496536 -0.02 0.980 -.9855287 .9608566 bf17 | .0006265 .0248268 0.03 0.980 -.0480332 .0492861 bf20 | -.0134138 .0231788 -0.58 0.563 -.0588434 .0320158 bf22 | -.0000815 .0001207 -0.67 0.500 -.000318 .0001551 bf29 | 0 (omitted) bf30 | -.0000914 .0002999 -0.30 0.760 -.0006791 .0004963 bf40 | .1554702 .1284498 1.21 0.226 -.0962868 .4072271 deaw2 | .7476902 .2584678 2.89 0.004 .2411026 1.254278 dvcew2 | 1.232183 1.276036 0.97 0.334 -1.268802 3.733168 sepaw2 | -1.82805 1.829743 -1.00 0.318 -5.41428 1.75818 accdw2 | -.7440138 .5452818 -1.36 0.172 -1.812746 .3247189 movew2 | -.5229006 .4948092 -1.06 0.291 -1.492709 .4469077 illw2 | -.1315546 .1759029 -0.75 0.455 -.4763179 .2132087 shfamw2 | -.0038201 .0062872 -0.61 0.543 -.0161429 .0085027 shhlw2 | -.0093005 .0066582 -1.40 0.162 -.0223502 .0037493 shjobw2 | .0003285 .005919 0.06 0.956 -.0112724 .0119295 shrelaw2 | -.0125743 .0072115 -1.74 0.081 -.0267086 .00156 suprtw2 | -.0093988 .0049637 -1.89 0.058 -.0191275 .0003299 suchrw2 | .0039959 .0055359 0.72 0.470 -.0068542 .0148461 havmilsq | -6.92e-07 2.48e-06 -0.28 0.780 -5.56e-06 4.18e-06 _cons | 12.62346 . . . . . ------------------------------------------------------------------------------ Note: 4 failures and 0 successes completely determined. Warning: convergence not achieved Logistic model for HP2hmcare -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 86 29 | 115 - | 34 209 | 243 -----------+--------------------------+----------- Total | 120 238 | 358 Classified + if predicted Pr(D) >= .5 True D defined as HP2hmcare != 0 -------------------------------------------------- Sensitivity Pr( +| D) 71.67% Specificity Pr( -|~D) 87.82% Positive predictive value Pr( D| +) 74.78% Negative predictive value Pr(~D| -) 86.01% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 12.18% False - rate for true D Pr( -| D) 28.33% False + rate for classified + Pr(~D| +) 25.22% False - rate for classified - Pr( D| -) 13.99% -------------------------------------------------- Correctly classified 82.40% -------------------------------------------------- Logistic model for HP2hmcare, goodness-of-fit test number of observations = 358 number of covariate patterns = 358 Pearson chi2(305) = 382.80 Prob > chi2 = 0.0016 Measures of Fit for logistic of HP2hmcare Log-Lik Intercept Only: -228.331 Log-Lik Full Model: -139.727 D(302): 279.455 LR(51): 177.208 Prob > LR: 0.000 McFadden's R2: 0.388 McFadden's Adj R2: 0.143 Maximum Likelihood R2: 0.390 Cragg & Uhler's R2: 0.542 McKelvey and Zavoina's R2: 0.930 Efron's R2: 0.437 Variance of y*: 47.071 Variance of error: 3.290 Count R2: 0.824 Adj Count R2: 0.475 AIC: 1.093 AIC*n: 391.455 BIC: -1496.466 BIC': 122.699 Full main model for HP2probsoc for wave= 2 chunk 2 H1 test:Gender= 1 model Wave = 2 for HP2hmcare i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: _Ieduc_4 != 0 predicts failure perfectly _Ieduc_4 dropped and 13 obs not used note: _Ieduc_7 != 0 predicts failure perfectly _Ieduc_7 dropped and 4 obs not used note: _Ieduc_8 != 0 predicts failure perfectly _Ieduc_8 dropped and 2 obs not used note: occ6w2 != 0 predicts failure perfectly occ6w2 dropped and 5 obs not used note: occ7w2 != 0 predicts failure perfectly occ7w2 dropped and 14 obs not used note: occ8w2 != 0 predicts failure perfectly occ8w2 dropped and 44 obs not used note: marrw22 != 0 predicts failure perfectly marrw22 dropped and 7 obs not used note: marrw26 != 0 predicts failure perfectly marrw26 dropped and 1 obs not used note: inc4w2 != 0 predicts failure perfectly inc4w2 dropped and 10 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 14 obs not used note: dvcew2 != 0 predicts failure perfectly dvcew2 dropped and 5 obs not used note: marrw25 != 0 predicts success perfectly marrw25 dropped and 1 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 1 obs not used note: _Ieduc_6 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 217 LR chi2(40) = 113.21 Prob > chi2 = 0.0000 Log likelihood = -47.096671 Pseudo R2 = 0.5459 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .1159429 .0419803 2.76 0.006 .0336631 .1982228 _Ieduc_2 | -.0479004 1.407174 -0.03 0.973 -2.80591 2.710109 _Ieduc_3 | -.8284731 .7880615 -1.05 0.293 -2.373045 .716099 _Ieduc_4 | 0 (omitted) _Ieduc_5 | .2454375 1.100079 0.22 0.823 -1.910677 2.401552 _Ieduc_6 | 0 (omitted) _Ieduc_7 | 0 (omitted) _Ieduc_8 | 0 (omitted) occ1w2 | -1.3725 16.54078 -0.08 0.934 -33.79183 31.04683 occ2w2 | -1.244674 16.53791 -0.08 0.940 -33.65838 31.16903 occ3w2 | -.7835828 16.55903 -0.05 0.962 -33.23868 31.67152 occ4w2 | -3.862736 16.57719 -0.23 0.816 -36.35344 28.62797 occ5w2 | -2.775301 16.58962 -0.17 0.867 -35.29036 29.73976 occ6w2 | 0 (omitted) occ7w2 | 0 (omitted) occ8w2 | 0 (omitted) marrw21 | 12.58162 1075.856 0.01 0.991 -2096.057 2121.22 marrw22 | 0 (omitted) marrw23 | 10.03646 1075.856 0.01 0.993 -2098.602 2118.675 marrw25 | 0 (omitted) marrw26 | 0 (omitted) inc1w2 | .0757099 16.62358 0.00 0.996 -32.50591 32.65733 inc2w2 | 2.619578 16.55703 0.16 0.874 -29.83159 35.07075 inc3w2 | 4.14916 16.572 0.25 0.802 -28.33137 36.62969 inc4w2 | 0 (omitted) radhlw2 | .0130005 .0142344 0.91 0.361 -.0148985 .0408995 havmil | -.002818 .0090337 -0.31 0.755 -.0205238 .0148877 avgcumdosew2 | .0114306 .0823526 0.14 0.890 -.1499776 .1728387 bf1 | .0037783 .0642153 0.06 0.953 -.1220814 .129638 bf4 | .016836 .3033169 0.06 0.956 -.5776542 .6113261 bf2 | .0006302 .0003126 2.02 0.044 .0000175 .0012428 bf4m | -.5319968 .2823116 -1.88 0.060 -1.085317 .0213237 bf5m | .0058806 .0028091 2.09 0.036 .0003749 .0113862 bf7m | .0013216 .0011466 1.15 0.249 -.0009257 .0035688 bf8 | -.0001829 .0000911 -2.01 0.045 -.0003616 -4.31e-06 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.0241214 .0536769 -0.45 0.653 -.1293262 .0810833 bf22 | .0004188 .0002855 1.47 0.142 -.0001407 .0009783 bf29 | -.000073 .0001017 -0.72 0.473 -.0002723 .0001263 bf30 | -.0016782 .0007996 -2.10 0.036 -.0032455 -.000111 bf40 | -.1773534 .3453449 -0.51 0.608 -.854217 .4995102 deaw2 | -.7039739 .7523846 -0.94 0.349 -2.178621 .7706728 dvcew2 | 0 (omitted) sepaw2 | 0 (omitted) accdw2 | -.0469037 .8655378 -0.05 0.957 -1.743327 1.649519 movew2 | 1.585081 .8835966 1.79 0.073 -.1467362 3.316899 illw2 | -.6496621 .6161215 -1.05 0.292 -1.857238 .5579138 shfamw2 | -.0160373 .0116025 -1.38 0.167 -.0387778 .0067033 shhlw2 | -.0105176 .0138341 -0.76 0.447 -.0376321 .0165968 shjobw2 | .0250231 .0129193 1.94 0.053 -.0002983 .0503445 shrelaw2 | -.0217826 .0114409 -1.90 0.057 -.0442063 .0006412 suprtw2 | .0083099 .0108445 0.77 0.444 -.012945 .0295648 suchrw2 | .0212997 .0111718 1.91 0.057 -.0005966 .0431959 havmilsq | 6.25e-06 .0000101 0.62 0.537 -.0000136 .0000261 _cons | -13.61182 1075.864 -0.01 0.990 -2122.266 2095.042 ------------------------------------------------------------------------------ Note: 2 failures and 0 successes completely determined. Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 26 4 | 30 - | 14 173 | 187 -----------+--------------------------+----------- Total | 40 177 | 217 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 65.00% Specificity Pr( -|~D) 97.74% Positive predictive value Pr( D| +) 86.67% Negative predictive value Pr(~D| -) 92.51% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 2.26% False - rate for true D Pr( -| D) 35.00% False + rate for classified + Pr(~D| +) 13.33% False - rate for classified - Pr( D| -) 7.49% -------------------------------------------------- Correctly classified 91.71% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 217 number of covariate patterns = 217 Pearson chi2(176) = 158.28 Prob > chi2 = 0.8270 Measures of Fit for logistic of HP2probsoc Log-Lik Intercept Only: -103.704 Log-Lik Full Model: -47.097 D(161): 94.193 LR(40): 113.215 Prob > LR: 0.000 McFadden's R2: 0.546 McFadden's Adj R2: 0.006 Maximum Likelihood R2: 0.407 Cragg & Uhler's R2: 0.660 McKelvey and Zavoina's R2: 0.840 Efron's R2: 0.562 Variance of y*: 20.586 Variance of error: 3.290 Count R2: 0.917 Adj Count R2: 0.550 AIC: 0.950 AIC*n: 206.193 BIC: -771.970 BIC': 101.981 Full main model for HP2probsoc for wave= 2 chunk 2 H1 test:Gender= 2 model Wave = 2 for HP2probsoc i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: occ6w2 != 0 predicts failure perfectly occ6w2 dropped and 9 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 341 LR chi2(50) = 174.30 Prob > chi2 = 0.0000 Log likelihood = -89.934458 Pseudo R2 = 0.4921 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .1034919 .0270041 3.83 0.000 .0505648 .1564191 _Ieduc_2 | -13.17654 1236.407 -0.01 0.991 -2436.49 2410.137 _Ieduc_3 | -13.38577 1236.407 -0.01 0.991 -2436.699 2409.928 _Ieduc_4 | -12.54323 1236.407 -0.01 0.992 -2435.857 2410.771 _Ieduc_5 | -12.47221 1236.407 -0.01 0.992 -2435.786 2410.842 _Ieduc_6 | -13.80248 1236.407 -0.01 0.991 -2437.116 2409.511 _Ieduc_7 | -15.17121 1236.497 -0.01 0.990 -2438.662 2408.319 _Ieduc_8 | 0 (omitted) occ1w2 | -1.147886 3.571925 -0.32 0.748 -8.14873 5.852957 occ2w2 | -1.271517 3.611241 -0.35 0.725 -8.349419 5.806385 occ3w2 | .2117582 3.586153 0.06 0.953 -6.816973 7.24049 occ4w2 | -1.534382 3.689834 -0.42 0.678 -8.766324 5.69756 occ5w2 | -2.414751 3.8284 -0.63 0.528 -9.918277 5.088775 occ6w2 | 0 (omitted) occ7w2 | -.6787306 3.585162 -0.19 0.850 -7.70552 6.348058 occ8w2 | 2.909649 3.844529 0.76 0.449 -4.625489 10.44479 marrw21 | -1.181403 1.729099 -0.68 0.494 -4.570375 2.207569 marrw22 | 1.066113 1.762045 0.61 0.545 -2.387432 4.519658 marrw23 | .6150888 .9283649 0.66 0.508 -1.204473 2.434651 marrw25 | .4424041 1.311023 0.34 0.736 -2.127154 3.011962 marrw26 | 0 (omitted) inc1w2 | .302891 3.591795 0.08 0.933 -6.736898 7.34268 inc2w2 | 1.142371 3.560505 0.32 0.748 -5.836091 8.120833 inc3w2 | .8314334 3.569583 0.23 0.816 -6.164821 7.827688 inc4w2 | .0290561 3.848853 0.01 0.994 -7.514557 7.57267 radhlw2 | .0111407 .0096438 1.16 0.248 -.0077607 .0300422 havmil | .0005515 .0077361 0.07 0.943 -.0146109 .0157139 avgcumdosew2 | .546822 .23001 2.38 0.017 .0960107 .9976333 bf1 | .0275412 .0390831 0.70 0.481 -.0490602 .1041427 bf4 | -.4078914 .2433269 -1.68 0.094 -.8848033 .0690205 bf2 | .0001427 .0001416 1.01 0.314 -.0001349 .0004204 bf4m | .1372093 .2260724 0.61 0.544 -.3058844 .5803031 bf5m | -.0016005 .0026021 -0.62 0.538 -.0067005 .0034995 bf7m | .0002743 .0007319 0.37 0.708 -.0011601 .0017087 bf8 | .0000233 .0000548 0.42 0.671 -.0000841 .0001306 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.04615 .0319322 -1.45 0.148 -.1087361 .016436 bf22 | 9.68e-06 .0001672 0.06 0.954 -.000318 .0003374 bf29 | -.0000266 .0000488 -0.54 0.586 -.0001221 .000069 bf30 | .0001087 .0003798 0.29 0.775 -.0006357 .0008531 bf40 | -.0077229 .1716746 -0.04 0.964 -.344199 .3287532 deaw2 | -.0328171 .2515674 -0.13 0.896 -.5258803 .460246 dvcew2 | 1.645199 1.729548 0.95 0.341 -1.744652 5.03505 sepaw2 | -.787699 2.185185 -0.36 0.718 -5.070584 3.495186 accdw2 | -.7482691 .7741464 -0.97 0.334 -2.265568 .7690299 movew2 | -.1983887 .9882329 -0.20 0.841 -2.13529 1.738512 illw2 | .037099 .2627647 0.14 0.888 -.4779104 .5521084 shfamw2 | -.0189411 .0086476 -2.19 0.028 -.03589 -.0019922 shhlw2 | .0013266 .0086367 0.15 0.878 -.0156011 .0182542 shjobw2 | -.0022444 .0078272 -0.29 0.774 -.0175855 .0130966 shrelaw2 | -.0057107 .0089259 -0.64 0.522 -.0232051 .0117837 suprtw2 | .0019185 .0068705 0.28 0.780 -.0115475 .0153845 suchrw2 | -.0030114 .0077253 -0.39 0.697 -.0181526 .0121298 havmilsq | -5.64e-06 .0000161 -0.35 0.725 -.0000371 .0000258 _cons | 8.438379 1236.411 0.01 0.995 -2414.883 2431.76 ------------------------------------------------------------------------------ Note: 2 failures and 0 successes completely determined. Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 52 13 | 65 - | 21 255 | 276 -----------+--------------------------+----------- Total | 73 268 | 341 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 71.23% Specificity Pr( -|~D) 95.15% Positive predictive value Pr( D| +) 80.00% Negative predictive value Pr(~D| -) 92.39% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 4.85% False - rate for true D Pr( -| D) 28.77% False + rate for classified + Pr(~D| +) 20.00% False - rate for classified - Pr( D| -) 7.61% -------------------------------------------------- Correctly classified 90.03% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 341 number of covariate patterns = 341 Pearson chi2(290) = 351.64 Prob > chi2 = 0.0077 Measures of Fit for logistic of HP2probsoc Log-Lik Intercept Only: -177.084 Log-Lik Full Model: -89.934 D(285): 179.869 LR(50): 174.299 Prob > LR: 0.000 McFadden's R2: 0.492 McFadden's Adj R2: 0.176 Maximum Likelihood R2: 0.400 Cragg & Uhler's R2: 0.619 McKelvey and Zavoina's R2: 0.858 Efron's R2: 0.531 Variance of y*: 23.160 Variance of error: 3.290 Count R2: 0.900 Adj Count R2: 0.534 AIC: 0.856 AIC*n: 291.869 BIC: -1482.218 BIC': 117.295 Full main model for HP2pbfhm for wave= 2 chunk 2 H1 test:Gender= 1 model Wave = 2 for HP2probsoc i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: _Ieduc_2 != 0 predicts failure perfectly _Ieduc_2 dropped and 10 obs not used note: _Ieduc_4 != 0 predicts failure perfectly _Ieduc_4 dropped and 13 obs not used note: _Ieduc_7 != 0 predicts failure perfectly _Ieduc_7 dropped and 4 obs not used note: _Ieduc_8 != 0 predicts failure perfectly _Ieduc_8 dropped and 2 obs not used note: occ5w2 != 0 predicts failure perfectly occ5w2 dropped and 17 obs not used note: occ6w2 != 0 predicts failure perfectly occ6w2 dropped and 5 obs not used note: occ8w2 != 0 predicts failure perfectly occ8w2 dropped and 44 obs not used note: marrw22 != 0 predicts failure perfectly marrw22 dropped and 7 obs not used note: marrw26 != 0 predicts failure perfectly marrw26 dropped and 3 obs not used note: inc1w2 != 0 predicts failure perfectly inc1w2 dropped and 13 obs not used note: inc4w2 != 0 predicts failure perfectly inc4w2 dropped and 10 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: bf29 != 0 predicts failure perfectly bf29 dropped and 4 obs not used note: dvcew2 != 0 predicts failure perfectly dvcew2 dropped and 4 obs not used note: marrw25 != 0 predicts success perfectly marrw25 dropped and 1 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 1 obs not used note: _Ieduc_6 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 188 LR chi2(37) = 54.88 Prob > chi2 = 0.0294 Log likelihood = -38.373257 Pseudo R2 = 0.4169 ------------------------------------------------------------------------------ HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0444593 .0428518 1.04 0.299 -.0395286 .1284472 _Ieduc_2 | 0 (omitted) _Ieduc_3 | -1.216759 1.079528 -1.13 0.260 -3.332594 .8990769 _Ieduc_4 | 0 (omitted) _Ieduc_5 | -.8969887 1.218662 -0.74 0.462 -3.285522 1.491544 _Ieduc_6 | 0 (omitted) _Ieduc_7 | 0 (omitted) _Ieduc_8 | 0 (omitted) occ1w2 | -.1675434 7.199614 -0.02 0.981 -14.27853 13.94344 occ2w2 | -.7756164 7.209263 -0.11 0.914 -14.90551 13.35428 occ3w2 | 1.02351 7.214423 0.14 0.887 -13.1165 15.16352 occ4w2 | 1.180132 7.249556 0.16 0.871 -13.02874 15.389 occ5w2 | 0 (omitted) occ6w2 | 0 (omitted) occ7w2 | -1.331976 7.411247 -0.18 0.857 -15.85775 13.1938 occ8w2 | 0 (omitted) marrw21 | 11.23886 2191.219 0.01 0.996 -4283.471 4305.948 marrw22 | 0 (omitted) marrw23 | 11.19426 2191.219 0.01 0.996 -4283.515 4305.904 marrw25 | 0 (omitted) marrw26 | 0 (omitted) inc1w2 | 0 (omitted) inc2w2 | 1.591635 7.274442 0.22 0.827 -12.66601 15.84928 inc3w2 | 2.001132 7.228455 0.28 0.782 -12.16638 16.16864 inc4w2 | 0 (omitted) radhlw2 | .0450707 .0190833 2.36 0.018 .0076681 .0824733 havmil | -.0149705 .0133639 -1.12 0.263 -.0411632 .0112223 avgcumdosew2 | -.0788123 .3549706 -0.22 0.824 -.7745419 .6169173 bf1 | -.1607809 .1318246 -1.22 0.223 -.4191523 .0975906 bf4 | -.2790034 .3019344 -0.92 0.355 -.8707839 .3127772 bf2 | .0006459 .0003574 1.81 0.071 -.0000546 .0013465 bf4m | .1083622 .270079 0.40 0.688 -.4209828 .6377072 bf5m | .0014293 .004266 0.34 0.738 -.0069319 .0097905 bf7m | .0014124 .0012328 1.15 0.252 -.0010038 .0038286 bf8 | -.0000455 .0000891 -0.51 0.610 -.0002201 .0001291 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | .0990519 .1141451 0.87 0.386 -.1246683 .3227721 bf22 | .0000324 .0003377 0.10 0.924 -.0006295 .0006943 bf29 | 0 (omitted) bf30 | -.0005162 .0007544 -0.68 0.494 -.0019949 .0009625 bf40 | -.3427693 .4881552 -0.70 0.483 -1.299536 .6139973 deaw2 | 1.277518 .6289072 2.03 0.042 .0448826 2.510153 dvcew2 | 0 (omitted) sepaw2 | 0 (omitted) accdw2 | -.8756107 1.266217 -0.69 0.489 -3.35735 1.606128 movew2 | .2828439 1.570553 0.18 0.857 -2.795383 3.36107 illw2 | 1.383819 .6666357 2.08 0.038 .0772372 2.690401 shfamw2 | -.0048065 .0139543 -0.34 0.731 -.0321564 .0225433 shhlw2 | -.0126567 .0158426 -0.80 0.424 -.0437076 .0183942 shjobw2 | .0113436 .0152838 0.74 0.458 -.018612 .0412992 shrelaw2 | -.0154518 .0162014 -0.95 0.340 -.047206 .0163025 suprtw2 | -.0271504 .0129849 -2.09 0.037 -.0526003 -.0017006 suchrw2 | .0166833 .0117501 1.42 0.156 -.0063465 .039713 havmilsq | .0000156 .0000131 1.19 0.234 -.00001 .0000412 _cons | -21.58182 2191.227 -0.01 0.992 -4316.307 4273.143 ------------------------------------------------------------------------------ Logistic model for HP2pbfhm -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 8 4 | 12 - | 13 163 | 176 -----------+--------------------------+----------- Total | 21 167 | 188 Classified + if predicted Pr(D) >= .5 True D defined as HP2pbfhm != 0 -------------------------------------------------- Sensitivity Pr( +| D) 38.10% Specificity Pr( -|~D) 97.60% Positive predictive value Pr( D| +) 66.67% Negative predictive value Pr(~D| -) 92.61% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 2.40% False - rate for true D Pr( -| D) 61.90% False + rate for classified + Pr(~D| +) 33.33% False - rate for classified - Pr( D| -) 7.39% -------------------------------------------------- Correctly classified 90.96% -------------------------------------------------- Logistic model for HP2pbfhm, goodness-of-fit test number of observations = 188 number of covariate patterns = 188 Pearson chi2(150) = 103.39 Prob > chi2 = 0.9986 Measures of Fit for logistic of HP2pbfhm Log-Lik Intercept Only: -65.811 Log-Lik Full Model: -38.373 D(132): 76.747 LR(37): 54.876 Prob > LR: 0.029 McFadden's R2: 0.417 McFadden's Adj R2: -0.434 Maximum Likelihood R2: 0.253 Cragg & Uhler's R2: 0.503 McKelvey and Zavoina's R2: 0.754 Efron's R2: 0.356 Variance of y*: 13.351 Variance of error: 3.290 Count R2: 0.910 Adj Count R2: 0.190 AIC: 1.004 AIC*n: 188.747 BIC: -614.464 BIC': 138.873 Full main model for HP2pbfhm for wave= 2 chunk 2 H1 test:Gender= 2 model Wave = 2 for HP2pbfhm i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: occ6w2 != 0 predicts failure perfectly occ6w2 dropped and 9 obs not used note: marrw22 != 0 predicts failure perfectly marrw22 dropped and 8 obs not used note: inc4w2 != 0 predicts failure perfectly inc4w2 dropped and 9 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 5 obs not used note: movew2 != 0 predicts failure perfectly movew2 dropped and 37 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity convergence not achieved Logistic regression Number of obs = 282 LR chi2(45) = 115.64 Prob > chi2 = 0.0000 Log likelihood = -69.237721 Pseudo R2 = 0.4551 ------------------------------------------------------------------------------ HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0573216 .0276334 2.07 0.038 .0031611 .1114822 _Ieduc_2 | 24.79783 5.419934 4.58 0.000 14.17495 35.4207 _Ieduc_3 | 25.40648 5.405293 4.70 0.000 14.8123 36.00065 _Ieduc_4 | 26.0435 5.498915 4.74 0.000 15.26582 36.82117 _Ieduc_5 | 25.99152 5.477282 4.75 0.000 15.25625 36.7268 _Ieduc_6 | 25.22458 5.399044 4.67 0.000 14.64265 35.80651 _Ieduc_7 | 24.91381 . . . . . _Ieduc_8 | 0 (omitted) occ1w2 | .1268694 2.741862 0.05 0.963 -5.247082 5.50082 occ2w2 | -2.180454 2.943693 -0.74 0.459 -7.949987 3.589079 occ3w2 | .8729362 2.776498 0.31 0.753 -4.568899 6.314772 occ4w2 | -1.353704 2.942107 -0.46 0.645 -7.120128 4.41272 occ5w2 | 2.7416 3.183927 0.86 0.389 -3.498782 8.981982 occ6w2 | 0 (omitted) occ7w2 | 1.723368 2.749238 0.63 0.531 -3.66504 7.111775 occ8w2 | 1.071406 3.004006 0.36 0.721 -4.816337 6.959149 marrw21 | 1.363708 1.652619 0.83 0.409 -1.875365 4.602781 marrw22 | 0 (omitted) marrw23 | .3462148 1.088773 0.32 0.750 -1.78774 2.48017 marrw25 | 1.049262 1.686487 0.62 0.534 -2.256193 4.354717 marrw26 | 0 (omitted) inc1w2 | -.4454501 2.822154 -0.16 0.875 -5.976771 5.085871 inc2w2 | 1.223562 2.740806 0.45 0.655 -4.148319 6.595444 inc3w2 | .8897543 2.773723 0.32 0.748 -4.546642 6.326151 inc4w2 | 0 (omitted) radhlw2 | .0162494 .0128872 1.26 0.207 -.0090091 .0415079 havmil | .0034284 .0174028 0.20 0.844 -.0306804 .0375372 avgcumdosew2 | .2841701 .2286177 1.24 0.214 -.1639122 .7322525 bf1 | -.0034211 .0428716 -0.08 0.936 -.0874479 .0806057 bf4 | -.5482161 .2947147 -1.86 0.063 -1.125846 .0294141 bf2 | .0001496 .0001782 0.84 0.401 -.0001996 .0004989 bf4m | .2436517 .2745867 0.89 0.375 -.2945285 .7818318 bf5m | -.0026475 .0036526 -0.72 0.469 -.0098065 .0045114 bf7m | -.0008652 .0008476 -1.02 0.307 -.0025264 .000796 bf8 | .0000586 .0000741 0.79 0.429 -.0000867 .0002038 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.0232359 .0348214 -0.67 0.505 -.0914845 .0450127 bf22 | .0000587 .0002121 0.28 0.782 -.0003569 .0004744 bf29 | .0000275 .0000434 0.63 0.527 -.0000576 .0001126 bf30 | .0000875 .0005016 0.17 0.862 -.0008956 .0010706 bf40 | -.3662342 .2446561 -1.50 0.134 -.8457514 .1132829 deaw2 | .0470792 .2393731 0.20 0.844 -.4220834 .5162419 dvcew2 | -1.779925 2.445695 -0.73 0.467 -6.573399 3.013549 sepaw2 | 0 (omitted) accdw2 | -2.474844 1.428768 -1.73 0.083 -5.275177 .3254891 movew2 | 0 (omitted) illw2 | -.2222062 .2628556 -0.85 0.398 -.7373938 .2929813 shfamw2 | .0141915 .0093043 1.53 0.127 -.0040446 .0324276 shhlw2 | .0087578 .0103731 0.84 0.399 -.0115731 .0290887 shjobw2 | -.0066068 .0093228 -0.71 0.479 -.0248791 .0116655 shrelaw2 | -.0301632 .0120406 -2.51 0.012 -.0537624 -.0065641 suprtw2 | -.0125721 .0077017 -1.63 0.103 -.0276672 .002523 suchrw2 | -.0060097 .0083821 -0.72 0.473 -.0224384 .010419 havmilsq | -.0000291 .000055 -0.53 0.597 -.0001368 .0000787 _cons | -27.59558 6.62482 -4.17 0.000 -40.57999 -14.61117 ------------------------------------------------------------------------------ Note: 3 failures and 0 successes completely determined. Warning: convergence not achieved Logistic model for HP2pbfhm -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 29 7 | 36 - | 18 228 | 246 -----------+--------------------------+----------- Total | 47 235 | 282 Classified + if predicted Pr(D) >= .5 True D defined as HP2pbfhm != 0 -------------------------------------------------- Sensitivity Pr( +| D) 61.70% Specificity Pr( -|~D) 97.02% Positive predictive value Pr( D| +) 80.56% Negative predictive value Pr(~D| -) 92.68% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 2.98% False - rate for true D Pr( -| D) 38.30% False + rate for classified + Pr(~D| +) 19.44% False - rate for classified - Pr( D| -) 7.32% -------------------------------------------------- Correctly classified 91.13% -------------------------------------------------- Logistic model for HP2pbfhm, goodness-of-fit test number of observations = 282 number of covariate patterns = 282 Pearson chi2(235) = 241.50 Prob > chi2 = 0.3715 Measures of Fit for logistic of HP2pbfhm Log-Lik Intercept Only: -127.058 Log-Lik Full Model: -69.238 D(226): 138.475 LR(45): 115.641 Prob > LR: 0.000 McFadden's R2: 0.455 McFadden's Adj R2: 0.014 Maximum Likelihood R2: 0.336 Cragg & Uhler's R2: 0.566 McKelvey and Zavoina's R2: 0.950 Efron's R2: 0.475 Variance of y*: 65.351 Variance of error: 3.290 Count R2: 0.911 Adj Count R2: 0.468 AIC: 0.888 AIC*n: 250.475 BIC: -1136.596 BIC': 138.245 Full main model for HP2sxlife for wave= 2 chunk 2 H1 test:Gender= 1 model Wave = 2 for HP2pbfhm i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: _Ieduc_4 != 0 predicts failure perfectly _Ieduc_4 dropped and 13 obs not used note: _Ieduc_7 != 0 predicts failure perfectly _Ieduc_7 dropped and 4 obs not used note: occ8w2 != 0 predicts failure perfectly occ8w2 dropped and 44 obs not used note: marrw22 != 0 predicts failure perfectly marrw22 dropped and 8 obs not used note: inc4w2 != 0 predicts failure perfectly inc4w2 dropped and 10 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 15 obs not used note: bf29 != 0 predicts failure perfectly bf29 dropped and 7 obs not used note: dvcew2 != 0 predicts failure perfectly dvcew2 dropped and 5 obs not used note: marrw25 != 0 predicts success perfectly marrw25 dropped and 1 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 1 obs not used note: _Ieduc_8 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 230 LR chi2(43) = 118.95 Prob > chi2 = 0.0000 Log likelihood = -79.289222 Pseudo R2 = 0.4286 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0432555 .0255157 1.70 0.090 -.0067543 .0932653 _Ieduc_2 | .5443827 2.506052 0.22 0.828 -4.367389 5.456154 _Ieduc_3 | .1796978 2.28739 0.08 0.937 -4.303504 4.6629 _Ieduc_4 | 0 (omitted) _Ieduc_5 | .4368682 2.317398 0.19 0.850 -4.105148 4.978884 _Ieduc_6 | .0186117 2.278221 0.01 0.993 -4.446619 4.483842 _Ieduc_7 | 0 (omitted) _Ieduc_8 | 0 (omitted) occ1w2 | -1.105479 2.816517 -0.39 0.695 -6.62575 4.414792 occ2w2 | -1.682569 2.827571 -0.60 0.552 -7.224506 3.859369 occ3w2 | -1.865477 2.89914 -0.64 0.520 -7.547687 3.816733 occ4w2 | -1.59066 2.872678 -0.55 0.580 -7.221004 4.039685 occ5w2 | -2.726982 3.04188 -0.90 0.370 -8.688957 3.234994 occ6w2 | -.2649406 3.127605 -0.08 0.932 -6.394934 5.865053 occ7w2 | -.4867933 2.955714 -0.16 0.869 -6.279885 5.306299 occ8w2 | 0 (omitted) marrw21 | 12.4944 2424.109 0.01 0.996 -4738.671 4763.66 marrw22 | 0 (omitted) marrw23 | 12.40066 2424.109 0.01 0.996 -4738.765 4763.566 marrw25 | 0 (omitted) marrw26 | 11.43457 2424.11 0.00 0.996 -4739.733 4762.602 inc1w2 | 2.196401 3.02039 0.73 0.467 -3.723454 8.116257 inc2w2 | 2.746044 2.89601 0.95 0.343 -2.930031 8.422118 inc3w2 | 2.442664 2.894545 0.84 0.399 -3.23054 8.115869 inc4w2 | 0 (omitted) radhlw2 | .017419 .0089507 1.95 0.052 -.000124 .0349621 havmil | -.00067 .0081563 -0.08 0.935 -.0166561 .0153161 avgcumdosew2 | .0428691 .0600095 0.71 0.475 -.0747474 .1604856 bf1 | .0291477 .0465434 0.63 0.531 -.0620756 .1203711 bf4 | -.2016289 .2378115 -0.85 0.397 -.667731 .2644731 bf2 | .0001142 .0001714 0.67 0.505 -.0002218 .0004501 bf4m | -.1000095 .2151889 -0.46 0.642 -.5217719 .321753 bf5m | .0017962 .0020545 0.87 0.382 -.0022305 .0058229 bf7m | .0013709 .0007292 1.88 0.060 -.0000582 .0028001 bf8 | -.0000501 .0000511 -0.98 0.327 -.0001502 .0000501 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.0313008 .0383214 -0.82 0.414 -.1064094 .0438077 bf22 | -.0000976 .0002072 -0.47 0.637 -.0005038 .0003085 bf29 | 0 (omitted) bf30 | -.000152 .0004096 -0.37 0.711 -.0009547 .0006507 bf40 | .4229922 .2840028 1.49 0.136 -.1336431 .9796275 deaw2 | -.1048722 .3851952 -0.27 0.785 -.8598409 .6500964 dvcew2 | 0 (omitted) sepaw2 | 0 (omitted) accdw2 | -.0405016 .5549457 -0.07 0.942 -1.128175 1.047172 movew2 | .116116 .6197485 0.19 0.851 -1.098569 1.330801 illw2 | .1039214 .3746121 0.28 0.781 -.6303047 .8381476 shfamw2 | -.0104564 .0087383 -1.20 0.231 -.0275832 .0066703 shhlw2 | -.0161825 .0102333 -1.58 0.114 -.0362395 .0038744 shjobw2 | .018921 .0100999 1.87 0.061 -.0008744 .0387165 shrelaw2 | -.008619 .0087825 -0.98 0.326 -.0258323 .0085944 suprtw2 | .0043231 .0078499 0.55 0.582 -.0110624 .0197086 suchrw2 | .006568 .0072888 0.90 0.368 -.0077177 .0208538 havmilsq | -9.54e-07 .000013 -0.07 0.941 -.0000264 .0000245 _cons | -15.57434 2424.111 -0.01 0.995 -4766.744 4735.596 ------------------------------------------------------------------------------ Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 46 14 | 60 - | 21 149 | 170 -----------+--------------------------+----------- Total | 67 163 | 230 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 68.66% Specificity Pr( -|~D) 91.41% Positive predictive value Pr( D| +) 76.67% Negative predictive value Pr(~D| -) 87.65% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 8.59% False - rate for true D Pr( -| D) 31.34% False + rate for classified + Pr(~D| +) 23.33% False - rate for classified - Pr( D| -) 12.35% -------------------------------------------------- Correctly classified 84.78% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 230 number of covariate patterns = 230 Pearson chi2(186) = 248.57 Prob > chi2 = 0.0015 Measures of Fit for logistic of HP2sxlife Log-Lik Intercept Only: -138.763 Log-Lik Full Model: -79.289 D(174): 158.578 LR(43): 118.947 Prob > LR: 0.000 McFadden's R2: 0.429 McFadden's Adj R2: 0.025 Maximum Likelihood R2: 0.404 Cragg & Uhler's R2: 0.576 McKelvey and Zavoina's R2: 0.727 Efron's R2: 0.487 Variance of y*: 12.053 Variance of error: 3.290 Count R2: 0.848 Adj Count R2: 0.478 AIC: 1.176 AIC*n: 270.578 BIC: -787.647 BIC': 114.891 Full main model for HP2sxlife for wave= 2 chunk 2 H1 test:Gender= 2 model Wave = 2 for HP2sxlife i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: bf29 != 0 predicts success perfectly bf29 dropped and 4 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 346 LR chi2(50) = 172.45 Prob > chi2 = 0.0000 Log likelihood = -111.04151 Pseudo R2 = 0.4371 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .1025465 .0220099 4.66 0.000 .0594079 .145685 _Ieduc_2 | -15.48089 5580.75 -0.00 0.998 -10953.55 10922.59 _Ieduc_3 | -13.96022 5580.75 -0.00 0.998 -10952.03 10924.11 _Ieduc_4 | -13.71505 5580.75 -0.00 0.998 -10951.78 10924.35 _Ieduc_5 | -14.90906 5580.75 -0.00 0.998 -10952.98 10923.16 _Ieduc_6 | -14.036 5580.75 -0.00 0.998 -10952.1 10924.03 _Ieduc_7 | -14.35246 5580.751 -0.00 0.998 -10952.42 10923.72 _Ieduc_8 | 0 (omitted) occ1w2 | -1.981003 1.626317 -1.22 0.223 -5.168525 1.20652 occ2w2 | -1.199181 1.677697 -0.71 0.475 -4.487407 2.089044 occ3w2 | -.7937582 1.670584 -0.48 0.635 -4.068042 2.480526 occ4w2 | -.8102305 1.76353 -0.46 0.646 -4.266686 2.646225 occ5w2 | -.5056346 1.89785 -0.27 0.790 -4.225353 3.214084 occ6w2 | -1.645496 1.931022 -0.85 0.394 -5.430229 2.139237 occ7w2 | -1.288749 1.611028 -0.80 0.424 -4.446306 1.868809 occ8w2 | -.2492981 1.896994 -0.13 0.895 -3.967338 3.468742 marrw21 | -.8001566 1.260909 -0.63 0.526 -3.271493 1.67118 marrw22 | -.5650635 1.458662 -0.39 0.698 -3.423989 2.293861 marrw23 | -1.064868 .8865967 -1.20 0.230 -2.802566 .6728293 marrw25 | -2.27112 1.443829 -1.57 0.116 -5.100972 .5587328 marrw26 | 0 (omitted) inc1w2 | .5165366 1.706152 0.30 0.762 -2.82746 3.860533 inc2w2 | .9386295 1.604285 0.59 0.558 -2.205712 4.082971 inc3w2 | -.0514022 1.646264 -0.03 0.975 -3.27802 3.175215 inc4w2 | .0543252 2.074029 0.03 0.979 -4.010697 4.119348 radhlw2 | .0162077 .0081749 1.98 0.047 .0001852 .0322303 havmil | -.0014745 .0033419 -0.44 0.659 -.0080244 .0050754 avgcumdosew2 | .1675129 .1251712 1.34 0.181 -.0778182 .412844 bf1 | -.0006699 .0354295 -0.02 0.985 -.0701104 .0687707 bf4 | -.6290339 .2665349 -2.36 0.018 -1.151433 -.1066351 bf2 | .0000625 .000131 0.48 0.633 -.0001943 .0003193 bf4m | .4940985 .2505491 1.97 0.049 .0030312 .9851658 bf5m | -.002308 .0018303 -1.26 0.207 -.0058953 .0012792 bf7m | .00018 .0006254 0.29 0.773 -.0010457 .0014058 bf8 | 6.11e-06 .0000425 0.14 0.886 -.0000772 .0000894 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.0009967 .0290168 -0.03 0.973 -.0578686 .0558752 bf22 | -.0001321 .0001453 -0.91 0.363 -.0004169 .0001527 bf29 | 0 (omitted) bf30 | -.0003317 .0003532 -0.94 0.348 -.001024 .0003605 bf40 | .149372 .145042 1.03 0.303 -.1349052 .4336491 deaw2 | .0989356 .2231863 0.44 0.658 -.3385014 .5363726 dvcew2 | -14.61462 1422.458 -0.01 0.992 -2802.582 2773.353 sepaw2 | 13.19252 1422.46 0.01 0.993 -2774.778 2801.163 accdw2 | -.3607593 .7071147 -0.51 0.610 -1.746679 1.02516 movew2 | .2363995 .5198633 0.45 0.649 -.7825137 1.255313 illw2 | .5572231 .2374511 2.35 0.019 .0918274 1.022619 shfamw2 | .001878 .0072955 0.26 0.797 -.0124209 .0161768 shhlw2 | .0092925 .0072946 1.27 0.203 -.0050046 .0235896 shjobw2 | -.0069643 .0067203 -1.04 0.300 -.0201358 .0062073 shrelaw2 | -.0169606 .0081044 -2.09 0.036 -.032845 -.0010762 suprtw2 | -.0129953 .0058047 -2.24 0.025 -.0243723 -.0016183 suchrw2 | .0118245 .0068566 1.72 0.085 -.0016142 .0252632 havmilsq | -6.39e-08 2.74e-06 -0.02 0.981 -5.43e-06 5.30e-06 _cons | 5.841436 5580.75 0.00 0.999 -10932.23 10943.91 ------------------------------------------------------------------------------ Note: 1 failure and 0 successes completely determined. Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 57 17 | 74 - | 32 240 | 272 -----------+--------------------------+----------- Total | 89 257 | 346 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 64.04% Specificity Pr( -|~D) 93.39% Positive predictive value Pr( D| +) 77.03% Negative predictive value Pr(~D| -) 88.24% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 6.61% False - rate for true D Pr( -| D) 35.96% False + rate for classified + Pr(~D| +) 22.97% False - rate for classified - Pr( D| -) 11.76% -------------------------------------------------- Correctly classified 85.84% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 346 number of covariate patterns = 346 Pearson chi2(295) = 312.67 Prob > chi2 = 0.2294 Measures of Fit for logistic of HP2sxlife Log-Lik Intercept Only: -197.267 Log-Lik Full Model: -111.042 D(290): 222.083 LR(50): 172.450 Prob > LR: 0.000 McFadden's R2: 0.437 McFadden's Adj R2: 0.153 Maximum Likelihood R2: 0.393 Cragg & Uhler's R2: 0.577 McKelvey and Zavoina's R2: 0.765 Efron's R2: 0.477 Variance of y*: 14.018 Variance of error: 3.290 Count R2: 0.858 Adj Count R2: 0.449 AIC: 0.966 AIC*n: 334.083 BIC: -1473.384 BIC': 119.872 Full main model for HP2inthob for wave= 2 chunk 2 H1 test:Gender= 1 model Wave = 2 for HP2sxlife i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: _Ieduc_4 != 0 predicts failure perfectly _Ieduc_4 dropped and 13 obs not used note: _Ieduc_7 != 0 predicts failure perfectly _Ieduc_7 dropped and 4 obs not used note: _Ieduc_8 != 0 predicts failure perfectly _Ieduc_8 dropped and 2 obs not used note: occ6w2 != 0 predicts failure perfectly occ6w2 dropped and 5 obs not used note: occ8w2 != 0 predicts failure perfectly occ8w2 dropped and 44 obs not used note: marrw22 != 0 predicts failure perfectly marrw22 dropped and 7 obs not used note: marrw26 != 0 predicts failure perfectly marrw26 dropped and 3 obs not used note: inc4w2 != 0 predicts failure perfectly inc4w2 dropped and 10 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 14 obs not used note: bf29 != 0 predicts failure perfectly bf29 dropped and 7 obs not used note: dvcew2 != 0 predicts failure perfectly dvcew2 dropped and 5 obs not used note: marrw25 != 0 predicts success perfectly marrw25 dropped and 1 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 1 obs not used note: _Ieduc_6 omitted because of collinearity note: bf17 omitted because of collinearity convergence not achieved Logistic regression Number of obs = 222 LR chi2(38) = 87.92 Prob > chi2 = 0.0000 Log likelihood = -56.065557 Pseudo R2 = 0.4395 ------------------------------------------------------------------------------ HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0628957 .0331927 1.89 0.058 -.0021608 .1279522 _Ieduc_2 | -1.6685 1.649966 -1.01 0.312 -4.902374 1.565374 _Ieduc_3 | -1.215713 .7907875 -1.54 0.124 -2.765628 .334202 _Ieduc_4 | 0 (omitted) _Ieduc_5 | -.4779232 .8553091 -0.56 0.576 -2.154298 1.198452 _Ieduc_6 | 0 (omitted) _Ieduc_7 | 0 (omitted) _Ieduc_8 | 0 (omitted) occ1w2 | -1.082181 4.607241 -0.23 0.814 -10.11221 7.947846 occ2w2 | -2.603476 4.649764 -0.56 0.576 -11.71685 6.509893 occ3w2 | -1.004632 4.641 -0.22 0.829 -10.10083 8.091562 occ4w2 | -2.019345 4.651526 -0.43 0.664 -11.13617 7.097479 occ5w2 | .6122404 4.674482 0.13 0.896 -8.549576 9.774057 occ6w2 | 0 (omitted) occ7w2 | -.673024 4.745659 -0.14 0.887 -9.974345 8.628297 occ8w2 | 0 (omitted) marrw21 | 20.61385 . . . . . marrw22 | 0 (omitted) marrw23 | 18.42435 .9174041 20.08 0.000 16.62627 20.22243 marrw25 | 0 (omitted) marrw26 | 0 (omitted) inc1w2 | -2.249864 4.853935 -0.46 0.643 -11.7634 7.263674 inc2w2 | -.5178361 4.628216 -0.11 0.911 -9.588972 8.5533 inc3w2 | .1239712 4.620272 0.03 0.979 -8.931596 9.179539 inc4w2 | 0 (omitted) radhlw2 | .0343977 .012966 2.65 0.008 .0089848 .0598107 havmil | .0008574 .0092743 0.09 0.926 -.01732 .0190348 avgcumdosew2 | -.193618 .3161247 -0.61 0.540 -.8132111 .425975 bf1 | -1.886122 .0917045 -20.57 0.000 -2.065859 -1.706384 bf4 | -.3784715 .2556706 -1.48 0.139 -.8795766 .1226337 bf2 | .0000547 .0002055 0.27 0.790 -.0003482 .0004575 bf4m | .0723497 .2259826 0.32 0.749 -.370568 .5152675 bf5m | .001341 .0024956 0.54 0.591 -.0035502 .0062323 bf7m | .0004844 .0009455 0.51 0.608 -.0013686 .0023375 bf8 | -.0000611 .0000655 -0.93 0.351 -.0001895 .0000674 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | 1.88387 .0868318 21.70 0.000 1.713682 2.054057 bf22 | .0002363 .000249 0.95 0.343 -.0002517 .0007243 bf29 | 0 (omitted) bf30 | -.0000596 .000538 -0.11 0.912 -.0011141 .0009949 bf40 | .1920076 .3256519 0.59 0.555 -.4462584 .8302735 deaw2 | .0505905 .5418118 0.09 0.926 -1.011341 1.112522 dvcew2 | 0 (omitted) sepaw2 | 0 (omitted) accdw2 | .6361143 .5519575 1.15 0.249 -.4457025 1.717931 movew2 | 1.255677 .7610419 1.65 0.099 -.2359377 2.747292 illw2 | -.8504102 .5855803 -1.45 0.146 -1.998126 .2973061 shfamw2 | -.0084797 .0105947 -0.80 0.423 -.029245 .0122856 shhlw2 | -.0041615 .0127463 -0.33 0.744 -.0291439 .0208209 shjobw2 | .0098894 .0132981 0.74 0.457 -.0161744 .0359532 shrelaw2 | -.0107778 .0098723 -1.09 0.275 -.0301272 .0085716 suprtw2 | -.0039714 .0091015 -0.44 0.663 -.0218101 .0138672 suchrw2 | .0137146 .0086956 1.58 0.115 -.0033283 .0307576 havmilsq | -2.48e-06 .0000157 -0.16 0.875 -.0000333 .0000283 _cons | -98.68478 . . . . . ------------------------------------------------------------------------------ Note: 21 failures and 0 successes completely determined. Warning: convergence not achieved Logistic model for HP2inthob -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 20 6 | 26 - | 17 179 | 196 -----------+--------------------------+----------- Total | 37 185 | 222 Classified + if predicted Pr(D) >= .5 True D defined as HP2inthob != 0 -------------------------------------------------- Sensitivity Pr( +| D) 54.05% Specificity Pr( -|~D) 96.76% Positive predictive value Pr( D| +) 76.92% Negative predictive value Pr(~D| -) 91.33% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 3.24% False - rate for true D Pr( -| D) 45.95% False + rate for classified + Pr(~D| +) 23.08% False - rate for classified - Pr( D| -) 8.67% -------------------------------------------------- Correctly classified 89.64% -------------------------------------------------- Logistic model for HP2inthob, goodness-of-fit test number of observations = 222 number of covariate patterns = 222 Pearson chi2(181) = 382.48 Prob > chi2 = 0.0000 Measures of Fit for logistic of HP2inthob Log-Lik Intercept Only: -100.025 Log-Lik Full Model: -56.066 D(166): 112.131 LR(38): 87.918 Prob > LR: 0.000 McFadden's R2: 0.439 McFadden's Adj R2: -0.120 Maximum Likelihood R2: 0.327 Cragg & Uhler's R2: 0.551 McKelvey and Zavoina's R2: 0.982 Efron's R2: 0.466 Variance of y*: 186.924 Variance of error: 3.290 Count R2: 0.896 Adj Count R2: 0.378 AIC: 1.010 AIC*n: 224.131 BIC: -784.713 BIC': 117.384 Full main model for HP2inthob for wave= 2 chunk 2 H1 test:Gender= 2 model Wave = 2 for HP2inthob i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 7 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 343 LR chi2(50) = 122.71 Prob > chi2 = 0.0000 Log likelihood = -105.17277 Pseudo R2 = 0.3684 ------------------------------------------------------------------------------ HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0823123 .0222811 3.69 0.000 .0386422 .1259824 _Ieduc_2 | -11.49207 1090.325 -0.01 0.992 -2148.489 2125.505 _Ieduc_3 | -10.49189 1090.324 -0.01 0.992 -2147.489 2126.505 _Ieduc_4 | -9.521531 1090.325 -0.01 0.993 -2146.519 2127.476 _Ieduc_5 | -10.03097 1090.325 -0.01 0.993 -2147.028 2126.966 _Ieduc_6 | -10.65519 1090.325 -0.01 0.992 -2147.652 2126.342 _Ieduc_7 | -10.56771 1090.33 -0.01 0.992 -2147.575 2126.44 _Ieduc_8 | 0 (omitted) occ1w2 | -1.444251 1.786645 -0.81 0.419 -4.94601 2.057508 occ2w2 | -1.72162 1.86422 -0.92 0.356 -5.375425 1.932185 occ3w2 | -.6960307 1.813742 -0.38 0.701 -4.2509 2.858838 occ4w2 | -1.340236 1.971898 -0.68 0.497 -5.205085 2.524613 occ5w2 | -1.866674 2.177453 -0.86 0.391 -6.134403 2.401055 occ6w2 | -.6630026 2.009767 -0.33 0.741 -4.602073 3.276067 occ7w2 | -.3132082 1.768051 -0.18 0.859 -3.778525 3.152109 occ8w2 | -.4424864 1.980995 -0.22 0.823 -4.325166 3.440193 marrw21 | 1.198754 1.252366 0.96 0.338 -1.255838 3.653346 marrw22 | -.723147 1.45696 -0.50 0.620 -3.578736 2.132442 marrw23 | -.1412732 .8614541 -0.16 0.870 -1.829692 1.547146 marrw25 | .030411 1.273943 0.02 0.981 -2.466472 2.527294 marrw26 | 0 (omitted) inc1w2 | .7639278 1.820866 0.42 0.675 -2.804904 4.33276 inc2w2 | 1.088119 1.754837 0.62 0.535 -2.351299 4.527537 inc3w2 | .8549853 1.806256 0.47 0.636 -2.685212 4.395182 inc4w2 | 2.107171 2.102907 1.00 0.316 -2.014451 6.228794 radhlw2 | .0162662 .0092193 1.76 0.078 -.0018033 .0343356 havmil | .0028568 .0035632 0.80 0.423 -.004127 .0098406 avgcumdosew2 | .1191237 .1112746 1.07 0.284 -.0989705 .3372179 bf1 | -.0579291 .0444461 -1.30 0.192 -.1450419 .0291837 bf4 | -.5134212 .2911522 -1.76 0.078 -1.084069 .0572265 bf2 | .0000225 .0001357 0.17 0.868 -.0002436 .0002885 bf4m | .3813738 .2769085 1.38 0.168 -.1613568 .9241045 bf5m | -.0013491 .0018728 -0.72 0.471 -.0050196 .0023214 bf7m | -.0007653 .0006452 -1.19 0.236 -.0020299 .0004993 bf8 | .0000124 .0000428 0.29 0.772 -.0000716 .0000964 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | .0409398 .0388667 1.05 0.292 -.0352376 .1171172 bf22 | .0000496 .0001446 0.34 0.732 -.0002338 .0003329 bf29 | .0000147 .0000386 0.38 0.704 -.000061 .0000904 bf30 | .0005975 .0003487 1.71 0.087 -.0000859 .0012809 bf40 | -.092913 .1571517 -0.59 0.554 -.4009247 .2150987 deaw2 | .1650922 .2350487 0.70 0.482 -.2955947 .6257792 dvcew2 | -.44027 1.751166 -0.25 0.801 -3.872493 2.991953 sepaw2 | 0 (omitted) accdw2 | .3072193 .6191348 0.50 0.620 -.9062626 1.520701 movew2 | -.4208456 .8150343 -0.52 0.606 -2.018283 1.176592 illw2 | .146668 .2184808 0.67 0.502 -.2815466 .5748825 shfamw2 | .0028218 .0077742 0.36 0.717 -.0124154 .018059 shhlw2 | .0082475 .0076919 1.07 0.284 -.0068283 .0233233 shjobw2 | -.0093426 .0067931 -1.38 0.169 -.0226569 .0039716 shrelaw2 | -.0142034 .0081278 -1.75 0.081 -.0301336 .0017268 suprtw2 | -.0099735 .0060387 -1.65 0.099 -.021809 .0018621 suchrw2 | -.0045113 .0068989 -0.65 0.513 -.0180329 .0090102 havmilsq | -2.60e-06 4.34e-06 -0.60 0.550 -.0000111 5.92e-06 _cons | 2.140849 1090.33 0.00 0.998 -2134.867 2139.148 ------------------------------------------------------------------------------ Note: 1 failure and 0 successes completely determined. Logistic model for HP2inthob -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 32 12 | 44 - | 33 266 | 299 -----------+--------------------------+----------- Total | 65 278 | 343 Classified + if predicted Pr(D) >= .5 True D defined as HP2inthob != 0 -------------------------------------------------- Sensitivity Pr( +| D) 49.23% Specificity Pr( -|~D) 95.68% Positive predictive value Pr( D| +) 72.73% Negative predictive value Pr(~D| -) 88.96% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 4.32% False - rate for true D Pr( -| D) 50.77% False + rate for classified + Pr(~D| +) 27.27% False - rate for classified - Pr( D| -) 11.04% -------------------------------------------------- Correctly classified 86.88% -------------------------------------------------- Logistic model for HP2inthob, goodness-of-fit test number of observations = 343 number of covariate patterns = 343 Pearson chi2(292) = 307.38 Prob > chi2 = 0.2568 Measures of Fit for logistic of HP2inthob Log-Lik Intercept Only: -166.528 Log-Lik Full Model: -105.173 D(287): 210.346 LR(50): 122.710 Prob > LR: 0.000 McFadden's R2: 0.368 McFadden's Adj R2: 0.032 Maximum Likelihood R2: 0.301 Cragg & Uhler's R2: 0.484 McKelvey and Zavoina's R2: 0.627 Efron's R2: 0.388 Variance of y*: 8.828 Variance of error: 3.290 Count R2: 0.869 Adj Count R2: 0.308 AIC: 0.940 AIC*n: 322.346 BIC: -1465.083 BIC': 169.177 Full main model for HP2vacatn for wave= 2 chunk 2 H1 test:Gender= 1 model Wave = 2 for HP2inthob i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: _Ieduc_4 != 0 predicts failure perfectly _Ieduc_4 dropped and 13 obs not used note: _Ieduc_7 != 0 predicts failure perfectly _Ieduc_7 dropped and 4 obs not used note: _Ieduc_8 != 0 predicts failure perfectly _Ieduc_8 dropped and 2 obs not used note: occ6w2 != 0 predicts failure perfectly occ6w2 dropped and 5 obs not used note: occ8w2 != 0 predicts failure perfectly occ8w2 dropped and 44 obs not used note: marrw22 != 0 predicts failure perfectly marrw22 dropped and 7 obs not used note: marrw26 != 0 predicts failure perfectly marrw26 dropped and 3 obs not used note: inc1w2 != 0 predicts failure perfectly inc1w2 dropped and 15 obs not used note: inc4w2 != 0 predicts failure perfectly inc4w2 dropped and 10 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 14 obs not used note: bf29 != 0 predicts failure perfectly bf29 dropped and 6 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 3 obs not used note: marrw25 != 0 predicts success perfectly marrw25 dropped and 2 obs not used note: dvcew2 != 0 predicts failure perfectly dvcew2 dropped and 1 obs not used note: _Ieduc_6 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 209 LR chi2(39) = 102.98 Prob > chi2 = 0.0000 Log likelihood = -47.602962 Pseudo R2 = 0.5196 ------------------------------------------------------------------------------ HP2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .1088786 .0407817 2.67 0.008 .028948 .1888093 _Ieduc_2 | -.9318847 1.583195 -0.59 0.556 -4.034891 2.171121 _Ieduc_3 | -.3013966 .8062315 -0.37 0.709 -1.881581 1.278788 _Ieduc_4 | 0 (omitted) _Ieduc_5 | .7382214 .9602436 0.77 0.442 -1.143821 2.620264 _Ieduc_6 | 0 (omitted) _Ieduc_7 | 0 (omitted) _Ieduc_8 | 0 (omitted) occ1w2 | .5124208 2.878835 0.18 0.859 -5.129992 6.154834 occ2w2 | -.4408554 2.792442 -0.16 0.875 -5.913941 5.03223 occ3w2 | -2.00202 3.038272 -0.66 0.510 -7.956923 3.952884 occ4w2 | -1.69795 2.828364 -0.60 0.548 -7.241441 3.84554 occ5w2 | .7563117 3.026518 0.25 0.803 -5.175555 6.688178 occ6w2 | 0 (omitted) occ7w2 | -1.936351 3.240951 -0.60 0.550 -8.288498 4.415797 occ8w2 | 0 (omitted) marrw21 | -1.786228 2.087814 -0.86 0.392 -5.878268 2.305811 marrw22 | 0 (omitted) marrw23 | -3.710107 2.110606 -1.76 0.079 -7.846818 .4266038 marrw25 | 0 (omitted) marrw26 | 0 (omitted) inc1w2 | 0 (omitted) inc2w2 | 6.638966 3.131338 2.12 0.034 .5016569 12.77628 inc3w2 | 6.255863 3.02429 2.07 0.039 .3283623 12.18336 inc4w2 | 0 (omitted) radhlw2 | .0154566 .0132775 1.16 0.244 -.0105668 .0414799 havmil | .0154293 .016452 0.94 0.348 -.016816 .0476745 avgcumdosew2 | -.0557184 .2274565 -0.24 0.806 -.501525 .3900881 bf1 | -.1915136 .1848361 -1.04 0.300 -.5537857 .1707584 bf4 | -1.014161 .384844 -2.64 0.008 -1.768442 -.2598809 bf2 | .0009651 .0003217 3.00 0.003 .0003345 .0015957 bf4m | .5255255 .2920885 1.80 0.072 -.0469573 1.098008 bf5m | -.0090927 .0046394 -1.96 0.050 -.0181857 3.32e-07 bf7m | .0026491 .0014384 1.84 0.066 -.0001701 .0054684 bf8 | -.0000651 .0001057 -0.62 0.538 -.0002723 .0001421 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | .1424239 .1725233 0.83 0.409 -.1957155 .4805633 bf22 | -.0003108 .0003594 -0.86 0.387 -.0010152 .0003936 bf29 | 0 (omitted) bf30 | -.0023793 .0008117 -2.93 0.003 -.0039701 -.0007885 bf40 | .9125294 .4548855 2.01 0.045 .0209702 1.804089 deaw2 | -.6840264 .6025074 -1.14 0.256 -1.864919 .4968663 dvcew2 | 0 (omitted) sepaw2 | 0 (omitted) accdw2 | 1.109415 .6826211 1.63 0.104 -.2284975 2.447328 movew2 | 1.822244 1.095619 1.66 0.096 -.3251288 3.969618 illw2 | -.4132514 .5817296 -0.71 0.477 -1.55342 .7269176 shfamw2 | .0127979 .0115841 1.10 0.269 -.0099065 .0355022 shhlw2 | .0134786 .0158032 0.85 0.394 -.0174951 .0444522 shjobw2 | -.038737 .0177181 -2.19 0.029 -.0734639 -.0040101 shrelaw2 | -.0185842 .0149559 -1.24 0.214 -.0478973 .0107288 suprtw2 | .013763 .0125463 1.10 0.273 -.0108273 .0383533 suchrw2 | -.0155904 .0122501 -1.27 0.203 -.0396002 .0084195 havmilsq | -.000036 .0000355 -1.01 0.312 -.0001056 .0000337 _cons | -17.72332 9.218306 -1.92 0.055 -35.79086 .3442328 ------------------------------------------------------------------------------ Note: 1 failure and 0 successes completely determined. Logistic model for HP2vacatn -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 24 8 | 32 - | 14 163 | 177 -----------+--------------------------+----------- Total | 38 171 | 209 Classified + if predicted Pr(D) >= .5 True D defined as HP2vacatn != 0 -------------------------------------------------- Sensitivity Pr( +| D) 63.16% Specificity Pr( -|~D) 95.32% Positive predictive value Pr( D| +) 75.00% Negative predictive value Pr(~D| -) 92.09% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 4.68% False - rate for true D Pr( -| D) 36.84% False + rate for classified + Pr(~D| +) 25.00% False - rate for classified - Pr( D| -) 7.91% -------------------------------------------------- Correctly classified 89.47% -------------------------------------------------- Logistic model for HP2vacatn, goodness-of-fit test number of observations = 209 number of covariate patterns = 209 Pearson chi2(169) = 254.11 Prob > chi2 = 0.0000 Measures of Fit for logistic of HP2vacatn Log-Lik Intercept Only: -99.095 Log-Lik Full Model: -47.603 D(153): 95.206 LR(39): 102.984 Prob > LR: 0.000 McFadden's R2: 0.520 McFadden's Adj R2: -0.045 Maximum Likelihood R2: 0.389 Cragg & Uhler's R2: 0.635 McKelvey and Zavoina's R2: 0.876 Efron's R2: 0.535 Variance of y*: 26.488 Variance of error: 3.290 Count R2: 0.895 Adj Count R2: 0.421 AIC: 0.991 AIC*n: 207.206 BIC: -722.171 BIC': 105.367 Full main model for HP2vacatn for wave= 2 chunk 2 H1 test:Gender= 2 model Wave = 2 for HP2vacatn i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 7 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 343 LR chi2(50) = 113.11 Prob > chi2 = 0.0000 Log likelihood = -105.52458 Pseudo R2 = 0.3489 ------------------------------------------------------------------------------ HP2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0854752 .0223994 3.82 0.000 .0415731 .1293772 _Ieduc_2 | -13.44949 933.6235 -0.01 0.989 -1843.318 1816.419 _Ieduc_3 | -13.23035 933.6235 -0.01 0.989 -1843.099 1816.638 _Ieduc_4 | -11.35201 933.6237 -0.01 0.990 -1841.221 1818.517 _Ieduc_5 | -12.71553 933.6236 -0.01 0.989 -1842.584 1817.153 _Ieduc_6 | -12.77592 933.6235 -0.01 0.989 -1842.644 1817.093 _Ieduc_7 | -13.29025 933.6257 -0.01 0.989 -1843.163 1816.582 _Ieduc_8 | 0 (omitted) occ1w2 | -1.13677 2.279208 -0.50 0.618 -5.603935 3.330396 occ2w2 | -.2856297 2.305316 -0.12 0.901 -4.803966 4.232706 occ3w2 | -.216915 2.29482 -0.09 0.925 -4.714679 4.280849 occ4w2 | -.6834035 2.371062 -0.29 0.773 -5.330599 3.963792 occ5w2 | -1.371419 2.630896 -0.52 0.602 -6.52788 3.785042 occ6w2 | .0514808 2.451681 0.02 0.983 -4.753725 4.856686 occ7w2 | -.181635 2.248007 -0.08 0.936 -4.587648 4.224378 occ8w2 | .8447418 2.549831 0.33 0.740 -4.152835 5.842319 marrw21 | -.3962422 1.357576 -0.29 0.770 -3.057042 2.264558 marrw22 | -.7030307 1.344505 -0.52 0.601 -3.338211 1.93215 marrw23 | -.2946063 .8362364 -0.35 0.725 -1.933599 1.344387 marrw25 | -.3847231 1.312392 -0.29 0.769 -2.956964 2.187518 marrw26 | 0 (omitted) inc1w2 | -.1652405 2.308326 -0.07 0.943 -4.689477 4.358996 inc2w2 | .5715205 2.259629 0.25 0.800 -3.857271 5.000312 inc3w2 | .4239257 2.273462 0.19 0.852 -4.031977 4.879829 inc4w2 | -.1023194 2.63109 -0.04 0.969 -5.25916 5.054522 radhlw2 | .0311839 .0092957 3.35 0.001 .0129647 .0494031 havmil | .0003102 .0031015 0.10 0.920 -.0057687 .006389 avgcumdosew2 | .0845928 .134995 0.63 0.531 -.1799926 .3491782 bf1 | -.0293287 .036543 -0.80 0.422 -.1009517 .0422942 bf4 | -.0358912 .2239004 -0.16 0.873 -.474728 .4029456 bf2 | .0000573 .0001382 0.41 0.678 -.0002135 .0003281 bf4m | -.0145732 .2099542 -0.07 0.945 -.4260759 .3969296 bf5m | -.0005729 .0016599 -0.35 0.730 -.0038263 .0026805 bf7m | -.0009478 .0006252 -1.52 0.130 -.0021731 .0002775 bf8 | -.0000327 .0000408 -0.80 0.422 -.0001126 .0000472 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | .0039961 .0309743 0.13 0.897 -.0567124 .0647046 bf22 | .0000865 .0001546 0.56 0.576 -.0002165 .0003895 bf29 | .0000586 .0000394 1.49 0.137 -.0000187 .000136 bf30 | -.0003297 .0003684 -0.89 0.371 -.0010518 .0003924 bf40 | -.198659 .1714101 -1.16 0.246 -.5346167 .1372987 deaw2 | .2948234 .2283214 1.29 0.197 -.1526784 .7423251 dvcew2 | 1.552638 1.538913 1.01 0.313 -1.463576 4.568851 sepaw2 | 0 (omitted) accdw2 | -.6724358 .784314 -0.86 0.391 -2.209663 .8647914 movew2 | -.2536718 .8260112 -0.31 0.759 -1.872624 1.36528 illw2 | .1943909 .2307856 0.84 0.400 -.2579405 .6467223 shfamw2 | .0107227 .0072538 1.48 0.139 -.0034944 .0249399 shhlw2 | .0100533 .0078834 1.28 0.202 -.0053979 .0255045 shjobw2 | -.0092742 .0068673 -1.35 0.177 -.0227339 .0041855 shrelaw2 | -.0224847 .0085804 -2.62 0.009 -.039302 -.0056674 suprtw2 | -.0043934 .0060874 -0.72 0.470 -.0163245 .0075377 suchrw2 | .0021142 .0069953 0.30 0.762 -.0115964 .0158248 havmilsq | -6.39e-07 2.42e-06 -0.26 0.792 -5.38e-06 4.10e-06 _cons | 8.078046 933.628 0.01 0.993 -1821.799 1837.955 ------------------------------------------------------------------------------ Logistic model for HP2vacatn -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 30 11 | 41 - | 32 270 | 302 -----------+--------------------------+----------- Total | 62 281 | 343 Classified + if predicted Pr(D) >= .5 True D defined as HP2vacatn != 0 -------------------------------------------------- Sensitivity Pr( +| D) 48.39% Specificity Pr( -|~D) 96.09% Positive predictive value Pr( D| +) 73.17% Negative predictive value Pr(~D| -) 89.40% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 3.91% False - rate for true D Pr( -| D) 51.61% False + rate for classified + Pr(~D| +) 26.83% False - rate for classified - Pr( D| -) 10.60% -------------------------------------------------- Correctly classified 87.46% -------------------------------------------------- Logistic model for HP2vacatn, goodness-of-fit test number of observations = 343 number of covariate patterns = 343 Pearson chi2(292) = 331.68 Prob > chi2 = 0.0548 Measures of Fit for logistic of HP2vacatn Log-Lik Intercept Only: -162.082 Log-Lik Full Model: -105.525 D(287): 211.049 LR(50): 113.114 Prob > LR: 0.000 McFadden's R2: 0.349 McFadden's Adj R2: 0.003 Maximum Likelihood R2: 0.281 Cragg & Uhler's R2: 0.460 McKelvey and Zavoina's R2: 0.590 Efron's R2: 0.377 Variance of y*: 8.031 Variance of error: 3.290 Count R2: 0.875 Adj Count R2: 0.306 AIC: 0.942 AIC*n: 323.049 BIC: -1464.379 BIC': 178.773 142 . 143 . title4 "trimming male models of dose and HP2work relationship in wave 2 " ------------------------------------------------------------------------------- trimming male models of dose and HP2work relationship in wave 2 ------------------------------------------------------------------------------- 144 . * male models 145 . forvalues j=2/2 { 2. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 3. di as input "Gender =1 HP2work model" 4. di _skip(5) 5. logit HP2work age inc1w2-inc4w2 bf2 bf4 bf5m bf30 bf40 illw`j' // > / > shhlw`j' radhlw2 havmilsq /// > avgcumdosew`j' if gender==1 6. estat class 7. estat gof 8. fitstat 9. } Gender =1 HP2work model Iteration 0: log likelihood = -172.87291 Iteration 1: log likelihood = -142.40034 Iteration 2: log likelihood = -139.93804 Iteration 3: log likelihood = -139.91728 Iteration 4: log likelihood = -139.91728 Logistic regression Number of obs = 340 LR chi2(15) = 65.91 Prob > chi2 = 0.0000 Log likelihood = -139.91728 Pseudo R2 = 0.1906 ------------------------------------------------------------------------------ HP2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0011282 .0148134 0.08 0.939 -.0279056 .030162 inc1w2 | -.9128308 1.006103 -0.91 0.364 -2.884756 1.059095 inc2w2 | .2695533 .6709232 0.40 0.688 -1.045432 1.584539 inc3w2 | .7986152 .6627163 1.21 0.228 -.5002849 2.097515 inc4w2 | -.2811072 1.2809 -0.22 0.826 -2.791626 2.229412 bf2 | .0000321 .0000932 0.34 0.730 -.0001506 .0002148 bf4 | -.1434674 .0383586 -3.74 0.000 -.2186488 -.0682859 bf5m | -.0001776 .0008906 -0.20 0.842 -.0019232 .0015679 bf30 | -.0001426 .0003057 -0.47 0.641 -.0007418 .0004566 bf40 | .3375085 .0997566 3.38 0.001 .1419893 .5330278 illw2 | -.0213359 .267186 -0.08 0.936 -.5450108 .502339 shhlw2 | .0011092 .0043627 0.25 0.799 -.0074416 .00966 radhlw2 | .000307 .0058685 0.05 0.958 -.011195 .011809 havmilsq | 2.49e-08 1.65e-06 0.02 0.988 -3.21e-06 3.26e-06 avgcumdosew2 | .0233352 .0524776 0.44 0.657 -.0795191 .1261894 _cons | -1.06417 1.235654 -0.86 0.389 -3.486007 1.357667 ------------------------------------------------------------------------------ Logistic model for HP2work -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 19 15 | 34 - | 51 255 | 306 -----------+--------------------------+----------- Total | 70 270 | 340 Classified + if predicted Pr(D) >= .5 True D defined as HP2work != 0 -------------------------------------------------- Sensitivity Pr( +| D) 27.14% Specificity Pr( -|~D) 94.44% Positive predictive value Pr( D| +) 55.88% Negative predictive value Pr(~D| -) 83.33% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 5.56% False - rate for true D Pr( -| D) 72.86% False + rate for classified + Pr(~D| +) 44.12% False - rate for classified - Pr( D| -) 16.67% -------------------------------------------------- Correctly classified 80.59% -------------------------------------------------- Logistic model for HP2work, goodness-of-fit test number of observations = 340 number of covariate patterns = 336 Pearson chi2(320) = 291.79 Prob > chi2 = 0.8693 Measures of Fit for logit of HP2work Log-Lik Intercept Only: -172.873 Log-Lik Full Model: -139.917 D(324): 279.835 LR(15): 65.911 Prob > LR: 0.000 McFadden's R2: 0.191 McFadden's Adj R2: 0.098 Maximum Likelihood R2: 0.176 Cragg & Uhler's R2: 0.276 McKelvey and Zavoina's R2: 0.292 Efron's R2: 0.182 Variance of y*: 4.649 Variance of error: 3.290 Count R2: 0.806 Adj Count R2: 0.057 AIC: 0.917 AIC*n: 311.835 BIC: -1608.744 BIC': 21.523 146 . 147 . 148 . forvalues j=2/2 { 2. title4 "trimmed HP2work main effects models wave `j' for H1 part 2 with do > se ns" 3. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 4. di as input "Gender = male HP2work model" 5. 149 . set more off 6. forvalues j=2/2 { 7. logistic HP2work age bf4 illw`j' shhlw`j' havmilsq /// > avgcumdosew`j' if gender==1, coef nolog 8. estat class 9. estat gof 10. fitstat 11. } 12. } ------------------------------------------------------------------------------- trimmed HP2work main effects models wave 2 for H1 part 2 with dose ns ------------------------------------------------------------------------------- Gender = male HP2work model Logistic regression Number of obs = 340 LR chi2(6) = 46.77 Prob > chi2 = 0.0000 Log likelihood = -149.49038 Pseudo R2 = 0.1353 ------------------------------------------------------------------------------ HP2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0127019 .0133613 0.95 0.342 -.0134858 .0388895 bf4 | -.1397142 .0305029 -4.58 0.000 -.1994988 -.0799296 illw2 | .2410569 .2214219 1.09 0.276 -.192922 .6750358 shhlw2 | .0043743 .0038731 1.13 0.259 -.0032168 .0119654 havmilsq | 2.84e-07 1.53e-06 0.19 0.852 -2.71e-06 3.28e-06 avgcumdosew2 | .0173789 .0498005 0.35 0.727 -.0802284 .1149862 _cons | -.7234318 .9036114 -0.80 0.423 -2.494478 1.047614 ------------------------------------------------------------------------------ Logistic model for HP2work -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 15 13 | 28 - | 55 257 | 312 -----------+--------------------------+----------- Total | 70 270 | 340 Classified + if predicted Pr(D) >= .5 True D defined as HP2work != 0 -------------------------------------------------- Sensitivity Pr( +| D) 21.43% Specificity Pr( -|~D) 95.19% Positive predictive value Pr( D| +) 53.57% Negative predictive value Pr(~D| -) 82.37% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 4.81% False - rate for true D Pr( -| D) 78.57% False + rate for classified + Pr(~D| +) 46.43% False - rate for classified - Pr( D| -) 17.63% -------------------------------------------------- Correctly classified 80.00% -------------------------------------------------- Logistic model for HP2work, goodness-of-fit test number of observations = 340 number of covariate patterns = 325 Pearson chi2(318) = 310.55 Prob > chi2 = 0.6069 Measures of Fit for logistic of HP2work Log-Lik Intercept Only: -172.873 Log-Lik Full Model: -149.490 D(333): 298.981 LR(6): 46.765 Prob > LR: 0.000 McFadden's R2: 0.135 McFadden's Adj R2: 0.095 Maximum Likelihood R2: 0.129 Cragg & Uhler's R2: 0.201 McKelvey and Zavoina's R2: 0.196 Efron's R2: 0.134 Variance of y*: 4.094 Variance of error: 3.290 Count R2: 0.800 Adj Count R2: 0.029 AIC: 0.921 AIC*n: 312.981 BIC: -1642.058 BIC': -11.791 150 . 151 . title4 "Simultaneous trimming" ------------------------------------------------------------------------------- Simultaneous trimming ------------------------------------------------------------------------------- 152 . forvalues j=2/2 { 2. logistic HP2work age bf8 illw`j' shhlw`j' /// > avgcumdosew`j' if gender==1, coef nolog 3. estat class 4. estat gof 5. fitstat 6. } Logistic regression Number of obs = 340 LR chi2(5) = 26.93 Prob > chi2 = 0.0001 Log likelihood = -159.40747 Pseudo R2 = 0.0779 ------------------------------------------------------------------------------ HP2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0348108 .0120851 2.88 0.004 .0111245 .0584972 bf8 | -.0000289 .0000207 -1.40 0.163 -.0000694 .0000117 illw2 | .4876542 .2119789 2.30 0.021 .0721831 .9031252 shhlw2 | .0088288 .0036112 2.44 0.014 .0017511 .0159066 avgcumdosew2 | .0118366 .0475514 0.25 0.803 -.0813624 .1050355 _cons | -3.658274 .6492228 -5.63 0.000 -4.930727 -2.385821 ------------------------------------------------------------------------------ Logistic model for HP2work -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 6 5 | 11 - | 64 265 | 329 -----------+--------------------------+----------- Total | 70 270 | 340 Classified + if predicted Pr(D) >= .5 True D defined as HP2work != 0 -------------------------------------------------- Sensitivity Pr( +| D) 8.57% Specificity Pr( -|~D) 98.15% Positive predictive value Pr( D| +) 54.55% Negative predictive value Pr(~D| -) 80.55% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 1.85% False - rate for true D Pr( -| D) 91.43% False + rate for classified + Pr(~D| +) 45.45% False - rate for classified - Pr( D| -) 19.45% -------------------------------------------------- Correctly classified 79.71% -------------------------------------------------- Logistic model for HP2work, goodness-of-fit test number of observations = 340 number of covariate patterns = 311 Pearson chi2(305) = 333.90 Prob > chi2 = 0.1226 Measures of Fit for logistic of HP2work Log-Lik Intercept Only: -172.873 Log-Lik Full Model: -159.407 D(334): 318.815 LR(5): 26.931 Prob > LR: 0.000 McFadden's R2: 0.078 McFadden's Adj R2: 0.043 Maximum Likelihood R2: 0.076 Cragg & Uhler's R2: 0.119 McKelvey and Zavoina's R2: 0.136 Efron's R2: 0.088 Variance of y*: 3.807 Variance of error: 3.290 Count R2: 0.797 Adj Count R2: 0.014 AIC: 0.973 AIC*n: 330.815 BIC: -1628.053 BIC': 2.214 153 . // HP2work for men washes out no dose impact 154 . 155 . scalar SigDoseWkFw2 = "no" 156 . 157 . 158 . des bf8 storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m 159 . * male sign main effects in main effects model: 4- age, bf8, illw2 shjobw2 160 . * male main effects model avgcumdosew2 was not signif. 161 . * male hp2wk w2 mediators: age illw2 ageXillw2 bf8 162 . * female signif main effects in main effects model 163 . * female hp2wk w2 mediators: radhlw2 age bf1 bf4 bf4m 164 . * hypothnuym pt dv wave gender signif 165 . 166 . 167 . title4 " 1. Summary matrix construction Paid employment partition: first two > rows" ------------------------------------------------------------------------------- 1. Summary matrix construction Paid employment partition: first two rows ------------------------------------------------------------------------------- 168 . 169 . matrix define HP2wkMw2 = J(1,8, 0) 170 . matrix define HP2wkFw2 = J(1,8, 0) 171 . matrix colnames HP2wkMw2= hypnum ptnum wave gender medsig numMAsig numModsig > numMed 172 . matrix colnames HP2wkFw2= hypnum ptnum wave gender medsig numMAsig numModsig > numMed 173 . matrix rownames HP2wkMw2 = workM 174 . matrix rownames HP2wkFw2 = workF 175 . matrix define HP2wkMw2= (1, 2, 3, 1, 0 ,4, 0, 4 ) 176 . matrix define HP2wkFw2= (1, 2, 3, 2, 0, 1, 0, 6 ) 177 . 178 . matrix define H1pt2w2 = (HP2wkMw2 \ HP2wkFw2) 179 . matrix colnames H1pt2w2 = hypnum ptnum wave medsig numMAsig numModsig n > umMed 180 . matrix rownames H1pt2w2 = HP2wkMw2 WHP2wkFw2 181 . matlist H1pt2w2 | hypnum ptnum wave medsig numMAsig numModsig > numMed numMed -------------+----------------------------------------------------------------- ----------------------- HP2wkMw2 | 1 2 3 1 0 4 > 0 4 WHP2wkFw2 | 1 2 3 2 0 1 > 0 6 182 . 183 . scalar SigDoseWKMw2 = "no" 184 . scalar MainEffwkMw2 = "workM: age bf8 illw2 shjobw2" 185 . 186 . scalar list `MainEffwkMw2' wkMedMw2 = bf8 age illw2 VactnMedFw2 = age illw2 radhlw2 VactnMedMw2 = age illw2 VacatnModFw2 = none MainEffVactnFw2 = age radhlw2 deaw2 SigDoseVactnFw2 = no vactnModMw2 = none MainEffVactnMw2 = age bf7m radhlw2 SigDoseVactnMw2 = no sxLifeMedFw2 = age bf4 bf4m sxLifeMedMw2 = age illw2 InthbModFw2 = none MainEffInthbFw2 = age radhlw2 bf4 SigdoseInthbFw2 = no InthbMw2 = none MainEffInthbMw2 = age radhlw2 shfamw2 SigDoseInthbMw2 = no sxlifeMedFw2 = age illw2 radhlw2 bf4 bf4m sxlifeMedMw2 = age illw2 sxlifeModFw2 = none MainEffsxlifeFw2 = age radhlw2 bf4 bf4m shrelaw2 shfamw2 SigDoseSxlifeFw2 = no sxlifeModMw2 = none SigDosesxlifeMw2 = no MainEffsxlifeMw2 = age bf4 illw2 radhlw2 PrbfmhmMedFw2 = age bf4 PrbfmhmMedMw2 = age PrbfmhmModFw2 = none MainEffPrbfmhmFw2 = age bf4 bf40 SigDosePrbfmhmFw2 = no PrbfmhmModw2 = none SigDosePrbfmhmMw2 = no SigDosePrbfhmMw2 = no MainEffPrbfhmMw2 = bf1 bf4 dvcew2 bf7m ProbsocMedFw2 = age radhlw2 ProbsocMedMw2 = age ProbsocModFw2 = none MainEffProbSocFw2 = age radhlw2 illw2 Shrelaw2 avgcumodsew2 SigDoseProbsocFw2 = yes ProbSocModMw2 = none SigDoseProbsocMw2 = no MainEffPrbsocMw2 = age radhlw2 shjobw2 hmcareMedFw2 = age illw2 hmcareMedMw2 = age illw2 hmcareModFw2 = none SigDoseWKFw2 = 0 SigdoseHmcareFw2 = no hmcareModMw2 = none MainEffhmcareMw2 = none SigDoseHmcareMw2 = no wkMedFw2 = radhlw2 age bf40 bf4m bf1 wkModFw2 = none wkModMw2 = none MainEffwkFw2 = age MainEffwkMw2 = workM: age bf8 illw2 shjobw2 SigDoseWKMw2 = no SigDoseWkFw2 = no hmcrMedFw1 = age icdxcnt shjobw1 bf4 BSIsoma WHPpain WHPsleep WHPel hmcrMedMw1 = age MainEffhmcrFw1 = illw1 age SigDosehmcrFw1 = no hmcrModMw1 = none MainEffhmcrMw1 = age shjobw1 SigDosehmcrMw1 = no wkMedFw1 = age b4 MainEffwkFw1 = age MainEffwkMw1 = age wkMedMw1 = bf40 WkMedMw1 = none WkModFw1 = none WKModMw1 = none SigDoseWkMw1 = no SigDoseWkFw1 = no SigDoseFw1 = no wkModFw1 = none wkModMw1 = none medsigFw1 = 1 prbsocnumMAsig = 8 187 . 188 . * moderator construction 189 . * none ussed because basic dose work relationship washes out 190 . 191 . sem(avgcumdosew2->bf4)(bf4->hp2work) /// > (avgcumdosew2->bf4m)(bf4m->hp2work)(avgcumdosew2->hp2work) if gender==2, no > capslatent Endogenous variables Observed: bf4 hp2work bf4m Exogenous variables Observed: avgcumdosew2 Fitting target model: Iteration 0: log likelihood = -3065.5103 Iteration 1: log likelihood = -3065.5103 Structural equation model Number of obs = 363 Estimation method = ml Log likelihood = -3065.5103 ------------------------------------------------------------------------------- --- | OIM | Coef. Std. Err. z P>|z| [95% Conf. Interv > al] -----------------+------------------------------------------------------------- --- Structural | bf4 <- | avgcumdosew2 | -.595012 .1955294 -3.04 0.002 -.9782426 -.2117 > 813 _cons | 11.02048 .321487 34.28 0.000 10.39037 11.65 > 058 ---------------+------------------------------------------------------------- --- hp2work <- | bf4 | -.1104133 .0240718 -4.59 0.000 -.1575931 -.0632 > 334 bf4m | .0772012 .0220571 3.50 0.000 .03397 .1204 > 324 avgcumdosew2 | .0267414 .0155054 1.72 0.085 -.0036486 .0571 > 315 _cons | -.022371 .1624855 -0.14 0.890 -.3408367 .2960 > 947 ---------------+------------------------------------------------------------- --- bf4m <- | avgcumdosew2 | -.599311 .2133886 -2.81 0.005 -1.017545 -.1810 > 771 _cons | 18.83424 .3508508 53.68 0.000 18.14659 19.5 > 219 -----------------+------------------------------------------------------------- --- Variance | e.bf4 | 26.38663 1.958599 22.81402 30.51 > 869 e.hp2work | .1612644 .0119702 .13943 .1865 > 179 e.bf4m | 31.42693 2.332725 27.17189 36.34 > 829 ------------------------------------------------------------------------------- --- LR test of model vs. saturated: chi2(1) = 1284.49, Prob > chi2 = 0.0000 192 . estat teffects, standardize Direct effects ------------------------------------------------------------------------------- --- | OIM | Coef. Std. Err. z P>|z| Std. Co > ef. -----------------+------------------------------------------------------------- --- Structural | bf4 <- | avgcumdosew2 | -.595012 .1955294 -3.04 0.002 -.1577 > 212 ---------------+------------------------------------------------------------- --- hp2work <- | bf4 | -.1104133 .0240718 -4.59 0.000 -.6994 > 476 bf4m | .0772012 .0220571 3.50 0.000 .5327 > 398 avgcumdosew2 | .0267414 .0155054 1.72 0.085 .0449 > 038 ---------------+------------------------------------------------------------- --- bf4m <- | avgcumdosew2 | -.599311 .2133886 -2.81 0.005 -.1458 > 343 ------------------------------------------------------------------------------- --- Indirect effects ------------------------------------------------------------------------------- --- | OIM | Coef. Std. Err. z P>|z| Std. Co > ef. -----------------+------------------------------------------------------------- --- Structural | bf4 <- | avgcumdosew2 | 0 (no path) > 0 ---------------+------------------------------------------------------------- --- hp2work <- | bf4 | 0 (no path) > 0 bf4m | 0 (no path) > 0 avgcumdosew2 | .0194297 .0272807 0.71 0.476 .032 > 626 ---------------+------------------------------------------------------------- --- bf4m <- | avgcumdosew2 | 0 (no path) > 0 ------------------------------------------------------------------------------- --- Total effects ------------------------------------------------------------------------------- --- | OIM | Coef. Std. Err. z P>|z| Std. Co > ef. -----------------+------------------------------------------------------------- --- Structural | bf4 <- | avgcumdosew2 | -.595012 .1955294 -3.04 0.002 -.1577 > 212 ---------------+------------------------------------------------------------- --- hp2work <- | bf4 | -.1104133 .0240718 -4.59 0.000 -.6994 > 476 bf4m | .0772012 .0220571 3.50 0.000 .5327 > 398 avgcumdosew2 | .0461711 .031163 1.48 0.138 .0775 > 298 ---------------+------------------------------------------------------------- --- bf4m <- | avgcumdosew2 | -.599311 .2133886 -2.81 0.005 -.1458 > 343 ------------------------------------------------------------------------------- --- 193 . 194 . 195 . 196 . title4 "testing female hp2work main effects for hypothesis 1 pt 2 wave 2" ------------------------------------------------------------------------------- testing female hp2work main effects for hypothesis 1 pt 2 wave 2 ------------------------------------------------------------------------------- 197 . * Dose work relationship washes out for males in wave 2 also 198 . cap gen hp2hmcare=HP2hmcare 199 . set more off 200 . forvalues j=2/2 { 2. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 3. di as input "For females HP2work on wave 2 with dose ns" 4. des age occ1w`j'-occ8w`j' inc1w`j'-inc4w`j' avgcumdosew`j' bf8 // > / > marrw`j'1-marrw`j'6 `w2bf' 5. logistic HP2work marrw`j'3-marrw`j'6 age havmilsq /// > avgcumdosew2 bf8 illw`j' shjobw`j' suprtw`j' if gender==2, co > ef nolog 6. estat gof 7. estat class 8. fitstat 9. } For females HP2work on wave 2 with dose ns storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age occ1w2 double %15.0g LABJ profess executive administration in 1996 occ2w2 double %15.0g LABJ technical sales admin support in 1996 occ3w2 double %15.0g LABJ service occup protective services in 1996 occ4w2 double %15.0g LABJ precision prod mechan craft construction in 1996 occ5w2 double %15.0g LABJ factory laborer machinist transp cleaner in 1996 occ6w2 double %15.0g LABJ farming agricul forestry fishing trapping logging in 1996 occ7w2 double %15.0g LABJ homemaking caregiving in 1996 occ8w2 double %15.0g LABJ student in 1996 inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf2 float %9.0g bf2 = max(0, efradw3 - 1.61252E-006) * bf1 bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m bf7m float %9.0g bf7m = max(0, radtlw3 - 2.12558E-007) * bf4m bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf17 float %9.0g bf17 = max(0, 20 - ecprw3) * bf15m bf20 float %9.0g bf20 = max(0, kzchorn - 2.53946E-006) bf22 float %9.0g bf22 = max(0, icdxcnt - 1.01635E-007) * bf7m bf29 float %9.0g bf29 = max(0, 18 - CSsocspt) * bf8 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) note: marrw24 != 0 predicts success perfectly marrw24 dropped and 1 obs not used Logistic regression Number of obs = 362 LR chi2(10) = 48.81 Prob > chi2 = 0.0000 Log likelihood = -180.79074 Pseudo R2 = 0.1189 ------------------------------------------------------------------------------ HP2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- marrw23 | .3465168 .4485933 0.77 0.440 -.53271 1.225743 marrw24 | 0 (omitted) marrw25 | .1476199 .8212357 0.18 0.857 -1.461973 1.757212 marrw26 | 1.010731 .7069238 1.43 0.153 -.3748139 2.396276 age | .0569149 .0131729 4.32 0.000 .0310966 .0827332 havmilsq | -8.40e-07 1.31e-06 -0.64 0.520 -3.40e-06 1.72e-06 avgcumdosew2 | .112931 .0849282 1.33 0.184 -.0535252 .2793872 bf8 | -8.51e-07 .0000122 -0.07 0.944 -.0000247 .000023 illw2 | .2083243 .1431057 1.46 0.145 -.0721576 .4888063 shjobw2 | -.0000502 .0033727 -0.01 0.988 -.0066606 .0065603 suprtw2 | .0007776 .0033741 0.23 0.818 -.0058355 .0073907 _cons | -4.607688 .7503726 -6.14 0.000 -6.078391 -3.136985 ------------------------------------------------------------------------------ Logistic model for HP2work, goodness-of-fit test number of observations = 362 number of covariate patterns = 360 Pearson chi2(349) = 381.32 Prob > chi2 = 0.1127 Logistic model for HP2work -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 21 7 | 28 - | 71 263 | 334 -----------+--------------------------+----------- Total | 92 270 | 362 Classified + if predicted Pr(D) >= .5 True D defined as HP2work != 0 -------------------------------------------------- Sensitivity Pr( +| D) 22.83% Specificity Pr( -|~D) 97.41% Positive predictive value Pr( D| +) 75.00% Negative predictive value Pr(~D| -) 78.74% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 2.59% False - rate for true D Pr( -| D) 77.17% False + rate for classified + Pr(~D| +) 25.00% False - rate for classified - Pr( D| -) 21.26% -------------------------------------------------- Correctly classified 78.45% -------------------------------------------------- Measures of Fit for logistic of HP2work Log-Lik Intercept Only: -205.197 Log-Lik Full Model: -180.791 D(350): 361.581 LR(10): 48.812 Prob > LR: 0.000 McFadden's R2: 0.119 McFadden's Adj R2: 0.060 Maximum Likelihood R2: 0.126 Cragg & Uhler's R2: 0.186 McKelvey and Zavoina's R2: 0.242 Efron's R2: 0.149 Variance of y*: 4.341 Variance of error: 3.290 Count R2: 0.785 Adj Count R2: 0.152 AIC: 1.065 AIC*n: 385.581 BIC: -1700.494 BIC': 10.104 201 . 202 . 203 . // female wave 2 homcare washes out 204 . 205 . 206 . 207 . scalar MainEffwkFw2 = "age" 208 . scalar SigDoseWKFw2 = 0 209 . 210 . 211 . 212 . forvalues j=2/2 { 2. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 3. set more off 4. di as input "For females HP2work on wave 2 with dose ns" 5. des age occ1w`j'-occ8w`j' inc1w`j'-inc4w`j' avgcumdosew`j' bf8 // > / > marrw`j'1-marrw`j'6 `w2bf' 6. logistic HP2work marrw`j'3-marrw`j'6 age havmilsq /// > avgcumdosew2 bf8 illw`j' shjobw`j' suprtw`j' if gender==1, co > ef nolog 7. estat gof 8. estat class 9. fitstat 10. } For females HP2work on wave 2 with dose ns storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age occ1w2 double %15.0g LABJ profess executive administration in 1996 occ2w2 double %15.0g LABJ technical sales admin support in 1996 occ3w2 double %15.0g LABJ service occup protective services in 1996 occ4w2 double %15.0g LABJ precision prod mechan craft construction in 1996 occ5w2 double %15.0g LABJ factory laborer machinist transp cleaner in 1996 occ6w2 double %15.0g LABJ farming agricul forestry fishing trapping logging in 1996 occ7w2 double %15.0g LABJ homemaking caregiving in 1996 occ8w2 double %15.0g LABJ student in 1996 inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf2 float %9.0g bf2 = max(0, efradw3 - 1.61252E-006) * bf1 bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m bf7m float %9.0g bf7m = max(0, radtlw3 - 2.12558E-007) * bf4m bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf17 float %9.0g bf17 = max(0, 20 - ecprw3) * bf15m bf20 float %9.0g bf20 = max(0, kzchorn - 2.53946E-006) bf22 float %9.0g bf22 = max(0, icdxcnt - 1.01635E-007) * bf7m bf29 float %9.0g bf29 = max(0, 18 - CSsocspt) * bf8 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) note: marrw24 != 0 predicts failure perfectly marrw24 dropped and 3 obs not used Logistic regression Number of obs = 337 LR chi2(10) = 28.86 Prob > chi2 = 0.0013 Log likelihood = -157.74569 Pseudo R2 = 0.0838 ------------------------------------------------------------------------------ HP2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- marrw23 | -.4766426 .3747451 -1.27 0.203 -1.21113 .2578444 marrw24 | 0 (omitted) marrw25 | .4073573 .955349 0.43 0.670 -1.465092 2.279807 marrw26 | -.9721074 1.293416 -0.75 0.452 -3.507156 1.562941 age | .0477474 .0139539 3.42 0.001 .0203982 .0750965 havmilsq | -2.10e-07 1.49e-06 -0.14 0.888 -3.12e-06 2.70e-06 avgcumdosew2 | .0155478 .048524 0.32 0.749 -.0795575 .1106532 bf8 | -.0000302 .0000209 -1.45 0.148 -.0000712 .0000107 illw2 | .5115257 .2184297 2.34 0.019 .0834114 .9396399 shjobw2 | .0088647 .0037067 2.39 0.017 .0015997 .0161296 suprtw2 | -.0044167 .0036236 -1.22 0.223 -.0115188 .0026855 _cons | -3.748494 .6958591 -5.39 0.000 -5.112352 -2.384635 ------------------------------------------------------------------------------ Logistic model for HP2work, goodness-of-fit test number of observations = 337 number of covariate patterns = 332 Pearson chi2(321) = 336.79 Prob > chi2 = 0.2612 Logistic model for HP2work -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 6 3 | 9 - | 64 264 | 328 -----------+--------------------------+----------- Total | 70 267 | 337 Classified + if predicted Pr(D) >= .5 True D defined as HP2work != 0 -------------------------------------------------- Sensitivity Pr( +| D) 8.57% Specificity Pr( -|~D) 98.88% Positive predictive value Pr( D| +) 66.67% Negative predictive value Pr(~D| -) 80.49% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 1.12% False - rate for true D Pr( -| D) 91.43% False + rate for classified + Pr(~D| +) 33.33% False - rate for classified - Pr( D| -) 19.51% -------------------------------------------------- Correctly classified 80.12% -------------------------------------------------- Measures of Fit for logistic of HP2work Log-Lik Intercept Only: -172.178 Log-Lik Full Model: -157.746 D(325): 315.491 LR(10): 28.864 Prob > LR: 0.001 McFadden's R2: 0.084 McFadden's Adj R2: 0.014 Maximum Likelihood R2: 0.082 Cragg & Uhler's R2: 0.128 McKelvey and Zavoina's R2: 0.142 Efron's R2: 0.099 Variance of y*: 3.835 Variance of error: 3.290 Count R2: 0.801 Adj Count R2: 0.043 AIC: 1.007 AIC*n: 339.491 BIC: -1576.036 BIC': 29.336 213 . forvalues j=2/2 { 2. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 3. set more off 4. di as input "For females trimmed HP2work on wave 2 with dose ns" 5. des age occ1w`j'-occ8w`j' inc1w`j'-inc4w`j' avgcumdosew`j' bf8 // > / > marrw`j'1-marrw`j'6 `w2bf' 6. logistic HP2work age /// > avgcumdosew2 bf8 illw`j' shjobw`j' radhlw`j' if gender==1, co > ef nolog 7. estat gof 8. estat class 9. fitstat 10. } For females trimmed HP2work on wave 2 with dose ns storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age occ1w2 double %15.0g LABJ profess executive administration in 1996 occ2w2 double %15.0g LABJ technical sales admin support in 1996 occ3w2 double %15.0g LABJ service occup protective services in 1996 occ4w2 double %15.0g LABJ precision prod mechan craft construction in 1996 occ5w2 double %15.0g LABJ factory laborer machinist transp cleaner in 1996 occ6w2 double %15.0g LABJ farming agricul forestry fishing trapping logging in 1996 occ7w2 double %15.0g LABJ homemaking caregiving in 1996 occ8w2 double %15.0g LABJ student in 1996 inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf2 float %9.0g bf2 = max(0, efradw3 - 1.61252E-006) * bf1 bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m bf7m float %9.0g bf7m = max(0, radtlw3 - 2.12558E-007) * bf4m bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf17 float %9.0g bf17 = max(0, 20 - ecprw3) * bf15m bf20 float %9.0g bf20 = max(0, kzchorn - 2.53946E-006) bf22 float %9.0g bf22 = max(0, icdxcnt - 1.01635E-007) * bf7m bf29 float %9.0g bf29 = max(0, 18 - CSsocspt) * bf8 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Logistic regression Number of obs = 340 LR chi2(6) = 34.24 Prob > chi2 = 0.0000 Log likelihood = -155.75258 Pseudo R2 = 0.0990 ------------------------------------------------------------------------------ HP2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0279967 .0125982 2.22 0.026 .0033047 .0526886 avgcumdosew2 | .0093245 .0489486 0.19 0.849 -.0866129 .1052619 bf8 | -.0000433 .0000214 -2.03 0.043 -.0000852 -1.44e-06 illw2 | .503088 .2180574 2.31 0.021 .0757033 .9304727 shjobw2 | .0054505 .0037374 1.46 0.145 -.0018746 .0127756 radhlw2 | .0133173 .0044637 2.98 0.003 .0045685 .022066 _cons | -3.81568 .6836898 -5.58 0.000 -5.155688 -2.475673 ------------------------------------------------------------------------------ Logistic model for HP2work, goodness-of-fit test number of observations = 340 number of covariate patterns = 332 Pearson chi2(325) = 343.93 Prob > chi2 = 0.2252 Logistic model for HP2work -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 6 5 | 11 - | 64 265 | 329 -----------+--------------------------+----------- Total | 70 270 | 340 Classified + if predicted Pr(D) >= .5 True D defined as HP2work != 0 -------------------------------------------------- Sensitivity Pr( +| D) 8.57% Specificity Pr( -|~D) 98.15% Positive predictive value Pr( D| +) 54.55% Negative predictive value Pr(~D| -) 80.55% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 1.85% False - rate for true D Pr( -| D) 91.43% False + rate for classified + Pr(~D| +) 45.45% False - rate for classified - Pr( D| -) 19.45% -------------------------------------------------- Correctly classified 79.71% -------------------------------------------------- Measures of Fit for logistic of HP2work Log-Lik Intercept Only: -172.873 Log-Lik Full Model: -155.753 D(333): 311.505 LR(6): 34.241 Prob > LR: 0.000 McFadden's R2: 0.099 McFadden's Adj R2: 0.059 Maximum Likelihood R2: 0.096 Cragg & Uhler's R2: 0.150 McKelvey and Zavoina's R2: 0.170 Efron's R2: 0.114 Variance of y*: 3.963 Variance of error: 3.290 Count R2: 0.797 Adj Count R2: 0.014 AIC: 0.957 AIC*n: 325.505 BIC: -1629.534 BIC': 0.733 214 . 215 . // female trimmed dose work washes out with does not signif. 216 . 217 . 218 . 219 . 220 . forvalues j=2/2 { 2. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 3. set more off 4. di as input "Full females HPprobsoc on wave 2 with dose ns" 5. des age occ1w`j'-occ8w`j' inc1w`j'-inc4w`j' avgcumdosew`j' bf8 // > / > marrw`j'1-marrw`j'6 radhlw`j' `w2bf' 6. logistic HP2probsoc marrw`j'3-marrw`j'6 age havmilsq radhlw2 /// > avgcumdosew2 bf8 illw`j' shjobw`j' suprtw`j' if gender==1, co > ef nolog 7. estat gof 8. estat class 9. fitstat 10. } Full females HPprobsoc on wave 2 with dose ns storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age occ1w2 double %15.0g LABJ profess executive administration in 1996 occ2w2 double %15.0g LABJ technical sales admin support in 1996 occ3w2 double %15.0g LABJ service occup protective services in 1996 occ4w2 double %15.0g LABJ precision prod mechan craft construction in 1996 occ5w2 double %15.0g LABJ factory laborer machinist transp cleaner in 1996 occ6w2 double %15.0g LABJ farming agricul forestry fishing trapping logging in 1996 occ7w2 double %15.0g LABJ homemaking caregiving in 1996 occ8w2 double %15.0g LABJ student in 1996 inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed radhlw2 double %8.0g how much believed personal health is affected by radiation in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf2 float %9.0g bf2 = max(0, efradw3 - 1.61252E-006) * bf1 bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m bf7m float %9.0g bf7m = max(0, radtlw3 - 2.12558E-007) * bf4m bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf17 float %9.0g bf17 = max(0, 20 - ecprw3) * bf15m bf20 float %9.0g bf20 = max(0, kzchorn - 2.53946E-006) bf22 float %9.0g bf22 = max(0, icdxcnt - 1.01635E-007) * bf7m bf29 float %9.0g bf29 = max(0, 18 - CSsocspt) * bf8 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) note: marrw24 != 0 predicts failure perfectly marrw24 dropped and 3 obs not used note: marrw26 != 0 predicts failure perfectly marrw26 dropped and 4 obs not used Logistic regression Number of obs = 333 LR chi2(10) = 62.55 Prob > chi2 = 0.0000 Log likelihood = -92.970187 Pseudo R2 = 0.2517 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- marrw23 | -.3736488 .5239634 -0.71 0.476 -1.400598 .6533007 marrw24 | 0 (omitted) marrw25 | .3178572 1.386011 0.23 0.819 -2.398675 3.034389 marrw26 | 0 (omitted) age | .068219 .0199083 3.43 0.001 .0291995 .1072385 havmilsq | -1.61e-06 5.43e-06 -0.30 0.767 -.0000122 9.03e-06 radhlw2 | .0237245 .0062836 3.78 0.000 .0114089 .0360402 avgcumdosew2 | .0249392 .0537004 0.46 0.642 -.0803117 .1301901 bf8 | -.0000369 .0000244 -1.51 0.130 -.0000846 .0000109 illw2 | .4654847 .2879266 1.62 0.106 -.098841 1.02981 shjobw2 | .013417 .0052952 2.53 0.011 .0030386 .0237954 suprtw2 | .0027116 .0050363 0.54 0.590 -.0071593 .0125825 _cons | -7.790191 1.174345 -6.63 0.000 -10.09186 -5.488517 ------------------------------------------------------------------------------ Logistic model for HP2probsoc, goodness-of-fit test number of observations = 333 number of covariate patterns = 331 Pearson chi2(320) = 376.50 Prob > chi2 = 0.0162 Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 11 4 | 15 - | 30 288 | 318 -----------+--------------------------+----------- Total | 41 292 | 333 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 26.83% Specificity Pr( -|~D) 98.63% Positive predictive value Pr( D| +) 73.33% Negative predictive value Pr(~D| -) 90.57% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 1.37% False - rate for true D Pr( -| D) 73.17% False + rate for classified + Pr(~D| +) 26.67% False - rate for classified - Pr( D| -) 9.43% -------------------------------------------------- Correctly classified 89.79% -------------------------------------------------- Measures of Fit for logistic of HP2probsoc Log-Lik Intercept Only: -124.243 Log-Lik Full Model: -92.970 D(320): 185.940 LR(10): 62.545 Prob > LR: 0.000 McFadden's R2: 0.252 McFadden's Adj R2: 0.147 Maximum Likelihood R2: 0.171 Cragg & Uhler's R2: 0.326 McKelvey and Zavoina's R2: 0.420 Efron's R2: 0.243 Variance of y*: 5.673 Variance of error: 3.290 Count R2: 0.898 Adj Count R2: 0.171 AIC: 0.636 AIC*n: 211.940 BIC: -1672.665 BIC': -4.464 221 . 222 . forvalues j=2/2 { 2. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 3. set more off 4. di as input "females trimmed HP2probsoc on wave 2 with dose ns" 5. des age occ1w`j'-occ8w`j' inc1w`j'-inc4w`j' avgcumdosew`j' bf8 // > / > marrw`j'1-marrw`j'6 `w2bf' 6. logistic HP2probsoc age /// > avgcumdosew2 bf8 illw`j' shjobw`j' radhlw`j' if gender==1, co > ef nolog 7. estat gof 8. estat class 9. fitstat 10. } females trimmed HP2probsoc on wave 2 with dose ns storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age occ1w2 double %15.0g LABJ profess executive administration in 1996 occ2w2 double %15.0g LABJ technical sales admin support in 1996 occ3w2 double %15.0g LABJ service occup protective services in 1996 occ4w2 double %15.0g LABJ precision prod mechan craft construction in 1996 occ5w2 double %15.0g LABJ factory laborer machinist transp cleaner in 1996 occ6w2 double %15.0g LABJ farming agricul forestry fishing trapping logging in 1996 occ7w2 double %15.0g LABJ homemaking caregiving in 1996 occ8w2 double %15.0g LABJ student in 1996 inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf2 float %9.0g bf2 = max(0, efradw3 - 1.61252E-006) * bf1 bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m bf7m float %9.0g bf7m = max(0, radtlw3 - 2.12558E-007) * bf4m bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf17 float %9.0g bf17 = max(0, 20 - ecprw3) * bf15m bf20 float %9.0g bf20 = max(0, kzchorn - 2.53946E-006) bf22 float %9.0g bf22 = max(0, icdxcnt - 1.01635E-007) * bf7m bf29 float %9.0g bf29 = max(0, 18 - CSsocspt) * bf8 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Logistic regression Number of obs = 340 LR chi2(6) = 60.41 Prob > chi2 = 0.0000 Log likelihood = -94.948223 Pseudo R2 = 0.2413 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0592362 .0176148 3.36 0.001 .0247118 .0937607 avgcumdosew2 | .0315514 .0525591 0.60 0.548 -.0714626 .1345653 bf8 | -.0000332 .0000239 -1.39 0.165 -.0000801 .0000136 illw2 | .5251102 .2757549 1.90 0.057 -.0153594 1.06558 shjobw2 | .0135312 .0051744 2.62 0.009 .0033897 .0236728 radhlw2 | .0242757 .006237 3.89 0.000 .0120514 .0365 _cons | -7.543673 1.131417 -6.67 0.000 -9.76121 -5.326137 ------------------------------------------------------------------------------ Logistic model for HP2probsoc, goodness-of-fit test number of observations = 340 number of covariate patterns = 332 Pearson chi2(325) = 382.40 Prob > chi2 = 0.0155 Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 12 3 | 15 - | 29 296 | 325 -----------+--------------------------+----------- Total | 41 299 | 340 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 29.27% Specificity Pr( -|~D) 99.00% Positive predictive value Pr( D| +) 80.00% Negative predictive value Pr(~D| -) 91.08% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 1.00% False - rate for true D Pr( -| D) 70.73% False + rate for classified + Pr(~D| +) 20.00% False - rate for classified - Pr( D| -) 8.92% -------------------------------------------------- Correctly classified 90.59% -------------------------------------------------- Measures of Fit for logistic of HP2probsoc Log-Lik Intercept Only: -125.152 Log-Lik Full Model: -94.948 D(333): 189.896 LR(6): 60.408 Prob > LR: 0.000 McFadden's R2: 0.241 McFadden's Adj R2: 0.185 Maximum Likelihood R2: 0.163 Cragg & Uhler's R2: 0.312 McKelvey and Zavoina's R2: 0.420 Efron's R2: 0.235 Variance of y*: 5.669 Variance of error: 3.290 Count R2: 0.906 Adj Count R2: 0.220 AIC: 0.600 AIC*n: 203.896 BIC: -1751.142 BIC': -25.435 223 . 224 . // female trimmed dose work washes out with does not signif. 225 . 226 . 227 . 228 . 229 . 230 . 231 . des bf8 storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m 232 . title4 "bf8 is a male mediator of paid employment" ------------------------------------------------------------------------------- bf8 is a male mediator of paid employment ------------------------------------------------------------------------------- 233 . local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 bf40 234 . foreach var in `w2bf'{ 2. glm `var' avgcumdosew2 if gender==1, family(gaussian) link(identity) 3. glm hp2work `var' illw2 avgcumdosew2 if gender==1, family(binomial) /// > irls scale(dev) link(probit) 4. } Iteration 0: log likelihood = -1551.9176 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 542.9132 Deviance = 183504.6626 (1/df) Deviance = 542.9132 Pearson = 183504.6626 (1/df) Pearson = 542.9132 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.140692 Log likelihood = -1551.917557 BIC = 181534.5 ------------------------------------------------------------------------------ | OIM bf1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.0294245 .5053831 -0.06 0.954 -1.019957 .9611082 _cons | 32.06654 1.353933 23.68 0.000 29.41288 34.7202 ------------------------------------------------------------------------------ Iteration 1: deviance = 327.3468 Iteration 2: deviance = 326.8667 Iteration 3: deviance = 326.8661 Iteration 4: deviance = 326.8661 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 326.8660563 (1/df) Deviance = .9728156 Pearson = 340.1753181 (1/df) Pearson = 1.012427 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1631.66 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf1 | .0109044 .0035032 3.11 0.002 .0040382 .0177705 illw2 | .2657677 .1244152 2.14 0.033 .0219184 .509617 avgcumdosew2 | .0146157 .0285488 0.51 0.609 -.041339 .0705704 _cons | -1.302985 .1496632 -8.71 0.000 -1.59632 -1.009651 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -1027.1225 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 24.7771 Deviance = 8374.659221 (1/df) Deviance = 24.7771 Pearson = 8374.659221 (1/df) Pearson = 24.7771 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.053662 Log likelihood = -1027.122509 BIC = 6404.476 ------------------------------------------------------------------------------ | OIM bf4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.0331637 .1079644 -0.31 0.759 -.2447701 .1784427 _cons | 12.52896 .2892393 43.32 0.000 11.96206 13.09586 ------------------------------------------------------------------------------ Iteration 1: deviance = 300.9022 Iteration 2: deviance = 299.8184 Iteration 3: deviance = 299.8108 Iteration 4: deviance = 299.8108 Iteration 5: deviance = 299.8108 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 299.8108296 (1/df) Deviance = .8922941 Pearson = 319.1468848 (1/df) Pearson = .9498419 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1658.715 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.0960279 .0152223 -6.31 0.000 -.125863 -.0661928 illw2 | .1615525 .1227138 1.32 0.188 -.0789623 .4020672 avgcumdosew2 | .0143706 .0276919 0.52 0.604 -.0399044 .0686457 _cons | .2173252 .204472 1.06 0.288 -.1834325 .6180829 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -3110.6399 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 5209030 Deviance = 1760652206 (1/df) Deviance = 5209030 Pearson = 1760652206 (1/df) Pearson = 5209030 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 18.30965 Log likelihood = -3110.639872 BIC = 1.76e+09 ------------------------------------------------------------------------------ | OIM bf2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -36.03645 49.50328 -0.73 0.467 -133.0611 60.9882 _cons | 1976.708 132.6205 14.91 0.000 1716.777 2236.64 ------------------------------------------------------------------------------ Iteration 1: deviance = 326.2564 Iteration 2: deviance = 326.0185 Iteration 3: deviance = 326.0184 Iteration 4: deviance = 326.0184 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 326.0184207 (1/df) Deviance = .9702929 Pearson = 340.5103126 (1/df) Pearson = 1.013424 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1632.507 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf2 | .0001126 .0000342 3.29 0.001 .0000456 .0001797 illw2 | .2301304 .1264315 1.82 0.069 -.0176708 .4779317 avgcumdosew2 | .0207807 .0278478 0.75 0.456 -.0338 .0753614 _cons | -1.166294 .1149488 -10.15 0.000 -1.391589 -.9409983 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -1060.7791 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 30.20174 Deviance = 10208.18969 (1/df) Deviance = 30.20174 Pearson = 10208.18969 (1/df) Pearson = 30.20174 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.251642 Log likelihood = -1060.779096 BIC = 8238.006 ------------------------------------------------------------------------------ | OIM bf4m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.0266037 .1191987 -0.22 0.823 -.2602289 .2070215 _cons | 20.34324 .3193362 63.70 0.000 19.71735 20.96913 ------------------------------------------------------------------------------ Iteration 1: deviance = 303.3546 Iteration 2: deviance = 302.5908 Iteration 3: deviance = 302.587 Iteration 4: deviance = 302.587 Iteration 5: deviance = 302.587 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 302.5870341 (1/df) Deviance = .9005566 Pearson = 319.7148309 (1/df) Pearson = .9515322 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1655.939 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4m | -.0822373 .0137721 -5.97 0.000 -.1092302 -.0552444 illw2 | .1770621 .1226767 1.44 0.149 -.0633798 .4175039 avgcumdosew2 | .0150542 .0276105 0.55 0.586 -.0390613 .0691697 _cons | .694465 .2897107 2.40 0.017 .1266424 1.262288 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -2273.2523 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 37801.15 Deviance = 12776789.76 (1/df) Deviance = 37801.15 Pearson = 12776789.76 (1/df) Pearson = 37801.15 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 13.38384 Log likelihood = -2273.252279 BIC = 1.28e+07 ------------------------------------------------------------------------------ | OIM bf5m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 9.243905 4.217043 2.19 0.028 .9786526 17.50916 _cons | 104.6228 11.29756 9.26 0.000 82.47996 126.7656 ------------------------------------------------------------------------------ Iteration 1: deviance = 336.3745 Iteration 2: deviance = 336.2755 Iteration 3: deviance = 336.2754 Iteration 4: deviance = 336.2754 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 336.2754021 (1/df) Deviance = 1.00082 Pearson = 339.5058665 (1/df) Pearson = 1.010434 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1622.25 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf5m | -.0001044 .000414 -0.25 0.801 -.0009159 .000707 illw2 | .3706764 .1252132 2.96 0.003 .1252631 .6160898 avgcumdosew2 | .0147823 .0283632 0.52 0.602 -.0408084 .0703731 _cons | -.9460378 .0985679 -9.60 0.000 -1.139227 -.7528483 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -2733.8869 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 567905.6 Deviance = 191952095.6 (1/df) Deviance = 567905.6 Pearson = 191952095.6 (1/df) Pearson = 567905.6 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 16.09345 Log likelihood = -2733.886934 BIC = 1.92e+08 ------------------------------------------------------------------------------ | OIM bf7m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 9.152801 16.34533 0.56 0.576 -22.88345 41.18905 _cons | 1025.425 43.78952 23.42 0.000 939.5992 1111.251 ------------------------------------------------------------------------------ Iteration 1: deviance = 336.2763 Iteration 2: deviance = 336.1772 Iteration 3: deviance = 336.1772 Iteration 4: deviance = 336.1772 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 336.177176 (1/df) Deviance = 1.000527 Pearson = 339.9627414 (1/df) Pearson = 1.011794 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1622.349 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf7m | -.0000423 .000105 -0.40 0.687 -.0002481 .0001634 illw2 | .3607427 .1214759 2.97 0.003 .1226542 .5988312 avgcumdosew2 | .014265 .0281752 0.51 0.613 -.0409575 .0694874 _cons | -.910979 .1415067 -6.44 0.000 -1.188327 -.633631 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -3529.9433 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 6.14e+07 Deviance = 2.07423e+10 (1/df) Deviance = 6.14e+07 Pearson = 2.07423e+10 (1/df) Pearson = 6.14e+07 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 20.77614 Log likelihood = -3529.943345 BIC = 2.07e+10 ------------------------------------------------------------------------------ | OIM bf8 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 357.2422 169.9126 2.10 0.036 24.2195 690.2648 _cons | 2822.831 455.2 6.20 0.000 1930.655 3715.006 ------------------------------------------------------------------------------ Iteration 1: deviance = 335.4034 Iteration 2: deviance = 335.2271 Iteration 3: deviance = 335.2265 Iteration 4: deviance = 335.2265 Iteration 5: deviance = 335.2265 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 335.2264779 (1/df) Deviance = .9976979 Pearson = 338.675469 (1/df) Pearson = 1.007963 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1623.299 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf8 | -.0000115 .0000109 -1.05 0.292 -.0000329 9.89e-06 illw2 | .3835488 .1226954 3.13 0.002 .1430703 .6240273 avgcumdosew2 | .0178965 .0281831 0.64 0.525 -.0373414 .0731343 _cons | -.9311558 .0948074 -9.82 0.000 -1.116975 -.7453367 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -2696.8707 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 456784.9 Deviance = 154393311.2 (1/df) Deviance = 456784.9 Pearson = 154393311.2 (1/df) Pearson = 456784.9 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 15.87571 Log likelihood = -2696.870705 BIC = 1.54e+08 ------------------------------------------------------------------------------ | OIM bf15m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -3.877137 14.65924 -0.26 0.791 -32.60872 24.85444 _cons | 115.2001 39.27245 2.93 0.003 38.22751 192.1727 ------------------------------------------------------------------------------ Iteration 1: deviance = 336.3366 Iteration 2: deviance = 336.2425 Iteration 3: deviance = 336.2425 Iteration 4: deviance = 336.2425 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 336.2424662 (1/df) Deviance = 1.000722 Pearson = 339.8130547 (1/df) Pearson = 1.011348 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1622.283 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf15m | .0000349 .0001107 0.32 0.752 -.000182 .0002518 illw2 | .3640012 .1216115 2.99 0.003 .1256471 .6023553 avgcumdosew2 | .0139566 .0282099 0.49 0.621 -.0413337 .0692469 _cons | -.9590254 .0937715 -10.23 0.000 -1.142814 -.7752366 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -3469.0038 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 4.29e+07 Deviance = 1.44936e+10 (1/df) Deviance = 4.29e+07 Pearson = 1.44936e+10 (1/df) Pearson = 4.29e+07 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 20.41767 Log likelihood = -3469.003753 BIC = 1.45e+10 ------------------------------------------------------------------------------ | OIM bf17 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -16.67211 142.0317 -0.12 0.907 -295.0491 261.7049 _cons | 429.2726 380.5063 1.13 0.259 -316.5061 1175.051 ------------------------------------------------------------------------------ Iteration 1: deviance = 336.1233 Iteration 2: deviance = 335.855 Iteration 3: deviance = 335.7482 Iteration 4: deviance = 335.4893 Iteration 5: deviance = 335.046 Iteration 6: deviance = 334.8623 Iteration 7: deviance = 334.7646 Iteration 8: deviance = 334.7046 Iteration 9: deviance = 334.6492 Iteration 10: deviance = 334.5367 Iteration 11: deviance = 334.466 Iteration 12: deviance = 334.4424 Iteration 13: deviance = 334.4338 Iteration 14: deviance = 334.4305 Iteration 15: deviance = 334.429 Iteration 16: deviance = 334.4282 Iteration 17: deviance = 334.4278 Iteration 18: deviance = 334.4276 Iteration 19: deviance = 334.4274 Iteration 20: deviance = 334.4273 Iteration 21: deviance = 334.4272 Iteration 22: deviance = 334.4271 Iteration 23: deviance = 334.4271 Iteration 24: deviance = 334.4271 Iteration 25: deviance = 334.427 Iteration 26: deviance = 334.427 Iteration 27: deviance = 334.427 Iteration 28: deviance = 334.4269 Iteration 29: deviance = 334.4269 Iteration 30: deviance = 334.4269 Iteration 31: deviance = 334.4269 Iteration 32: deviance = 334.4269 Iteration 33: deviance = 334.4269 Iteration 34: deviance = 334.4269 Iteration 35: deviance = 334.4269 Iteration 36: deviance = 334.4269 Iteration 37: deviance = 334.4269 Iteration 38: deviance = 334.4268 Iteration 39: deviance = 334.4268 Iteration 40: deviance = 334.4268 Iteration 41: deviance = 334.4268 Iteration 42: deviance = 334.4268 Iteration 43: deviance = 334.4268 Iteration 44: deviance = 334.4268 Iteration 45: deviance = 334.4268 Iteration 46: deviance = 334.4268 Iteration 47: deviance = 334.4268 Iteration 48: deviance = 334.4268 Iteration 49: deviance = 334.4268 Iteration 50: deviance = 334.4268 Iteration 51: deviance = 334.4268 Iteration 52: deviance = 334.4268 Iteration 53: deviance = 334.4268 Iteration 54: deviance = 334.4268 Iteration 55: deviance = 334.4268 Iteration 56: deviance = 334.4268 Iteration 57: deviance = 334.4268 Iteration 58: deviance = 334.4268 Iteration 59: deviance = 334.4268 Iteration 60: deviance = 334.4268 Iteration 61: deviance = 334.4268 Iteration 62: deviance = 334.4268 Iteration 63: deviance = 334.4268 Iteration 64: deviance = 334.4268 Iteration 65: deviance = 334.4268 Iteration 66: deviance = 334.4268 Iteration 67: deviance = 334.4268 Iteration 68: deviance = 334.4268 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 334.426778 (1/df) Deviance = .9953178 Pearson = 334.5406031 (1/df) Pearson = .9956566 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1624.099 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf17 | -.0063351 .0141649 -0.45 0.655 -.0340978 .0214276 illw2 | .3553446 .1212371 2.93 0.003 .1177242 .5929649 avgcumdosew2 | .0135183 .02812 0.48 0.631 -.0415959 .0686325 _cons | -.9421698 .0926941 -10.16 0.000 -1.123847 -.7604927 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -1596.6381 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 706.2799 Deviance = 238722.6207 (1/df) Deviance = 706.2799 Pearson = 238722.6207 (1/df) Pearson = 706.2799 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.403754 Log likelihood = -1596.638134 BIC = 236752.4 ------------------------------------------------------------------------------ | OIM bf20 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.1996497 .5764265 -0.35 0.729 -1.329425 .9301255 _cons | 70.37734 1.54426 45.57 0.000 67.35065 73.40404 ------------------------------------------------------------------------------ Iteration 1: deviance = 326.4244 Iteration 2: deviance = 325.6691 Iteration 3: deviance = 325.6663 Iteration 4: deviance = 325.6663 Iteration 5: deviance = 325.6663 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 325.6663467 (1/df) Deviance = .9692451 Pearson = 338.4324666 (1/df) Pearson = 1.007239 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1632.859 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf20 | .0104867 .0032097 3.27 0.001 .0041958 .0167776 illw2 | .2668849 .1239534 2.15 0.031 .0239406 .5098291 avgcumdosew2 | .0159959 .0289453 0.55 0.581 -.0407358 .0727275 _cons | -1.696899 .2514028 -6.75 0.000 -2.189639 -1.204158 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -3143.8431 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 6332576 Deviance = 2140410669 (1/df) Deviance = 6332576 Pearson = 2140410669 (1/df) Pearson = 6332576 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 18.50496 Log likelihood = -3143.84315 BIC = 2.14e+09 ------------------------------------------------------------------------------ | OIM bf22 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 69.44601 54.58153 1.27 0.203 -37.53183 176.4239 _cons | 2129.507 146.2252 14.56 0.000 1842.911 2416.103 ------------------------------------------------------------------------------ Iteration 1: deviance = 329.663 Iteration 2: deviance = 329.5329 Iteration 3: deviance = 329.5327 Iteration 4: deviance = 329.5327 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 329.5326856 (1/df) Deviance = .980752 Pearson = 336.2324364 (1/df) Pearson = 1.000692 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1628.993 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf22 | .0000783 .0000296 2.64 0.008 .0000202 .0001364 illw2 | .3005113 .1241463 2.42 0.015 .0571889 .5438337 avgcumdosew2 | .0091767 .0283699 0.32 0.746 -.0464273 .0647808 _cons | -1.120001 .1128358 -9.93 0.000 -1.341155 -.8988465 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -3683.6132 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 1.52e+08 Deviance = 5.12192e+10 (1/df) Deviance = 1.52e+08 Pearson = 5.12192e+10 (1/df) Pearson = 1.52e+08 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 21.68008 Log likelihood = -3683.613157 BIC = 5.12e+10 ------------------------------------------------------------------------------ | OIM bf29 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 82.80099 267.0015 0.31 0.756 -440.5124 606.1144 _cons | 1325.352 715.3035 1.85 0.064 -76.61736 2727.321 ------------------------------------------------------------------------------ Iteration 1: deviance = 336.3754 Iteration 2: deviance = 336.2738 Iteration 3: deviance = 336.2737 Iteration 4: deviance = 336.2737 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 336.273687 (1/df) Deviance = 1.000815 Pearson = 339.164143 (1/df) Pearson = 1.009417 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1622.252 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf29 | 1.48e-06 5.82e-06 0.25 0.799 -9.92e-06 .0000129 illw2 | .3622929 .1215962 2.98 0.003 .1239688 .600617 avgcumdosew2 | .0137461 .028224 0.49 0.626 -.0415719 .0690641 _cons | -.9567327 .092818 -10.31 0.000 -1.138653 -.7748128 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -2661.1745 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 370270.9 Deviance = 125151569.5 (1/df) Deviance = 370270.9 Pearson = 125151569.5 (1/df) Pearson = 370270.9 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 15.66573 Log likelihood = -2661.174486 BIC = 1.25e+08 ------------------------------------------------------------------------------ | OIM bf30 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -9.387797 13.19822 -0.71 0.477 -35.25583 16.48024 _cons | 497.7068 35.35834 14.08 0.000 428.4057 567.0079 ------------------------------------------------------------------------------ Iteration 1: deviance = 331.3314 Iteration 2: deviance = 331.1891 Iteration 3: deviance = 331.1888 Iteration 4: deviance = 331.1888 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 331.1888247 (1/df) Deviance = .985681 Pearson = 342.2972093 (1/df) Pearson = 1.018742 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1627.337 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf30 | .0002835 .0001242 2.28 0.022 .0000401 .0005269 illw2 | .3537185 .1220211 2.90 0.004 .1145615 .5928754 avgcumdosew2 | .0172486 .0281673 0.61 0.540 -.0379583 .0724554 _cons | -1.105489 .1160104 -9.53 0.000 -1.332866 -.8781133 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -659.07396 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 2.843195 Deviance = 960.9998238 (1/df) Deviance = 2.843195 Pearson = 960.9998238 (1/df) Pearson = 2.843195 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 3.88867 Log likelihood = -659.0739647 BIC = -1009.184 ------------------------------------------------------------------------------ | OIM bf40 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .0482004 .0365729 1.32 0.188 -.0234811 .1198818 _cons | 2.097753 .0979795 21.41 0.000 1.905716 2.289789 ------------------------------------------------------------------------------ Iteration 1: deviance = 315.0711 Iteration 2: deviance = 314.451 Iteration 3: deviance = 314.4494 Iteration 4: deviance = 314.4494 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 314.4493978 (1/df) Deviance = .9358613 Pearson = 332.2461013 (1/df) Pearson = .9888277 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1644.076 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf40 | .2308417 .0484278 4.77 0.000 .1359249 .3257585 illw2 | .1331906 .1291807 1.03 0.303 -.1199989 .3863801 avgcumdosew2 | .0062786 .0280086 0.22 0.823 -.0486172 .0611743 _cons | -1.427269 .1387712 -10.29 0.000 -1.699256 -1.155283 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 235 . 236 . **** male mediators of dose paid employment: bf8, age, illw2 ageXillw2 237 . scalar wkMedMw2 = "bf8 age illw2 " 238 . 239 . title4 "Female mediator models for dose-paid employment" ------------------------------------------------------------------------------- Female mediator models for dose-paid employment ------------------------------------------------------------------------------- 240 . glm radhlw2 avgcumdosew2 if gender==2, fam(gaussian) link(identity) Iteration 0: log likelihood = -1791.2233 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 1137.567 Deviance = 410661.5604 (1/df) Deviance = 1137.567 Pearson = 410661.5604 (1/df) Pearson = 1137.567 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.880018 Log likelihood = -1791.223306 BIC = 408533.7 ------------------------------------------------------------------------------ | OIM radhlw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 3.302288 1.283833 2.57 0.010 .7860214 5.818555 _cons | 56.95167 2.110863 26.98 0.000 52.81445 61.08888 ------------------------------------------------------------------------------ 241 . glm hp2work radhlw2 if gender==2, fam(binomial) irls scale(dev) link(probit) Iteration 1: deviance = 395.8837 Iteration 2: deviance = 395.5308 Iteration 3: deviance = 395.5307 Iteration 4: deviance = 395.5307 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 395.5307336 (1/df) Deviance = 1.095653 Pearson = 363.4160364 (1/df) Pearson = 1.006693 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1732.349 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- radhlw2 | .0090691 .0023064 3.93 0.000 .0045487 .0135895 _cons | -1.229859 .1685308 -7.30 0.000 -1.560173 -.8995443 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 242 . glm age avgcumdosew2 if gender==2, fam(gaussian) link(identity) Iteration 0: log likelihood = -1406.9403 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 136.9184 Deviance = 49427.52828 (1/df) Deviance = 136.9184 Pearson = 49427.52828 (1/df) Pearson = 136.9184 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.762756 Log likelihood = -1406.940271 BIC = 47299.65 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.502324 .4454009 3.37 0.001 .6293547 2.375294 _cons | 48.86944 .7323225 66.73 0.000 47.43412 50.30477 ------------------------------------------------------------------------------ 243 . glm hp2work age if gender==2, fam(binomial) irls scale(dev) link(probit) Iteration 1: deviance = 372.7391 Iteration 2: deviance = 372.3711 Iteration 3: deviance = 372.3711 Iteration 4: deviance = 372.3711 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 372.3710546 (1/df) Deviance = 1.031499 Pearson = 375.1783727 (1/df) Pearson = 1.039275 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1755.508 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0398718 .0066503 6.00 0.000 .0268375 .052906 _cons | -2.722762 .3594297 -7.58 0.000 -3.427231 -2.018292 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 244 . glm illw2 avgcumdosew2 if gender==2, fam(gaussian) link(identity) Iteration 0: log likelihood = -463.51524 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = .756881 Deviance = 273.2340487 (1/df) Deviance = .756881 Pearson = 273.2340487 (1/df) Pearson = .756881 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 2.564822 Log likelihood = -463.5152411 BIC = -1854.645 ------------------------------------------------------------------------------ | OIM illw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .1249912 .0331157 3.77 0.000 .0600856 .1898968 _cons | .301285 .0544484 5.53 0.000 .194568 .4080019 ------------------------------------------------------------------------------ 245 . glm hp2work illw2 if gender==2, fam(binomial) irls scale(dev) link(probit) Iteration 1: deviance = 405.7435 Iteration 2: deviance = 405.3085 Iteration 3: deviance = 405.3081 Iteration 4: deviance = 405.3081 Iteration 5: deviance = 405.3081 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 405.3080692 (1/df) Deviance = 1.122737 Pearson = 363.1923247 (1/df) Pearson = 1.006073 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1722.571 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2 | .2093544 .0854065 2.45 0.014 .0419606 .3767481 _cons | -.7522712 .0853534 -8.81 0.000 -.9195607 -.5849817 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 246 . 247 . 248 . cap gen illw2Xd2=illw2*avgcumdosew2 249 . 250 . * interaction of illw2Xd is not a mediator 251 . glm illw2 avgcumdosew2 if gender==2, fam(gaussian) link(identity) nolog Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = .756881 Deviance = 273.2340487 (1/df) Deviance = .756881 Pearson = 273.2340487 (1/df) Pearson = .756881 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 2.564822 Log likelihood = -463.5152411 BIC = -1854.645 ------------------------------------------------------------------------------ | OIM illw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .1249912 .0331157 3.77 0.000 .0600856 .1898968 _cons | .301285 .0544484 5.53 0.000 .194568 .4080019 ------------------------------------------------------------------------------ 252 . glm hp2work illw2Xd2 illw2 avgcumdosew2 if gender==2, fam(binomial) /// > nolog irls scale(dev) link(probit) Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 359 (IRLS EIM) Scale parameter = 1 Deviance = 400.8217749 (1/df) Deviance = 1.116495 Pearson = 360.6634088 (1/df) Pearson = 1.004633 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1715.269 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2Xd2 | -.0002555 .0352447 -0.01 0.994 -.0693338 .0688228 illw2 | .1843288 .1001774 1.84 0.066 -.0120152 .3806729 avgcumdosew2 | .1074757 .0718444 1.50 0.135 -.0333367 .2482882 _cons | -.8405461 .1053904 -7.98 0.000 -1.047107 -.6339848 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 253 . 254 . 255 . 256 . **** Basis function mediators of paid employment 257 . ** female b4 b4m b40 258 . 259 . title4 "bf8 is a male mediator of paid employment" ------------------------------------------------------------------------------- bf8 is a male mediator of paid employment ------------------------------------------------------------------------------- 260 . local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 bf40 261 . foreach var in `w2bf'{ 2. glm `var' avgcumdosew2 if gender==2, family(gaussian) link(identity) 3. glm hp2work `var' avgcumdosew2 if gender==2, family(binomial) irls scale( > dev) link(probit) 4. } Iteration 0: log likelihood = -1648.0584 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 516.905 Deviance = 186602.7154 (1/df) Deviance = 516.905 Pearson = 186602.7154 (1/df) Pearson = 516.905 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.091231 Log likelihood = -1648.05836 BIC = 184474.8 ------------------------------------------------------------------------------ | OIM bf1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.592699 .8654173 1.84 0.066 -.1034879 3.288886 _cons | 37.79953 1.422908 26.56 0.000 35.01068 40.58837 ------------------------------------------------------------------------------ Iteration 1: deviance = 400.85 Iteration 2: deviance = 400.4115 Iteration 3: deviance = 400.4114 Iteration 4: deviance = 400.4114 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 400.4114259 (1/df) Deviance = 1.112254 Pearson = 362.4247002 (1/df) Pearson = 1.006735 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1721.574 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf1 | .0079775 .0034614 2.30 0.021 .0011932 .0147618 avgcumdosew2 | .1139711 .0531295 2.15 0.032 .0098392 .2181029 _cons | -1.08756 .1662101 -6.54 0.000 -1.413326 -.7617939 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -1109.0983 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 26.53281 Deviance = 9578.344971 (1/df) Deviance = 26.53281 Pearson = 9578.344971 (1/df) Pearson = 26.53281 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.121754 Log likelihood = -1109.098281 BIC = 7450.466 ------------------------------------------------------------------------------ | OIM bf4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.595012 .1960703 -3.03 0.002 -.9793027 -.2107212 _cons | 11.02048 .3223763 34.19 0.000 10.38863 11.65232 ------------------------------------------------------------------------------ Iteration 1: deviance = 368.2526 Iteration 2: deviance = 367.9545 Iteration 3: deviance = 367.9542 Iteration 4: deviance = 367.9542 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 367.9541653 (1/df) Deviance = 1.022095 Pearson = 355.981786 (1/df) Pearson = .9888383 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1754.031 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.0878252 .0146171 -6.01 0.000 -.1164742 -.0591762 avgcumdosew2 | .0839548 .051694 1.62 0.104 -.0173635 .1852731 _cons | .1183858 .1696362 0.70 0.485 -.2140951 .4508667 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -3335.9882 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 5653290 Deviance = 2040837774 (1/df) Deviance = 5653290 Pearson = 2040837774 (1/df) Pearson = 5653290 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 18.39112 Log likelihood = -3335.988235 BIC = 2.04e+09 ------------------------------------------------------------------------------ | OIM bf2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -6.46457 90.50464 -0.07 0.943 -183.8504 170.9213 _cons | 2475.004 148.8066 16.63 0.000 2183.349 2766.66 ------------------------------------------------------------------------------ Iteration 1: deviance = 400.9948 Iteration 2: deviance = 400.598 Iteration 3: deviance = 400.598 Iteration 4: deviance = 400.598 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 400.5979742 (1/df) Deviance = 1.112772 Pearson = 362.9767308 (1/df) Pearson = 1.008269 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1721.387 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf2 | .0000727 .0000317 2.29 0.022 .0000106 .0001349 avgcumdosew2 | .1275153 .0528818 2.41 0.016 .0238688 .2311617 _cons | -.9638935 .1245337 -7.74 0.000 -1.207975 -.719812 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -1140.8259 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 31.60104 Deviance = 11407.97484 (1/df) Deviance = 31.60104 Pearson = 11407.97484 (1/df) Pearson = 31.60104 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.296561 Log likelihood = -1140.825904 BIC = 9280.095 ------------------------------------------------------------------------------ | OIM bf4m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.599311 .2139789 -2.80 0.005 -1.018702 -.1799202 _cons | 18.83424 .3518214 53.53 0.000 18.14469 19.5238 ------------------------------------------------------------------------------ Iteration 1: deviance = 376.0339 Iteration 2: deviance = 375.8988 Iteration 3: deviance = 375.8985 Iteration 4: deviance = 375.8985 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 375.8985331 (1/df) Deviance = 1.044163 Pearson = 352.5709603 (1/df) Pearson = .9793638 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1746.086 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4m | -.0703561 .013277 -5.30 0.000 -.0963785 -.0443336 avgcumdosew2 | .0920064 .0520406 1.77 0.077 -.0099913 .1940041 _cons | .491873 .2531477 1.94 0.052 -.0042874 .9880333 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -2474.5823 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 49104.71 Deviance = 17726801.77 (1/df) Deviance = 49104.71 Pearson = 17726801.77 (1/df) Pearson = 49104.71 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 13.64508 Log likelihood = -2474.582345 BIC = 1.77e+07 ------------------------------------------------------------------------------ | OIM bf5m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 13.38431 8.434936 1.59 0.113 -3.147864 29.91648 _cons | 147.9804 13.86862 10.67 0.000 120.7984 175.1624 ------------------------------------------------------------------------------ Iteration 1: deviance = 406.9626 Iteration 2: deviance = 406.4247 Iteration 3: deviance = 406.4246 Iteration 4: deviance = 406.4246 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 406.4245618 (1/df) Deviance = 1.128957 Pearson = 362.4207333 (1/df) Pearson = 1.006724 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1715.56 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf5m | .0000409 .0003437 0.12 0.905 -.0006328 .0007146 avgcumdosew2 | .1250263 .0533787 2.34 0.019 .020406 .2296465 _cons | -.7796998 .1041776 -7.48 0.000 -.9838841 -.5755154 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -2919.8439 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 570884.5 Deviance = 206089311.1 (1/df) Deviance = 570884.5 Pearson = 206089311.1 (1/df) Pearson = 570884.5 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 16.09831 Log likelihood = -2919.843936 BIC = 2.06e+08 ------------------------------------------------------------------------------ | OIM bf7m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -14.75682 28.76036 -0.51 0.608 -71.12609 41.61245 _cons | 1193.681 47.28742 25.24 0.000 1101 1286.363 ------------------------------------------------------------------------------ Iteration 1: deviance = 406.5017 Iteration 2: deviance = 405.9725 Iteration 3: deviance = 405.9723 Iteration 4: deviance = 405.9723 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 405.9722985 (1/df) Deviance = 1.127701 Pearson = 362.2389826 (1/df) Pearson = 1.006219 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1716.013 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf7m | -.0000668 .0001013 -0.66 0.509 -.0002652 .0001317 avgcumdosew2 | .1244811 .0531995 2.34 0.019 .020212 .2287502 _cons | -.6947121 .1494363 -4.65 0.000 -.9876019 -.4018223 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -3888.9836 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 1.19e+08 Deviance = 4.29556e+10 (1/df) Deviance = 1.19e+08 Pearson = 4.29556e+10 (1/df) Pearson = 1.19e+08 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 21.43793 Log likelihood = -3888.983569 BIC = 4.30e+10 ------------------------------------------------------------------------------ | OIM bf8 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 560.4936 415.2182 1.35 0.177 -253.3192 1374.306 _cons | 5567.66 682.6966 8.16 0.000 4229.599 6905.72 ------------------------------------------------------------------------------ Iteration 1: deviance = 406.9011 Iteration 2: deviance = 406.3651 Iteration 3: deviance = 406.3649 Iteration 4: deviance = 406.3649 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 406.3649265 (1/df) Deviance = 1.128791 Pearson = 362.4412012 (1/df) Pearson = 1.006781 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1715.62 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf8 | 1.81e-06 6.92e-06 0.26 0.793 -.0000118 .0000154 avgcumdosew2 | .1246875 .0532751 2.34 0.019 .0202703 .2291048 _cons | -.7839399 .0989719 -7.92 0.000 -.9779212 -.5899586 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -2865.4285 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 423002.2 Deviance = 152703798.3 (1/df) Deviance = 423002.2 Pearson = 152703798.3 (1/df) Pearson = 423002.2 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 15.7985 Log likelihood = -2865.428505 BIC = 1.53e+08 ------------------------------------------------------------------------------ | OIM bf15m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -20.7721 24.75663 -0.84 0.401 -69.29421 27.75 _cons | 118.713 40.70454 2.92 0.004 38.93359 198.4925 ------------------------------------------------------------------------------ Iteration 1: deviance = 403.5605 Iteration 2: deviance = 401.8807 Iteration 3: deviance = 401.3347 Iteration 4: deviance = 400.9492 Iteration 5: deviance = 400.5446 Iteration 6: deviance = 400.2059 Iteration 7: deviance = 400.0413 Iteration 8: deviance = 399.9425 Iteration 9: deviance = 399.8645 Iteration 10: deviance = 399.8166 Iteration 11: deviance = 399.8 Iteration 12: deviance = 399.7944 Iteration 13: deviance = 399.7924 Iteration 14: deviance = 399.7917 Iteration 15: deviance = 399.7914 Iteration 16: deviance = 399.7913 Iteration 17: deviance = 399.7912 Iteration 18: deviance = 399.7912 Iteration 19: deviance = 399.7912 Iteration 20: deviance = 399.7912 Iteration 21: deviance = 399.7912 Iteration 22: deviance = 399.7912 Iteration 23: deviance = 399.7912 Iteration 24: deviance = 399.7912 Iteration 25: deviance = 399.7912 Iteration 26: deviance = 399.7912 Iteration 27: deviance = 399.7912 Iteration 28: deviance = 399.7912 Iteration 29: deviance = 399.7912 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 399.7911696 (1/df) Deviance = 1.110531 Pearson = 350.5970313 (1/df) Pearson = .9738806 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1722.194 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf15m | -.0186941 .1404434 -0.13 0.894 -.293958 .2565699 avgcumdosew2 | .1198992 .0526195 2.28 0.023 .0167668 .2230316 _cons | -.7426095 .0909405 -8.17 0.000 -.9208497 -.5643693 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -3826.3245 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 8.43e+07 Deviance = 3.04151e+10 (1/df) Deviance = 8.43e+07 Pearson = 3.04151e+10 (1/df) Pearson = 8.43e+07 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 21.0927 Log likelihood = -3826.324541 BIC = 3.04e+10 ------------------------------------------------------------------------------ | OIM bf17 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -273.4502 349.3904 -0.78 0.434 -958.2427 411.3423 _cons | 1275.196 574.4633 2.22 0.026 149.269 2401.124 ------------------------------------------------------------------------------ Iteration 1: deviance = 405.2053 Iteration 2: deviance = 404.0203 Iteration 3: deviance = 403.6855 Iteration 4: deviance = 403.4175 Iteration 5: deviance = 403.1159 Iteration 6: deviance = 402.9417 Iteration 7: deviance = 402.8368 Iteration 8: deviance = 402.7416 Iteration 9: deviance = 402.6786 Iteration 10: deviance = 402.6575 Iteration 11: deviance = 402.6503 Iteration 12: deviance = 402.6478 Iteration 13: deviance = 402.647 Iteration 14: deviance = 402.6466 Iteration 15: deviance = 402.6465 Iteration 16: deviance = 402.6465 Iteration 17: deviance = 402.6465 Iteration 18: deviance = 402.6464 Iteration 19: deviance = 402.6464 Iteration 20: deviance = 402.6464 Iteration 21: deviance = 402.6464 Iteration 22: deviance = 402.6464 Iteration 23: deviance = 402.6464 Iteration 24: deviance = 402.6464 Iteration 25: deviance = 402.6464 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 402.6464206 (1/df) Deviance = 1.118462 Pearson = 355.5597944 (1/df) Pearson = .9876661 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1719.339 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf17 | -.0009496 .0098579 -0.10 0.923 -.0202707 .0183715 avgcumdosew2 | .1218174 .0528492 2.30 0.021 .0182348 .2253999 _cons | -.755413 .0909044 -8.31 0.000 -.9335823 -.5772436 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -1709.1559 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 723.7781 Deviance = 261283.9058 (1/df) Deviance = 723.7781 Pearson = 261283.9058 (1/df) Pearson = 723.7781 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.427856 Log likelihood = -1709.155932 BIC = 259156 ------------------------------------------------------------------------------ | OIM bf20 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.580896 1.024054 1.54 0.123 -.4262136 3.588005 _cons | 75.9561 1.683737 45.11 0.000 72.65604 79.25616 ------------------------------------------------------------------------------ Iteration 1: deviance = 400.9593 Iteration 2: deviance = 400.4773 Iteration 3: deviance = 400.4772 Iteration 4: deviance = 400.4772 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 400.4771542 (1/df) Deviance = 1.112437 Pearson = 362.8420021 (1/df) Pearson = 1.007894 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1721.508 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf20 | .0068438 .0030242 2.26 0.024 .0009166 .012771 avgcumdosew2 | .1159481 .0531408 2.18 0.029 .011794 .2201022 _cons | -1.306413 .2554428 -5.11 0.000 -1.807072 -.8057543 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -3441.897 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 1.01e+07 Deviance = 3657865066 (1/df) Deviance = 1.01e+07 Pearson = 3657865066 (1/df) Pearson = 1.01e+07 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 18.97464 Log likelihood = -3441.896981 BIC = 3.66e+09 ------------------------------------------------------------------------------ | OIM bf22 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 220.8529 121.1659 1.82 0.068 -16.62788 458.3337 _cons | 3140.533 199.2195 15.76 0.000 2750.07 3530.996 ------------------------------------------------------------------------------ Iteration 1: deviance = 405.124 Iteration 2: deviance = 404.6331 Iteration 3: deviance = 404.6329 Iteration 4: deviance = 404.6329 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 404.6329168 (1/df) Deviance = 1.12398 Pearson = 360.9276967 (1/df) Pearson = 1.002577 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1717.352 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf22 | .0000304 .0000232 1.31 0.191 -.0000151 .0000759 avgcumdosew2 | .1199526 .0533515 2.25 0.025 .0153855 .2245196 _cons | -.8734808 .1187957 -7.35 0.000 -1.106316 -.6406455 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -3484.9777 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 1.28e+07 Deviance = 4637795801 (1/df) Deviance = 1.28e+07 Pearson = 4637795801 (1/df) Pearson = 1.28e+07 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 19.212 Log likelihood = -3484.977739 BIC = 4.64e+09 ------------------------------------------------------------------------------ | OIM bf29 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 52.50345 136.4339 0.38 0.700 -214.9022 319.909 _cons | 267.0292 224.323 1.19 0.234 -172.6358 706.6941 ------------------------------------------------------------------------------ Iteration 1: deviance = 406.0224 Iteration 2: deviance = 405.5022 Iteration 3: deviance = 405.5021 Iteration 4: deviance = 405.5021 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 405.5020775 (1/df) Deviance = 1.126395 Pearson = 362.236176 (1/df) Pearson = 1.006212 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1716.483 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf29 | .0000177 .0000198 0.89 0.371 -.0000211 .0000564 avgcumdosew2 | .1248052 .0530425 2.35 0.019 .0208439 .2287666 _cons | -.7795525 .0909171 -8.57 0.000 -.9577467 -.6013582 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -2855.8596 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 401278.8 Deviance = 144861628.9 (1/df) Deviance = 401278.8 Pearson = 144861628.9 (1/df) Pearson = 401278.8 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 15.74578 Log likelihood = -2855.859629 BIC = 1.45e+08 ------------------------------------------------------------------------------ | OIM bf30 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 58.2065 24.11256 2.41 0.016 10.94675 105.4662 _cons | 476.5581 39.64556 12.02 0.000 398.8542 554.2619 ------------------------------------------------------------------------------ Iteration 1: deviance = 404.5558 Iteration 2: deviance = 404.0864 Iteration 3: deviance = 404.0863 Iteration 4: deviance = 404.0863 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 404.0863003 (1/df) Deviance = 1.122462 Pearson = 361.7532879 (1/df) Pearson = 1.00487 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1717.899 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf30 | .0001722 .0001187 1.45 0.147 -.0000605 .0004048 avgcumdosew2 | .116572 .0537488 2.17 0.030 .0112264 .2219177 _cons | -.8607769 .1094838 -7.86 0.000 -1.075361 -.6461925 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) Iteration 0: log likelihood = -818.51169 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 5.351378 Deviance = 1931.847477 (1/df) Deviance = 5.351378 Pearson = 1931.847477 (1/df) Pearson = 5.351378 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 4.520726 Log likelihood = -818.5116931 BIC = -196.0319 ------------------------------------------------------------------------------ | OIM bf40 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .1794903 .0880548 2.04 0.042 .0069061 .3520745 _cons | 3.004543 .1447786 20.75 0.000 2.720783 3.288304 ------------------------------------------------------------------------------ Iteration 1: deviance = 400.9844 Iteration 2: deviance = 400.6024 Iteration 3: deviance = 400.6022 Iteration 4: deviance = 400.6022 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 400.6022459 (1/df) Deviance = 1.112784 Pearson = 359.7424008 (1/df) Pearson = .9992844 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1721.383 ------------------------------------------------------------------------------ | EIM hp2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf40 | .0741875 .0318129 2.33 0.020 .0118353 .1365397 avgcumdosew2 | .1139391 .0533719 2.13 0.033 .009332 .2185461 _cons | -1.008205 .1365187 -7.39 0.000 -1.275776 -.740633 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 262 . * Saving mediators as scalars for Dose=work relationship 263 . **** female mediators of dose-paid employment: radhlw2, age,b40, bf4m, bf4, b > f1 264 . scalar wkMedFw2 = "radhlw2 age bf40 bf4m bf1" 265 . scalar list wkMedMw2 = bf8 age illw2 VactnMedFw2 = age illw2 radhlw2 VactnMedMw2 = age illw2 VacatnModFw2 = none MainEffVactnFw2 = age radhlw2 deaw2 SigDoseVactnFw2 = no vactnModMw2 = none MainEffVactnMw2 = age bf7m radhlw2 SigDoseVactnMw2 = no sxLifeMedFw2 = age bf4 bf4m sxLifeMedMw2 = age illw2 InthbModFw2 = none MainEffInthbFw2 = age radhlw2 bf4 SigdoseInthbFw2 = no InthbMw2 = none MainEffInthbMw2 = age radhlw2 shfamw2 SigDoseInthbMw2 = no sxlifeMedFw2 = age illw2 radhlw2 bf4 bf4m sxlifeMedMw2 = age illw2 sxlifeModFw2 = none MainEffsxlifeFw2 = age radhlw2 bf4 bf4m shrelaw2 shfamw2 SigDoseSxlifeFw2 = no sxlifeModMw2 = none SigDosesxlifeMw2 = no MainEffsxlifeMw2 = age bf4 illw2 radhlw2 PrbfmhmMedFw2 = age bf4 PrbfmhmMedMw2 = age PrbfmhmModFw2 = none MainEffPrbfmhmFw2 = age bf4 bf40 SigDosePrbfmhmFw2 = no PrbfmhmModw2 = none SigDosePrbfmhmMw2 = no SigDosePrbfhmMw2 = no MainEffPrbfhmMw2 = bf1 bf4 dvcew2 bf7m ProbsocMedFw2 = age radhlw2 ProbsocMedMw2 = age ProbsocModFw2 = none MainEffProbSocFw2 = age radhlw2 illw2 Shrelaw2 avgcumodsew2 SigDoseProbsocFw2 = yes ProbSocModMw2 = none SigDoseProbsocMw2 = no MainEffPrbsocMw2 = age radhlw2 shjobw2 hmcareMedFw2 = age illw2 hmcareMedMw2 = age illw2 hmcareModFw2 = none SigDoseWKFw2 = 0 SigdoseHmcareFw2 = no hmcareModMw2 = none MainEffhmcareMw2 = none SigDoseHmcareMw2 = no wkMedFw2 = radhlw2 age bf40 bf4m bf1 wkModFw2 = none wkModMw2 = none MainEffwkFw2 = age MainEffwkMw2 = workM: age bf8 illw2 shjobw2 SigDoseWKMw2 = no SigDoseWkFw2 = no hmcrMedFw1 = age icdxcnt shjobw1 bf4 BSIsoma WHPpain WHPsleep WHPel hmcrMedMw1 = age MainEffhmcrFw1 = illw1 age SigDosehmcrFw1 = no hmcrModMw1 = none MainEffhmcrMw1 = age shjobw1 SigDosehmcrMw1 = no wkMedFw1 = age b4 MainEffwkFw1 = age MainEffwkMw1 = age wkMedMw1 = bf40 WkMedMw1 = none WkModFw1 = none WKModMw1 = none SigDoseWkMw1 = no SigDoseWkFw1 = no SigDoseFw1 = no wkModFw1 = none wkModMw1 = none medsigFw1 = 1 prbsocnumMAsig = 8 266 . 267 . set linesize 100 268 . 269 . ***************************************************************************** > *** 270 . ******* We summarize the main effects models that were supposed to be signifi > cant 271 . ***************************************************************************** > *** 272 . set linesize 100 273 . est clear 274 . 275 . 276 . * work 277 . forvalues j=2/2 { 2. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 3. set more off 4. di as input "For females trimmed HP2work on wave 2 with dose ns" 5. des age occ1w`j'-occ8w`j' inc1w`j'-inc4w`j' avgcumdosew`j' bf8 // > / > marrw`j'1-marrw`j'6 `w2bf' 6. logistic HP2work age /// > avgcumdosew2 bf8 illw`j' shjobw`j' radhlw`j' if gender==1, co > ef nolog 7. 278 . eststo paidwk 8. } For females trimmed HP2work on wave 2 with dose ns storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age occ1w2 double %15.0g LABJ profess executive administration in 1996 occ2w2 double %15.0g LABJ technical sales admin support in 1996 occ3w2 double %15.0g LABJ service occup protective services in 1996 occ4w2 double %15.0g LABJ precision prod mechan craft construction in 1996 occ5w2 double %15.0g LABJ factory laborer machinist transp cleaner in 1996 occ6w2 double %15.0g LABJ farming agricul forestry fishing trapping logging in 1996 occ7w2 double %15.0g LABJ homemaking caregiving in 1996 occ8w2 double %15.0g LABJ student in 1996 inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf2 float %9.0g bf2 = max(0, efradw3 - 1.61252E-006) * bf1 bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m bf7m float %9.0g bf7m = max(0, radtlw3 - 2.12558E-007) * bf4m bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf17 float %9.0g bf17 = max(0, 20 - ecprw3) * bf15m bf20 float %9.0g bf20 = max(0, kzchorn - 2.53946E-006) bf22 float %9.0g bf22 = max(0, icdxcnt - 1.01635E-007) * bf7m bf29 float %9.0g bf29 = max(0, 18 - CSsocspt) * bf8 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Logistic regression Number of obs = 340 LR chi2(6) = 34.24 Prob > chi2 = 0.0000 Log likelihood = -155.75258 Pseudo R2 = 0.0990 ------------------------------------------------------------------------------ HP2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0279967 .0125982 2.22 0.026 .0033047 .0526886 avgcumdosew2 | .0093245 .0489486 0.19 0.849 -.0866129 .1052619 bf8 | -.0000433 .0000214 -2.03 0.043 -.0000852 -1.44e-06 illw2 | .503088 .2180574 2.31 0.021 .0757033 .9304727 shjobw2 | .0054505 .0037374 1.46 0.145 -.0018746 .0127756 radhlw2 | .0133173 .0044637 2.98 0.003 .0045685 .022066 _cons | -3.81568 .6836898 -5.58 0.000 -5.155688 -2.475673 ------------------------------------------------------------------------------ 279 . 280 . 281 . * social life 282 . forvalues j=2/2 { 2. set more off 3. di as input "females trimmed HP2probsoc on wave 2 with dose ns" 4. des age occ1w`j'-occ8w`j' inc1w`j'-inc4w`j' avgcumdosew`j' bf8 // > / > marrw`j'1-marrw`j'6 `w2bf' 5. logistic HP2probsoc age /// > avgcumdosew2 bf8 illw`j' shjobw`j' radhlw`j' if gender==1, co > ef nolog 6. eststo socprobs 7. } females trimmed HP2probsoc on wave 2 with dose ns storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age occ1w2 double %15.0g LABJ profess executive administration in 1996 occ2w2 double %15.0g LABJ technical sales admin support in 1996 occ3w2 double %15.0g LABJ service occup protective services in 1996 occ4w2 double %15.0g LABJ precision prod mechan craft construction in 1996 occ5w2 double %15.0g LABJ factory laborer machinist transp cleaner in 1996 occ6w2 double %15.0g LABJ farming agricul forestry fishing trapping logging in 1996 occ7w2 double %15.0g LABJ homemaking caregiving in 1996 occ8w2 double %15.0g LABJ student in 1996 inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf2 float %9.0g bf2 = max(0, efradw3 - 1.61252E-006) * bf1 bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m bf7m float %9.0g bf7m = max(0, radtlw3 - 2.12558E-007) * bf4m bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf17 float %9.0g bf17 = max(0, 20 - ecprw3) * bf15m bf20 float %9.0g bf20 = max(0, kzchorn - 2.53946E-006) bf22 float %9.0g bf22 = max(0, icdxcnt - 1.01635E-007) * bf7m bf29 float %9.0g bf29 = max(0, 18 - CSsocspt) * bf8 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Logistic regression Number of obs = 340 LR chi2(6) = 60.41 Prob > chi2 = 0.0000 Log likelihood = -94.948223 Pseudo R2 = 0.2413 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0592362 .0176148 3.36 0.001 .0247118 .0937607 avgcumdosew2 | .0315514 .0525591 0.60 0.548 -.0714626 .1345653 bf8 | -.0000332 .0000239 -1.39 0.165 -.0000801 .0000136 illw2 | .5251102 .2757549 1.90 0.057 -.0153594 1.06558 shjobw2 | .0135312 .0051744 2.62 0.009 .0033897 .0236728 radhlw2 | .0242757 .006237 3.89 0.000 .0120514 .0365 _cons | -7.543673 1.131417 -6.67 0.000 -9.76121 -5.326137 ------------------------------------------------------------------------------ 283 . 284 . * sex life 285 . title "Chunk 7 trimmed male model of dose and HP2sxlife relationship in wave > 2" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****Chunk 7 trimmed male model of dose and HP2sxlife relationship in wave 2*** > ** ***** ***** ***** ***** ***** 1 Jul 2012 15:01:04 ***** ******************************************************************************* ******************************************************************************* 286 . * male models 287 . forvalues j=2/2 { 2. set more off 3. di as input "trimmed HP2sexlife main effects models wave 1 for H1 part 2 > with dose ns" 4. di as input "wave 2 male dose avgcumdosew`j' main effect not signif" 5. logit HP2sxlife age bf4 illw`j' /// > avgcumdosew`j' radhlw`j' if gender==1 6. 288 . eststo sexlife 7. } trimmed HP2sexlife main effects models wave 1 for H1 part 2 with dose ns wave 2 male dose avgcumdosew2 main effect not signif Iteration 0: log likelihood = -171.51396 Iteration 1: log likelihood = -121.56573 Iteration 2: log likelihood = -115.51071 Iteration 3: log likelihood = -115.37175 Iteration 4: log likelihood = -115.3714 Iteration 5: log likelihood = -115.3714 Logistic regression Number of obs = 340 LR chi2(5) = 112.29 Prob > chi2 = 0.0000 Log likelihood = -115.3714 Pseudo R2 = 0.3273 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0668709 .0154805 4.32 0.000 .0365295 .0972122 bf4 | -.1650598 .0368511 -4.48 0.000 -.2372867 -.0928329 illw2 | .3077459 .2454954 1.25 0.210 -.1734162 .788908 avgcumdosew2 | .0621309 .0473611 1.31 0.190 -.0306951 .1549569 radhlw2 | .010562 .0054615 1.93 0.053 -.0001422 .0212663 _cons | -3.881348 1.050876 -3.69 0.000 -5.941027 -1.82167 ------------------------------------------------------------------------------ 289 . 290 . 291 . 292 . 293 . 294 . * inthob 295 . 296 . forvalues j=2/2 { 2. set more off 3. 297 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 298 . foreach var in HP2inthob { 5. forvalues k=2/2 { 6. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 7. 299 . di as input "trimmed main model for `var' for wave= `j' " 8. di _skip(4) 9. di as input "chunk 8 H1 test:Gender= `k' model Wave = `j' for `e(depvar > )' " 10. di _skip(4) 11. title "Full Nottingham Part 2 subscale models for male and then females" 12. di as input "Model for gender==`k' and wave == `j'" 13. di _skip(2) 14. logit `var' age /// > avgcumdosew`j' bf4 bf30 /// > shrelaw`j' suprtw`j' /// > radhlw2 if gender==2, difficult iterate(50) nolog 15. 300 . eststo inthobbies 16. } 17. } 18. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) trimmed main model for HP2inthob for wave= 2 chunk 8 H1 test:Gender= 2 model Wave = 2 for HP2sxlife ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for male and then females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:01:05 ***** ******************************************************************************* ******************************************************************************* Model for gender==2 and wave == 2 Logistic regression Number of obs = 363 LR chi2(7) = 101.28 Prob > chi2 = 0.0000 Log likelihood = -121.47255 Pseudo R2 = 0.2942 ------------------------------------------------------------------------------ HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0729699 .016233 4.50 0.000 .0411537 .104786 avgcumdosew2 | .0713471 .0870475 0.82 0.412 -.0992629 .2419571 bf4 | -.12277 .0348875 -3.52 0.000 -.1911482 -.0543918 bf30 | .0005237 .0002568 2.04 0.041 .0000204 .001027 shrelaw2 | -.0121549 .0056037 -2.17 0.030 -.0231379 -.001172 suprtw2 | -.0103307 .0040941 -2.52 0.012 -.018355 -.0023065 radhlw2 | .0119091 .0056652 2.10 0.036 .0008055 .0230128 _cons | -4.716583 1.190104 -3.96 0.000 -7.049144 -2.384021 ------------------------------------------------------------------------------ 301 . 302 . 303 . 304 . * vacation plans 305 . 306 . di _skip(1) 307 . di as input "For females trimmed vacation plans model on wave2 and d2 is not > signif " For females trimmed vacation plans model on wave2 and d2 is not signif 308 . di _skip(1) 309 . logistic hp2vacatn age deaw2 shjobw2 bf7m havmil /// > radhlw2 avgcumdosew2 if /// > gender==2, coef nolog Logistic regression Number of obs = 363 LR chi2(7) = 76.69 Prob > chi2 = 0.0000 Log likelihood = -129.17206 Pseudo R2 = 0.2289 ------------------------------------------------------------------------------ hp2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .079348 .0155749 5.09 0.000 .0488217 .1098742 deaw2 | .1646231 .1715933 0.96 0.337 -.1716935 .5009397 shjobw2 | -.0056137 .0041432 -1.35 0.175 -.0137341 .0025068 bf7m | -.0006133 .00025 -2.45 0.014 -.0011032 -.0001234 havmil | -.0001958 .0011139 -0.18 0.860 -.0023791 .0019875 radhlw2 | .0233367 .005861 3.98 0.000 .0118494 .034824 avgcumdosew2 | .0651649 .0909818 0.72 0.474 -.1131561 .243486 _cons | -6.606305 1.018983 -6.48 0.000 -8.603475 -4.609135 ------------------------------------------------------------------------------ 310 . eststo vacatn 311 . 312 . 313 . estout *, cells(b(star fmt(%9.3f)) se(par) t p) /// > stats(r2_p bic N, fmt(%9.3f %9.0g) ) /// > legend collabels(none) varlabels(_cons Constant) /// > mlabels(workw2 socprobsw2 sexlifew2 inthobw2 vactnw2) /// > title("main effects regression coefficients" > /// > "of reconstructed dose for females in wave 2") main effects regression coefficients of reconstructed dose for females in wave > 2 ------------------------------------------------------------------------------- ------------- workw2 socprobsw2 sexlifew2 inthobw2 > vactnw2 ------------------------------------------------------------------------------- ------------- main > age 0.028* 0.059*** 0.067*** 0.073*** > 0.079*** (0.013) (0.018) (0.015) (0.016) > (0.016) 2.222 3.363 4.320 4.495 > 5.095 0.026 0.001 0.000 0.000 > 0.000 avgcumdosew2 0.009 0.032 0.062 0.071 > 0.065 (0.049) (0.053) (0.047) (0.087) > (0.091) 0.190 0.600 1.312 0.820 > 0.716 0.849 0.548 0.190 0.412 > 0.474 bf8 -0.000* -0.000 > (0.000) (0.000) > -2.028 -1.389 > 0.043 0.165 > illw2 0.503* 0.525 0.308 > (0.218) (0.276) (0.245) > 2.307 1.904 1.254 > 0.021 0.057 0.210 > shjobw2 0.005 0.014** > -0.006 (0.004) (0.005) > (0.004) 1.458 2.615 > -1.355 0.145 0.009 > 0.175 radhlw2 0.013** 0.024*** 0.011 0.012* > 0.023*** (0.004) (0.006) (0.005) (0.006) > (0.006) 2.983 3.892 1.934 2.102 > 3.982 0.003 0.000 0.053 0.036 > 0.000 bf4 -0.165*** -0.123*** > (0.037) (0.035) > -4.479 -3.519 > 0.000 0.000 > bf30 0.001* > (0.000) > 2.039 > 0.041 > shrelaw2 -0.012* > (0.006) > -2.169 > 0.030 > suprtw2 -0.010* > (0.004) > -2.523 > 0.012 > deaw2 > 0.165 > (0.172) > 0.959 > 0.337 bf7m > -0.001* > (0.000) > -2.454 > 0.014 havmil > -0.000 > (0.001) > -0.176 > 0.860 Constant -3.816*** -7.544*** -3.881*** -4.717*** > -6.606*** (0.684) (1.131) (1.051) (1.190) > (1.019) -5.581 -6.667 -3.693 -3.963 > -6.483 0.000 0.000 0.000 0.000 > 0.000 ------------------------------------------------------------------------------- ------------- r2_p 0.099 0.241 0.327 0.294 > 0.229 bic 352.3078 230.6991 265.7165 290.1003 > 305.4993 N 340 340 340 363 > 363 ------------------------------------------------------------------------------- ------------- * p<0.05, ** p<0.01, *** p<0.001 314 . 315 . 316 . 317 . 318 . 319 . 320 . 321 . 322 . *xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx > xxx 323 . *------ Chunk 3 Dose=> hp2hmcare impact for males and females 324 . *xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx > xxx 325 . 326 . title "2. wave 2 part2 H1: dose home care relationship " /// > " wave 2 Dose - HP2home care Main effects identification" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** 2. wave 2 part2 H1: dose home care relationship ***** ***** wave 2 Dose - HP2home care Main effects identification ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:01:08 ***** ******************************************************************************* ******************************************************************************* 327 . 328 . * review of general model for men and women 329 . 330 . forvalues j=2/2 { 2. set more off 3. 331 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 332 . foreach var in HP2hmcare { 5. 333 . forvalues k=1/2 { 6. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 > bf40 7. title "chunk 3 H1 test pt 2 :Gender= `k' model Wave = `j' for `e(dep > var)' " 8. di _skip(4) 9. 334 . 335 . xi: logistic `var' age i.educ occ1w`j'-occ8w`j' /// > marrw`j'1- marrw`j'3 marrw`j'5-marrw`j'6 inc1w`j'-inc4w`j' // > / > radhlw`j' havmil avgcumdosew`j' `w`j'bf' /// > deaw`j' dvcew`j' sepaw`j' accdw`j' movew`j' /// > illw2 shfamw`j' shhlw`j' shjobw`j' shrelaw`j' suprtw`j' suchr > w`j' /// > havmilsq if gender==`k', coef nolog difficult iterate(50) 10. estat class 11. estat gof 12. fitstat 13. } 14. } 15. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** chunk 3 H1 test pt 2 :Gender= 1 model Wave = 2 for hp2vacatn ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:01:08 ***** ******************************************************************************* ******************************************************************************* i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: _Ieduc_4 != 0 predicts failure perfectly _Ieduc_4 dropped and 13 obs not used note: _Ieduc_8 != 0 predicts failure perfectly _Ieduc_8 dropped and 2 obs not used note: occ8w2 != 0 predicts failure perfectly occ8w2 dropped and 44 obs not used note: bf17 != 0 predicts failure perfectly bf17 dropped and 3 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 6 obs not used note: _Ieduc_7 omitted because of collinearity Logistic regression Number of obs = 270 LR chi2(49) = 112.39 Prob > chi2 = 0.0000 Log likelihood = -98.319474 Pseudo R2 = 0.3637 ------------------------------------------------------------------------------ HP2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0339056 .0231493 1.46 0.143 -.0114661 .0792774 _Ieduc_2 | -4.046801 2.06481 -1.96 0.050 -8.093754 .0001521 _Ieduc_3 | -2.332961 1.654719 -1.41 0.159 -5.576151 .9102284 _Ieduc_4 | 0 (omitted) _Ieduc_5 | -2.109138 1.662617 -1.27 0.205 -5.367807 1.149531 _Ieduc_6 | -2.575578 1.598895 -1.61 0.107 -5.709355 .5581988 _Ieduc_7 | 0 (omitted) _Ieduc_8 | 0 (omitted) occ1w2 | .2791915 3.076664 0.09 0.928 -5.750959 6.309342 occ2w2 | -.2043325 3.082144 -0.07 0.947 -6.245223 5.836559 occ3w2 | .5280791 3.097215 0.17 0.865 -5.542352 6.59851 occ4w2 | .3530379 3.103258 0.11 0.909 -5.729235 6.435311 occ5w2 | 1.030726 3.128223 0.33 0.742 -5.100479 7.161931 occ6w2 | 1.834194 3.454783 0.53 0.595 -4.937056 8.605444 occ7w2 | .6310877 3.205439 0.20 0.844 -5.651457 6.913633 occ8w2 | 0 (omitted) marrw21 | -1.774375 1.681557 -1.06 0.291 -5.070167 1.521417 marrw22 | -3.603201 2.051956 -1.76 0.079 -7.624961 .4185589 marrw23 | -3.822298 1.717545 -2.23 0.026 -7.188624 -.4559723 marrw25 | -.1941937 2.536946 -0.08 0.939 -5.166517 4.77813 marrw26 | -3.213845 2.602626 -1.23 0.217 -8.314899 1.887208 inc1w2 | 2.821191 3.187221 0.89 0.376 -3.425647 9.068029 inc2w2 | 2.680447 3.109489 0.86 0.389 -3.41404 8.774935 inc3w2 | 2.9859 3.107015 0.96 0.337 -3.103737 9.075536 inc4w2 | 1.060877 3.414563 0.31 0.756 -5.631544 7.753298 radhlw2 | .0009802 .0078741 0.12 0.901 -.0144527 .0164132 havmil | .0033106 .008219 0.40 0.687 -.0127982 .0194195 avgcumdosew2 | -.0408898 .0892564 -0.46 0.647 -.2158292 .1340496 bf1 | -.0224294 .0411105 -0.55 0.585 -.1030045 .0581457 bf4 | -.1579765 .2337949 -0.68 0.499 -.616206 .300253 bf2 | .0000992 .0001626 0.61 0.542 -.0002196 .000418 bf4m | -.1739281 .2154639 -0.81 0.420 -.5962296 .2483734 bf5m | .0006314 .0017487 0.36 0.718 -.002796 .0040589 bf7m | .0005115 .000594 0.86 0.389 -.0006527 .0016757 bf8 | -.0000403 .0000473 -0.85 0.394 -.0001331 .0000525 bf15m | -.0001301 .0003714 -0.35 0.726 -.000858 .0005979 bf17 | 0 (omitted) bf20 | .0027387 .0345917 0.08 0.937 -.0650597 .0705372 bf22 | -.0000529 .0001896 -0.28 0.780 -.0004245 .0003187 bf29 | 2.63e-06 .0000221 0.12 0.905 -.0000407 .0000459 bf30 | -.0002608 .0004218 -0.62 0.536 -.0010875 .0005659 bf40 | .3296594 .2457937 1.34 0.180 -.1520874 .8114062 deaw2 | .0069355 .3395872 0.02 0.984 -.6586432 .6725142 dvcew2 | -.8346564 3.597118 -0.23 0.817 -7.884878 6.215565 sepaw2 | 0 (omitted) accdw2 | .1481124 .4606844 0.32 0.748 -.7548124 1.051037 movew2 | .2192344 .5237343 0.42 0.676 -.807266 1.245735 illw2 | -.2794365 .3922624 -0.71 0.476 -1.048257 .4893838 shfamw2 | -.0130582 .0087013 -1.50 0.133 -.0301125 .0039961 shhlw2 | -.0041104 .0095817 -0.43 0.668 -.0228901 .0146694 shjobw2 | .0000993 .009716 0.01 0.992 -.0189438 .0191423 shrelaw2 | .0041598 .0074695 0.56 0.578 -.0104802 .0187997 suprtw2 | .0129457 .0070817 1.83 0.068 -.0009342 .0268256 suchrw2 | -.0024472 .0063512 -0.39 0.700 -.0148953 .010001 havmilsq | -8.45e-06 .000015 -0.56 0.573 -.0000379 .000021 _cons | 3.897935 3.960914 0.98 0.325 -3.865314 11.66118 ------------------------------------------------------------------------------ Logistic model for HP2hmcare -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 41 17 | 58 - | 29 183 | 212 -----------+--------------------------+----------- Total | 70 200 | 270 Classified + if predicted Pr(D) >= .5 True D defined as HP2hmcare != 0 -------------------------------------------------- Sensitivity Pr( +| D) 58.57% Specificity Pr( -|~D) 91.50% Positive predictive value Pr( D| +) 70.69% Negative predictive value Pr(~D| -) 86.32% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 8.50% False - rate for true D Pr( -| D) 41.43% False + rate for classified + Pr(~D| +) 29.31% False - rate for classified - Pr( D| -) 13.68% -------------------------------------------------- Correctly classified 82.96% -------------------------------------------------- Logistic model for HP2hmcare, goodness-of-fit test number of observations = 270 number of covariate patterns = 270 Pearson chi2(220) = 227.64 Prob > chi2 = 0.3476 Measures of Fit for logistic of HP2hmcare Log-Lik Intercept Only: -154.516 Log-Lik Full Model: -98.319 D(214): 196.639 LR(49): 112.393 Prob > LR: 0.000 McFadden's R2: 0.364 McFadden's Adj R2: 0.001 Maximum Likelihood R2: 0.340 Cragg & Uhler's R2: 0.500 McKelvey and Zavoina's R2: 0.594 Efron's R2: 0.390 Variance of y*: 8.112 Variance of error: 3.290 Count R2: 0.830 Adj Count R2: 0.343 AIC: 1.143 AIC*n: 308.639 BIC: -1001.423 BIC': 161.930 ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** chunk 3 H1 test pt 2 :Gender= 2 model Wave = 2 for HP2hmcare ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:01:10 ***** ******************************************************************************* ******************************************************************************* i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: bf29 != 0 predicts success perfectly bf29 dropped and 4 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity convergence not achieved Logistic regression Number of obs = 358 LR chi2(51) = 177.21 Prob > chi2 = 0.0000 Log likelihood = -139.72732 Pseudo R2 = 0.3881 ------------------------------------------------------------------------------ HP2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0689184 .0184884 3.73 0.000 .0326818 .105155 _Ieduc_2 | -17.33023 2.370203 -7.31 0.000 -21.97575 -12.68472 _Ieduc_3 | -17.85228 2.271622 -7.86 0.000 -22.30458 -13.39998 _Ieduc_4 | -16.22367 2.322199 -6.99 0.000 -20.7751 -11.67225 _Ieduc_5 | -17.51753 2.330541 -7.52 0.000 -22.0853 -12.94975 _Ieduc_6 | -18.46499 2.320848 -7.96 0.000 -23.01377 -13.91621 _Ieduc_7 | -17.72595 3.034199 -5.84 0.000 -23.67287 -11.77903 _Ieduc_8 | 0 (omitted) occ1w2 | -2.066459 1.51974 -1.36 0.174 -5.045095 .9121779 occ2w2 | -2.243078 1.553998 -1.44 0.149 -5.288858 .8027026 occ3w2 | -1.29392 1.568233 -0.83 0.409 -4.367601 1.77976 occ4w2 | -3.169244 1.685231 -1.88 0.060 -6.472236 .1337472 occ5w2 | -4.174961 1.966604 -2.12 0.034 -8.029433 -.3204881 occ6w2 | -5.016961 2.033875 -2.47 0.014 -9.003283 -1.030639 occ7w2 | -1.338521 1.557092 -0.86 0.390 -4.390365 1.713323 occ8w2 | .0102882 1.823109 0.01 0.995 -3.56294 3.583517 marrw21 | -.4364242 1.195764 -0.36 0.715 -2.780078 1.907229 marrw22 | .2779117 1.516808 0.18 0.855 -2.694977 3.2508 marrw23 | 1.982364 .8407589 2.36 0.018 .3345069 3.630221 marrw25 | .7265557 1.247315 0.58 0.560 -1.718137 3.171248 marrw26 | 0 (omitted) inc1w2 | 2.1918 1.615301 1.36 0.175 -.9741308 5.357731 inc2w2 | 3.722591 1.551917 2.40 0.016 .6808895 6.764292 inc3w2 | 3.205652 1.561296 2.05 0.040 .1455678 6.265737 inc4w2 | 3.577016 1.917029 1.87 0.062 -.1802924 7.334325 radhlw2 | -.0017814 .0069396 -0.26 0.797 -.0153829 .01182 havmil | .0004744 .0029523 0.16 0.872 -.005312 .0062609 avgcumdosew2 | -.232422 .1275125 -1.82 0.068 -.4823419 .017498 bf1 | -.0155252 .0283044 -0.55 0.583 -.0710008 .0399503 bf4 | -.6331543 .2104116 -3.01 0.003 -1.045553 -.2207551 bf2 | .0002047 .0001148 1.78 0.075 -.0000203 .0004296 bf4m | .369727 .1901433 1.94 0.052 -.0029469 .742401 bf5m | -.0009929 .001436 -0.69 0.489 -.0038073 .0018215 bf7m | .0003779 .0005084 0.74 0.457 -.0006186 .0013744 bf8 | -5.36e-07 .0000355 -0.02 0.988 -.0000702 .0000691 bf15m | -.0123361 .496536 -0.02 0.980 -.9855287 .9608566 bf17 | .0006265 .0248268 0.03 0.980 -.0480332 .0492861 bf20 | -.0134138 .0231788 -0.58 0.563 -.0588434 .0320158 bf22 | -.0000815 .0001207 -0.67 0.500 -.000318 .0001551 bf29 | 0 (omitted) bf30 | -.0000914 .0002999 -0.30 0.760 -.0006791 .0004963 bf40 | .1554702 .1284498 1.21 0.226 -.0962868 .4072271 deaw2 | .7476902 .2584678 2.89 0.004 .2411026 1.254278 dvcew2 | 1.232183 1.276036 0.97 0.334 -1.268802 3.733168 sepaw2 | -1.82805 1.829743 -1.00 0.318 -5.41428 1.75818 accdw2 | -.7440138 .5452818 -1.36 0.172 -1.812746 .3247189 movew2 | -.5229006 .4948092 -1.06 0.291 -1.492709 .4469077 illw2 | -.1315546 .1759029 -0.75 0.455 -.4763179 .2132087 shfamw2 | -.0038201 .0062872 -0.61 0.543 -.0161429 .0085027 shhlw2 | -.0093005 .0066582 -1.40 0.162 -.0223502 .0037493 shjobw2 | .0003285 .005919 0.06 0.956 -.0112724 .0119295 shrelaw2 | -.0125743 .0072115 -1.74 0.081 -.0267086 .00156 suprtw2 | -.0093988 .0049637 -1.89 0.058 -.0191275 .0003299 suchrw2 | .0039959 .0055359 0.72 0.470 -.0068542 .0148461 havmilsq | -6.92e-07 2.48e-06 -0.28 0.780 -5.56e-06 4.18e-06 _cons | 12.62346 . . . . . ------------------------------------------------------------------------------ Note: 4 failures and 0 successes completely determined. Warning: convergence not achieved Logistic model for HP2hmcare -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 86 29 | 115 - | 34 209 | 243 -----------+--------------------------+----------- Total | 120 238 | 358 Classified + if predicted Pr(D) >= .5 True D defined as HP2hmcare != 0 -------------------------------------------------- Sensitivity Pr( +| D) 71.67% Specificity Pr( -|~D) 87.82% Positive predictive value Pr( D| +) 74.78% Negative predictive value Pr(~D| -) 86.01% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 12.18% False - rate for true D Pr( -| D) 28.33% False + rate for classified + Pr(~D| +) 25.22% False - rate for classified - Pr( D| -) 13.99% -------------------------------------------------- Correctly classified 82.40% -------------------------------------------------- Logistic model for HP2hmcare, goodness-of-fit test number of observations = 358 number of covariate patterns = 358 Pearson chi2(305) = 382.80 Prob > chi2 = 0.0016 Measures of Fit for logistic of HP2hmcare Log-Lik Intercept Only: -228.331 Log-Lik Full Model: -139.727 D(302): 279.455 LR(51): 177.208 Prob > LR: 0.000 McFadden's R2: 0.388 McFadden's Adj R2: 0.143 Maximum Likelihood R2: 0.390 Cragg & Uhler's R2: 0.542 McKelvey and Zavoina's R2: 0.930 Efron's R2: 0.437 Variance of y*: 47.071 Variance of error: 3.290 Count R2: 0.824 Adj Count R2: 0.475 AIC: 1.093 AIC*n: 391.455 BIC: -1496.466 BIC': 122.699 336 . 337 . title4 "Male Main effects model for dose=> homcare wave 2" ------------------------------------------------------------------------------- Male Main effects model for dose=> homcare wave 2 ------------------------------------------------------------------------------- 338 . logit hp2hmcare age radhlw2 avgcumdosew2 bf4 bf4m bf40 if gender==1 Iteration 0: log likelihood = -172.87291 Iteration 1: log likelihood = -133.32556 Iteration 2: log likelihood = -130.68368 Iteration 3: log likelihood = -130.58803 Iteration 4: log likelihood = -130.58773 Iteration 5: log likelihood = -130.58773 Logistic regression Number of obs = 340 LR chi2(6) = 84.57 Prob > chi2 = 0.0000 Log likelihood = -130.58773 Pseudo R2 = 0.2446 ------------------------------------------------------------------------------ hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0212175 .0144823 1.47 0.143 -.0071673 .0496022 radhlw2 | .0004287 .0051507 0.08 0.934 -.0096666 .0105239 avgcumdosew2 | .0002129 .0577761 0.00 0.997 -.1130262 .113452 bf4 | -.0235753 .2097912 -0.11 0.911 -.4347586 .3876079 bf4m | -.1707442 .1936458 -0.88 0.378 -.5502831 .2087946 bf40 | .141308 .0923159 1.53 0.126 -.0396278 .3222439 _cons | .6763131 1.703403 0.40 0.691 -2.662295 4.014921 ------------------------------------------------------------------------------ 339 . 340 . 341 . // male main effects dose-work model washes out 342 . 343 . di as input "Male trimmed model for dose-home care impact in wv 3: dose not s > ignif" Male trimmed model for dose-home care impact in wv 3: dose not signif 344 . di as input " male dose is not signif as main effect in dose - homecare impac > t " male dose is not signif as main effect in dose - homecare impact 345 . di as input " no male moderate interactions for dose-homecare impact" no male moderate interactions for dose-homecare impact 346 . 347 . scalar SigDoseHmcareMw2 = "no" 348 . scalar MainEffhmcareMw2= "none" 349 . scalar list wkMedMw2 = bf8 age illw2 VactnMedFw2 = age illw2 radhlw2 VactnMedMw2 = age illw2 VacatnModFw2 = none MainEffVactnFw2 = age radhlw2 deaw2 SigDoseVactnFw2 = no vactnModMw2 = none MainEffVactnMw2 = age bf7m radhlw2 SigDoseVactnMw2 = no sxLifeMedFw2 = age bf4 bf4m sxLifeMedMw2 = age illw2 InthbModFw2 = none MainEffInthbFw2 = age radhlw2 bf4 SigdoseInthbFw2 = no InthbMw2 = none MainEffInthbMw2 = age radhlw2 shfamw2 SigDoseInthbMw2 = no sxlifeMedFw2 = age illw2 radhlw2 bf4 bf4m sxlifeMedMw2 = age illw2 sxlifeModFw2 = none MainEffsxlifeFw2 = age radhlw2 bf4 bf4m shrelaw2 shfamw2 SigDoseSxlifeFw2 = no sxlifeModMw2 = none SigDosesxlifeMw2 = no MainEffsxlifeMw2 = age bf4 illw2 radhlw2 PrbfmhmMedFw2 = age bf4 PrbfmhmMedMw2 = age PrbfmhmModFw2 = none MainEffPrbfmhmFw2 = age bf4 bf40 SigDosePrbfmhmFw2 = no PrbfmhmModw2 = none SigDosePrbfmhmMw2 = no SigDosePrbfhmMw2 = no MainEffPrbfhmMw2 = bf1 bf4 dvcew2 bf7m ProbsocMedFw2 = age radhlw2 ProbsocMedMw2 = age ProbsocModFw2 = none MainEffProbSocFw2 = age radhlw2 illw2 Shrelaw2 avgcumodsew2 SigDoseProbsocFw2 = yes ProbSocModMw2 = none SigDoseProbsocMw2 = no MainEffPrbsocMw2 = age radhlw2 shjobw2 hmcareMedFw2 = age illw2 hmcareMedMw2 = age illw2 hmcareModFw2 = none SigDoseWKFw2 = 0 SigdoseHmcareFw2 = no hmcareModMw2 = none MainEffhmcareMw2 = none SigDoseHmcareMw2 = no wkMedFw2 = radhlw2 age bf40 bf4m bf1 wkModFw2 = none wkModMw2 = none MainEffwkFw2 = age MainEffwkMw2 = workM: age bf8 illw2 shjobw2 SigDoseWKMw2 = no SigDoseWkFw2 = no hmcrMedFw1 = age icdxcnt shjobw1 bf4 BSIsoma WHPpain WHPsleep WHPel hmcrMedMw1 = age MainEffhmcrFw1 = illw1 age SigDosehmcrFw1 = no hmcrModMw1 = none MainEffhmcrMw1 = age shjobw1 SigDosehmcrMw1 = no wkMedFw1 = age b4 MainEffwkFw1 = age MainEffwkMw1 = age wkMedMw1 = bf40 WkMedMw1 = none WkModFw1 = none WKModMw1 = none SigDoseWkMw1 = no SigDoseWkFw1 = no SigDoseFw1 = no wkModFw1 = none wkModMw1 = none medsigFw1 = 1 prbsocnumMAsig = 8 350 . 351 . title4 "male main effect plus interaction model" ------------------------------------------------------------------------------- male main effect plus interaction model ------------------------------------------------------------------------------- 352 . logit hp2hmcare age radhlw2 avgcumdosew2 ageXd3 illw2 if gender==1 Iteration 0: log likelihood = -172.87291 Iteration 1: log likelihood = -151.63199 Iteration 2: log likelihood = -150.23873 Iteration 3: log likelihood = -150.14009 Iteration 4: log likelihood = -150.13472 Iteration 5: log likelihood = -150.13471 Logistic regression Number of obs = 340 LR chi2(5) = 45.48 Prob > chi2 = 0.0000 Log likelihood = -150.13471 Pseudo R2 = 0.1315 ------------------------------------------------------------------------------ hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0283791 .0164052 1.73 0.084 -.0037745 .0605327 radhlw2 | .0154894 .0043054 3.60 0.000 .007051 .0239279 avgcumdosew2 | -1.218369 1.156272 -1.05 0.292 -3.48462 1.047882 ageXd3 | .0170005 .0151684 1.12 0.262 -.012729 .0467301 illw2 | .3201382 .2199825 1.46 0.146 -.1110197 .751296 _cons | -3.730027 .9027785 -4.13 0.000 -5.49944 -1.960613 ------------------------------------------------------------------------------ 353 . cap gen radhlw2Xillw2 = radhlw2*illw2 354 . cap drop radhlw2Xd1 355 . cap gen radhlw2Xd3 = radhlw2*avgcumdosew2 356 . cap drop illw2Xd1 357 . cap gen illw2Xd3 = illw2*avgcumdosew2 358 . 359 . 360 . * there are no significant moderators for male dose=hmcare relationship 361 . scalar hmcareModMw2 = "none" 362 . 363 . *--------- Male moderator tests for Dose-Hmcare in wave 2: ---------------- > ---- 364 . 365 . title "Dose-home care impact relationship reveals no male moderators" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Dose-home care impact relationship reveals no male moderators ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:01:15 ***** ******************************************************************************* ******************************************************************************* 366 . logit hp2hmcare radhlw2 avgcumdosew2 illw2 radhlw2Xillw2 radhlw2Xd3 illw2Xd3 > /// > if gender==1, nolog Logistic regression Number of obs = 340 LR chi2(6) = 34.02 Prob > chi2 = 0.0000 Log likelihood = -155.8647 Pseudo R2 = 0.0984 ------------------------------------------------------------------------------- hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- radhlw2 | .0124854 .006471 1.93 0.054 -.0001976 .0251683 avgcumdosew2 | -.6591079 .694783 -0.95 0.343 -2.020858 .7026418 illw2 | -.0271833 .4678058 -0.06 0.954 -.9440659 .8896993 radhlw2Xillw2 | .0076663 .0070763 1.08 0.279 -.006203 .0215357 radhlw2Xd3 | .0069713 .0070473 0.99 0.323 -.0068412 .0207839 illw2Xd3 | .0895034 .2486877 0.36 0.719 -.3979155 .5769223 _cons | -2.001833 .530832 -3.77 0.000 -3.042245 -.9614216 ------------------------------------------------------------------------------- 367 . 368 . fitstat Measures of Fit for logit of hp2hmcare Log-Lik Intercept Only: -172.873 Log-Lik Full Model: -155.865 D(333): 311.729 LR(6): 34.016 Prob > LR: 0.000 McFadden's R2: 0.098 McFadden's Adj R2: 0.058 Maximum Likelihood R2: 0.095 Cragg & Uhler's R2: 0.149 McKelvey and Zavoina's R2: 0.259 Efron's R2: 0.109 Variance of y*: 4.438 Variance of error: 3.290 Count R2: 0.806 Adj Count R2: 0.057 AIC: 0.958 AIC*n: 325.729 BIC: -1629.309 BIC': 0.957 369 . 370 . 371 . title "testing female moderators for hp2hmcare" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** testing female moderators for hp2hmcare ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:01:16 ***** ******************************************************************************* ******************************************************************************* 372 . local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 bf40 373 . * Dose work relationship for females in wave 2 washes out also 374 . set more off 375 . forvalues j=2/2 { 2. di _skip(4) 3. di as input "For females hp2hmcare on wave 1 with dose ns" 4. des age avgcumdosew`j' `w2bf' 5. logistic HP2work age havmilsq radhlw2 /// > avgcumdosew2 shhlw`j' shjobw`j' shrelaw`j' suprtw`j' if gender==2, coef no > log 6. 376 . } For females hp2hmcare on wave 1 with dose ns storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf2 float %9.0g bf2 = max(0, efradw3 - 1.61252E-006) * bf1 bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m bf7m float %9.0g bf7m = max(0, radtlw3 - 2.12558E-007) * bf4m bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf17 float %9.0g bf17 = max(0, 20 - ecprw3) * bf15m bf20 float %9.0g bf20 = max(0, kzchorn - 2.53946E-006) bf22 float %9.0g bf22 = max(0, icdxcnt - 1.01635E-007) * bf7m bf29 float %9.0g bf29 = max(0, 18 - CSsocspt) * bf8 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Logistic regression Number of obs = 363 LR chi2(8) = 60.77 Prob > chi2 = 0.0000 Log likelihood = -176.17616 Pseudo R2 = 0.1471 ------------------------------------------------------------------------------ HP2work | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0605771 .012257 4.94 0.000 .0365537 .0846004 havmilsq | -6.47e-07 1.17e-06 -0.55 0.579 -2.93e-06 1.64e-06 radhlw2 | .0108128 .0042449 2.55 0.011 .0024929 .0191327 avgcumdosew2 | .1024263 .084282 1.22 0.224 -.0627633 .2676159 shhlw2 | .0099394 .0048118 2.07 0.039 .0005085 .0193703 shjobw2 | -.00289 .0045988 -0.63 0.530 -.0119035 .0061234 shrelaw2 | -.0105367 .0047951 -2.20 0.028 -.019935 -.0011384 suprtw2 | .0029696 .0033321 0.89 0.373 -.0035611 .0095003 _cons | -5.319428 .7716311 -6.89 0.000 -6.831797 -3.807059 ------------------------------------------------------------------------------ 377 . 378 . scalar SigdoseHmcareFw2="no" 379 . 380 . * capturing significant vars to test as moderators 381 . local cn4: colnames(e(b)) 382 . di "`cn4'" age havmilsq radhlw2 avgcumdosew2 shhlw2 shjobw2 shrelaw2 suprtw2 _cons 383 . local leng4 = length( "`cn4'") 384 . di `leng4' 71 385 . local leng4b `leng4'-6 386 . di `leng3b' 387 . local nuvlist4 = substr("`cn4'",4,`leng4b') 388 . di "`nuvlist4'" havmilsq radhlw2 avgcumdosew2 shhlw2 shjobw2 shrelaw2 suprtw2 _c 389 . local rhsvars4 = "`nuvlist4'" 390 . local nuvlist4= "`nuvlist4'" 391 . local nuvlist4= substr("`cn4'",1,`leng4b') 392 . di "`nuvlist4'" age havmilsq radhlw2 avgcumdosew2 shhlw2 shjobw2 shrelaw2 suprtw2 393 . 394 . cap gen shhlw2Xd3 = shhlw2*avgcumdosew2 395 . 396 . title "No sig female moderators for dose-hmcare impact are found here" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** No sig female moderators for dose-hmcare impact are found here ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:01:18 ***** ******************************************************************************* ******************************************************************************* 397 . logit hp2hmcare age shhlw2 avgcumdosew2 ageXd3 shhlw2Xd3 if gender==2 Iteration 0: log likelihood = -233.72859 Iteration 1: log likelihood = -195.05719 Iteration 2: log likelihood = -194.19863 Iteration 3: log likelihood = -194.19128 Iteration 4: log likelihood = -194.19128 Logistic regression Number of obs = 363 LR chi2(5) = 79.07 Prob > chi2 = 0.0000 Log likelihood = -194.19128 Pseudo R2 = 0.1692 ------------------------------------------------------------------------------ hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .084796 .0149038 5.69 0.000 .0555851 .1140068 shhlw2 | -.000082 .004448 -0.02 0.985 -.0087999 .0086358 avgcumdosew2 | -.7437036 .8125992 -0.92 0.360 -2.336369 .8489616 ageXd3 | .0110146 .0113714 0.97 0.333 -.0112729 .033302 shhlw2Xd3 | -.0030423 .0040693 -0.75 0.455 -.011018 .0049333 _cons | -4.921691 .7788456 -6.32 0.000 -6.448201 -3.395182 ------------------------------------------------------------------------------ 398 . 399 . set more off 400 . *---------- Mediator relationships for home care are tested below: ---------- > --- 401 . title "Mediator relationships of Dose - home care impact are tested" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Mediator relationships of Dose - home care impact are tested ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:01:19 ***** ******************************************************************************* ******************************************************************************* 402 . forvalues k=1/2 { 2. forvalues j=2/2 { 3. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 4. di _skip(2) 5. di as input "Trimmed gender=`k' hp2hmcare impact of dose in wave `j' " 6. des age occ1w`j'-occ8w`j' inc1w`j'-inc4w`j' avgcumdosew`j' `w2bf' 7. sw, pr(.1): logistic hp2hmcare age havmilsq /// > avgcumdosew2 ageXd3 illw`j' shjobw`j' suprtw`j' if gender==`k', coef nolo > g 8. estat gof 9. estat class 10. fitstat 11. } 12. } Trimmed gender=1 hp2hmcare impact of dose in wave 2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age occ1w2 double %15.0g LABJ profess executive administration in 1996 occ2w2 double %15.0g LABJ technical sales admin support in 1996 occ3w2 double %15.0g LABJ service occup protective services in 1996 occ4w2 double %15.0g LABJ precision prod mechan craft construction in 1996 occ5w2 double %15.0g LABJ factory laborer machinist transp cleaner in 1996 occ6w2 double %15.0g LABJ farming agricul forestry fishing trapping logging in 1996 occ7w2 double %15.0g LABJ homemaking caregiving in 1996 occ8w2 double %15.0g LABJ student in 1996 inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf2 float %9.0g bf2 = max(0, efradw3 - 1.61252E-006) * bf1 bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m bf7m float %9.0g bf7m = max(0, radtlw3 - 2.12558E-007) * bf4m bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf17 float %9.0g bf17 = max(0, 20 - ecprw3) * bf15m bf20 float %9.0g bf20 = max(0, kzchorn - 2.53946E-006) bf22 float %9.0g bf22 = max(0, icdxcnt - 1.01635E-007) * bf7m bf29 float %9.0g bf29 = max(0, 18 - CSsocspt) * bf8 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) begin with full model p = 0.4827 >= 0.1000 removing havmilsq p = 0.4147 >= 0.1000 removing suprtw2 p = 0.2849 >= 0.1000 removing shjobw2 p = 0.2522 >= 0.1000 removing avgcumdosew2 p = 0.9715 >= 0.1000 removing ageXd3 Logistic regression Number of obs = 340 LR chi2(2) = 28.54 Prob > chi2 = 0.0000 Log likelihood = -158.60117 Pseudo R2 = 0.0826 ------------------------------------------------------------------------------ hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0538093 .0119121 4.52 0.000 .030462 .0771566 illw2 | .347449 .2097345 1.66 0.098 -.063623 .7585211 _cons | -4.244759 .6492495 -6.54 0.000 -5.517264 -2.972253 ------------------------------------------------------------------------------ Logistic model for hp2hmcare, goodness-of-fit test number of observations = 340 number of covariate patterns = 91 Pearson chi2(88) = 90.18 Prob > chi2 = 0.4155 Logistic model for hp2hmcare -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 3 2 | 5 - | 67 268 | 335 -----------+--------------------------+----------- Total | 70 270 | 340 Classified + if predicted Pr(D) >= .5 True D defined as hp2hmcare != 0 -------------------------------------------------- Sensitivity Pr( +| D) 4.29% Specificity Pr( -|~D) 99.26% Positive predictive value Pr( D| +) 60.00% Negative predictive value Pr(~D| -) 80.00% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 0.74% False - rate for true D Pr( -| D) 95.71% False + rate for classified + Pr(~D| +) 40.00% False - rate for classified - Pr( D| -) 20.00% -------------------------------------------------- Correctly classified 79.71% -------------------------------------------------- Measures of Fit for logistic of hp2hmcare Log-Lik Intercept Only: -172.873 Log-Lik Full Model: -158.601 D(337): 317.202 LR(2): 28.543 Prob > LR: 0.000 McFadden's R2: 0.083 McFadden's Adj R2: 0.065 Maximum Likelihood R2: 0.081 Cragg & Uhler's R2: 0.126 McKelvey and Zavoina's R2: 0.140 Efron's R2: 0.094 Variance of y*: 3.826 Variance of error: 3.290 Count R2: 0.797 Adj Count R2: 0.014 AIC: 0.951 AIC*n: 323.202 BIC: -1647.152 BIC': -16.886 Trimmed gender=2 hp2hmcare impact of dose in wave 2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age occ1w2 double %15.0g LABJ profess executive administration in 1996 occ2w2 double %15.0g LABJ technical sales admin support in 1996 occ3w2 double %15.0g LABJ service occup protective services in 1996 occ4w2 double %15.0g LABJ precision prod mechan craft construction in 1996 occ5w2 double %15.0g LABJ factory laborer machinist transp cleaner in 1996 occ6w2 double %15.0g LABJ farming agricul forestry fishing trapping logging in 1996 occ7w2 double %15.0g LABJ homemaking caregiving in 1996 occ8w2 double %15.0g LABJ student in 1996 inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf2 float %9.0g bf2 = max(0, efradw3 - 1.61252E-006) * bf1 bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m bf7m float %9.0g bf7m = max(0, radtlw3 - 2.12558E-007) * bf4m bf8 float %9.0g bf8 = max(0, radtlw3 - 40) * bf5m bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf17 float %9.0g bf17 = max(0, 20 - ecprw3) * bf15m bf20 float %9.0g bf20 = max(0, kzchorn - 2.53946E-006) bf22 float %9.0g bf22 = max(0, icdxcnt - 1.01635E-007) * bf7m bf29 float %9.0g bf29 = max(0, 18 - CSsocspt) * bf8 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) begin with full model p = 0.7388 >= 0.1000 removing suprtw2 p = 0.7127 >= 0.1000 removing illw2 p = 0.7000 >= 0.1000 removing shjobw2 p = 0.4326 >= 0.1000 removing havmilsq p = 0.4184 >= 0.1000 removing ageXd3 p = 0.1321 >= 0.1000 removing avgcumdosew2 Logistic regression Number of obs = 363 LR chi2(1) = 74.63 Prob > chi2 = 0.0000 Log likelihood = -196.41342 Pseudo R2 = 0.1597 ------------------------------------------------------------------------------ hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0907219 .0119185 7.61 0.000 .0673621 .1140817 _cons | -5.350036 .6457493 -8.29 0.000 -6.615682 -4.084391 ------------------------------------------------------------------------------ Logistic model for hp2hmcare, goodness-of-fit test number of observations = 363 number of covariate patterns = 49 Pearson chi2(47) = 39.93 Prob > chi2 = 0.7580 Logistic model for hp2hmcare -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 62 33 | 95 - | 63 205 | 268 -----------+--------------------------+----------- Total | 125 238 | 363 Classified + if predicted Pr(D) >= .5 True D defined as hp2hmcare != 0 -------------------------------------------------- Sensitivity Pr( +| D) 49.60% Specificity Pr( -|~D) 86.13% Positive predictive value Pr( D| +) 65.26% Negative predictive value Pr(~D| -) 76.49% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 13.87% False - rate for true D Pr( -| D) 50.40% False + rate for classified + Pr(~D| +) 34.74% False - rate for classified - Pr( D| -) 23.51% -------------------------------------------------- Correctly classified 73.55% -------------------------------------------------- Measures of Fit for logistic of hp2hmcare Log-Lik Intercept Only: -233.729 Log-Lik Full Model: -196.413 D(361): 392.827 LR(1): 74.630 Prob > LR: 0.000 McFadden's R2: 0.160 McFadden's Adj R2: 0.151 Maximum Likelihood R2: 0.186 Cragg & Uhler's R2: 0.257 McKelvey and Zavoina's R2: 0.261 Efron's R2: 0.204 Variance of y*: 4.449 Variance of error: 3.290 Count R2: 0.736 Adj Count R2: 0.232 AIC: 1.093 AIC*n: 396.827 BIC: -1735.053 BIC': -68.736 403 . 404 . scalar hmcareModFw2 = "none" 405 . 406 . 407 . 408 . * age and illw2 are a female mediators of dose - home care impact 409 . 410 . * age is a male and female mediator with impact on home care 411 . glm age avgcumdosew2 if gender==1, fam(gaussian) link(identity) Iteration 0: log likelihood = -1330.6004 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 147.6853 Deviance = 49917.64009 (1/df) Deviance = 147.6853 Pearson = 49917.64009 (1/df) Pearson = 147.6853 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.838826 Log likelihood = -1330.6004 BIC = 47947.46 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .5832314 .2635871 2.21 0.027 .0666101 1.099853 _cons | 48.62133 .7061562 68.85 0.000 47.23729 50.00537 ------------------------------------------------------------------------------ 412 . glm hp2hmcare age if gender==1, fam(binomial) irls scale(dev) link(probit) Iteration 1: deviance = 320.8295 Iteration 2: deviance = 320.0337 Iteration 3: deviance = 320.0328 Iteration 4: deviance = 320.0328 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 320.0327771 (1/df) Deviance = .9468425 Pearson = 341.699231 (1/df) Pearson = 1.010944 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1650.151 ------------------------------------------------------------------------------ | EIM hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .032545 .0064212 5.07 0.000 .0199596 .0451304 _cons | -2.483665 .3435565 -7.23 0.000 -3.157023 -1.810306 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 413 . 414 . glm age avgcumdosew2 if gender==2, fam(gaussian) link(identity) Iteration 0: log likelihood = -1406.9403 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 136.9184 Deviance = 49427.52828 (1/df) Deviance = 136.9184 Pearson = 49427.52828 (1/df) Pearson = 136.9184 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.762756 Log likelihood = -1406.940271 BIC = 47299.65 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.502324 .4454009 3.37 0.001 .6293547 2.375294 _cons | 48.86944 .7323225 66.73 0.000 47.43412 50.30477 ------------------------------------------------------------------------------ 415 . glm hp2hmcare age if gender==2 , fam(binomial) irls scale(dev) link(probit) Iteration 1: deviance = 393.7958 Iteration 2: deviance = 393.6955 Iteration 3: deviance = 393.6955 Iteration 4: deviance = 393.6955 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 393.6954976 (1/df) Deviance = 1.090569 Pearson = 375.4421438 (1/df) Pearson = 1.040006 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1734.184 ------------------------------------------------------------------------------ | EIM hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0533501 .0069587 7.67 0.000 .0397113 .0669889 _cons | -3.144653 .3710751 -8.47 0.000 -3.871946 -2.417359 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 416 . 417 . 418 . * illw2 is a male mediator wrt dose home care impact 419 . glm illw2 avgcumdosew2 if gender==1, fam(gaussian) link(identity) Iteration 0: log likelihood = -303.59609 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = .3512988 Deviance = 118.7390083 (1/df) Deviance = .3512988 Pearson = 118.7390083 (1/df) Pearson = .3512988 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 1.797624 Log likelihood = -303.5960853 BIC = -1851.445 ------------------------------------------------------------------------------ | OIM illw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .0085423 .0128556 0.66 0.506 -.0166543 .0337389 _cons | .2741359 .0344406 7.96 0.000 .2066336 .3416382 ------------------------------------------------------------------------------ 420 . glm hp2hmcare illw2 if gender==1, fam(binomial) irls scale(dev) link(probit) Iteration 1: deviance = 338.889 Iteration 2: deviance = 338.7479 Iteration 3: deviance = 338.7478 Iteration 4: deviance = 338.7478 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 338.7478085 (1/df) Deviance = 1.002212 Pearson = 338.1071125 (1/df) Pearson = 1.000317 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1631.436 ------------------------------------------------------------------------------ | EIM hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2 | .3223509 .1214283 2.65 0.008 .0843558 .560346 _cons | -.926217 .0878912 -10.54 0.000 -1.098481 -.7539535 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 421 . 422 . glm illw2 avgcumdosew2 if gender==2, fam(gaussian) link(identity) Iteration 0: log likelihood = -463.51524 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = .756881 Deviance = 273.2340487 (1/df) Deviance = .756881 Pearson = 273.2340487 (1/df) Pearson = .756881 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 2.564822 Log likelihood = -463.5152411 BIC = -1854.645 ------------------------------------------------------------------------------ | OIM illw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .1249912 .0331157 3.77 0.000 .0600856 .1898968 _cons | .301285 .0544484 5.53 0.000 .194568 .4080019 ------------------------------------------------------------------------------ 423 . glm hp2hmcare illw2 if gender==2, fam(binomial) irls scale(dev) link(probit) Iteration 1: deviance = 466.1156 Iteration 2: deviance = 465.4957 Iteration 3: deviance = 465.4956 Iteration 4: deviance = 465.4956 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 465.4955997 (1/df) Deviance = 1.289461 Pearson = 362.823507 (1/df) Pearson = 1.005051 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1662.384 ------------------------------------------------------------------------------ | EIM hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2 | .1070451 .0863777 1.24 0.215 -.0622522 .2763423 _cons | -.4461214 .0854256 -5.22 0.000 -.6135525 -.2786904 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 424 . 425 . 426 . 427 . scalar hmcareMedMw2 = "age illw2" 428 . scalar hmcareMedFw2 = "age illw2" 429 . scalar list wkMedMw2 = bf8 age illw2 VactnMedFw2 = age illw2 radhlw2 VactnMedMw2 = age illw2 VacatnModFw2 = none MainEffVactnFw2 = age radhlw2 deaw2 SigDoseVactnFw2 = no vactnModMw2 = none MainEffVactnMw2 = age bf7m radhlw2 SigDoseVactnMw2 = no sxLifeMedFw2 = age bf4 bf4m sxLifeMedMw2 = age illw2 InthbModFw2 = none MainEffInthbFw2 = age radhlw2 bf4 SigdoseInthbFw2 = no InthbMw2 = none MainEffInthbMw2 = age radhlw2 shfamw2 SigDoseInthbMw2 = no sxlifeMedFw2 = age illw2 radhlw2 bf4 bf4m sxlifeMedMw2 = age illw2 sxlifeModFw2 = none MainEffsxlifeFw2 = age radhlw2 bf4 bf4m shrelaw2 shfamw2 SigDoseSxlifeFw2 = no sxlifeModMw2 = none SigDosesxlifeMw2 = no MainEffsxlifeMw2 = age bf4 illw2 radhlw2 PrbfmhmMedFw2 = age bf4 PrbfmhmMedMw2 = age PrbfmhmModFw2 = none MainEffPrbfmhmFw2 = age bf4 bf40 SigDosePrbfmhmFw2 = no PrbfmhmModw2 = none SigDosePrbfmhmMw2 = no SigDosePrbfhmMw2 = no MainEffPrbfhmMw2 = bf1 bf4 dvcew2 bf7m ProbsocMedFw2 = age radhlw2 ProbsocMedMw2 = age ProbsocModFw2 = none MainEffProbSocFw2 = age radhlw2 illw2 Shrelaw2 avgcumodsew2 SigDoseProbsocFw2 = yes ProbSocModMw2 = none SigDoseProbsocMw2 = no MainEffPrbsocMw2 = age radhlw2 shjobw2 hmcareMedFw2 = age illw2 hmcareMedMw2 = age illw2 hmcareModFw2 = none SigDoseWKFw2 = 0 SigdoseHmcareFw2 = no hmcareModMw2 = none MainEffhmcareMw2 = none SigDoseHmcareMw2 = no wkMedFw2 = radhlw2 age bf40 bf4m bf1 wkModFw2 = none wkModMw2 = none MainEffwkFw2 = age MainEffwkMw2 = workM: age bf8 illw2 shjobw2 SigDoseWKMw2 = no SigDoseWkFw2 = no hmcrMedFw1 = age icdxcnt shjobw1 bf4 BSIsoma WHPpain WHPsleep WHPel hmcrMedMw1 = age MainEffhmcrFw1 = illw1 age SigDosehmcrFw1 = no hmcrModMw1 = none MainEffhmcrMw1 = age shjobw1 SigDosehmcrMw1 = no wkMedFw1 = age b4 MainEffwkFw1 = age MainEffwkMw1 = age wkMedMw1 = bf40 WkMedMw1 = none WkModFw1 = none WKModMw1 = none SigDoseWkMw1 = no SigDoseWkFw1 = no SigDoseFw1 = no wkModFw1 = none wkModMw1 = none medsigFw1 = 1 prbsocnumMAsig = 8 430 . 431 . * conclusion "age & illw2 are main effects as possible male & female mediator > s" 432 . * conclusion title "their interaction is not a mediator" 433 . 434 . 435 . 436 . 437 . * Other non-signif test results for mediators of the dose-homecare impact 438 . 439 . glm illw2Xd3 illw2 avgcumdosew2 if gender==1, fam(gaussian) link(identity) Iteration 0: log likelihood = -561.33689 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 337 Scale parameter = 1.604749 Deviance = 540.8004038 (1/df) Deviance = 1.604749 Pearson = 540.8004038 (1/df) Pearson = 1.604749 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 3.319629 Log likelihood = -561.3368924 BIC = -1423.554 ------------------------------------------------------------------------------ | OIM illw2Xd3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2 | .6995256 .1162537 6.02 0.000 .4716726 .9273787 avgcumdosew2 | .5560421 .0274943 20.22 0.000 .5021543 .6099299 _cons | -.4073759 .0802126 -5.08 0.000 -.5645897 -.2501622 ------------------------------------------------------------------------------ 440 . glm hp2hmcare illw2Xd3 illw2 avgcumdosew2 if gender==1, fam(binomial) /// > irls scale(dev) link(probit) Iteration 1: deviance = 338.5042 Iteration 2: deviance = 338.3318 Iteration 3: deviance = 338.3296 Iteration 4: deviance = 338.3295 Iteration 5: deviance = 338.3295 Iteration 6: deviance = 338.3295 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 338.3295375 (1/df) Deviance = 1.006933 Pearson = 337.9090682 (1/df) Pearson = 1.005682 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1620.196 ------------------------------------------------------------------------------ | EIM hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2Xd3 | .043344 .0744303 0.58 0.560 -.1025368 .1892247 illw2 | .2934865 .1303698 2.25 0.024 .0379663 .5490066 avgcumdosew2 | -.02475 .0655641 -0.38 0.706 -.1532532 .1037532 _cons | -.9097069 .1011012 -9.00 0.000 -1.107862 -.7115522 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 441 . glm illw2Xd3 illw2 avgcumdosew2 if gender==2, fam(gaussian) link(identity) Iteration 0: log likelihood = -802.15873 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 360 Scale parameter = 4.903891 Deviance = 1765.400887 (1/df) Deviance = 4.903891 Pearson = 1765.400887 (1/df) Pearson = 4.903891 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 4.436136 Log likelihood = -802.1587263 BIC = -356.5841 ------------------------------------------------------------------------------ | OIM illw2Xd3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2 | 1.477942 .1339686 11.03 0.000 1.215369 1.740516 avgcumdosew2 | 1.48752 .0859399 17.31 0.000 1.319081 1.655959 _cons | -1.335179 .1443511 -9.25 0.000 -1.618101 -1.052256 ------------------------------------------------------------------------------ 442 . glm hp2hmcare illw2Xd3 illw2 avgcumdosew2 if gender==2, fam(binomial) /// > irls scale(dev) link(probit) Iteration 1: deviance = 463.5448 Iteration 2: deviance = 462.9346 Iteration 3: deviance = 462.934 Iteration 4: deviance = 462.934 Iteration 5: deviance = 462.934 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 359 (IRLS EIM) Scale parameter = 1 Deviance = 462.93402 (1/df) Deviance = 1.28951 Pearson = 361.7870308 (1/df) Pearson = 1.007763 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1653.157 ------------------------------------------------------------------------------ | EIM hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2Xd3 | -.0539164 .0417648 -1.29 0.197 -.1357739 .0279411 illw2 | .1869825 .1045198 1.79 0.074 -.0178725 .3918376 avgcumdosew2 | .0522197 .0762058 0.69 0.493 -.097141 .2015804 _cons | -.4961458 .106924 -4.64 0.000 -.7057129 -.2865786 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 443 . 444 . 445 . glm havmilsq avgcumdosew2 if gender==1, fam(gaussian) link(identity) Iteration 0: log likelihood = -4357.4808 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 7.98e+09 Deviance = 2.69740e+12 (1/df) Deviance = 7.98e+09 Pearson = 2.69740e+12 (1/df) Pearson = 7.98e+09 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 25.644 Log likelihood = -4357.480823 BIC = 2.70e+12 ------------------------------------------------------------------------------ | OIM havmilsq | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -1562.571 1937.625 -0.81 0.420 -5360.246 2235.104 _cons | 18476.58 5190.943 3.56 0.000 8302.515 28650.64 ------------------------------------------------------------------------------ 446 . glm hp2hmcare havmilsq if gender==1, fam(binomial) irls scale(dev) link(probi > t) Iteration 1: deviance = 345.527 Iteration 2: deviance = 345.1721 Iteration 3: deviance = 345.1646 Iteration 4: deviance = 345.1644 Iteration 5: deviance = 345.1644 Iteration 6: deviance = 345.1644 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 345.1644251 (1/df) Deviance = 1.021197 Pearson = 339.1838387 (1/df) Pearson = 1.003502 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1625.019 ------------------------------------------------------------------------------ | EIM hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- havmilsq | -9.19e-07 1.39e-06 -0.66 0.509 -3.65e-06 1.81e-06 _cons | -.8078353 .0797621 -10.13 0.000 -.9641661 -.6515045 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 447 . glm havmilsq avgcumdosew2 if gender==2, fam(gaussian) link(identity) Iteration 0: log likelihood = -5315.5316 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 3.08e+11 Deviance = 1.11295e+14 (1/df) Deviance = 3.08e+11 Pearson = 1.11295e+14 (1/df) Pearson = 3.08e+11 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 29.29769 Log likelihood = -5315.53158 BIC = 1.11e+14 ------------------------------------------------------------------------------ | OIM havmilsq | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -2103.378 21135.06 -0.10 0.921 -43527.34 39320.58 _cons | 62705.69 34750 1.80 0.071 -5403.056 130814.4 ------------------------------------------------------------------------------ 448 . glm hp2hmcare havmilsq if gender==2, fam(binomial) irls scale(dev) link(probi > t) Iteration 1: deviance = 466.1751 Iteration 2: deviance = 465.0334 Iteration 3: deviance = 464.73 Iteration 4: deviance = 464.6076 Iteration 5: deviance = 464.5797 Iteration 6: deviance = 464.5779 Iteration 7: deviance = 464.5779 Iteration 8: deviance = 464.5779 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 464.5778963 (1/df) Deviance = 1.286919 Pearson = 360.2475414 (1/df) Pearson = .9979156 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1663.302 ------------------------------------------------------------------------------ | EIM hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- havmilsq | -7.46e-07 1.04e-06 -0.72 0.475 -2.79e-06 1.30e-06 _cons | -.3815177 .0787742 -4.84 0.000 -.5359124 -.227123 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 449 . 450 . glm radhlw2 avgcumdosew2 if gender==1, fam(gaussian) link(identity) Iteration 0: log likelihood = -1693.4076 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 1247.933 Deviance = 421801.4584 (1/df) Deviance = 1247.933 Pearson = 421801.4584 (1/df) Pearson = 1247.933 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.972986 Log likelihood = -1693.407647 BIC = 419831.3 ------------------------------------------------------------------------------ | OIM radhlw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.220373 .766216 1.59 0.111 -.2813831 2.722128 _cons | 45.63198 2.052711 22.23 0.000 41.60874 49.65522 ------------------------------------------------------------------------------ 451 . glm hp2hmcare radhlw2 if gender==1, fam(binomial) irls scale(dev) link(probit > ) Iteration 1: deviance = 320.2142 Iteration 2: deviance = 319.4143 Iteration 3: deviance = 319.4134 Iteration 4: deviance = 319.4134 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 319.4134212 (1/df) Deviance = .9450101 Pearson = 343.7631942 (1/df) Pearson = 1.017051 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1650.77 ------------------------------------------------------------------------------ | EIM hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- radhlw2 | .0113273 .0022041 5.14 0.000 .0070074 .0156472 _cons | -1.415444 .1449647 -9.76 0.000 -1.69957 -1.131318 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 452 . glm radhlw2 avgcumdosew2 if gender==2, fam(gaussian) link(identity) Iteration 0: log likelihood = -1791.2233 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 1137.567 Deviance = 410661.5604 (1/df) Deviance = 1137.567 Pearson = 410661.5604 (1/df) Pearson = 1137.567 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.880018 Log likelihood = -1791.223306 BIC = 408533.7 ------------------------------------------------------------------------------ | OIM radhlw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 3.302288 1.283833 2.57 0.010 .7860214 5.818555 _cons | 56.95167 2.110863 26.98 0.000 52.81445 61.08888 ------------------------------------------------------------------------------ 453 . glm hp2hmcare radhlw2 if gender==2, fam(binomial) irls scale(dev) link(probit > ) Iteration 1: deviance = 465.9397 Iteration 2: deviance = 465.344 Iteration 3: deviance = 465.3439 Iteration 4: deviance = 465.3439 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 465.3438602 (1/df) Deviance = 1.289041 Pearson = 362.9626143 (1/df) Pearson = 1.005437 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1662.536 ------------------------------------------------------------------------------ | EIM hp2hmcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- radhlw2 | .0029148 .0022776 1.28 0.201 -.0015493 .0073789 _cons | -.5772317 .1586961 -3.64 0.000 -.8882703 -.266193 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 454 . 455 . title4 "*------- summary matrix construction for dose home care" ------------------------------------------------------------------------------- *------- summary matrix construction for dose home care ------------------------------------------------------------------------------- 456 . 457 . matrix define HP2hmcrMw2 = J(1,8, 0) 458 . matrix define HP2hmcrFw2 = J(1,8, 0) 459 . matrix colnames HP2hmcrMw2= hypnum ptnum wave gender medsig numMAsig numMods > ig /// > numMed 460 . matrix colnames HP2hmcrFw2= hypnum ptnum wave gender medsig numMAsig numMods > ig /// > numMed 461 . matrix rownames HP2hmcrMw2 = hmcareM 462 . matrix rownames HP2hmcrFw2 = hmcareF 463 . matrix define HP2hmcrFw2= (1, 2, 3, 2, 0 ,2, 0 , 2 ) 464 . matrix define HP2hmcrMw2= (1, 2, 3, 1, 0 , 4, 0 , 2 ) 465 . matrix define H1pt2w2 = (HP2wkMw2 \ HP2wkFw2 \ HP2hmcrMw2 \HP2hmcrFw2 > ) 466 . matrix colnames H1pt2w2 = hypnum ptnum wave gender medsig numMAsig numMo > dsig numMed 467 . matrix colnames H1pt2w2 = hypnum ptnum wave gender medsig numMAsig numMo > dsig numMed 468 . matrix rownames H1pt2w2 = HP2wkMw2 WHP2wkFw2 HP2hmcrMw2 HP2hmcrFw2 469 . matlist H1pt2w2 | hypnum ptnum wave gender medsig numMAsig > numModsig -------------+----------------------------------------------------------------- ------------ HP2wkMw2 | 1 2 3 1 0 4 > 0 WHP2wkFw2 | 1 2 3 2 0 1 > 0 HP2hmcrMw2 | 1 2 3 1 0 4 > 0 HP2hmcrFw2 | 1 2 3 2 0 2 > 0 | numMed -------------+----------- HP2wkMw2 | 4 WHP2wkFw2 | 6 HP2hmcrMw2 | 2 HP2hmcrFw2 | 2 470 . 471 . * see scalar list for names of variables 472 . 473 . 474 . * X * missing the number of main effects in the trimmed models 475 . 476 . //////////////////////////////////////////////////////////////////////// > *--------- Chunk 4 Dose prob soc impact relationship HP2probsoc 477 . *---------------------------------------------------------------------------- > --- 478 . * General model for all part 2 of Nottingham Health Profile 479 . 480 . title " 3. wave 2 part2 H1: Test of hypothesis 1 Part 2 " ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** 3. wave 2 part2 H1: Test of hypothesis 1 Part 2 ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:07 ***** ******************************************************************************* ******************************************************************************* 481 . title " wave 2 Dose - HP2probsoc Main effects identification" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** wave 2 Dose - HP2probsoc Main effects identification ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:07 ***** ******************************************************************************* ******************************************************************************* 482 . forvalues j=2/2 { 2. set more off 3. 483 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 484 . foreach var in HP2probsoc { 5. forvalues k=1/2 { 6. di as input "Full main model for `var' for wave= `j' " 7. di _skip(4) 8. di as input "chunk 4 H1 test:Gender= `k' model Wave = `j' for `e(depvar > )' " 9. di _skip(4) 10. title "Full Nottingham Part 2 subscale models for male and then females" 11. 485 . xi: logistic `var' age i.educ occ1w`j'-occ8w`j' /// > marrw`j'1- marrw`j'3 marrw`j'5-marrw`j'6 inc1w`j'-inc4w`j' // > / > radhlw`j' havmil avgcumdosew`j' `w`j'bf' /// > deaw`j' dvcew`j' sepaw`j' accdw`j' movew`j' /// > illw`j' shfamw`j' shhlw`j' shjobw`j' shrelaw`j' suprtw`j' suc > hrw`j' /// > havmilsq if gender==`k', coef nolog difficult iterate(50) 12. estat class 13. estat gof 14. fitstat 15. } 16. } 17. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Full main model for HP2probsoc for wave= 2 chunk 4 H1 test:Gender= 1 model Wave = 2 for hp2hmcare ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for male and then females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:07 ***** ******************************************************************************* ******************************************************************************* i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: _Ieduc_4 != 0 predicts failure perfectly _Ieduc_4 dropped and 13 obs not used note: _Ieduc_7 != 0 predicts failure perfectly _Ieduc_7 dropped and 4 obs not used note: _Ieduc_8 != 0 predicts failure perfectly _Ieduc_8 dropped and 2 obs not used note: occ6w2 != 0 predicts failure perfectly occ6w2 dropped and 5 obs not used note: occ7w2 != 0 predicts failure perfectly occ7w2 dropped and 14 obs not used note: occ8w2 != 0 predicts failure perfectly occ8w2 dropped and 44 obs not used note: marrw22 != 0 predicts failure perfectly marrw22 dropped and 7 obs not used note: marrw26 != 0 predicts failure perfectly marrw26 dropped and 1 obs not used note: inc4w2 != 0 predicts failure perfectly inc4w2 dropped and 10 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 14 obs not used note: dvcew2 != 0 predicts failure perfectly dvcew2 dropped and 5 obs not used note: marrw25 != 0 predicts success perfectly marrw25 dropped and 1 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 1 obs not used note: _Ieduc_6 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 217 LR chi2(40) = 113.21 Prob > chi2 = 0.0000 Log likelihood = -47.096671 Pseudo R2 = 0.5459 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .1159429 .0419803 2.76 0.006 .0336631 .1982228 _Ieduc_2 | -.0479004 1.407174 -0.03 0.973 -2.80591 2.710109 _Ieduc_3 | -.8284731 .7880615 -1.05 0.293 -2.373045 .716099 _Ieduc_4 | 0 (omitted) _Ieduc_5 | .2454375 1.100079 0.22 0.823 -1.910677 2.401552 _Ieduc_6 | 0 (omitted) _Ieduc_7 | 0 (omitted) _Ieduc_8 | 0 (omitted) occ1w2 | -1.3725 16.54078 -0.08 0.934 -33.79183 31.04683 occ2w2 | -1.244674 16.53791 -0.08 0.940 -33.65838 31.16903 occ3w2 | -.7835828 16.55903 -0.05 0.962 -33.23868 31.67152 occ4w2 | -3.862736 16.57719 -0.23 0.816 -36.35344 28.62797 occ5w2 | -2.775301 16.58962 -0.17 0.867 -35.29036 29.73976 occ6w2 | 0 (omitted) occ7w2 | 0 (omitted) occ8w2 | 0 (omitted) marrw21 | 12.58162 1075.856 0.01 0.991 -2096.057 2121.22 marrw22 | 0 (omitted) marrw23 | 10.03646 1075.856 0.01 0.993 -2098.602 2118.675 marrw25 | 0 (omitted) marrw26 | 0 (omitted) inc1w2 | .0757099 16.62358 0.00 0.996 -32.50591 32.65733 inc2w2 | 2.619578 16.55703 0.16 0.874 -29.83159 35.07075 inc3w2 | 4.14916 16.572 0.25 0.802 -28.33137 36.62969 inc4w2 | 0 (omitted) radhlw2 | .0130005 .0142344 0.91 0.361 -.0148985 .0408995 havmil | -.002818 .0090337 -0.31 0.755 -.0205238 .0148877 avgcumdosew2 | .0114306 .0823526 0.14 0.890 -.1499776 .1728387 bf1 | .0037783 .0642153 0.06 0.953 -.1220814 .129638 bf4 | .016836 .3033169 0.06 0.956 -.5776542 .6113261 bf2 | .0006302 .0003126 2.02 0.044 .0000175 .0012428 bf4m | -.5319968 .2823116 -1.88 0.060 -1.085317 .0213237 bf5m | .0058806 .0028091 2.09 0.036 .0003749 .0113862 bf7m | .0013216 .0011466 1.15 0.249 -.0009257 .0035688 bf8 | -.0001829 .0000911 -2.01 0.045 -.0003616 -4.31e-06 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.0241214 .0536769 -0.45 0.653 -.1293262 .0810833 bf22 | .0004188 .0002855 1.47 0.142 -.0001407 .0009783 bf29 | -.000073 .0001017 -0.72 0.473 -.0002723 .0001263 bf30 | -.0016782 .0007996 -2.10 0.036 -.0032455 -.000111 bf40 | -.1773534 .3453449 -0.51 0.608 -.854217 .4995102 deaw2 | -.7039739 .7523846 -0.94 0.349 -2.178621 .7706728 dvcew2 | 0 (omitted) sepaw2 | 0 (omitted) accdw2 | -.0469037 .8655378 -0.05 0.957 -1.743327 1.649519 movew2 | 1.585081 .8835966 1.79 0.073 -.1467362 3.316899 illw2 | -.6496621 .6161215 -1.05 0.292 -1.857238 .5579138 shfamw2 | -.0160373 .0116025 -1.38 0.167 -.0387778 .0067033 shhlw2 | -.0105176 .0138341 -0.76 0.447 -.0376321 .0165968 shjobw2 | .0250231 .0129193 1.94 0.053 -.0002983 .0503445 shrelaw2 | -.0217826 .0114409 -1.90 0.057 -.0442063 .0006412 suprtw2 | .0083099 .0108445 0.77 0.444 -.012945 .0295648 suchrw2 | .0212997 .0111718 1.91 0.057 -.0005966 .0431959 havmilsq | 6.25e-06 .0000101 0.62 0.537 -.0000136 .0000261 _cons | -13.61182 1075.864 -0.01 0.990 -2122.266 2095.042 ------------------------------------------------------------------------------ Note: 2 failures and 0 successes completely determined. Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 26 4 | 30 - | 14 173 | 187 -----------+--------------------------+----------- Total | 40 177 | 217 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 65.00% Specificity Pr( -|~D) 97.74% Positive predictive value Pr( D| +) 86.67% Negative predictive value Pr(~D| -) 92.51% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 2.26% False - rate for true D Pr( -| D) 35.00% False + rate for classified + Pr(~D| +) 13.33% False - rate for classified - Pr( D| -) 7.49% -------------------------------------------------- Correctly classified 91.71% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 217 number of covariate patterns = 217 Pearson chi2(176) = 158.28 Prob > chi2 = 0.8270 Measures of Fit for logistic of HP2probsoc Log-Lik Intercept Only: -103.704 Log-Lik Full Model: -47.097 D(161): 94.193 LR(40): 113.215 Prob > LR: 0.000 McFadden's R2: 0.546 McFadden's Adj R2: 0.006 Maximum Likelihood R2: 0.407 Cragg & Uhler's R2: 0.660 McKelvey and Zavoina's R2: 0.840 Efron's R2: 0.562 Variance of y*: 20.586 Variance of error: 3.290 Count R2: 0.917 Adj Count R2: 0.550 AIC: 0.950 AIC*n: 206.193 BIC: -771.970 BIC': 101.981 Full main model for HP2probsoc for wave= 2 chunk 4 H1 test:Gender= 2 model Wave = 2 for HP2probsoc ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for male and then females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:08 ***** ******************************************************************************* ******************************************************************************* i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: occ6w2 != 0 predicts failure perfectly occ6w2 dropped and 9 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 341 LR chi2(50) = 174.30 Prob > chi2 = 0.0000 Log likelihood = -89.934458 Pseudo R2 = 0.4921 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .1034919 .0270041 3.83 0.000 .0505648 .1564191 _Ieduc_2 | -13.17654 1236.407 -0.01 0.991 -2436.49 2410.137 _Ieduc_3 | -13.38577 1236.407 -0.01 0.991 -2436.699 2409.928 _Ieduc_4 | -12.54323 1236.407 -0.01 0.992 -2435.857 2410.771 _Ieduc_5 | -12.47221 1236.407 -0.01 0.992 -2435.786 2410.842 _Ieduc_6 | -13.80248 1236.407 -0.01 0.991 -2437.116 2409.511 _Ieduc_7 | -15.17121 1236.497 -0.01 0.990 -2438.662 2408.319 _Ieduc_8 | 0 (omitted) occ1w2 | -1.147886 3.571925 -0.32 0.748 -8.14873 5.852957 occ2w2 | -1.271517 3.611241 -0.35 0.725 -8.349419 5.806385 occ3w2 | .2117582 3.586153 0.06 0.953 -6.816973 7.24049 occ4w2 | -1.534382 3.689834 -0.42 0.678 -8.766324 5.69756 occ5w2 | -2.414751 3.8284 -0.63 0.528 -9.918277 5.088775 occ6w2 | 0 (omitted) occ7w2 | -.6787306 3.585162 -0.19 0.850 -7.70552 6.348058 occ8w2 | 2.909649 3.844529 0.76 0.449 -4.625489 10.44479 marrw21 | -1.181403 1.729099 -0.68 0.494 -4.570375 2.207569 marrw22 | 1.066113 1.762045 0.61 0.545 -2.387432 4.519658 marrw23 | .6150888 .9283649 0.66 0.508 -1.204473 2.434651 marrw25 | .4424041 1.311023 0.34 0.736 -2.127154 3.011962 marrw26 | 0 (omitted) inc1w2 | .302891 3.591795 0.08 0.933 -6.736898 7.34268 inc2w2 | 1.142371 3.560505 0.32 0.748 -5.836091 8.120833 inc3w2 | .8314334 3.569583 0.23 0.816 -6.164821 7.827688 inc4w2 | .0290561 3.848853 0.01 0.994 -7.514557 7.57267 radhlw2 | .0111407 .0096438 1.16 0.248 -.0077607 .0300422 havmil | .0005515 .0077361 0.07 0.943 -.0146109 .0157139 avgcumdosew2 | .546822 .23001 2.38 0.017 .0960107 .9976333 bf1 | .0275412 .0390831 0.70 0.481 -.0490602 .1041427 bf4 | -.4078914 .2433269 -1.68 0.094 -.8848033 .0690205 bf2 | .0001427 .0001416 1.01 0.314 -.0001349 .0004204 bf4m | .1372093 .2260724 0.61 0.544 -.3058844 .5803031 bf5m | -.0016005 .0026021 -0.62 0.538 -.0067005 .0034995 bf7m | .0002743 .0007319 0.37 0.708 -.0011601 .0017087 bf8 | .0000233 .0000548 0.42 0.671 -.0000841 .0001306 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.04615 .0319322 -1.45 0.148 -.1087361 .016436 bf22 | 9.68e-06 .0001672 0.06 0.954 -.000318 .0003374 bf29 | -.0000266 .0000488 -0.54 0.586 -.0001221 .000069 bf30 | .0001087 .0003798 0.29 0.775 -.0006357 .0008531 bf40 | -.0077229 .1716746 -0.04 0.964 -.344199 .3287532 deaw2 | -.0328171 .2515674 -0.13 0.896 -.5258803 .460246 dvcew2 | 1.645199 1.729548 0.95 0.341 -1.744652 5.03505 sepaw2 | -.787699 2.185185 -0.36 0.718 -5.070584 3.495186 accdw2 | -.7482691 .7741464 -0.97 0.334 -2.265568 .7690299 movew2 | -.1983887 .9882329 -0.20 0.841 -2.13529 1.738512 illw2 | .037099 .2627647 0.14 0.888 -.4779104 .5521084 shfamw2 | -.0189411 .0086476 -2.19 0.028 -.03589 -.0019922 shhlw2 | .0013266 .0086367 0.15 0.878 -.0156011 .0182542 shjobw2 | -.0022444 .0078272 -0.29 0.774 -.0175855 .0130966 shrelaw2 | -.0057107 .0089259 -0.64 0.522 -.0232051 .0117837 suprtw2 | .0019185 .0068705 0.28 0.780 -.0115475 .0153845 suchrw2 | -.0030114 .0077253 -0.39 0.697 -.0181526 .0121298 havmilsq | -5.64e-06 .0000161 -0.35 0.725 -.0000371 .0000258 _cons | 8.438379 1236.411 0.01 0.995 -2414.883 2431.76 ------------------------------------------------------------------------------ Note: 2 failures and 0 successes completely determined. Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 52 13 | 65 - | 21 255 | 276 -----------+--------------------------+----------- Total | 73 268 | 341 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 71.23% Specificity Pr( -|~D) 95.15% Positive predictive value Pr( D| +) 80.00% Negative predictive value Pr(~D| -) 92.39% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 4.85% False - rate for true D Pr( -| D) 28.77% False + rate for classified + Pr(~D| +) 20.00% False - rate for classified - Pr( D| -) 7.61% -------------------------------------------------- Correctly classified 90.03% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 341 number of covariate patterns = 341 Pearson chi2(290) = 351.64 Prob > chi2 = 0.0077 Measures of Fit for logistic of HP2probsoc Log-Lik Intercept Only: -177.084 Log-Lik Full Model: -89.934 D(285): 179.869 LR(50): 174.299 Prob > LR: 0.000 McFadden's R2: 0.492 McFadden's Adj R2: 0.176 Maximum Likelihood R2: 0.400 Cragg & Uhler's R2: 0.619 McKelvey and Zavoina's R2: 0.858 Efron's R2: 0.531 Variance of y*: 23.160 Variance of error: 3.290 Count R2: 0.900 Adj Count R2: 0.534 AIC: 0.856 AIC*n: 291.869 BIC: -1482.218 BIC': 117.295 486 . 487 . *-----Chunk 4 dose3 social problem impact--------------no sig dose main effec > t-- 488 . title4 "Chunk 4 trimmed models of dose and HP2work relationship in wave 2" ------------------------------------------------------------------------------- Chunk 4 trimmed models of dose and HP2work relationship in wave 2 ------------------------------------------------------------------------------- 489 . * male models 490 . set more off 491 . forvalues j=2/2 { 2. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 3. title3 "trimmed HP2probsoc main effects models wave 2 for H1 part 2 with d > ose ns" 4. title "wave 2 dose HP2probsoc relationship but avgcumdosew`j': Dose not si > gnif" 5. xi:logit HP2probsoc age i.educ radhlw2 accdw`j' bf8 shjobw`j' suchrw2 hav > milsq /// > avgcumdosew`j' if gender==1 6. estat class 7. estat gof 8. fitstat 9. } ------------------------------------------------------------------------------- title3 : trimmed HP2probsoc main effects models wave 2 for H1 part 2 with dose > ns 1 Jul 2012 15:02:10 computer Macintosh (Intel 64-bit) folder /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/ > h1pt2 Data file chwide1jul2012.dta currrently has 2395 variables and 703 observ > ations ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****wave 2 dose HP2probsoc relationship but avgcumdosew2: Dose not signif***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:10 ***** ******************************************************************************* ******************************************************************************* i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: _Ieduc_4 != 0 predicts failure perfectly _Ieduc_4 dropped and 14 obs not used note: _Ieduc_7 != 0 predicts failure perfectly _Ieduc_7 dropped and 4 obs not used note: _Ieduc_8 != 0 predicts failure perfectly _Ieduc_8 dropped and 2 obs not used note: _Ieduc_6 omitted because of collinearity Iteration 0: log likelihood = -122.49818 Iteration 1: log likelihood = -97.278367 Iteration 2: log likelihood = -91.419426 Iteration 3: log likelihood = -91.263695 Iteration 4: log likelihood = -91.263586 Iteration 5: log likelihood = -91.263586 Logistic regression Number of obs = 320 LR chi2(11) = 62.47 Prob > chi2 = 0.0000 Log likelihood = -91.263586 Pseudo R2 = 0.2550 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0708065 .0177532 3.99 0.000 .0360108 .1056021 _Ieduc_2 | .0385275 .9109987 0.04 0.966 -1.746997 1.824052 _Ieduc_3 | -.5726249 .461212 -1.24 0.214 -1.476584 .3313341 _Ieduc_4 | 0 (omitted) _Ieduc_5 | -.2324702 .5513297 -0.42 0.673 -1.313057 .8481161 _Ieduc_6 | 0 (omitted) _Ieduc_7 | 0 (omitted) _Ieduc_8 | 0 (omitted) radhlw2 | .0254018 .006594 3.85 0.000 .0124778 .0383257 accdw2 | -.5083029 .6480971 -0.78 0.433 -1.77855 .7619441 bf8 | -.0000267 .0000244 -1.09 0.274 -.0000745 .0000212 shjobw2 | .0157373 .0055054 2.86 0.004 .0049469 .0265278 suchrw2 | .00109 .0046549 0.23 0.815 -.0080334 .0102134 havmilsq | -1.25e-06 5.36e-06 -0.23 0.815 -.0000118 9.25e-06 avgcumdosew2 | .0268182 .0517674 0.52 0.604 -.0746441 .1282805 _cons | -7.869049 1.225988 -6.42 0.000 -10.27194 -5.466157 ------------------------------------------------------------------------------ Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 12 2 | 14 - | 29 277 | 306 -----------+--------------------------+----------- Total | 41 279 | 320 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 29.27% Specificity Pr( -|~D) 99.28% Positive predictive value Pr( D| +) 85.71% Negative predictive value Pr(~D| -) 90.52% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 0.72% False - rate for true D Pr( -| D) 70.73% False + rate for classified + Pr(~D| +) 14.29% False - rate for classified - Pr( D| -) 9.48% -------------------------------------------------- Correctly classified 90.31% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 320 number of covariate patterns = 317 Pearson chi2(305) = 353.85 Prob > chi2 = 0.0282 Measures of Fit for logit of HP2probsoc Log-Lik Intercept Only: -122.498 Log-Lik Full Model: -91.264 D(304): 182.527 LR(11): 62.469 Prob > LR: 0.000 McFadden's R2: 0.255 McFadden's Adj R2: 0.124 Maximum Likelihood R2: 0.177 Cragg & Uhler's R2: 0.332 McKelvey and Zavoina's R2: 0.447 Efron's R2: 0.255 Variance of y*: 5.948 Variance of error: 3.290 Count R2: 0.903 Adj Count R2: 0.244 AIC: 0.670 AIC*n: 214.527 BIC: -1571.042 BIC': 0.982 492 . 493 . 494 . forvalues j=2/2 { 2. title "trimmed HP2probsoc main effects models wave `j'" " for H1 part 2 wi > th dose ns" 3. title2 "Wave `j dose HP2work relationship but avgcumdosew`j': Dose not sig > nif" 4. } ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** trimmed HP2probsoc main effects models wave 2 ***** ***** for H1 part 2 with dose ns ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:12 ***** ******************************************************************************* ******************************************************************************* ------------------------------------------------------------------------------- title2: Wave `j dose HP2work relationship but avgcumdosew2: Dose not signif Date and time: 1 Jul 2012 15:02:12 Working directory: /Users/robertyaffee/ > Documents/data/research/chwk/phase3/Htests/H1tests/h1pt2 Stata data file: chwide1jul2012.dta ha > s 2395 variables and 703 observations Wave `j dose HP2work relationship but avgcumdosew2: Dose not signif ------------------------------------------------------------------------------- 495 . 496 . foreach var in suchrw2 radhlw2 shjobw2 { 2. cap gen `var'Xd3= `var'*avgcumdosew2 3. } 497 . 498 . forvalues j=2/2 { 2. title "Main effects Dose ProbSoc model for males" 3. logit HP2probsoc age avgcumdosew2 radhlw2 shjobw`j' if gender==1 4. estat class 5. estat gof 6. fitstat 7. } ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Main effects Dose ProbSoc model for males ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:12 ***** ******************************************************************************* ******************************************************************************* Iteration 0: log likelihood = -125.15243 Iteration 1: log likelihood = -102.49803 Iteration 2: log likelihood = -97.612007 Iteration 3: log likelihood = -97.514638 Iteration 4: log likelihood = -97.514623 Iteration 5: log likelihood = -97.514623 Logistic regression Number of obs = 340 LR chi2(4) = 55.28 Prob > chi2 = 0.0000 Log likelihood = -97.514623 Pseudo R2 = 0.2208 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0650468 .0171243 3.80 0.000 .0314838 .0986098 avgcumdosew2 | .0250528 .0516618 0.48 0.628 -.0762025 .1263081 radhlw2 | .0217043 .0059664 3.64 0.000 .0100104 .0333982 shjobw2 | .0153749 .0050929 3.02 0.003 .0053929 .0253569 _cons | -7.726421 1.13381 -6.81 0.000 -9.948647 -5.504194 ------------------------------------------------------------------------------ Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 9 1 | 10 - | 32 298 | 330 -----------+--------------------------+----------- Total | 41 299 | 340 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 21.95% Specificity Pr( -|~D) 99.67% Positive predictive value Pr( D| +) 90.00% Negative predictive value Pr(~D| -) 90.30% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 0.33% False - rate for true D Pr( -| D) 78.05% False + rate for classified + Pr(~D| +) 10.00% False - rate for classified - Pr( D| -) 9.70% -------------------------------------------------- Correctly classified 90.29% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 340 number of covariate patterns = 327 Pearson chi2(322) = 384.78 Prob > chi2 = 0.0093 Measures of Fit for logit of HP2probsoc Log-Lik Intercept Only: -125.152 Log-Lik Full Model: -97.515 D(335): 195.029 LR(4): 55.276 Prob > LR: 0.000 McFadden's R2: 0.221 McFadden's Adj R2: 0.181 Maximum Likelihood R2: 0.150 Cragg & Uhler's R2: 0.288 McKelvey and Zavoina's R2: 0.411 Efron's R2: 0.210 Variance of y*: 5.584 Variance of error: 3.290 Count R2: 0.903 Adj Count R2: 0.195 AIC: 0.603 AIC*n: 205.029 BIC: -1757.668 BIC': -31.960 499 . 500 . scalar MainEffPrbsocMw2 = "age radhlw2 shjobw2" 501 . 502 . 503 . forvalues j=2/2 { 2. logit HP2probsoc age radhlw2 shjobw`j' /// > avgcumdosew`j' /// > shjobw2Xd3 if gender==1 3. estat class 4. estat gof 5. fitstat 6. } Iteration 0: log likelihood = -125.15243 Iteration 1: log likelihood = -102.40367 Iteration 2: log likelihood = -97.802085 Iteration 3: log likelihood = -96.367187 Iteration 4: log likelihood = -96.245667 Iteration 5: log likelihood = -96.239265 Iteration 6: log likelihood = -96.239247 Iteration 7: log likelihood = -96.239247 Logistic regression Number of obs = 340 LR chi2(5) = 57.83 Prob > chi2 = 0.0000 Log likelihood = -96.239247 Pseudo R2 = 0.2310 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0645913 .0169521 3.81 0.000 .0313658 .0978168 radhlw2 | .0222475 .0060296 3.69 0.000 .0104297 .0340653 shjobw2 | .0092759 .0068028 1.36 0.173 -.0040574 .0226091 avgcumdosew2 | -.7357041 .6602672 -1.11 0.265 -2.029804 .5583958 shjobw2Xd3 | .0081692 .0068728 1.19 0.235 -.0053013 .0216397 _cons | -7.172995 1.201125 -5.97 0.000 -9.527157 -4.818833 ------------------------------------------------------------------------------ Note: 1 failure and 0 successes completely determined. Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 10 2 | 12 - | 31 297 | 328 -----------+--------------------------+----------- Total | 41 299 | 340 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 24.39% Specificity Pr( -|~D) 99.33% Positive predictive value Pr( D| +) 83.33% Negative predictive value Pr(~D| -) 90.55% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 0.67% False - rate for true D Pr( -| D) 75.61% False + rate for classified + Pr(~D| +) 16.67% False - rate for classified - Pr( D| -) 9.45% -------------------------------------------------- Correctly classified 90.29% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 340 number of covariate patterns = 327 Pearson chi2(321) = 353.15 Prob > chi2 = 0.1047 Measures of Fit for logit of HP2probsoc Log-Lik Intercept Only: -125.152 Log-Lik Full Model: -96.239 D(334): 192.478 LR(5): 57.826 Prob > LR: 0.000 McFadden's R2: 0.231 McFadden's Adj R2: 0.183 Maximum Likelihood R2: 0.156 Cragg & Uhler's R2: 0.300 McKelvey and Zavoina's R2: 0.495 Efron's R2: 0.219 Variance of y*: 6.518 Variance of error: 3.290 Count R2: 0.903 Adj Count R2: 0.195 AIC: 0.601 AIC*n: 204.478 BIC: -1754.389 BIC': -28.682 504 . scalar SigDoseProbsocMw2 = "no" 505 . * xx no signific radhwl3 by dose effect 506 . * xx for males no signif dose social problem effect 507 . * xx for males no significant moderator in dose social problem effect 508 . scalar ProbSocModMw2 = "none" 509 . 510 . 511 . ***************************************************************************** > *** 512 . * female dose social problems wave 2 models 513 . 514 . forvalues j=2/2 { 2. set more off 3. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 4. title3 "partly trimmed HP2probsoc main effects models wave 2 for H1 part 2 > with dose ns" 5. title "wave 2 dose HP2probsoc relationship but avgcumdosew`j': Dose signif > here" 6. logit HP2probsoc age radhlw2 accdw`j' sepaw`j' illw`j' shjobw`j' //// > shrelaw`j' suchrw2 havmilsq avgcumdosew`j' if gender==2, nolog 7. estat class 8. estat gof 9. fitstat 10. } ------------------------------------------------------------------------------- title3 : partly trimmed HP2probsoc main effects models wave 2 for H1 part 2 wit > h dose ns 1 Jul 2012 15:02:14 computer Macintosh (Intel 64-bit) folder /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/ > h1pt2 Data file chwide1jul2012.dta currrently has 2397 variables and 703 observ > ations ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****wave 2 dose HP2probsoc relationship but avgcumdosew2: Dose signif here**** > * ***** ***** ***** ***** ***** 1 Jul 2012 15:02:14 ***** ******************************************************************************* ******************************************************************************* Logistic regression Number of obs = 363 LR chi2(10) = 125.06 Prob > chi2 = 0.0000 Log likelihood = -121.03832 Pseudo R2 = 0.3406 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .1153677 .0182285 6.33 0.000 .0796406 .1510948 radhlw2 | .0186525 .0056295 3.31 0.001 .0076189 .0296861 accdw2 | -.4572324 .6196198 -0.74 0.461 -1.671665 .7572 sepaw2 | -.2264106 1.149974 -0.20 0.844 -2.480318 2.027497 illw2 | .2070637 .1818612 1.14 0.255 -.1493778 .5635051 shjobw2 | -.0011404 .0044896 -0.25 0.799 -.0099399 .007659 shrelaw2 | -.0074957 .0057617 -1.30 0.193 -.0187885 .003797 suchrw2 | .0002657 .0041153 0.06 0.949 -.0078001 .0083315 havmilsq | -6.22e-06 6.94e-06 -0.90 0.370 -.0000198 7.38e-06 avgcumdosew2 | .4048448 .1418923 2.85 0.004 .1267411 .6829486 _cons | -9.109529 1.208597 -7.54 0.000 -11.47834 -6.740722 ------------------------------------------------------------------------------ Note: 3 failures and 0 successes completely determined. Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 37 11 | 48 - | 37 278 | 315 -----------+--------------------------+----------- Total | 74 289 | 363 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 50.00% Specificity Pr( -|~D) 96.19% Positive predictive value Pr( D| +) 77.08% Negative predictive value Pr(~D| -) 88.25% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 3.81% False - rate for true D Pr( -| D) 50.00% False + rate for classified + Pr(~D| +) 22.92% False - rate for classified - Pr( D| -) 11.75% -------------------------------------------------- Correctly classified 86.78% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 363 number of covariate patterns = 362 Pearson chi2(351) = 479.72 Prob > chi2 = 0.0000 Measures of Fit for logit of HP2probsoc Log-Lik Intercept Only: -183.570 Log-Lik Full Model: -121.038 D(352): 242.077 LR(10): 125.064 Prob > LR: 0.000 McFadden's R2: 0.341 McFadden's Adj R2: 0.281 Maximum Likelihood R2: 0.291 Cragg & Uhler's R2: 0.458 McKelvey and Zavoina's R2: 0.831 Efron's R2: 0.386 Variance of y*: 19.413 Variance of error: 3.290 Count R2: 0.868 Adj Count R2: 0.351 AIC: 0.727 AIC*n: 264.077 BIC: -1832.753 BIC': -66.120 515 . 516 . scalar SigDoseProbsocFw2 = "yes" 517 . scalar MainEffProbSocFw2 = "age radhlw2 illw2 Shrelaw2 avgcumodsew2" 518 . 519 . * trimmed female model with basis functions 520 . forvalues j=2/2 { 2. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 3. title "trimmed HP2probsoc main effects models wave 2 for H1 part 2 " "Dose > is signif Females" 4. title "wave 2 dose HP2probsoc relationship but avgcumdosew`j':" 5. title "Dose is signif but this trimming is too path dependent" 6. sw, pr(.1): logit HP2probsoc age radhlw2 illw`j' `w2bf' //// > shrelaw`j' avgcumdosew`j' if gender==2 7. estat class 8. estat gof 9. fitstat 10. } ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** trimmed HP2probsoc main effects models wave 2 for H1 part 2 ***** ***** Dose is signif Females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:16 ***** ******************************************************************************* ******************************************************************************* ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** wave 2 dose HP2probsoc relationship but avgcumdosew2: ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:16 ***** ******************************************************************************* ******************************************************************************* ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Dose is signif but this trimming is too path dependent ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:16 ***** ******************************************************************************* ******************************************************************************* note: bf15m dropped because of estimability note: bf17 dropped because of estimability note: o.bf15m dropped because of estimability note: o.bf17 dropped because of estimability note: 12 obs. dropped because of estimability begin with full model p = 0.9612 >= 0.1000 removing bf40 p = 0.9509 >= 0.1000 removing bf29 p = 0.7921 >= 0.1000 removing illw2 p = 0.7993 >= 0.1000 removing bf8 p = 0.6044 >= 0.1000 removing bf22 p = 0.5736 >= 0.1000 removing bf2 p = 0.5392 >= 0.1000 removing bf30 p = 0.3501 >= 0.1000 removing bf7m p = 0.2282 >= 0.1000 removing bf5m p = 0.2737 >= 0.1000 removing bf1 p = 0.2645 >= 0.1000 removing bf20 p = 0.2181 >= 0.1000 removing bf4m Logistic regression Number of obs = 351 LR chi2(5) = 143.75 Prob > chi2 = 0.0000 Log likelihood = -108.90897 Pseudo R2 = 0.3976 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0962607 .0184725 5.21 0.000 .0600553 .1324662 radhlw2 | .0145499 .005748 2.53 0.011 .003284 .0258158 avgcumdosew2 | .4041686 .1517163 2.66 0.008 .1068102 .7015271 shrelaw2 | -.0146954 .0058729 -2.50 0.012 -.0262061 -.0031847 bf4 | -.1767354 .0368357 -4.80 0.000 -.2489321 -.1045388 _cons | -6.140261 1.256194 -4.89 0.000 -8.602356 -3.678166 ------------------------------------------------------------------------------ Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 45 10 | 55 - | 29 267 | 296 -----------+--------------------------+----------- Total | 74 277 | 351 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 60.81% Specificity Pr( -|~D) 96.39% Positive predictive value Pr( D| +) 81.82% Negative predictive value Pr(~D| -) 90.20% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 3.61% False - rate for true D Pr( -| D) 39.19% False + rate for classified + Pr(~D| +) 18.18% False - rate for classified - Pr( D| -) 9.80% -------------------------------------------------- Correctly classified 88.89% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 351 number of covariate patterns = 350 Pearson chi2(344) = 405.23 Prob > chi2 = 0.0127 Measures of Fit for logit of HP2probsoc Log-Lik Intercept Only: -180.782 Log-Lik Full Model: -108.909 D(345): 217.818 LR(5): 143.747 Prob > LR: 0.000 McFadden's R2: 0.398 McFadden's Adj R2: 0.364 Maximum Likelihood R2: 0.336 Cragg & Uhler's R2: 0.523 McKelvey and Zavoina's R2: 0.578 Efron's R2: 0.445 Variance of y*: 7.801 Variance of error: 3.290 Count R2: 0.889 Adj Count R2: 0.473 AIC: 0.655 AIC*n: 229.818 BIC: -1804.153 BIC': -114.443 521 . 522 . 523 . * trimmed female model with basis functions 524 . forvalues j=2/2 { 2. set more off 3. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 4. title "partly trimmed HP2probsoc main effects models wave 2 for H1 part 2 > " "Dose is signif Females" 5. title "wave 2 dose HP2probsoc relationship but avgcumdosew`j':" 6. title "Dose is not signif when fully trimmed " 7. title "Dose significance is too path dependent to be stable" 8. logit HP2probsoc age radhlw2 illw`j' `w2bf' //// > shrelaw`j' avgcumdosew`j' if gender==2, nolog 9. estat class 10. estat gof 11. fitstat 12. } ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****partly trimmed HP2probsoc main effects models wave 2 for H1 part 2 ***** ***** Dose is signif Females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:35 ***** ******************************************************************************* ******************************************************************************* ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** wave 2 dose HP2probsoc relationship but avgcumdosew2: ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:35 ***** ******************************************************************************* ******************************************************************************* ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Dose is not signif when fully trimmed ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:35 ***** ******************************************************************************* ******************************************************************************* ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Dose significance is too path dependent to be stable ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:35 ***** ******************************************************************************* ******************************************************************************* note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: bf17 omitted because of collinearity Logistic regression Number of obs = 351 LR chi2(17) = 151.27 Prob > chi2 = 0.0000 Log likelihood = -105.14863 Pseudo R2 = 0.4184 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0968128 .0198903 4.87 0.000 .0578286 .1357971 radhlw2 | .0131268 .0081938 1.60 0.109 -.0029327 .0291864 illw2 | .0512501 .1902124 0.27 0.788 -.3215593 .4240595 bf1 | .0297711 .0318436 0.93 0.350 -.0326412 .0921834 bf4 | -.3702467 .1933363 -1.92 0.055 -.7491789 .0086855 bf2 | .0000552 .0001012 0.54 0.586 -.0001433 .0002536 bf4m | .1574353 .1794504 0.88 0.380 -.194281 .5091516 bf5m | -.0017729 .0021884 -0.81 0.418 -.006062 .0025162 bf7m | .0004786 .0006133 0.78 0.435 -.0007234 .0016805 bf8 | .000013 .0000438 0.30 0.767 -.0000729 .0000989 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.0360318 .0249913 -1.44 0.149 -.0850139 .0129503 bf22 | -.0000485 .0001411 -0.34 0.731 -.0003252 .0002281 bf29 | -2.02e-06 .0000345 -0.06 0.953 -.0000697 .0000656 bf30 | .0001996 .0003187 0.63 0.531 -.0004251 .0008243 bf40 | .0074913 .1539797 0.05 0.961 -.2943034 .309286 shrelaw2 | -.0131188 .0063308 -2.07 0.038 -.025527 -.0007106 avgcumdosew2 | .4534463 .1712457 2.65 0.008 .1178109 .7890817 _cons | -5.944128 2.135376 -2.78 0.005 -10.12939 -1.758869 ------------------------------------------------------------------------------ Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 47 12 | 59 - | 27 265 | 292 -----------+--------------------------+----------- Total | 74 277 | 351 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 63.51% Specificity Pr( -|~D) 95.67% Positive predictive value Pr( D| +) 79.66% Negative predictive value Pr(~D| -) 90.75% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 4.33% False - rate for true D Pr( -| D) 36.49% False + rate for classified + Pr(~D| +) 20.34% False - rate for classified - Pr( D| -) 9.25% -------------------------------------------------- Correctly classified 88.89% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 351 number of covariate patterns = 351 Pearson chi2(333) = 432.31 Prob > chi2 = 0.0002 Measures of Fit for logit of HP2probsoc Log-Lik Intercept Only: -180.782 Log-Lik Full Model: -105.149 D(331): 210.297 LR(17): 151.267 Prob > LR: 0.000 McFadden's R2: 0.418 McFadden's Adj R2: 0.308 Maximum Likelihood R2: 0.350 Cragg & Uhler's R2: 0.544 McKelvey and Zavoina's R2: 0.610 Efron's R2: 0.465 Variance of y*: 8.437 Variance of error: 3.290 Count R2: 0.889 Adj Count R2: 0.473 AIC: 0.713 AIC*n: 250.297 BIC: -1729.623 BIC': -51.634 525 . 526 . 527 . * trimmed female model with basis functions 528 . forvalues j=2/2 { 2. set more off 3. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 4. title "fully trimmed HP2probsoc main effects models wave 2 for H1 part 2 " > "Dose is not signif for Females" 5. title "wave 2 dose HP2probsoc relationship but avgcumdosew`j':" 6. title "Dose is not signif " 7. logit HP2probsoc age radhlw2 b4 //// > shrelaw`j' avgcumdosew`j' if gender==2, nolog 8. estat class 9. estat gof 10. fitstat 11. } ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** fully trimmed HP2probsoc main effects models wave 2 for H1 part 2 ***** ***** Dose is not signif for Females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:36 ***** ******************************************************************************* ******************************************************************************* ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** wave 2 dose HP2probsoc relationship but avgcumdosew2: ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:36 ***** ******************************************************************************* ******************************************************************************* ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Dose is not signif ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:36 ***** ******************************************************************************* ******************************************************************************* Logistic regression Number of obs = 363 LR chi2(5) = 121.71 Prob > chi2 = 0.0000 Log likelihood = -122.71719 Pseudo R2 = 0.3315 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .111496 .0203766 5.47 0.000 .0715586 .1514333 radhlw2 | .0196292 .0053673 3.66 0.000 .0091095 .030149 b4Xd3 | .0068784 .0106999 0.64 0.520 -.0140931 .0278498 shrelaw2 | -.0088391 .0053365 -1.66 0.098 -.0192985 .0016202 avgcumdosew2 | -.0685999 .7594291 -0.09 0.928 -1.557053 1.419854 _cons | -9.015948 1.232733 -7.31 0.000 -11.43206 -6.599837 ------------------------------------------------------------------------------ Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 38 10 | 48 - | 36 279 | 315 -----------+--------------------------+----------- Total | 74 289 | 363 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 51.35% Specificity Pr( -|~D) 96.54% Positive predictive value Pr( D| +) 79.17% Negative predictive value Pr(~D| -) 88.57% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 3.46% False - rate for true D Pr( -| D) 48.65% False + rate for classified + Pr(~D| +) 20.83% False - rate for classified - Pr( D| -) 11.43% -------------------------------------------------- Correctly classified 87.33% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 363 number of covariate patterns = 357 Pearson chi2(351) = 464.33 Prob > chi2 = 0.0000 Measures of Fit for logit of HP2probsoc Log-Lik Intercept Only: -183.570 Log-Lik Full Model: -122.717 D(357): 245.434 LR(5): 121.706 Prob > LR: 0.000 McFadden's R2: 0.331 McFadden's Adj R2: 0.299 Maximum Likelihood R2: 0.285 Cragg & Uhler's R2: 0.448 McKelvey and Zavoina's R2: 0.521 Efron's R2: 0.377 Variance of y*: 6.863 Variance of error: 3.290 Count R2: 0.873 Adj Count R2: 0.378 AIC: 0.709 AIC*n: 257.434 BIC: -1858.867 BIC': -92.234 529 . 530 . 531 . 532 . 533 . 534 . 535 . foreach var in b4 shrelaw2 { 2. cap gen `var'Xd3 = age*avgcumdosew2 3. } 536 . 537 . * testing the female moderator model with basis functions 538 . forvalues j=2/2 { 2. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 3. title "trimmed HP2socprob main effects wv 3 for Hyp1 pt 2" "dose is signi > f for females" 4. title "wave 2 dose HP2socprob relationship but avgcumdosew`j'" " Dose is s > ignif for females" 5. title "problem is that stepwise backward is too path dependent" "and shoul > d be checked by simultaneous trim" 6. sw, pr(.1): logit HP2probsoc age radhlw2 illw`j' `w2bf' //// > shrelaw`j' avgcumdosew`j' b4Xd3 /// > if gender==2 7. estat class 8. estat gof 9. fitstat 10. } ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** trimmed HP2socprob main effects wv 3 for Hyp1 pt 2 ***** ***** dose is signif for females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:38 ***** ******************************************************************************* ******************************************************************************* ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** wave 2 dose HP2socprob relationship but avgcumdosew2 ***** ***** Dose is signif for females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:38 ***** ******************************************************************************* ******************************************************************************* ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** problem is that stepwise backward is too path dependent ***** ***** and should be checked by simultaneous trim ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:02:38 ***** ******************************************************************************* ******************************************************************************* note: bf15m dropped because of estimability note: bf17 dropped because of estimability note: o.bf15m dropped because of estimability note: o.bf17 dropped because of estimability note: 12 obs. dropped because of estimability begin with full model p = 0.9383 >= 0.1000 removing bf29 p = 0.9284 >= 0.1000 removing bf40 p = 0.7417 >= 0.1000 removing illw2 p = 0.7055 >= 0.1000 removing bf8 p = 0.6496 >= 0.1000 removing avgcumdosew2 p = 0.5938 >= 0.1000 removing bf2 p = 0.5881 >= 0.1000 removing bf22 p = 0.5207 >= 0.1000 removing bf30 p = 0.3390 >= 0.1000 removing bf7m p = 0.2342 >= 0.1000 removing bf1 p = 0.2810 >= 0.1000 removing bf20 p = 0.2334 >= 0.1000 removing bf5m p = 0.2198 >= 0.1000 removing bf4m Logistic regression Number of obs = 351 LR chi2(5) = 144.26 Prob > chi2 = 0.0000 Log likelihood = -108.65463 Pseudo R2 = 0.3990 ------------------------------------------------------------------------------ HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0909062 .018443 4.93 0.000 .0547586 .1270537 radhlw2 | .0146177 .0057691 2.53 0.011 .0033105 .025925 shrelaw2 | -.0146099 .0058727 -2.49 0.013 -.0261202 -.0030996 b4Xd3 | .005736 .0020674 2.77 0.006 .0016839 .009788 bf4 | -.1769505 .0368092 -4.81 0.000 -.2490952 -.1048059 _cons | -5.869429 1.246027 -4.71 0.000 -8.311597 -3.427261 ------------------------------------------------------------------------------ Logistic model for HP2probsoc -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 45 9 | 54 - | 29 268 | 297 -----------+--------------------------+----------- Total | 74 277 | 351 Classified + if predicted Pr(D) >= .5 True D defined as HP2probsoc != 0 -------------------------------------------------- Sensitivity Pr( +| D) 60.81% Specificity Pr( -|~D) 96.75% Positive predictive value Pr( D| +) 83.33% Negative predictive value Pr(~D| -) 90.24% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 3.25% False - rate for true D Pr( -| D) 39.19% False + rate for classified + Pr(~D| +) 16.67% False - rate for classified - Pr( D| -) 9.76% -------------------------------------------------- Correctly classified 89.17% -------------------------------------------------- Logistic model for HP2probsoc, goodness-of-fit test number of observations = 351 number of covariate patterns = 350 Pearson chi2(344) = 405.40 Prob > chi2 = 0.0126 Measures of Fit for logit of HP2probsoc Log-Lik Intercept Only: -180.782 Log-Lik Full Model: -108.655 D(345): 217.309 LR(5): 144.255 Prob > LR: 0.000 McFadden's R2: 0.399 McFadden's Adj R2: 0.366 Maximum Likelihood R2: 0.337 Cragg & Uhler's R2: 0.524 McKelvey and Zavoina's R2: 0.579 Efron's R2: 0.447 Variance of y*: 7.808 Variance of error: 3.290 Count R2: 0.892 Adj Count R2: 0.486 AIC: 0.653 AIC*n: 229.309 BIC: -1804.662 BIC': -114.951 539 . 540 . scalar ProbsocModFw2 = "none" 541 . 542 . title4 " testing for male and female dose probsoc mediators" ------------------------------------------------------------------------------- testing for male and female dose probsoc mediators ------------------------------------------------------------------------------- 543 . * Male mediator dose social problem response models 544 . 545 . 546 . 547 . // age is a male mediator 548 . glm age avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1330.6004 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 147.6853 Deviance = 49917.64009 (1/df) Deviance = 147.6853 Pearson = 49917.64009 (1/df) Pearson = 147.6853 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.838826 Log likelihood = -1330.6004 BIC = 47947.46 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .5832314 .2635871 2.21 0.027 .0666101 1.099853 _cons | 48.62133 .7061562 68.85 0.000 47.23729 50.00537 ------------------------------------------------------------------------------ 549 . glm HP2probsoc age if gender==1, fam(bin) irls link(probit) scale(dev) Iteration 1: deviance = 230.0069 Iteration 2: deviance = 223.5454 Iteration 3: deviance = 223.2469 Iteration 4: deviance = 223.2456 Iteration 5: deviance = 223.2456 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 223.2456447 (1/df) Deviance = .6604901 Pearson = 331.0340472 (1/df) Pearson = .9793907 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1746.938 ------------------------------------------------------------------------------ | EIM HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0392343 .0064286 6.10 0.000 .0266344 .0518342 _cons | -3.234772 .3587306 -9.02 0.000 -3.937871 -2.531673 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 550 . 551 . glm radhlw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1693.4076 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 1247.933 Deviance = 421801.4584 (1/df) Deviance = 1247.933 Pearson = 421801.4584 (1/df) Pearson = 1247.933 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.972986 Log likelihood = -1693.407647 BIC = 419831.3 ------------------------------------------------------------------------------ | OIM radhlw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.220373 .766216 1.59 0.111 -.2813831 2.722128 _cons | 45.63198 2.052711 22.23 0.000 41.60874 49.65522 ------------------------------------------------------------------------------ 552 . glm HP2probsoc radhlw2 if gender==1,fam(bin) irls link(probit) scale(dev) Iteration 1: deviance = 226.5911 Iteration 2: deviance = 218.8478 Iteration 3: deviance = 218.3822 Iteration 4: deviance = 218.3787 Iteration 5: deviance = 218.3787 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 218.378733 (1/df) Deviance = .6460909 Pearson = 326.2667817 (1/df) Pearson = .9652863 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1751.805 ------------------------------------------------------------------------------ | EIM HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- radhlw2 | .0149459 .0022581 6.62 0.000 .0105201 .0193717 _cons | -2.034921 .1640547 -12.40 0.000 -2.356462 -1.71338 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 553 . 554 . glm shjobw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1730.3274 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 1550.634 Deviance = 524114.2615 (1/df) Deviance = 1550.634 Pearson = 524114.2615 (1/df) Pearson = 1550.634 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 10.19016 Log likelihood = -1730.327396 BIC = 522144.1 ------------------------------------------------------------------------------ | OIM shjobw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .7146559 .8541028 0.84 0.403 -.9593549 2.388667 _cons | 49.09491 2.288162 21.46 0.000 44.61019 53.57962 ------------------------------------------------------------------------------ 555 . glm HP2probsoc shjobw2 if gender==1,fam(bin) irls link(probit) scale(dev) Iteration 1: deviance = 241.5668 Iteration 2: deviance = 238.4954 Iteration 3: deviance = 238.438 Iteration 4: deviance = 238.438 Iteration 5: deviance = 238.438 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 238.4379517 (1/df) Deviance = .7054377 Pearson = 344.7520389 (1/df) Pearson = 1.019976 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1731.746 ------------------------------------------------------------------------------ | EIM HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- shjobw2 | .0079142 .0019981 3.96 0.000 .003998 .0118303 _cons | -1.622345 .1436613 -11.29 0.000 -1.903915 -1.340774 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 556 . 557 . // shjobw2Xd3 is almost significant but not quite 558 . glm radhlw2 shjobw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1684.8126 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 337 Scale parameter = 1189.928 Deviance = 401005.7796 (1/df) Deviance = 1189.928 Pearson = 401005.7796 (1/df) Pearson = 1189.928 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.92831 Log likelihood = -1684.812637 BIC = 399041.4 ------------------------------------------------------------------------------ | OIM radhlw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- shjobw2 | .1991928 .0476483 4.18 0.000 .1058038 .2925817 avgcumdosew2 | 1.078018 .7489714 1.44 0.150 -.3899386 2.545975 _cons | 35.85263 3.080591 11.64 0.000 29.81478 41.89047 ------------------------------------------------------------------------------ 559 . glm HP2probsoc shjobw2 avgcumdosew2 shjobw2Xd3 if gender==1,fam(bin) /// > irls scale(dev) link(probit) Iteration 1: deviance = 238.9844 Iteration 2: deviance = 235.7842 Iteration 3: deviance = 235.5578 Iteration 4: deviance = 235.4233 Iteration 5: deviance = 235.4108 Iteration 6: deviance = 235.4106 Iteration 7: deviance = 235.4106 Iteration 8: deviance = 235.4106 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 336 (IRLS EIM) Scale parameter = 1 Deviance = 235.4106098 (1/df) Deviance = .7006268 Pearson = 342.9227819 (1/df) Pearson = 1.020604 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1723.115 ------------------------------------------------------------------------------ | EIM HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- shjobw2 | .005567 .0027192 2.05 0.041 .0002374 .0108965 avgcumdosew2 | -.2189419 .2705013 -0.81 0.418 -.7491148 .311231 shjobw2Xd3 | .002944 .0028701 1.03 0.305 -.0026813 .0085693 _cons | -1.466051 .2263255 -6.48 0.000 -1.909641 -1.022461 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 560 . 561 . scalar ProbsocMedMw2 = "age" 562 . 563 . * female mediator tests 564 . 565 . // age is a female mediator 566 . glm age avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1406.9403 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 136.9184 Deviance = 49427.52828 (1/df) Deviance = 136.9184 Pearson = 49427.52828 (1/df) Pearson = 136.9184 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.762756 Log likelihood = -1406.940271 BIC = 47299.65 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.502324 .4454009 3.37 0.001 .6293547 2.375294 _cons | 48.86944 .7323225 66.73 0.000 47.43412 50.30477 ------------------------------------------------------------------------------ 567 . glm HP2probsoc age if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 289.3253 Iteration 2: deviance = 280.9528 Iteration 3: deviance = 280.5176 Iteration 4: deviance = 280.5162 Iteration 5: deviance = 280.5162 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 280.5161869 (1/df) Deviance = .7770531 Pearson = 406.2926804 (1/df) Pearson = 1.125464 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1847.363 ------------------------------------------------------------------------------ | EIM HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0683554 .007474 9.15 0.000 .0537067 .083004 _cons | -4.505136 .424587 -10.61 0.000 -5.337311 -3.672961 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 568 . 569 . // radhlw2 is a female mediator 570 . glm radhlw2 avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1791.2233 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 1137.567 Deviance = 410661.5604 (1/df) Deviance = 1137.567 Pearson = 410661.5604 (1/df) Pearson = 1137.567 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.880018 Log likelihood = -1791.223306 BIC = 408533.7 ------------------------------------------------------------------------------ | OIM radhlw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 3.302288 1.283833 2.57 0.010 .7860214 5.818555 _cons | 56.95167 2.110863 26.98 0.000 52.81445 61.08888 ------------------------------------------------------------------------------ 571 . glm HP2probsoc radhlw2 if gender==2,fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 335.706 Iteration 2: deviance = 333.7569 Iteration 3: deviance = 333.7467 Iteration 4: deviance = 333.7467 Iteration 5: deviance = 333.7467 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 333.7467432 (1/df) Deviance = .9245062 Pearson = 373.5069806 (1/df) Pearson = 1.034645 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1794.133 ------------------------------------------------------------------------------ | EIM HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- radhlw2 | .0135849 .002393 5.68 0.000 .0088947 .0182751 _cons | -1.729418 .1840212 -9.40 0.000 -2.090093 -1.368743 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 572 . 573 . glm bf4 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1027.1225 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 24.7771 Deviance = 8374.659221 (1/df) Deviance = 24.7771 Pearson = 8374.659221 (1/df) Pearson = 24.7771 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.053662 Log likelihood = -1027.122509 BIC = 6404.476 ------------------------------------------------------------------------------ | OIM bf4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.0331637 .1079644 -0.31 0.759 -.2447701 .1784427 _cons | 12.52896 .2892393 43.32 0.000 11.96206 13.09586 ------------------------------------------------------------------------------ 574 . glm HP2probsoc bf4 if gender==1,fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 194.8196 Iteration 2: deviance = 182.741 Iteration 3: deviance = 181.7875 Iteration 4: deviance = 181.7753 Iteration 5: deviance = 181.7753 Iteration 6: deviance = 181.7753 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 181.7752898 (1/df) Deviance = .5377967 Pearson = 293.1088357 (1/df) Pearson = .8671859 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1788.408 ------------------------------------------------------------------------------ | EIM HP2probsoc | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.1440261 .0137935 -10.44 0.000 -.1710608 -.1169913 _cons | .3344471 .1497689 2.23 0.026 .0409054 .6279887 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 575 . 576 . 577 . scalar ProbsocMedFw2 = "age radhlw2" 578 . 579 . title4 "summary matrix for dose Hp2probsoc impact in wave 2" ------------------------------------------------------------------------------- summary matrix for dose Hp2probsoc impact in wave 2 ------------------------------------------------------------------------------- 580 . * male hp2spM w2 mediators: age 581 . * dose is not significant main effect for males 582 . * female hp2spF w2 mediators: age radhlw2 583 . * dose is not sig main effect for males 584 . 585 . scalar SigDoseProbsocMw2 = "no" 586 . 587 . matrix define HP2spMw2 = J(1,8, 0) 588 . matrix define HP2spFw2 = J(1,8, 0) 589 . matrix colnames HP2spMw2= hypnum ptnum wave gender medsig numMAsig numModsig > numMed 590 . matrix colnames HP2spFw2= hypnum ptnum wave gender medsig numMAsig numModsig > numMed 591 . matrix define HP2spFw2= (1, 2, 3, 2, 1, 5, 5, 2 ) 592 . matrix define HP2spMw2= (1, 2, 3, 1, 0, 2, 0, 1 ) 593 . matrix rowname HP2spMw2 = HP2spMw2 594 . matrix rowname HP2spFw2 = HP2spFw2 595 . matlist HP2spMw2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ HP2spMw2 | 1 2 3 1 0 2 > 0 | c8 -------------+----------- HP2spMw2 | 1 596 . matlist HP2spFw2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ HP2spFw2 | 1 2 3 2 1 5 > 5 | c8 -------------+----------- HP2spFw2 | 2 597 . matrix define H1pt2w2 = ( HP2wkMw2 \ HP2wkFw2 \ HP2hmcrMw2 \ HP2hmcrF > w2 \ HP2spMw2 \ HP2spFw2 ) 598 . 599 . matlist H1pt2w2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ r1 | 1 2 3 1 0 4 > 0 r1 | 1 2 3 2 0 1 > 0 r1 | 1 2 3 1 0 4 > 0 r1 | 1 2 3 2 0 2 > 0 HP2spMw2 | 1 2 3 1 0 2 > 0 HP2spFw2 | 1 2 3 2 1 5 > 5 | c8 -------------+----------- r1 | 4 r1 | 6 r1 | 2 r1 | 2 HP2spMw2 | 1 HP2spFw2 | 2 600 . matrix colnames H1pt2w2 = hypnum ptnum wave gender medsig numMAsig numMo > dsig numMed 601 . matrix rownames H1pt2w2 = HP2wkMw2 HP2wkFw2 HP2hmcrMw2 HP2hmcrFw2 HP2so > cprbMw2 HP2socprbFw2 602 . matlist H1pt2w2 | hypnum ptnum wave gender medsig numMAsig > numModsig -------------+----------------------------------------------------------------- ------------ HP2wkMw2 | 1 2 3 1 0 4 > 0 HP2wkFw2 | 1 2 3 2 0 1 > 0 HP2hmcrMw2 | 1 2 3 1 0 4 > 0 HP2hmcrFw2 | 1 2 3 2 0 2 > 0 HP2socprbMw2 | 1 2 3 1 0 2 > 0 HP2socprbFw2 | 1 2 3 2 1 5 > 5 | numMed -------------+----------- HP2wkMw2 | 4 HP2wkFw2 | 6 HP2hmcrMw2 | 2 HP2hmcrFw2 | 2 HP2socprbMw2 | 1 HP2socprbFw2 | 2 603 . 604 . 605 . *xx significant dose effect for females 606 . scalar ProbsocModFw2 = "none" 607 . *xx no female moderators for Dose Social problem impact relationship 608 . scalar list wkMedMw2 = bf8 age illw2 VactnMedFw2 = age illw2 radhlw2 VactnMedMw2 = age illw2 VacatnModFw2 = none MainEffVactnFw2 = age radhlw2 deaw2 SigDoseVactnFw2 = no vactnModMw2 = none MainEffVactnMw2 = age bf7m radhlw2 SigDoseVactnMw2 = no sxLifeMedFw2 = age bf4 bf4m sxLifeMedMw2 = age illw2 InthbModFw2 = none MainEffInthbFw2 = age radhlw2 bf4 SigdoseInthbFw2 = no InthbMw2 = none MainEffInthbMw2 = age radhlw2 shfamw2 SigDoseInthbMw2 = no sxlifeMedFw2 = age illw2 radhlw2 bf4 bf4m sxlifeMedMw2 = age illw2 sxlifeModFw2 = none MainEffsxlifeFw2 = age radhlw2 bf4 bf4m shrelaw2 shfamw2 SigDoseSxlifeFw2 = no sxlifeModMw2 = none SigDosesxlifeMw2 = no MainEffsxlifeMw2 = age bf4 illw2 radhlw2 PrbfmhmMedFw2 = age bf4 PrbfmhmMedMw2 = age PrbfmhmModFw2 = none MainEffPrbfmhmFw2 = age bf4 bf40 SigDosePrbfmhmFw2 = no PrbfmhmModw2 = none SigDosePrbfmhmMw2 = no SigDosePrbfhmMw2 = no MainEffPrbfhmMw2 = bf1 bf4 dvcew2 bf7m ProbsocMedFw2 = age radhlw2 ProbsocMedMw2 = age ProbsocModFw2 = none MainEffProbSocFw2 = age radhlw2 illw2 Shrelaw2 avgcumodsew2 SigDoseProbsocFw2 = yes ProbSocModMw2 = none SigDoseProbsocMw2 = no MainEffPrbsocMw2 = age radhlw2 shjobw2 hmcareMedFw2 = age illw2 hmcareMedMw2 = age illw2 hmcareModFw2 = none SigDoseWKFw2 = 0 SigdoseHmcareFw2 = no hmcareModMw2 = none MainEffhmcareMw2 = none SigDoseHmcareMw2 = no wkMedFw2 = radhlw2 age bf40 bf4m bf1 wkModFw2 = none wkModMw2 = none MainEffwkFw2 = age MainEffwkMw2 = workM: age bf8 illw2 shjobw2 SigDoseWKMw2 = no SigDoseWkFw2 = no hmcrMedFw1 = age icdxcnt shjobw1 bf4 BSIsoma WHPpain WHPsleep WHPel hmcrMedMw1 = age MainEffhmcrFw1 = illw1 age SigDosehmcrFw1 = no hmcrModMw1 = none MainEffhmcrMw1 = age shjobw1 SigDosehmcrMw1 = no wkMedFw1 = age b4 MainEffwkFw1 = age MainEffwkMw1 = age wkMedMw1 = bf40 WkMedMw1 = none WkModFw1 = none WKModMw1 = none SigDoseWkMw1 = no SigDoseWkFw1 = no SigDoseFw1 = no wkModFw1 = none wkModMw1 = none medsigFw1 = 1 prbsocnumMAsig = 8 609 . 610 . *----------- Chunk 5 Dose => Problems with the Family at home Impact 611 . * this washes out in the trimmed models 612 . 613 . 614 . title "4. wave 2 part2 hypothesis 1 Pt2 Probs with Fam at home" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** 4. wave 2 part2 hypothesis 1 Pt2 Probs with Fam at home ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:03:18 ***** ******************************************************************************* ******************************************************************************* 615 . forvalues j=2/2 { 2. set more off 3. 616 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 617 . foreach var in HP2pbfhm { 5. forvalues k=1/2 { 6. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 7. 618 . di as input "Full main model for `var' for wave= `j' " 8. di _skip(4) 9. di as input "chunk 5 H1 test:Gender= `k' model Wave = `j' for `e(depvar > )' " 10. di _skip(4) 11. title "Full Nottingham Part 2 subscale models for male and then females" 12. 619 . xi: logistic `var' age i.educ occ1w`j'-occ8w`j' /// > marrw`j'1- marrw`j'3 marrw`j'5-marrw`j'6 inc1w`j'-inc4w`j' // > / > radhlw`j' havmil avgcumdosew`j' `w`j'bf' /// > deaw`j' dvcew`j' sepaw`j' accdw`j' movew`j' /// > illw`j' shfamw`j' shhlw`j' shjobw`j' shrelaw`j' suprtw`j' suc > hrw`j' /// > havmilsq if gender==`k', coef nolog difficult iterate(50) 13. estat class 14. estat gof 15. fitstat 16. } 17. } 18. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Full main model for HP2pbfhm for wave= 2 chunk 5 H1 test:Gender= 1 model Wave = 2 for HP2probsoc ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for male and then females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:03:18 ***** ******************************************************************************* ******************************************************************************* i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: _Ieduc_2 != 0 predicts failure perfectly _Ieduc_2 dropped and 10 obs not used note: _Ieduc_4 != 0 predicts failure perfectly _Ieduc_4 dropped and 13 obs not used note: _Ieduc_7 != 0 predicts failure perfectly _Ieduc_7 dropped and 4 obs not used note: _Ieduc_8 != 0 predicts failure perfectly _Ieduc_8 dropped and 2 obs not used note: occ5w2 != 0 predicts failure perfectly occ5w2 dropped and 17 obs not used note: occ6w2 != 0 predicts failure perfectly occ6w2 dropped and 5 obs not used note: occ8w2 != 0 predicts failure perfectly occ8w2 dropped and 44 obs not used note: marrw22 != 0 predicts failure perfectly marrw22 dropped and 7 obs not used note: marrw26 != 0 predicts failure perfectly marrw26 dropped and 3 obs not used note: inc1w2 != 0 predicts failure perfectly inc1w2 dropped and 13 obs not used note: inc4w2 != 0 predicts failure perfectly inc4w2 dropped and 10 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: bf29 != 0 predicts failure perfectly bf29 dropped and 4 obs not used note: dvcew2 != 0 predicts failure perfectly dvcew2 dropped and 4 obs not used note: marrw25 != 0 predicts success perfectly marrw25 dropped and 1 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 1 obs not used note: _Ieduc_6 omitted because of collinearity note: bf17 omitted because of collinearity Logistic regression Number of obs = 188 LR chi2(37) = 54.88 Prob > chi2 = 0.0294 Log likelihood = -38.373257 Pseudo R2 = 0.4169 ------------------------------------------------------------------------------ HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0444593 .0428518 1.04 0.299 -.0395286 .1284472 _Ieduc_2 | 0 (omitted) _Ieduc_3 | -1.216759 1.079528 -1.13 0.260 -3.332594 .8990769 _Ieduc_4 | 0 (omitted) _Ieduc_5 | -.8969887 1.218662 -0.74 0.462 -3.285522 1.491544 _Ieduc_6 | 0 (omitted) _Ieduc_7 | 0 (omitted) _Ieduc_8 | 0 (omitted) occ1w2 | -.1675434 7.199614 -0.02 0.981 -14.27853 13.94344 occ2w2 | -.7756164 7.209263 -0.11 0.914 -14.90551 13.35428 occ3w2 | 1.02351 7.214423 0.14 0.887 -13.1165 15.16352 occ4w2 | 1.180132 7.249556 0.16 0.871 -13.02874 15.389 occ5w2 | 0 (omitted) occ6w2 | 0 (omitted) occ7w2 | -1.331976 7.411247 -0.18 0.857 -15.85775 13.1938 occ8w2 | 0 (omitted) marrw21 | 11.23886 2191.219 0.01 0.996 -4283.471 4305.948 marrw22 | 0 (omitted) marrw23 | 11.19426 2191.219 0.01 0.996 -4283.515 4305.904 marrw25 | 0 (omitted) marrw26 | 0 (omitted) inc1w2 | 0 (omitted) inc2w2 | 1.591635 7.274442 0.22 0.827 -12.66601 15.84928 inc3w2 | 2.001132 7.228455 0.28 0.782 -12.16638 16.16864 inc4w2 | 0 (omitted) radhlw2 | .0450707 .0190833 2.36 0.018 .0076681 .0824733 havmil | -.0149705 .0133639 -1.12 0.263 -.0411632 .0112223 avgcumdosew2 | -.0788123 .3549706 -0.22 0.824 -.7745419 .6169173 bf1 | -.1607809 .1318246 -1.22 0.223 -.4191523 .0975906 bf4 | -.2790034 .3019344 -0.92 0.355 -.8707839 .3127772 bf2 | .0006459 .0003574 1.81 0.071 -.0000546 .0013465 bf4m | .1083622 .270079 0.40 0.688 -.4209828 .6377072 bf5m | .0014293 .004266 0.34 0.738 -.0069319 .0097905 bf7m | .0014124 .0012328 1.15 0.252 -.0010038 .0038286 bf8 | -.0000455 .0000891 -0.51 0.610 -.0002201 .0001291 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | .0990519 .1141451 0.87 0.386 -.1246683 .3227721 bf22 | .0000324 .0003377 0.10 0.924 -.0006295 .0006943 bf29 | 0 (omitted) bf30 | -.0005162 .0007544 -0.68 0.494 -.0019949 .0009625 bf40 | -.3427693 .4881552 -0.70 0.483 -1.299536 .6139973 deaw2 | 1.277518 .6289072 2.03 0.042 .0448826 2.510153 dvcew2 | 0 (omitted) sepaw2 | 0 (omitted) accdw2 | -.8756107 1.266217 -0.69 0.489 -3.35735 1.606128 movew2 | .2828439 1.570553 0.18 0.857 -2.795383 3.36107 illw2 | 1.383819 .6666357 2.08 0.038 .0772372 2.690401 shfamw2 | -.0048065 .0139543 -0.34 0.731 -.0321564 .0225433 shhlw2 | -.0126567 .0158426 -0.80 0.424 -.0437076 .0183942 shjobw2 | .0113436 .0152838 0.74 0.458 -.018612 .0412992 shrelaw2 | -.0154518 .0162014 -0.95 0.340 -.047206 .0163025 suprtw2 | -.0271504 .0129849 -2.09 0.037 -.0526003 -.0017006 suchrw2 | .0166833 .0117501 1.42 0.156 -.0063465 .039713 havmilsq | .0000156 .0000131 1.19 0.234 -.00001 .0000412 _cons | -21.58182 2191.227 -0.01 0.992 -4316.307 4273.143 ------------------------------------------------------------------------------ Logistic model for HP2pbfhm -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 8 4 | 12 - | 13 163 | 176 -----------+--------------------------+----------- Total | 21 167 | 188 Classified + if predicted Pr(D) >= .5 True D defined as HP2pbfhm != 0 -------------------------------------------------- Sensitivity Pr( +| D) 38.10% Specificity Pr( -|~D) 97.60% Positive predictive value Pr( D| +) 66.67% Negative predictive value Pr(~D| -) 92.61% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 2.40% False - rate for true D Pr( -| D) 61.90% False + rate for classified + Pr(~D| +) 33.33% False - rate for classified - Pr( D| -) 7.39% -------------------------------------------------- Correctly classified 90.96% -------------------------------------------------- Logistic model for HP2pbfhm, goodness-of-fit test number of observations = 188 number of covariate patterns = 188 Pearson chi2(150) = 103.39 Prob > chi2 = 0.9986 Measures of Fit for logistic of HP2pbfhm Log-Lik Intercept Only: -65.811 Log-Lik Full Model: -38.373 D(132): 76.747 LR(37): 54.876 Prob > LR: 0.029 McFadden's R2: 0.417 McFadden's Adj R2: -0.434 Maximum Likelihood R2: 0.253 Cragg & Uhler's R2: 0.503 McKelvey and Zavoina's R2: 0.754 Efron's R2: 0.356 Variance of y*: 13.351 Variance of error: 3.290 Count R2: 0.910 Adj Count R2: 0.190 AIC: 1.004 AIC*n: 188.747 BIC: -614.464 BIC': 138.873 Full main model for HP2pbfhm for wave= 2 chunk 5 H1 test:Gender= 2 model Wave = 2 for HP2pbfhm ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for male and then females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:03:20 ***** ******************************************************************************* ******************************************************************************* i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: occ6w2 != 0 predicts failure perfectly occ6w2 dropped and 9 obs not used note: marrw22 != 0 predicts failure perfectly marrw22 dropped and 8 obs not used note: inc4w2 != 0 predicts failure perfectly inc4w2 dropped and 9 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 5 obs not used note: movew2 != 0 predicts failure perfectly movew2 dropped and 37 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity convergence not achieved Logistic regression Number of obs = 282 LR chi2(45) = 115.64 Prob > chi2 = 0.0000 Log likelihood = -69.237721 Pseudo R2 = 0.4551 ------------------------------------------------------------------------------ HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0573216 .0276334 2.07 0.038 .0031611 .1114822 _Ieduc_2 | 24.79783 5.419934 4.58 0.000 14.17495 35.4207 _Ieduc_3 | 25.40648 5.405293 4.70 0.000 14.8123 36.00065 _Ieduc_4 | 26.0435 5.498915 4.74 0.000 15.26582 36.82117 _Ieduc_5 | 25.99152 5.477282 4.75 0.000 15.25625 36.7268 _Ieduc_6 | 25.22458 5.399044 4.67 0.000 14.64265 35.80651 _Ieduc_7 | 24.91381 . . . . . _Ieduc_8 | 0 (omitted) occ1w2 | .1268694 2.741862 0.05 0.963 -5.247082 5.50082 occ2w2 | -2.180454 2.943693 -0.74 0.459 -7.949987 3.589079 occ3w2 | .8729362 2.776498 0.31 0.753 -4.568899 6.314772 occ4w2 | -1.353704 2.942107 -0.46 0.645 -7.120128 4.41272 occ5w2 | 2.7416 3.183927 0.86 0.389 -3.498782 8.981982 occ6w2 | 0 (omitted) occ7w2 | 1.723368 2.749238 0.63 0.531 -3.66504 7.111775 occ8w2 | 1.071406 3.004006 0.36 0.721 -4.816337 6.959149 marrw21 | 1.363708 1.652619 0.83 0.409 -1.875365 4.602781 marrw22 | 0 (omitted) marrw23 | .3462148 1.088773 0.32 0.750 -1.78774 2.48017 marrw25 | 1.049262 1.686487 0.62 0.534 -2.256193 4.354717 marrw26 | 0 (omitted) inc1w2 | -.4454501 2.822154 -0.16 0.875 -5.976771 5.085871 inc2w2 | 1.223562 2.740806 0.45 0.655 -4.148319 6.595444 inc3w2 | .8897543 2.773723 0.32 0.748 -4.546642 6.326151 inc4w2 | 0 (omitted) radhlw2 | .0162494 .0128872 1.26 0.207 -.0090091 .0415079 havmil | .0034284 .0174028 0.20 0.844 -.0306804 .0375372 avgcumdosew2 | .2841701 .2286177 1.24 0.214 -.1639122 .7322525 bf1 | -.0034211 .0428716 -0.08 0.936 -.0874479 .0806057 bf4 | -.5482161 .2947147 -1.86 0.063 -1.125846 .0294141 bf2 | .0001496 .0001782 0.84 0.401 -.0001996 .0004989 bf4m | .2436517 .2745867 0.89 0.375 -.2945285 .7818318 bf5m | -.0026475 .0036526 -0.72 0.469 -.0098065 .0045114 bf7m | -.0008652 .0008476 -1.02 0.307 -.0025264 .000796 bf8 | .0000586 .0000741 0.79 0.429 -.0000867 .0002038 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.0232359 .0348214 -0.67 0.505 -.0914845 .0450127 bf22 | .0000587 .0002121 0.28 0.782 -.0003569 .0004744 bf29 | .0000275 .0000434 0.63 0.527 -.0000576 .0001126 bf30 | .0000875 .0005016 0.17 0.862 -.0008956 .0010706 bf40 | -.3662342 .2446561 -1.50 0.134 -.8457514 .1132829 deaw2 | .0470792 .2393731 0.20 0.844 -.4220834 .5162419 dvcew2 | -1.779925 2.445695 -0.73 0.467 -6.573399 3.013549 sepaw2 | 0 (omitted) accdw2 | -2.474844 1.428768 -1.73 0.083 -5.275177 .3254891 movew2 | 0 (omitted) illw2 | -.2222062 .2628556 -0.85 0.398 -.7373938 .2929813 shfamw2 | .0141915 .0093043 1.53 0.127 -.0040446 .0324276 shhlw2 | .0087578 .0103731 0.84 0.399 -.0115731 .0290887 shjobw2 | -.0066068 .0093228 -0.71 0.479 -.0248791 .0116655 shrelaw2 | -.0301632 .0120406 -2.51 0.012 -.0537624 -.0065641 suprtw2 | -.0125721 .0077017 -1.63 0.103 -.0276672 .002523 suchrw2 | -.0060097 .0083821 -0.72 0.473 -.0224384 .010419 havmilsq | -.0000291 .000055 -0.53 0.597 -.0001368 .0000787 _cons | -27.59558 6.62482 -4.17 0.000 -40.57999 -14.61117 ------------------------------------------------------------------------------ Note: 3 failures and 0 successes completely determined. Warning: convergence not achieved Logistic model for HP2pbfhm -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 29 7 | 36 - | 18 228 | 246 -----------+--------------------------+----------- Total | 47 235 | 282 Classified + if predicted Pr(D) >= .5 True D defined as HP2pbfhm != 0 -------------------------------------------------- Sensitivity Pr( +| D) 61.70% Specificity Pr( -|~D) 97.02% Positive predictive value Pr( D| +) 80.56% Negative predictive value Pr(~D| -) 92.68% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 2.98% False - rate for true D Pr( -| D) 38.30% False + rate for classified + Pr(~D| +) 19.44% False - rate for classified - Pr( D| -) 7.32% -------------------------------------------------- Correctly classified 91.13% -------------------------------------------------- Logistic model for HP2pbfhm, goodness-of-fit test number of observations = 282 number of covariate patterns = 282 Pearson chi2(235) = 241.50 Prob > chi2 = 0.3715 Measures of Fit for logistic of HP2pbfhm Log-Lik Intercept Only: -127.058 Log-Lik Full Model: -69.238 D(226): 138.475 LR(45): 115.641 Prob > LR: 0.000 McFadden's R2: 0.455 McFadden's Adj R2: 0.014 Maximum Likelihood R2: 0.336 Cragg & Uhler's R2: 0.566 McKelvey and Zavoina's R2: 0.950 Efron's R2: 0.475 Variance of y*: 65.351 Variance of error: 3.290 Count R2: 0.911 Adj Count R2: 0.468 AIC: 0.888 AIC*n: 250.475 BIC: -1136.596 BIC': 138.245 620 . 621 . 622 . title "Partly Trimmed male wave 2 Dose => Problems with Family at home model > s" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****Partly Trimmed male wave 2 Dose => Problems with Family at home models*** > ** ***** ***** ***** ***** ***** 1 Jul 2012 15:03:22 ***** ******************************************************************************* ******************************************************************************* 623 . local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 bf40 624 . logit HP2pbfhm age sepaw2 dvcew2 radhlw2 avgcumdosew2 suprtw2 /// > havmilsq illw2 `w2bf' if gender==1, iterate(50) note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 6 obs not used note: dvcew2 != 0 predicts failure perfectly dvcew2 dropped and 4 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 20 obs not used note: bf29 != 0 predicts failure perfectly bf29 dropped and 8 obs not used note: bf17 omitted because of collinearity Iteration 0: log likelihood = -78.804936 Iteration 1: log likelihood = -67.490685 Iteration 2: log likelihood = -61.940594 Iteration 3: log likelihood = -61.733617 Iteration 4: log likelihood = -61.732595 Iteration 5: log likelihood = -61.732594 Logistic regression Number of obs = 302 LR chi2(17) = 34.14 Prob > chi2 = 0.0080 Log likelihood = -61.732594 Pseudo R2 = 0.2166 ------------------------------------------------------------------------------ HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0292956 .0235579 1.24 0.214 -.016877 .0754681 sepaw2 | 0 (omitted) dvcew2 | 0 (omitted) radhlw2 | .0182205 .0109536 1.66 0.096 -.0032483 .0396892 avgcumdosew2 | -.090545 .2561457 -0.35 0.724 -.5925814 .4114914 suprtw2 | -.0052645 .0059622 -0.88 0.377 -.0169502 .0064212 havmilsq | -1.61e-06 7.15e-06 -0.23 0.822 -.0000156 .0000124 illw2 | .7307941 .4092131 1.79 0.074 -.0712488 1.532837 bf1 | -.0825023 .0733724 -1.12 0.261 -.2263094 .0613049 bf4 | -.1790987 .2180029 -0.82 0.411 -.6063764 .2481791 bf2 | .0002682 .0002153 1.25 0.213 -.0001537 .0006901 bf4m | .0150156 .1990066 0.08 0.940 -.3750301 .4050613 bf5m | .0001665 .0028981 0.06 0.954 -.0055136 .0058466 bf7m | .0013857 .000794 1.75 0.081 -.0001704 .0029419 bf8 | -.0000382 .0000618 -0.62 0.537 -.0001593 .0000829 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | .0450852 .0634467 0.71 0.477 -.0792681 .1694385 bf22 | -.0000253 .0002352 -0.11 0.914 -.0004863 .0004357 bf29 | 0 (omitted) bf30 | -.0003567 .0004856 -0.73 0.463 -.0013085 .0005951 bf40 | -.229251 .3317905 -0.69 0.490 -.8795485 .4210465 _cons | -5.211948 3.446845 -1.51 0.131 -11.96764 1.543744 ------------------------------------------------------------------------------ 625 . 626 . estat class Logistic model for HP2pbfhm -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 0 1 | 1 - | 22 279 | 301 -----------+--------------------------+----------- Total | 22 280 | 302 Classified + if predicted Pr(D) >= .5 True D defined as HP2pbfhm != 0 -------------------------------------------------- Sensitivity Pr( +| D) 0.00% Specificity Pr( -|~D) 99.64% Positive predictive value Pr( D| +) 0.00% Negative predictive value Pr(~D| -) 92.69% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 0.36% False - rate for true D Pr( -| D) 100.00% False + rate for classified + Pr(~D| +) 100.00% False - rate for classified - Pr( D| -) 7.31% -------------------------------------------------- Correctly classified 92.38% -------------------------------------------------- 627 . estat gof Logistic model for HP2pbfhm, goodness-of-fit test number of observations = 302 number of covariate patterns = 301 Pearson chi2(283) = 231.95 Prob > chi2 = 0.9882 628 . fitstat Measures of Fit for logit of HP2pbfhm Log-Lik Intercept Only: -78.805 Log-Lik Full Model: -61.733 D(279): 123.465 LR(17): 34.145 Prob > LR: 0.008 McFadden's R2: 0.217 McFadden's Adj R2: -0.075 Maximum Likelihood R2: 0.107 Cragg & Uhler's R2: 0.263 McKelvey and Zavoina's R2: 0.433 Efron's R2: 0.127 Variance of y*: 5.806 Variance of error: 3.290 Count R2: 0.924 Adj Count R2: -0.045 AIC: 0.561 AIC*n: 169.465 BIC: -1469.744 BIC': 62.933 629 . 630 . title "fully Trimmed male main effects wv 3" " Dose => Problems with Family a > t home models" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** fully Trimmed male main effects wv 3 ***** ***** Dose => Problems with Family at home models ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:03:24 ***** ******************************************************************************* ******************************************************************************* 631 . local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 bf40 632 . sw, pr(.1):logit HP2pbfhm age sepaw2 dvcew2 radhlw2 avgcumdosew2 suprtw2 /// > havmilsq illw2 `w2bf' if gender==1, iterate(50) note: sepaw2 dropped because of estimability note: dvcew2 dropped because of estimability note: bf15m dropped because of estimability note: bf17 dropped because of estimability note: bf29 dropped because of estimability note: o.sepaw2 dropped because of estimability note: o.dvcew2 dropped because of estimability note: o.bf15m dropped because of estimability note: o.bf17 dropped because of estimability note: o.bf29 dropped because of estimability note: 38 obs. dropped because of estimability begin with full model p = 0.9542 >= 0.1000 removing bf5m p = 0.9265 >= 0.1000 removing bf4m p = 0.9130 >= 0.1000 removing bf22 p = 0.8252 >= 0.1000 removing havmilsq p = 0.7278 >= 0.1000 removing avgcumdosew2 p = 0.4697 >= 0.1000 removing bf20 p = 0.4787 >= 0.1000 removing bf30 p = 0.3553 >= 0.1000 removing bf8 p = 0.3688 >= 0.1000 removing suprtw2 p = 0.3130 >= 0.1000 removing age p = 0.3217 >= 0.1000 removing bf40 p = 0.2872 >= 0.1000 removing bf2 p = 0.1449 >= 0.1000 removing bf1 p = 0.2454 >= 0.1000 removing radhlw2 p = 0.1939 >= 0.1000 removing illw2 Logistic regression Number of obs = 302 LR chi2(2) = 22.57 Prob > chi2 = 0.0000 Log likelihood = -67.522012 Pseudo R2 = 0.1432 ------------------------------------------------------------------------------ HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.2376169 .0586559 -4.05 0.000 -.3525804 -.1226534 bf7m | .0012872 .0004582 2.81 0.005 .0003891 .0021853 _cons | -1.530756 .4468865 -3.43 0.001 -2.406637 -.6548742 ------------------------------------------------------------------------------ 633 . 634 . estat class Logistic model for HP2pbfhm -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 0 0 | 0 - | 22 280 | 302 -----------+--------------------------+----------- Total | 22 280 | 302 Classified + if predicted Pr(D) >= .5 True D defined as HP2pbfhm != 0 -------------------------------------------------- Sensitivity Pr( +| D) 0.00% Specificity Pr( -|~D) 100.00% Positive predictive value Pr( D| +) .% Negative predictive value Pr(~D| -) 92.72% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 0.00% False - rate for true D Pr( -| D) 100.00% False + rate for classified + Pr(~D| +) .% False - rate for classified - Pr( D| -) 7.28% -------------------------------------------------- Correctly classified 92.72% -------------------------------------------------- 635 . estat gof Logistic model for HP2pbfhm, goodness-of-fit test number of observations = 302 number of covariate patterns = 117 Pearson chi2(114) = 146.75 Prob > chi2 = 0.0210 636 . fitstat Measures of Fit for logit of HP2pbfhm Log-Lik Intercept Only: -78.805 Log-Lik Full Model: -67.522 D(299): 135.044 LR(2): 22.566 Prob > LR: 0.000 McFadden's R2: 0.143 McFadden's Adj R2: 0.105 Maximum Likelihood R2: 0.072 Cragg & Uhler's R2: 0.177 McKelvey and Zavoina's R2: 0.333 Efron's R2: 0.089 Variance of y*: 4.936 Variance of error: 3.290 Count R2: 0.927 Adj Count R2: 0.000 AIC: 0.467 AIC*n: 141.044 BIC: -1572.374 BIC': -11.145 637 . 638 . scalar MainEffPrbfhmMw2 = "bf1 bf4 dvcew2 bf7m" 639 . scalar SigDosePrbfhmMw2 = "no" 640 . // construction of moderators for male model 641 . 642 . foreach var in bf1 bf4 dvcew2 bf2 bf7m { 2. cap gen `var'Xd3 = `var'*avgcumdosew2 3. } 643 . 644 . 645 . 646 . 647 . ***************************************************************************** > * 648 . *-------chunk 6 continued -testing moderators and none found for males 649 . local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 bf40 650 . 651 . 652 . title "fully Trimmed male main effects wv 3" /// > "Dose => Problems with Family at home models" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** fully Trimmed male main effects wv 3 ***** ***** Dose => Problems with Family at home models ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:03:48 ***** ******************************************************************************* ******************************************************************************* 653 . local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 bf40 654 . sw, pr(.1):logit HP2pbfhm age sepaw2 dvcew2 radhlw2 avgcumdosew2 suprtw2 /// > havmilsq illw2 `w2bf' bf1Xd3 bf4Xd3 bf2Xd3 bf7mXd3 dvcew2Xd3 if /// > gender==1, iterate(50) note: sepaw2 dropped because of estimability note: dvcew2 dropped because of estimability note: bf15m dropped because of estimability note: bf17 dropped because of estimability note: bf29 dropped because of estimability note: dvcew2Xd3 dropped because of estimability note: o.sepaw2 dropped because of estimability note: o.dvcew2 dropped because of estimability note: o.bf15m dropped because of estimability note: o.bf17 dropped because of estimability note: o.bf29 dropped because of estimability note: o.dvcew2Xd3 dropped because of estimability note: 38 obs. dropped because of estimability begin with full model p = 0.9625 >= 0.1000 removing bf22 p = 0.9571 >= 0.1000 removing bf4m p = 0.8570 >= 0.1000 removing bf5m p = 0.8130 >= 0.1000 removing havmilsq p = 0.7588 >= 0.1000 removing bf2Xd3 p = 0.6682 >= 0.1000 removing bf7mXd3 p = 0.7871 >= 0.1000 removing bf1Xd3 p = 0.4970 >= 0.1000 removing avgcumdosew2 p = 0.7875 >= 0.1000 removing bf4Xd3 p = 0.4697 >= 0.1000 removing bf20 p = 0.4787 >= 0.1000 removing bf30 p = 0.3553 >= 0.1000 removing bf8 p = 0.3688 >= 0.1000 removing suprtw2 p = 0.3130 >= 0.1000 removing age p = 0.3217 >= 0.1000 removing bf40 p = 0.2872 >= 0.1000 removing bf2 p = 0.1449 >= 0.1000 removing bf1 p = 0.2454 >= 0.1000 removing radhlw2 p = 0.1939 >= 0.1000 removing illw2 Logistic regression Number of obs = 302 LR chi2(2) = 22.57 Prob > chi2 = 0.0000 Log likelihood = -67.522012 Pseudo R2 = 0.1432 ------------------------------------------------------------------------------ HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.2376169 .0586559 -4.05 0.000 -.3525804 -.1226534 bf7m | .0012872 .0004582 2.81 0.005 .0003891 .0021853 _cons | -1.530756 .4468865 -3.43 0.001 -2.406637 -.6548742 ------------------------------------------------------------------------------ 655 . 656 . estat class Logistic model for HP2pbfhm -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 0 0 | 0 - | 22 280 | 302 -----------+--------------------------+----------- Total | 22 280 | 302 Classified + if predicted Pr(D) >= .5 True D defined as HP2pbfhm != 0 -------------------------------------------------- Sensitivity Pr( +| D) 0.00% Specificity Pr( -|~D) 100.00% Positive predictive value Pr( D| +) .% Negative predictive value Pr(~D| -) 92.72% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 0.00% False - rate for true D Pr( -| D) 100.00% False + rate for classified + Pr(~D| +) .% False - rate for classified - Pr( D| -) 7.28% -------------------------------------------------- Correctly classified 92.72% -------------------------------------------------- 657 . estat gof Logistic model for HP2pbfhm, goodness-of-fit test number of observations = 302 number of covariate patterns = 117 Pearson chi2(114) = 146.75 Prob > chi2 = 0.0210 658 . fitstat Measures of Fit for logit of HP2pbfhm Log-Lik Intercept Only: -78.805 Log-Lik Full Model: -67.522 D(299): 135.044 LR(2): 22.566 Prob > LR: 0.000 McFadden's R2: 0.143 McFadden's Adj R2: 0.105 Maximum Likelihood R2: 0.072 Cragg & Uhler's R2: 0.177 McKelvey and Zavoina's R2: 0.333 Efron's R2: 0.089 Variance of y*: 4.936 Variance of error: 3.290 Count R2: 0.927 Adj Count R2: 0.000 AIC: 0.467 AIC*n: 141.044 BIC: -1572.374 BIC': -11.145 659 . 660 . scalar SigDosePrbfmhmMw2 = "no" 661 . scalar PrbfmhmModw2 = "none" 662 . 663 . * 3 main effects signif no main effect for dose for males 664 . 665 . 666 . ***************************************************************************** > *** 667 . title4 "Chunk 6 continued -testing meditors for females" ------------------------------------------------------------------------------- Chunk 6 continued -testing meditors for females ------------------------------------------------------------------------------- 668 . title4 "Partly Trimmed female wave 2" "Dose => Problems with Family at home > models" ------------------------------------------------------------------------------- Partly Trimmed female wave 2 Dose => Problems with Family at home models ------------------------------------------------------------------------------- 669 . local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 bf40 670 . logit HP2pbfhm age sepaw2 dvcew2 radhlw2 avgcumdosew2 bf4 bf8 bf40 /// > shrelaw2 suchrw2 suprtw2 /// > havmilsq illw2 `w2bf' if gender==2, iterate(50) note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 8 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 11 obs not used note: bf4 omitted because of collinearity note: bf8 omitted because of collinearity note: bf17 omitted because of collinearity note: bf40 omitted because of collinearity Iteration 0: log likelihood = -137.18535 Iteration 1: log likelihood = -103.93355 Iteration 2: log likelihood = -95.703442 Iteration 3: log likelihood = -95.15807 Iteration 4: log likelihood = -94.770663 Iteration 5: log likelihood = -94.32988 Iteration 6: log likelihood = -94.321437 Iteration 7: log likelihood = -94.321422 Iteration 8: log likelihood = -94.321422 Logistic regression Number of obs = 344 LR chi2(21) = 85.73 Prob > chi2 = 0.0000 Log likelihood = -94.321422 Pseudo R2 = 0.3125 ------------------------------------------------------------------------------ HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0495582 .0188768 2.63 0.009 .0125605 .086556 sepaw2 | 0 (omitted) dvcew2 | .1260682 1.324676 0.10 0.924 -2.470249 2.722386 radhlw2 | .0105681 .0093019 1.14 0.256 -.0076632 .0287993 avgcumdosew2 | .0737234 .1141614 0.65 0.518 -.1500289 .2974756 bf4 | -.4430413 .2263763 -1.96 0.050 -.8867306 .000648 bf8 | .0000574 .0000561 1.02 0.306 -.0000524 .0001673 bf40 | -.1754276 .1875146 -0.94 0.350 -.5429494 .1920941 shrelaw2 | -.0156329 .0070882 -2.21 0.027 -.0295256 -.0017402 suchrw2 | -.0041523 .0059924 -0.69 0.488 -.0158972 .0075927 suprtw2 | -.007471 .0054359 -1.37 0.169 -.0181252 .0031831 havmilsq | -.000013 .0000109 -1.19 0.233 -.0000344 8.39e-06 illw2 | -.0615734 .1862759 -0.33 0.741 -.4266675 .3035207 bf1 | .007936 .0350726 0.23 0.821 -.060805 .0766769 bf4 | 0 (omitted) bf2 | -.0000527 .0001277 -0.41 0.680 -.0003031 .0001977 bf4m | .1913739 .2129046 0.90 0.369 -.2259115 .6086594 bf5m | -.0025742 .0029442 -0.87 0.382 -.0083446 .0031963 bf7m | -.0002656 .0006721 -0.40 0.693 -.0015828 .0010516 bf8 | 0 (omitted) bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.0160588 .0276726 -0.58 0.562 -.0702962 .0381785 bf22 | .0000785 .0001703 0.46 0.645 -.0002554 .0004124 bf29 | .000024 .0000382 0.63 0.529 -.0000508 .0000989 bf30 | -.0003425 .0003633 -0.94 0.346 -.0010546 .0003696 bf40 | 0 (omitted) _cons | -1.808076 2.335383 -0.77 0.439 -6.385343 2.76919 ------------------------------------------------------------------------------ Note: 3 failures and 0 successes completely determined. 671 . 672 . estat class Logistic model for HP2pbfhm -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 17 8 | 25 - | 30 289 | 319 -----------+--------------------------+----------- Total | 47 297 | 344 Classified + if predicted Pr(D) >= .5 True D defined as HP2pbfhm != 0 -------------------------------------------------- Sensitivity Pr( +| D) 36.17% Specificity Pr( -|~D) 97.31% Positive predictive value Pr( D| +) 68.00% Negative predictive value Pr(~D| -) 90.60% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 2.69% False - rate for true D Pr( -| D) 63.83% False + rate for classified + Pr(~D| +) 32.00% False - rate for classified - Pr( D| -) 9.40% -------------------------------------------------- Correctly classified 88.95% -------------------------------------------------- 673 . estat gof Logistic model for HP2pbfhm, goodness-of-fit test number of observations = 344 number of covariate patterns = 344 Pearson chi2(322) = 324.25 Prob > chi2 = 0.4544 674 . fitstat Measures of Fit for logit of HP2pbfhm Log-Lik Intercept Only: -137.185 Log-Lik Full Model: -94.321 D(316): 188.643 LR(21): 85.728 Prob > LR: 0.000 McFadden's R2: 0.312 McFadden's Adj R2: 0.108 Maximum Likelihood R2: 0.221 Cragg & Uhler's R2: 0.401 McKelvey and Zavoina's R2: 0.947 Efron's R2: 0.303 Variance of y*: 61.833 Variance of error: 3.290 Count R2: 0.890 Adj Count R2: 0.191 AIC: 0.711 AIC*n: 244.643 BIC: -1657.000 BIC': 36.926 675 . 676 . *-------Chunk 6 continued -testing meditors for females 677 . title "More partly female Trimmed wave 2" "Dose => Problems with Family at h > ome models" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** More partly female Trimmed wave 2 ***** ***** Dose => Problems with Family at home models ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:19 ***** ******************************************************************************* ******************************************************************************* 678 . local w2bf bf1 bf4 bf2 bf8 bf15m bf17 bf20 bf22 bf29 bf30 bf40 679 . logit HP2pbfhm age bf4 bf40 if gender==2, iterate(50) Iteration 0: log likelihood = -139.89675 Iteration 1: log likelihood = -112.36921 Iteration 2: log likelihood = -106.24923 Iteration 3: log likelihood = -106.12137 Iteration 4: log likelihood = -106.12091 Iteration 5: log likelihood = -106.12091 Logistic regression Number of obs = 363 LR chi2(3) = 67.55 Prob > chi2 = 0.0000 Log likelihood = -106.12091 Pseudo R2 = 0.2414 ------------------------------------------------------------------------------ HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0636233 .017269 3.68 0.000 .0297767 .0974699 bf4 | -.2029758 .0392585 -5.17 0.000 -.2799211 -.1260305 bf40 | -.1942568 .091411 -2.13 0.034 -.373419 -.0150946 _cons | -3.085643 1.113267 -2.77 0.006 -5.267607 -.9036792 ------------------------------------------------------------------------------ 680 . 681 . estat class Logistic model for HP2pbfhm -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 12 3 | 15 - | 35 313 | 348 -----------+--------------------------+----------- Total | 47 316 | 363 Classified + if predicted Pr(D) >= .5 True D defined as HP2pbfhm != 0 -------------------------------------------------- Sensitivity Pr( +| D) 25.53% Specificity Pr( -|~D) 99.05% Positive predictive value Pr( D| +) 80.00% Negative predictive value Pr(~D| -) 89.94% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 0.95% False - rate for true D Pr( -| D) 74.47% False + rate for classified + Pr(~D| +) 20.00% False - rate for classified - Pr( D| -) 10.06% -------------------------------------------------- Correctly classified 89.53% -------------------------------------------------- 682 . estat gof Logistic model for HP2pbfhm, goodness-of-fit test number of observations = 363 number of covariate patterns = 341 Pearson chi2(337) = 366.00 Prob > chi2 = 0.1331 683 . fitstat Measures of Fit for logit of HP2pbfhm Log-Lik Intercept Only: -139.897 Log-Lik Full Model: -106.121 D(359): 212.242 LR(3): 67.552 Prob > LR: 0.000 McFadden's R2: 0.241 McFadden's Adj R2: 0.213 Maximum Likelihood R2: 0.170 Cragg & Uhler's R2: 0.316 McKelvey and Zavoina's R2: 0.399 Efron's R2: 0.224 Variance of y*: 5.470 Variance of error: 3.290 Count R2: 0.895 Adj Count R2: 0.191 AIC: 0.607 AIC*n: 220.242 BIC: -1903.849 BIC': -49.868 684 . 685 . scalar SigDosePrbfmhmFw2 = "no" 686 . scalar MainEffPrbfmhmFw2 = "age bf4 bf40" 687 . * 3 significant main effects for females 688 . * no significant main effect for dose 689 . 690 . * constructing moderators 691 . 692 . foreach var in bf4 bf40 { 2. cap gen `var'Xd3 = `var'*avgcumdosew2 3. } 693 . 694 . 695 . *- testing female moderator effects: no moderator effects for females 696 . 697 . sw, pr(.1): logit HP2pbfhm age bf4 bf40 ageXd3 bf4Xd3 bf40Xd3 if gender==2, i > terate(50) begin with full model p = 0.7256 >= 0.1000 removing bf4Xd3 p = 0.5514 >= 0.1000 removing bf40 Logistic regression Number of obs = 363 LR chi2(4) = 69.05 Prob > chi2 = 0.0000 Log likelihood = -105.37103 Pseudo R2 = 0.2468 ------------------------------------------------------------------------------ HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0553962 .0175151 3.16 0.002 .0210671 .0897253 bf4 | -.1901097 .0380586 -5.00 0.000 -.2647032 -.1155163 bf40Xd3 | -.1318421 .0668537 -1.97 0.049 -.262873 -.0008113 ageXd3 | .0078148 .0040047 1.95 0.051 -.0000342 .0156639 _cons | -3.363212 1.084085 -3.10 0.002 -5.487979 -1.238446 ------------------------------------------------------------------------------ 698 . estat gof Logistic model for HP2pbfhm, goodness-of-fit test number of observations = 363 number of covariate patterns = 359 Pearson chi2(354) = 358.75 Prob > chi2 = 0.4197 699 . estat class Logistic model for HP2pbfhm -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 12 4 | 16 - | 35 312 | 347 -----------+--------------------------+----------- Total | 47 316 | 363 Classified + if predicted Pr(D) >= .5 True D defined as HP2pbfhm != 0 -------------------------------------------------- Sensitivity Pr( +| D) 25.53% Specificity Pr( -|~D) 98.73% Positive predictive value Pr( D| +) 75.00% Negative predictive value Pr(~D| -) 89.91% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 1.27% False - rate for true D Pr( -| D) 74.47% False + rate for classified + Pr(~D| +) 25.00% False - rate for classified - Pr( D| -) 10.09% -------------------------------------------------- Correctly classified 89.26% -------------------------------------------------- 700 . fitstat Measures of Fit for logit of HP2pbfhm Log-Lik Intercept Only: -139.897 Log-Lik Full Model: -105.371 D(358): 210.742 LR(4): 69.051 Prob > LR: 0.000 McFadden's R2: 0.247 McFadden's Adj R2: 0.211 Maximum Likelihood R2: 0.173 Cragg & Uhler's R2: 0.322 McKelvey and Zavoina's R2: 0.417 Efron's R2: 0.228 Variance of y*: 5.645 Variance of error: 3.290 Count R2: 0.893 Adj Count R2: 0.170 AIC: 0.608 AIC*n: 220.742 BIC: -1899.454 BIC': -45.474 701 . 702 . scalar PrbfmhmModFw2="none" 703 . 704 . ***************************************************************************** > *** 705 . *---------Chunk 6 continued testing mediating effects for Problems with famil > y 706 . * at home 707 . 708 . * age is a mediating effect for males for Dose=> problems with family at home 709 . des bf4 bf40 storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) 710 . glm age avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1330.6004 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 147.6853 Deviance = 49917.64009 (1/df) Deviance = 147.6853 Pearson = 49917.64009 (1/df) Pearson = 147.6853 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.838826 Log likelihood = -1330.6004 BIC = 47947.46 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .5832314 .2635871 2.21 0.027 .0666101 1.099853 _cons | 48.62133 .7061562 68.85 0.000 47.23729 50.00537 ------------------------------------------------------------------------------ 711 . glm HP2pbfhm age if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 163.1004 Iteration 2: deviance = 152.8997 Iteration 3: deviance = 151.9532 Iteration 4: deviance = 151.9347 Iteration 5: deviance = 151.9347 Iteration 6: deviance = 151.9347 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 151.9346518 (1/df) Deviance = .4495108 Pearson = 324.8005072 (1/df) Pearson = .9609482 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1818.249 ------------------------------------------------------------------------------ | EIM HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0296385 .0061334 4.83 0.000 .0176173 .0416597 _cons | -3.073816 .3430721 -8.96 0.000 -3.746225 -2.401407 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 712 . glm bf4 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1027.1225 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 24.7771 Deviance = 8374.659221 (1/df) Deviance = 24.7771 Pearson = 8374.659221 (1/df) Pearson = 24.7771 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.053662 Log likelihood = -1027.122509 BIC = 6404.476 ------------------------------------------------------------------------------ | OIM bf4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.0331637 .1079644 -0.31 0.759 -.2447701 .1784427 _cons | 12.52896 .2892393 43.32 0.000 11.96206 13.09586 ------------------------------------------------------------------------------ 713 . glm HP2pbfhm bf4 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 160.0024 Iteration 2: deviance = 148.9095 Iteration 3: deviance = 148.0472 Iteration 4: deviance = 148.0358 Iteration 5: deviance = 148.0358 Iteration 6: deviance = 148.0358 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 148.0358223 (1/df) Deviance = .4379758 Pearson = 318.8927909 (1/df) Pearson = .9434698 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1822.148 ------------------------------------------------------------------------------ | EIM HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.0752171 .0127773 -5.89 0.000 -.1002602 -.0501741 _cons | -.6910419 .1483563 -4.66 0.000 -.9818149 -.400269 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 714 . 715 . * age is a mediating effect for females for Dose=> Problems with family at ho > me 716 . glm age avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1406.9403 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 136.9184 Deviance = 49427.52828 (1/df) Deviance = 136.9184 Pearson = 49427.52828 (1/df) Pearson = 136.9184 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.762756 Log likelihood = -1406.940271 BIC = 47299.65 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.502324 .4454009 3.37 0.001 .6293547 2.375294 _cons | 48.86944 .7323225 66.73 0.000 47.43412 50.30477 ------------------------------------------------------------------------------ 717 . glm HP2pbfhm age if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 252.4902 Iteration 2: deviance = 245.4181 Iteration 3: deviance = 245.1325 Iteration 4: deviance = 245.1321 Iteration 5: deviance = 245.1321 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 245.1320769 (1/df) Deviance = .6790362 Pearson = 382.7456824 (1/df) Pearson = 1.060237 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1882.747 ------------------------------------------------------------------------------ | EIM HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .043836 .0066445 6.60 0.000 .030813 .056859 _cons | -3.477625 .3761287 -9.25 0.000 -4.214824 -2.740426 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 718 . 719 . * bf4 is a mediting effect for females for Dose=> Problems with family at hom > e 720 . glm bf4 avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1109.0983 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 26.53281 Deviance = 9578.344971 (1/df) Deviance = 26.53281 Pearson = 9578.344971 (1/df) Pearson = 26.53281 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.121754 Log likelihood = -1109.098281 BIC = 7450.466 ------------------------------------------------------------------------------ | OIM bf4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.595012 .1960703 -3.03 0.002 -.9793027 -.2107212 _cons | 11.02048 .3223763 34.19 0.000 10.38863 11.65232 ------------------------------------------------------------------------------ 721 . glm HP2pbfhm bf4 if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 240.0481 Iteration 2: deviance = 229.4916 Iteration 3: deviance = 228.6166 Iteration 4: deviance = 228.5986 Iteration 5: deviance = 228.5985 Iteration 6: deviance = 228.5985 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 228.5985175 (1/df) Deviance = .6332369 Pearson = 299.6186696 (1/df) Pearson = .8299686 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1899.281 ------------------------------------------------------------------------------ | EIM HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.1229375 .0145262 -8.46 0.000 -.1514083 -.0944667 _cons | -.0739521 .1322299 -0.56 0.576 -.3331179 .1852138 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 722 . 723 . 724 . glm bf40 avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -818.51169 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 5.351378 Deviance = 1931.847477 (1/df) Deviance = 5.351378 Pearson = 1931.847477 (1/df) Pearson = 5.351378 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 4.520726 Log likelihood = -818.5116931 BIC = -196.0319 ------------------------------------------------------------------------------ | OIM bf40 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .1794903 .0880548 2.04 0.042 .0069061 .3520745 _cons | 3.004543 .1447786 20.75 0.000 2.720783 3.288304 ------------------------------------------------------------------------------ 725 . glm HP2pbfhm bf40 if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 280.0603 Iteration 2: deviance = 279.7443 Iteration 3: deviance = 279.7441 Iteration 4: deviance = 279.7441 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 279.7440687 (1/df) Deviance = .7749143 Pearson = 362.9453702 (1/df) Pearson = 1.005389 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1848.135 ------------------------------------------------------------------------------ | EIM HP2pbfhm | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf40 | .0081494 .0313367 0.26 0.795 -.0532693 .0695682 _cons | -1.154869 .1244365 -9.28 0.000 -1.398761 -.9109783 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 726 . 727 . scalar PrbfmhmMedMw2 = "age" 728 . scalar PrbfmhmMedFw2 = "age bf4" 729 . 730 . title4 " Summary of dose=problems with family at home mediating effects " ------------------------------------------------------------------------------- Summary of dose=problems with family at home mediating effects ------------------------------------------------------------------------------- 731 . * males mediators age 1 732 . * females mediators age and BSIsoma rescaled (bf4) 2 733 . 734 . matrix define HP2prbfamMw2 = J(1,8, 0) 735 . matrix define HP2prbfamFw2 = J(1,8, 0) 736 . matrix colnames HP2prbfamMw2 = hypnum ptnum wave gender medsig numMAsig numM > odsig numMed 737 . matrix colnames HP2prbfamFw2= hypnum ptnum wave gender medsig numMAs > ig numModsig numMed 738 . matrix define HP2prbfamMw2= (1, 2, 3, 1, 0, 5, 0, 1 ) 739 . matrix define HP2prbfamFw2= (1, 2, 3, 2, 0, 2, 2, 2) 740 . matrix rowname HP2prbfamMw2 = HP2prbfamM 741 . matrix rowname HP2prbfamFw2 = HP2prbfamF 742 . matlist HP2prbfamMw2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ HP2prbfamM | 1 2 3 1 0 5 > 0 | c8 -------------+----------- HP2prbfamM | 1 743 . matlist HP2prbfamFw2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ HP2prbfamF | 1 2 3 2 0 2 > 2 | c8 -------------+----------- HP2prbfamF | 2 744 . matrix define H1pt2w2 = ( HP2wkMw2 \ HP2wkFw2 \ HP2hmcrMw2 \ HP2hmcrF > w2 \HP2spMw2 \HP2spFw2 \ HP2prbfamMw2 \ HP2prbfamFw2 ) 745 . 746 . matlist H1pt2w2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ r1 | 1 2 3 1 0 4 > 0 r1 | 1 2 3 2 0 1 > 0 r1 | 1 2 3 1 0 4 > 0 r1 | 1 2 3 2 0 2 > 0 HP2spMw2 | 1 2 3 1 0 2 > 0 HP2spFw2 | 1 2 3 2 1 5 > 5 HP2prbfamM | 1 2 3 1 0 5 > 0 HP2prbfamF | 1 2 3 2 0 2 > 2 | c8 -------------+----------- r1 | 4 r1 | 6 r1 | 2 r1 | 2 HP2spMw2 | 1 HP2spFw2 | 2 HP2prbfamM | 1 HP2prbfamF | 2 747 . matrix colnames H1pt2w2 = hypnum ptnum wave gender medsig numMAsig numMo > dsig numMed 748 . matrix rownames H1pt2w2 = HP2wkMw2 HP2wkFw2 HP2hmcrMw2 HP2hmcrFw2 HP2pr > bfhmMw2 HP2prbfhmFw2 749 . matlist H1pt2w2 | hypnum ptnum wave gender medsig numMAsig > numModsig -------------+----------------------------------------------------------------- ------------ HP2wkMw2 | 1 2 3 1 0 4 > 0 HP2wkFw2 | 1 2 3 2 0 1 > 0 HP2hmcrMw2 | 1 2 3 1 0 4 > 0 HP2hmcrFw2 | 1 2 3 2 0 2 > 0 HP2prbfhmMw2 | 1 2 3 1 0 2 > 0 HP2prbfhmFw2 | 1 2 3 2 1 5 > 5 HP2prbfhmFw2 | 1 2 3 1 0 5 > 0 HP2prbfhmFw2 | 1 2 3 2 0 2 > 2 | numMed -------------+----------- HP2wkMw2 | 4 HP2wkFw2 | 6 HP2hmcrMw2 | 2 HP2hmcrFw2 | 2 HP2prbfhmMw2 | 1 HP2prbfhmFw2 | 2 HP2prbfhmFw2 | 1 HP2prbfhmFw2 | 2 750 . 751 . 752 . *---------------------------------------------------------------------------- > --- 753 . ***************************************************************************** > *** 754 . *-------Chunk 7 Dose==> problems with sex life impact 755 . * Chunk 7 General model for all part 2 of Nottingham Health Profile 756 . * washes out for men 757 . * female main effects wash out in trimmed model 758 . title "5. wave 2 part2 H1: Test of hypothesis 1 with Male and Female Respond > ents" /// > " wave 2 Main effects Dose=> sexlife impact identification" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****5. wave 2 part2 H1: Test of hypothesis 1 with Male and Female Respondents > ***** ***** wave 2 Main effects Dose=> sexlife impact identification ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:41 ***** ******************************************************************************* ******************************************************************************* 759 . forvalues j=2/2 { 2. set more off 3. 760 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 761 . foreach var in HP2sxlife { 5. forvalues k=2/2 { 6. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 7. title "Full Nottingham Part 2 subscale models for male & females" /// > "Full main model for `var' for wave= `j' " /// > "chunk 7 H1 test:Gender= `k' model Wave = `j' for `e(depvar)' " 8. di _skip(4) 9. 762 . di _skip(4) 10. 763 . 764 . xi: logistic `var' age i.educ occ1w`j'-occ8w`j' /// > marrw`j'1- marrw`j'3 marrw`j'5-marrw`j'6 inc1w`j'-inc4w`j' // > / > radhlw`j' havmil avgcumdosew`j' `w`j'bf' /// > deaw`j' dvcew`j' sepaw`j' accdw`j' movew`j' /// > illw`j' shfamw`j' shhlw`j' shjobw`j' shrelaw`j' suprtw`j' suc > hrw`j' /// > havmilsq radhlw2 if gender==`k', coef nolog difficult itera > te(50) 11. estat class 12. estat gof 13. fitstat 14. } 15. } 16. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for male & females ***** ***** Full main model for HP2sxlife for wave= 2 ***** ***** chunk 7 H1 test:Gender= 2 model Wave = 2 for HP2pbfhm ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:41 ***** ******************************************************************************* ******************************************************************************* i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: bf29 != 0 predicts success perfectly bf29 dropped and 4 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity note: radhlw2 omitted because of collinearity Logistic regression Number of obs = 346 LR chi2(50) = 172.45 Prob > chi2 = 0.0000 Log likelihood = -111.04151 Pseudo R2 = 0.4371 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .1025465 .0220099 4.66 0.000 .0594079 .145685 _Ieduc_2 | -15.48089 5580.75 -0.00 0.998 -10953.55 10922.59 _Ieduc_3 | -13.96022 5580.75 -0.00 0.998 -10952.03 10924.11 _Ieduc_4 | -13.71505 5580.75 -0.00 0.998 -10951.78 10924.35 _Ieduc_5 | -14.90906 5580.75 -0.00 0.998 -10952.98 10923.16 _Ieduc_6 | -14.036 5580.75 -0.00 0.998 -10952.1 10924.03 _Ieduc_7 | -14.35246 5580.751 -0.00 0.998 -10952.42 10923.72 _Ieduc_8 | 0 (omitted) occ1w2 | -1.981003 1.626317 -1.22 0.223 -5.168525 1.20652 occ2w2 | -1.199181 1.677697 -0.71 0.475 -4.487407 2.089044 occ3w2 | -.7937582 1.670584 -0.48 0.635 -4.068042 2.480526 occ4w2 | -.8102305 1.76353 -0.46 0.646 -4.266686 2.646225 occ5w2 | -.5056346 1.89785 -0.27 0.790 -4.225353 3.214084 occ6w2 | -1.645496 1.931022 -0.85 0.394 -5.430229 2.139237 occ7w2 | -1.288749 1.611028 -0.80 0.424 -4.446306 1.868809 occ8w2 | -.2492981 1.896994 -0.13 0.895 -3.967338 3.468742 marrw21 | -.8001566 1.260909 -0.63 0.526 -3.271493 1.67118 marrw22 | -.5650635 1.458662 -0.39 0.698 -3.423989 2.293861 marrw23 | -1.064868 .8865967 -1.20 0.230 -2.802566 .6728293 marrw25 | -2.27112 1.443829 -1.57 0.116 -5.100972 .5587328 marrw26 | 0 (omitted) inc1w2 | .5165366 1.706152 0.30 0.762 -2.82746 3.860533 inc2w2 | .9386295 1.604285 0.59 0.558 -2.205712 4.082971 inc3w2 | -.0514022 1.646264 -0.03 0.975 -3.27802 3.175215 inc4w2 | .0543252 2.074029 0.03 0.979 -4.010697 4.119348 radhlw2 | .0162077 .0081749 1.98 0.047 .0001852 .0322303 havmil | -.0014745 .0033419 -0.44 0.659 -.0080244 .0050754 avgcumdosew2 | .1675129 .1251712 1.34 0.181 -.0778182 .412844 bf1 | -.0006699 .0354295 -0.02 0.985 -.0701104 .0687707 bf4 | -.6290339 .2665349 -2.36 0.018 -1.151433 -.1066351 bf2 | .0000625 .000131 0.48 0.633 -.0001943 .0003193 bf4m | .4940985 .2505491 1.97 0.049 .0030312 .9851658 bf5m | -.002308 .0018303 -1.26 0.207 -.0058953 .0012792 bf7m | .00018 .0006254 0.29 0.773 -.0010457 .0014058 bf8 | 6.11e-06 .0000425 0.14 0.886 -.0000772 .0000894 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.0009967 .0290168 -0.03 0.973 -.0578686 .0558752 bf22 | -.0001321 .0001453 -0.91 0.363 -.0004169 .0001527 bf29 | 0 (omitted) bf30 | -.0003317 .0003532 -0.94 0.348 -.001024 .0003605 bf40 | .149372 .145042 1.03 0.303 -.1349052 .4336491 deaw2 | .0989356 .2231863 0.44 0.658 -.3385014 .5363726 dvcew2 | -14.61462 1422.458 -0.01 0.992 -2802.582 2773.353 sepaw2 | 13.19252 1422.46 0.01 0.993 -2774.778 2801.163 accdw2 | -.3607593 .7071147 -0.51 0.610 -1.746679 1.02516 movew2 | .2363995 .5198633 0.45 0.649 -.7825137 1.255313 illw2 | .5572231 .2374511 2.35 0.019 .0918274 1.022619 shfamw2 | .001878 .0072955 0.26 0.797 -.0124209 .0161768 shhlw2 | .0092925 .0072946 1.27 0.203 -.0050046 .0235896 shjobw2 | -.0069643 .0067203 -1.04 0.300 -.0201358 .0062073 shrelaw2 | -.0169606 .0081044 -2.09 0.036 -.032845 -.0010762 suprtw2 | -.0129953 .0058047 -2.24 0.025 -.0243723 -.0016183 suchrw2 | .0118245 .0068566 1.72 0.085 -.0016142 .0252632 havmilsq | -6.39e-08 2.74e-06 -0.02 0.981 -5.43e-06 5.30e-06 radhlw2 | 0 (omitted) _cons | 5.841436 5580.75 0.00 0.999 -10932.23 10943.91 ------------------------------------------------------------------------------ Note: 1 failure and 0 successes completely determined. Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 57 17 | 74 - | 32 240 | 272 -----------+--------------------------+----------- Total | 89 257 | 346 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 64.04% Specificity Pr( -|~D) 93.39% Positive predictive value Pr( D| +) 77.03% Negative predictive value Pr(~D| -) 88.24% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 6.61% False - rate for true D Pr( -| D) 35.96% False + rate for classified + Pr(~D| +) 22.97% False - rate for classified - Pr( D| -) 11.76% -------------------------------------------------- Correctly classified 85.84% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 346 number of covariate patterns = 346 Pearson chi2(295) = 312.67 Prob > chi2 = 0.2294 Measures of Fit for logistic of HP2sxlife Log-Lik Intercept Only: -197.267 Log-Lik Full Model: -111.042 D(289): 222.083 LR(50): 172.450 Prob > LR: 0.000 McFadden's R2: 0.437 McFadden's Adj R2: 0.148 Maximum Likelihood R2: 0.393 Cragg & Uhler's R2: 0.577 McKelvey and Zavoina's R2: 0.765 Efron's R2: 0.477 Variance of y*: 14.018 Variance of error: 3.290 Count R2: 0.858 Adj Count R2: 0.449 AIC: 0.971 AIC*n: 336.083 BIC: -1467.538 BIC': 119.872 765 . // full model washes out 766 . 767 . title "5. wave 2 part2 H1: Test of hypothesis 1 with Male and Female Respond > ents" /// > " wave 2 Main effects Dose=> sexlife impact identification" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****5. wave 2 part2 H1: Test of hypothesis 1 with Male and Female Respondents > ***** ***** wave 2 Main effects Dose=> sexlife impact identification ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:43 ***** ******************************************************************************* ******************************************************************************* 768 . forvalues j=2/2 { 2. set more off 3. 769 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 770 . foreach var in HP2sxlife { 5. forvalues k=2/2 { 6. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 7. title "Full Nottingham Part 2 subscale models for male & females" /// > "Full main model for `var' for wave= `j' " /// > "chunk 7 H1 test:Gender= `k' model Wave = `j' for `e(depvar)' " 8. di _skip(4) 9. 771 . di _skip(4) 10. 772 . 773 . xi: logistic `var' age i.educ occ1w`j'-occ8w`j' /// > marrw`j'1- marrw`j'3 marrw`j'5-marrw`j'6 inc1w`j'-inc4w`j' // > / > radhlw`j' havmil avgcumdosew`j' `w`j'bf' /// > deaw`j' dvcew`j' sepaw`j' accdw`j' movew`j' /// > illw`j' shfamw`j' shhlw`j' shjobw`j' shrelaw`j' suprtw`j' suc > hrw`j' /// > havmilsq radhlw2 if gender==`k', coef nolog difficult itera > te(50) 11. estat class 12. estat gof 13. fitstat 14. } 15. } 16. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for male & females ***** ***** Full main model for HP2sxlife for wave= 2 ***** ***** chunk 7 H1 test:Gender= 2 model Wave = 2 for HP2sxlife ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:43 ***** ******************************************************************************* ******************************************************************************* i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: bf29 != 0 predicts success perfectly bf29 dropped and 4 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity note: radhlw2 omitted because of collinearity Logistic regression Number of obs = 346 LR chi2(50) = 172.45 Prob > chi2 = 0.0000 Log likelihood = -111.04151 Pseudo R2 = 0.4371 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .1025465 .0220099 4.66 0.000 .0594079 .145685 _Ieduc_2 | -15.48089 5580.75 -0.00 0.998 -10953.55 10922.59 _Ieduc_3 | -13.96022 5580.75 -0.00 0.998 -10952.03 10924.11 _Ieduc_4 | -13.71505 5580.75 -0.00 0.998 -10951.78 10924.35 _Ieduc_5 | -14.90906 5580.75 -0.00 0.998 -10952.98 10923.16 _Ieduc_6 | -14.036 5580.75 -0.00 0.998 -10952.1 10924.03 _Ieduc_7 | -14.35246 5580.751 -0.00 0.998 -10952.42 10923.72 _Ieduc_8 | 0 (omitted) occ1w2 | -1.981003 1.626317 -1.22 0.223 -5.168525 1.20652 occ2w2 | -1.199181 1.677697 -0.71 0.475 -4.487407 2.089044 occ3w2 | -.7937582 1.670584 -0.48 0.635 -4.068042 2.480526 occ4w2 | -.8102305 1.76353 -0.46 0.646 -4.266686 2.646225 occ5w2 | -.5056346 1.89785 -0.27 0.790 -4.225353 3.214084 occ6w2 | -1.645496 1.931022 -0.85 0.394 -5.430229 2.139237 occ7w2 | -1.288749 1.611028 -0.80 0.424 -4.446306 1.868809 occ8w2 | -.2492981 1.896994 -0.13 0.895 -3.967338 3.468742 marrw21 | -.8001566 1.260909 -0.63 0.526 -3.271493 1.67118 marrw22 | -.5650635 1.458662 -0.39 0.698 -3.423989 2.293861 marrw23 | -1.064868 .8865967 -1.20 0.230 -2.802566 .6728293 marrw25 | -2.27112 1.443829 -1.57 0.116 -5.100972 .5587328 marrw26 | 0 (omitted) inc1w2 | .5165366 1.706152 0.30 0.762 -2.82746 3.860533 inc2w2 | .9386295 1.604285 0.59 0.558 -2.205712 4.082971 inc3w2 | -.0514022 1.646264 -0.03 0.975 -3.27802 3.175215 inc4w2 | .0543252 2.074029 0.03 0.979 -4.010697 4.119348 radhlw2 | .0162077 .0081749 1.98 0.047 .0001852 .0322303 havmil | -.0014745 .0033419 -0.44 0.659 -.0080244 .0050754 avgcumdosew2 | .1675129 .1251712 1.34 0.181 -.0778182 .412844 bf1 | -.0006699 .0354295 -0.02 0.985 -.0701104 .0687707 bf4 | -.6290339 .2665349 -2.36 0.018 -1.151433 -.1066351 bf2 | .0000625 .000131 0.48 0.633 -.0001943 .0003193 bf4m | .4940985 .2505491 1.97 0.049 .0030312 .9851658 bf5m | -.002308 .0018303 -1.26 0.207 -.0058953 .0012792 bf7m | .00018 .0006254 0.29 0.773 -.0010457 .0014058 bf8 | 6.11e-06 .0000425 0.14 0.886 -.0000772 .0000894 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | -.0009967 .0290168 -0.03 0.973 -.0578686 .0558752 bf22 | -.0001321 .0001453 -0.91 0.363 -.0004169 .0001527 bf29 | 0 (omitted) bf30 | -.0003317 .0003532 -0.94 0.348 -.001024 .0003605 bf40 | .149372 .145042 1.03 0.303 -.1349052 .4336491 deaw2 | .0989356 .2231863 0.44 0.658 -.3385014 .5363726 dvcew2 | -14.61462 1422.458 -0.01 0.992 -2802.582 2773.353 sepaw2 | 13.19252 1422.46 0.01 0.993 -2774.778 2801.163 accdw2 | -.3607593 .7071147 -0.51 0.610 -1.746679 1.02516 movew2 | .2363995 .5198633 0.45 0.649 -.7825137 1.255313 illw2 | .5572231 .2374511 2.35 0.019 .0918274 1.022619 shfamw2 | .001878 .0072955 0.26 0.797 -.0124209 .0161768 shhlw2 | .0092925 .0072946 1.27 0.203 -.0050046 .0235896 shjobw2 | -.0069643 .0067203 -1.04 0.300 -.0201358 .0062073 shrelaw2 | -.0169606 .0081044 -2.09 0.036 -.032845 -.0010762 suprtw2 | -.0129953 .0058047 -2.24 0.025 -.0243723 -.0016183 suchrw2 | .0118245 .0068566 1.72 0.085 -.0016142 .0252632 havmilsq | -6.39e-08 2.74e-06 -0.02 0.981 -5.43e-06 5.30e-06 radhlw2 | 0 (omitted) _cons | 5.841436 5580.75 0.00 0.999 -10932.23 10943.91 ------------------------------------------------------------------------------ Note: 1 failure and 0 successes completely determined. Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 57 17 | 74 - | 32 240 | 272 -----------+--------------------------+----------- Total | 89 257 | 346 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 64.04% Specificity Pr( -|~D) 93.39% Positive predictive value Pr( D| +) 77.03% Negative predictive value Pr(~D| -) 88.24% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 6.61% False - rate for true D Pr( -| D) 35.96% False + rate for classified + Pr(~D| +) 22.97% False - rate for classified - Pr( D| -) 11.76% -------------------------------------------------- Correctly classified 85.84% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 346 number of covariate patterns = 346 Pearson chi2(295) = 312.67 Prob > chi2 = 0.2294 Measures of Fit for logistic of HP2sxlife Log-Lik Intercept Only: -197.267 Log-Lik Full Model: -111.042 D(289): 222.083 LR(50): 172.450 Prob > LR: 0.000 McFadden's R2: 0.437 McFadden's Adj R2: 0.148 Maximum Likelihood R2: 0.393 Cragg & Uhler's R2: 0.577 McKelvey and Zavoina's R2: 0.765 Efron's R2: 0.477 Variance of y*: 14.018 Variance of error: 3.290 Count R2: 0.858 Adj Count R2: 0.449 AIC: 0.971 AIC*n: 336.083 BIC: -1467.538 BIC': 119.872 774 . 775 . *-----Chunk 7 female dose2 => sex life impact---------------------------- 776 . * wash out in the trimmed model 777 . title "Chunk 7 trimmed male model of dose and HP2sxlife relationship in wave > 2" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****Chunk 7 trimmed male model of dose and HP2sxlife relationship in wave 2*** > ** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:44 ***** ******************************************************************************* ******************************************************************************* 778 . * male models 779 . forvalues j=2/2 { 2. di as input "trimmed HP2sexlife main effects models wave 1 for H1 part 2 > with dose ns" 3. di as input "wave 2 male dose avgcumdosew`j' main effect not signif" 4. logit HP2sxlife age marrw21-marrw26 bf4 bf4m dvcew`j' illw`j' shhl > w`j' /// > havmilsq shrelaw`j' suchrw2 /// > avgcumdosew`j' radhlw`j' if gender==1 5. estat class 6. estat gof 7. fitstat 8. } trimmed HP2sexlife main effects models wave 1 for H1 part 2 with dose ns wave 2 male dose avgcumdosew2 main effect not signif note: marrw22 != 0 predicts failure perfectly marrw22 dropped and 11 obs not used note: marrw24 != 0 predicts failure perfectly marrw24 dropped and 3 obs not used note: dvcew2 != 0 predicts failure perfectly dvcew2 dropped and 8 obs not used Iteration 0: log likelihood = -166.08818 Iteration 1: log likelihood = -113.32233 Iteration 2: log likelihood = -107.02172 Iteration 3: log likelihood = -106.73421 Iteration 4: log likelihood = -106.72877 Iteration 5: log likelihood = -106.72753 Iteration 6: log likelihood = -106.72723 Iteration 7: log likelihood = -106.72717 Iteration 8: log likelihood = -106.72716 Logistic regression Number of obs = 317 LR chi2(14) = 118.72 Prob > chi2 = 0.0000 Log likelihood = -106.72716 Pseudo R2 = 0.3574 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0644457 .0181912 3.54 0.000 .0287916 .1000998 marrw21 | 8.652724 607.7898 0.01 0.989 -1182.593 1199.899 marrw22 | 0 (omitted) marrw23 | 8.99536 607.7899 0.01 0.988 -1182.251 1200.242 marrw24 | 0 (omitted) marrw25 | 10.60048 607.791 0.02 0.986 -1180.648 1201.849 marrw26 | 8.365677 607.7915 0.01 0.989 -1182.884 1199.615 bf4 | -.2024234 .1925354 -1.05 0.293 -.5797859 .174939 bf4m | .0200376 .172285 0.12 0.907 -.3176348 .35771 dvcew2 | 0 (omitted) illw2 | .3945351 .2569746 1.54 0.125 -.1091258 .8981961 shhlw2 | .0030985 .0058298 0.53 0.595 -.0083277 .0145248 havmilsq | -2.23e-06 4.62e-06 -0.48 0.630 -.0000113 6.84e-06 shrelaw2 | -.010479 .006183 -1.69 0.090 -.0225974 .0016394 suchrw2 | .0070631 .0045745 1.54 0.123 -.0019027 .0160288 avgcumdosew2 | .0643464 .0493461 1.30 0.192 -.0323701 .161063 radhlw2 | .0146535 .0060343 2.43 0.015 .0028265 .0264806 _cons | -13.11105 607.7916 -0.02 0.983 -1204.361 1178.139 ------------------------------------------------------------------------------ Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 35 15 | 50 - | 34 233 | 267 -----------+--------------------------+----------- Total | 69 248 | 317 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 50.72% Specificity Pr( -|~D) 93.95% Positive predictive value Pr( D| +) 70.00% Negative predictive value Pr(~D| -) 87.27% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 6.05% False - rate for true D Pr( -| D) 49.28% False + rate for classified + Pr(~D| +) 30.00% False - rate for classified - Pr( D| -) 12.73% -------------------------------------------------- Correctly classified 84.54% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 317 number of covariate patterns = 312 Pearson chi2(297) = 255.20 Prob > chi2 = 0.9621 Measures of Fit for logit of HP2sxlife Log-Lik Intercept Only: -166.088 Log-Lik Full Model: -106.727 D(299): 213.454 LR(14): 118.722 Prob > LR: 0.000 McFadden's R2: 0.357 McFadden's Adj R2: 0.249 Maximum Likelihood R2: 0.312 Cragg & Uhler's R2: 0.481 McKelvey and Zavoina's R2: 0.539 Efron's R2: 0.374 Variance of y*: 7.131 Variance of error: 3.290 Count R2: 0.845 Adj Count R2: 0.290 AIC: 0.787 AIC*n: 249.454 BIC: -1508.457 BIC': -38.097 780 . title "Chunk 7 trimmed male model of dose and HP2sxlife relationship in wave > 2" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****Chunk 7 trimmed male model of dose and HP2sxlife relationship in wave 2*** > ** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:46 ***** ******************************************************************************* ******************************************************************************* 781 . * male models 782 . forvalues j=2/2 { 2. set more off 3. di as input "trimmed HP2sexlife main effects models wave 1 for H1 part 2 > with dose ns" 4. di as input "wave 2 male dose avgcumdosew`j' main effect not signif" 5. logit HP2sxlife age bf4 illw`j' /// > avgcumdosew`j' radhlw`j' if gender==1 6. estat class 7. estat gof 8. fitstat 9. } trimmed HP2sexlife main effects models wave 1 for H1 part 2 with dose ns wave 2 male dose avgcumdosew2 main effect not signif Iteration 0: log likelihood = -171.51396 Iteration 1: log likelihood = -121.56573 Iteration 2: log likelihood = -115.51071 Iteration 3: log likelihood = -115.37175 Iteration 4: log likelihood = -115.3714 Iteration 5: log likelihood = -115.3714 Logistic regression Number of obs = 340 LR chi2(5) = 112.29 Prob > chi2 = 0.0000 Log likelihood = -115.3714 Pseudo R2 = 0.3273 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0668709 .0154805 4.32 0.000 .0365295 .0972122 bf4 | -.1650598 .0368511 -4.48 0.000 -.2372867 -.0928329 illw2 | .3077459 .2454954 1.25 0.210 -.1734162 .788908 avgcumdosew2 | .0621309 .0473611 1.31 0.190 -.0306951 .1549569 radhlw2 | .010562 .0054615 1.93 0.053 -.0001422 .0212663 _cons | -3.881348 1.050876 -3.69 0.000 -5.941027 -1.82167 ------------------------------------------------------------------------------ Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 32 15 | 47 - | 37 256 | 293 -----------+--------------------------+----------- Total | 69 271 | 340 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 46.38% Specificity Pr( -|~D) 94.46% Positive predictive value Pr( D| +) 68.09% Negative predictive value Pr(~D| -) 87.37% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 5.54% False - rate for true D Pr( -| D) 53.62% False + rate for classified + Pr(~D| +) 31.91% False - rate for classified - Pr( D| -) 12.63% -------------------------------------------------- Correctly classified 84.71% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 340 number of covariate patterns = 330 Pearson chi2(324) = 287.38 Prob > chi2 = 0.9292 Measures of Fit for logit of HP2sxlife Log-Lik Intercept Only: -171.514 Log-Lik Full Model: -115.371 D(334): 230.743 LR(5): 112.285 Prob > LR: 0.000 McFadden's R2: 0.327 McFadden's Adj R2: 0.292 Maximum Likelihood R2: 0.281 Cragg & Uhler's R2: 0.443 McKelvey and Zavoina's R2: 0.462 Efron's R2: 0.344 Variance of y*: 6.119 Variance of error: 3.290 Count R2: 0.847 Adj Count R2: 0.246 AIC: 0.714 AIC*n: 242.743 BIC: -1716.125 BIC': -83.140 783 . 784 . * sex life washes out 785 . 786 . 787 . scalar MainEffsxlifeMw2 = "age bf4 illw2 radhlw2" 788 . scalar SigDosesxlifeMw2 = "no" 789 . 790 . 791 . forvalues j=2/2 { 2. title "trimmed HP2sxlife main effects models wave `j' for H1 part 2 with d > ose ns" 3. title2 "Wave `j dose HPsxlife relationship but avgcumdosew`j': Dose not si > gnif" 4. } ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****trimmed HP2sxlife main effects models wave 2 for H1 part 2 with dose ns*** > ** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:47 ***** ******************************************************************************* ******************************************************************************* ------------------------------------------------------------------------------- title2: Wave `j dose HPsxlife relationship but avgcumdosew2: Dose not signif Date and time: 1 Jul 2012 15:04:47 Working directory: /Users/robertyaffee/ > Documents/data/research/chwk/phase3/Htests/H1tests/h1pt2 Stata data file: chwide1jul2012.dta ha > s 2399 variables and 703 observations Wave `j dose HPsxlife relationship but avgcumdosew2: Dose not signif ------------------------------------------------------------------------------- 792 . 793 . 794 . cap gen radhlw2Xd3 = radhlw2*avgcumdosew2 795 . 796 . set more off 797 . des bf4 storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) 798 . forvalues j=2/2 { 2. sw, pr(.1):logistic HP2sxlife age bf4 illw`j' /// > avgcumdosew`j' radhlw`j' ageXd3 radhlw2Xd3 illw2Xd3 if gender > ==1, coef nolog 3. estat class 4. estat gof 5. fitstat 6. } begin with full model p = 0.6505 >= 0.1000 removing avgcumdosew2 p = 0.4079 >= 0.1000 removing illw2Xd3 Logistic regression Number of obs = 340 LR chi2(6) = 121.14 Prob > chi2 = 0.0000 Log likelihood = -110.94454 Pseudo R2 = 0.3531 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0529331 .0169001 3.13 0.002 .0198095 .0860566 bf4 | -.1653141 .0375369 -4.40 0.000 -.2388852 -.0917431 illw2 | .4168743 .2517861 1.66 0.098 -.0766174 .9103659 radhlw2Xd3 | -.0118911 .0064749 -1.84 0.066 -.0245816 .0007994 radhlw2 | .0203277 .0073575 2.76 0.006 .0059072 .0347481 ageXd3 | .0144933 .0077512 1.87 0.062 -.0006989 .0296854 _cons | -3.925242 1.078928 -3.64 0.000 -6.039901 -1.810582 ------------------------------------------------------------------------------ Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 33 11 | 44 - | 36 260 | 296 -----------+--------------------------+----------- Total | 69 271 | 340 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 47.83% Specificity Pr( -|~D) 95.94% Positive predictive value Pr( D| +) 75.00% Negative predictive value Pr(~D| -) 87.84% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 4.06% False - rate for true D Pr( -| D) 52.17% False + rate for classified + Pr(~D| +) 25.00% False - rate for classified - Pr( D| -) 12.16% -------------------------------------------------- Correctly classified 86.18% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 340 number of covariate patterns = 330 Pearson chi2(323) = 291.21 Prob > chi2 = 0.8976 Measures of Fit for logistic of HP2sxlife Log-Lik Intercept Only: -171.514 Log-Lik Full Model: -110.945 D(333): 221.889 LR(6): 121.139 Prob > LR: 0.000 McFadden's R2: 0.353 McFadden's Adj R2: 0.312 Maximum Likelihood R2: 0.300 Cragg & Uhler's R2: 0.472 McKelvey and Zavoina's R2: 0.539 Efron's R2: 0.379 Variance of y*: 7.135 Variance of error: 3.290 Count R2: 0.862 Adj Count R2: 0.319 AIC: 0.694 AIC*n: 235.889 BIC: -1719.150 BIC': -86.165 799 . 800 . scalar sxlifeModMw2 = "none" 801 . *xx male moderators: no main significant dose effect 802 . *xx no male moderators for sexlife impact 803 . 804 . 805 . * female models 806 . *-----Chunk 7 dose3 moderator => sex life impact---------------------------- 807 . di as input "chunk 7 female wave=3" chunk 7 female wave=3 808 . title "Chunk 7 trimmed female model:" "dose and HP2sxlife relationship in wav > e 2" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Chunk 7 trimmed female model: ***** ***** dose and HP2sxlife relationship in wave 2 ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:53 ***** ******************************************************************************* ******************************************************************************* 809 . * female models 810 . forvalues j=2/2 { 2. 811 . set more off 3. des bf4 bf4m shfamw2 shrelaw2 avgcumdosew2 4. title "trimmed HP2sexlife main effects models" "wave 2 for H1 part 2 with > dose ns" 5. title "wave 2 dose HP2sexlife relationship" "avgcumdosew`j' Dose not signi > f" 6. logit HP2sxlife age marrw21-marrw26 radhlw`j' bf4 bf4m /// > shfamw`j' shrelaw`j' avgcumdosew`j' if gender==2 7. estat class 8. estat gof 9. fitstat 10. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) shfamw2 double %8.0g Percentage of strains and hassles related to family in 1996 shrelaw2 double %8.0g Percentage of strains and hassles related to relationships in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** trimmed HP2sexlife main effects models ***** ***** wave 2 for H1 part 2 with dose ns ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:53 ***** ******************************************************************************* ******************************************************************************* ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** wave 2 dose HP2sexlife relationship ***** ***** avgcumdosew2 Dose not signif ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:53 ***** ******************************************************************************* ******************************************************************************* note: marrw24 omitted because of collinearity note: marrw26 omitted because of collinearity Iteration 0: log likelihood = -206.26609 Iteration 1: log likelihood = -143.57745 Iteration 2: log likelihood = -138.68519 Iteration 3: log likelihood = -138.62333 Iteration 4: log likelihood = -138.62326 Iteration 5: log likelihood = -138.62326 Logistic regression Number of obs = 362 LR chi2(11) = 135.29 Prob > chi2 = 0.0000 Log likelihood = -138.62326 Pseudo R2 = 0.3279 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .076392 .0165292 4.62 0.000 .0439954 .1087886 marrw21 | -.5305645 .8871345 -0.60 0.550 -2.269316 1.208187 marrw22 | .2470266 1.206929 0.20 0.838 -2.11851 2.612563 marrw23 | -.9086585 .7011467 -1.30 0.195 -2.282881 .4655638 marrw24 | 0 (omitted) marrw25 | -1.473965 1.020033 -1.45 0.148 -3.473194 .525263 marrw26 | 0 (omitted) radhlw2 | .0078658 .0048473 1.62 0.105 -.0016346 .0173663 bf4 | -.6041323 .1886755 -3.20 0.001 -.9739294 -.2343351 bf4m | .40554 .1714694 2.37 0.018 .0694661 .7416139 shfamw2 | -.0013231 .0052527 -0.25 0.801 -.0116181 .008972 shrelaw2 | -.0119535 .0060123 -1.99 0.047 -.0237374 -.0001695 avgcumdosew2 | .185182 .1129898 1.64 0.101 -.0362739 .4066379 _cons | -6.005196 1.747399 -3.44 0.001 -9.430035 -2.580358 ------------------------------------------------------------------------------ Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 53 20 | 73 - | 40 249 | 289 -----------+--------------------------+----------- Total | 93 269 | 362 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 56.99% Specificity Pr( -|~D) 92.57% Positive predictive value Pr( D| +) 72.60% Negative predictive value Pr(~D| -) 86.16% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 7.43% False - rate for true D Pr( -| D) 43.01% False + rate for classified + Pr(~D| +) 27.40% False - rate for classified - Pr( D| -) 13.84% -------------------------------------------------- Correctly classified 83.43% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 362 number of covariate patterns = 361 Pearson chi2(349) = 408.71 Prob > chi2 = 0.0151 Measures of Fit for logit of HP2sxlife Log-Lik Intercept Only: -206.266 Log-Lik Full Model: -138.623 D(348): 277.247 LR(11): 135.286 Prob > LR: 0.000 McFadden's R2: 0.328 McFadden's Adj R2: 0.260 Maximum Likelihood R2: 0.312 Cragg & Uhler's R2: 0.459 McKelvey and Zavoina's R2: 0.482 Efron's R2: 0.384 Variance of y*: 6.352 Variance of error: 3.290 Count R2: 0.834 Adj Count R2: 0.355 AIC: 0.843 AIC*n: 305.247 BIC: -1773.046 BIC': -70.478 812 . scalar SigDoseSxlifeFw2 = "no" 813 . scalar MainEffsxlifeFw2 = "age marrw23 radhlw2 bf4 bf4m shrelaw2" 814 . *----- constructing possible moderators 815 . 816 . foreach var in marrw21 marrw22 marrw23 marrw24 marrw25 marrw26 /// > bf4 bf4m shfamw2 shrelaw2 radhlw2 { 2. cap gen `var'Xd3 = `var'*avgcumdosew2 3. } 817 . 818 . title "Chunk 7 trimmed female model:" "dose and HP2sxlife relationship in wav > e 2" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Chunk 7 trimmed female model: ***** ***** dose and HP2sxlife relationship in wave 2 ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:54 ***** ******************************************************************************* ******************************************************************************* 819 . * female models 820 . forvalues j=2/2 { 2. 821 . set more off 3. des bf4 bf4m shfamw2 shrelaw2 avgcumdosew2 4. title "trimmed HP2sexlife main effects models" "wave 2 for H1 part 2 with > dose ns" 5. title "wave 2 dose HP2sexlife relationship" "avgcumdosew`j' Dose not signi > f" 6. logit HP2sxlife age marrw21-marrw26 radhlw`j' bf4 bf4m /// > shfamw`j' shrelaw`j' avgcumdosew`j' ageXd3 marrw21Xd3 marrw22Xd3 > /// > marrw23Xd3 marrw24Xd3 marrw25Xd3 bf4Xd3 bf4mXd3 shrelaw2Xd3 /// > if gender==2 7. estat class 8. estat gof 9. fitstat 10. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) shfamw2 double %8.0g Percentage of strains and hassles related to family in 1996 shrelaw2 double %8.0g Percentage of strains and hassles related to relationships in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** trimmed HP2sexlife main effects models ***** ***** wave 2 for H1 part 2 with dose ns ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:54 ***** ******************************************************************************* ******************************************************************************* ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** wave 2 dose HP2sexlife relationship ***** ***** avgcumdosew2 Dose not signif ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:54 ***** ******************************************************************************* ******************************************************************************* note: marrw24 omitted because of collinearity note: marrw26 omitted because of collinearity note: marrw24Xd3 omitted because of collinearity Iteration 0: log likelihood = -206.26609 Iteration 1: log likelihood = -141.90135 Iteration 2: log likelihood = -137.13734 Iteration 3: log likelihood = -137.05422 Iteration 4: log likelihood = -137.05402 Iteration 5: log likelihood = -137.05402 Logistic regression Number of obs = 362 LR chi2(19) = 138.42 Prob > chi2 = 0.0000 Log likelihood = -137.05402 Pseudo R2 = 0.3355 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0771657 .0249115 3.10 0.002 .02834 .1259913 marrw21 | -1.451767 1.441353 -1.01 0.314 -4.276767 1.373234 marrw22 | .0685249 1.464872 0.05 0.963 -2.802572 2.939622 marrw23 | -1.092581 .9071979 -1.20 0.228 -2.870657 .6854937 marrw24 | 0 (omitted) marrw25 | -1.837988 1.514456 -1.21 0.225 -4.806268 1.130292 marrw26 | 0 (omitted) radhlw2 | .0079384 .0048969 1.62 0.105 -.0016593 .0175361 bf4 | -.363408 .3781896 -0.96 0.337 -1.104646 .3778301 bf4m | .2325195 .3434991 0.68 0.498 -.4407264 .9057654 shfamw2 | -.0007843 .0054412 -0.14 0.885 -.011449 .0098803 shrelaw2 | -.0124226 .0061868 -2.01 0.045 -.0245484 -.0002967 avgcumdosew2 | -1.023812 3.170547 -0.32 0.747 -7.23797 5.190346 ageXd3 | .0002661 .0332903 0.01 0.994 -.0649818 .0655139 marrw21Xd3 | 1.328931 1.506104 0.88 0.378 -1.622979 4.28084 marrw22Xd3 | .1769697 .9861833 0.18 0.858 -1.755914 2.109853 marrw23Xd3 | .2926788 .7754937 0.38 0.706 -1.227261 1.812619 marrw24Xd3 | 0 (omitted) marrw25Xd3 | .2934835 .9361317 0.31 0.754 -1.541301 2.128268 bf4Xd3 | -.2172939 .2938598 -0.74 0.460 -.7932486 .3586608 bf4mXd3 | .14997 .2614038 0.57 0.566 -.362372 .6623121 shrelaw2Xd3 | .0002655 .0431225 0.01 0.995 -.084253 .084784 _cons | -5.03786 3.098692 -1.63 0.104 -11.11118 1.035465 ------------------------------------------------------------------------------ Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 55 20 | 75 - | 38 249 | 287 -----------+--------------------------+----------- Total | 93 269 | 362 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 59.14% Specificity Pr( -|~D) 92.57% Positive predictive value Pr( D| +) 73.33% Negative predictive value Pr(~D| -) 86.76% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 7.43% False - rate for true D Pr( -| D) 40.86% False + rate for classified + Pr(~D| +) 26.67% False - rate for classified - Pr( D| -) 13.24% -------------------------------------------------- Correctly classified 83.98% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 362 number of covariate patterns = 361 Pearson chi2(341) = 404.96 Prob > chi2 = 0.0097 Measures of Fit for logit of HP2sxlife Log-Lik Intercept Only: -206.266 Log-Lik Full Model: -137.054 D(339): 274.108 LR(19): 138.424 Prob > LR: 0.000 McFadden's R2: 0.336 McFadden's Adj R2: 0.224 Maximum Likelihood R2: 0.318 Cragg & Uhler's R2: 0.467 McKelvey and Zavoina's R2: 0.494 Efron's R2: 0.389 Variance of y*: 6.498 Variance of error: 3.290 Count R2: 0.840 Adj Count R2: 0.376 AIC: 0.884 AIC*n: 320.108 BIC: -1723.159 BIC': -26.483 822 . scalar sxlifeModFw2="none" 823 . 824 . 825 . 826 . 827 . *----- testing female moderators 828 . title "partly trimmed female moderator model of dose & HP2sxlife relationship > in wv 3" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****partly trimmed female moderator model of dose & HP2sxlife relationship in > wv 3***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:56 ***** ******************************************************************************* ******************************************************************************* 829 . * male models 830 . forvalues j=2/2 { 2. set more off 3. des bf4 bf4m shfamw2 shrelaw2 avgcumdosew2 4. title3 "trimmed HP2sexlife main effects models wave 1 for H1 part 2 with d > ose ns" 5. title "wave 2 dose HP2sexlife relationship but avgcumdosew`j': Dose not si > gnif" 6. logit HP2sxlife age marrw21-marrw26 radhlw`j' bf4 bf4m /// > shfamw`j' shrelaw`j' avgcumdosew`j' marrw21Xd3 marrw22Xd3 /// > marrw23Xd3 marrw24Xd3 marrw25Xd3 marrw26Xd3 radhlw`j'Xd3 /// > bf4Xd3 shfamw2Xd3 shrelaw2Xd3 if gender==2 7. estat class 8. estat gof 9. fitstat 10. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) shfamw2 double %8.0g Percentage of strains and hassles related to family in 1996 shrelaw2 double %8.0g Percentage of strains and hassles related to relationships in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 ------------------------------------------------------------------------------- title3 : trimmed HP2sexlife main effects models wave 1 for H1 part 2 with dose > ns 1 Jul 2012 15:04:56 computer Macintosh (Intel 64-bit) folder /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/ > h1pt2 Data file chwide1jul2012.dta currrently has 2406 variables and 703 observ > ations ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****wave 2 dose HP2sexlife relationship but avgcumdosew2: Dose not signif***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:56 ***** ******************************************************************************* ******************************************************************************* note: marrw24 omitted because of collinearity note: marrw26 omitted because of collinearity note: marrw24Xd3 omitted because of collinearity note: marrw26Xd3 omitted because of collinearity Iteration 0: log likelihood = -206.26609 Iteration 1: log likelihood = -142.57705 Iteration 2: log likelihood = -137.51325 Iteration 3: log likelihood = -137.42461 Iteration 4: log likelihood = -137.42437 Iteration 5: log likelihood = -137.42437 Logistic regression Number of obs = 362 LR chi2(19) = 137.68 Prob > chi2 = 0.0000 Log likelihood = -137.42437 Pseudo R2 = 0.3338 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0881857 .0228618 3.86 0.000 .0433774 .132994 marrw21 | -1.190817 1.437325 -0.83 0.407 -4.007921 1.626288 marrw22 | .0152843 1.480232 0.01 0.992 -2.885916 2.916485 marrw23 | -1.100839 .8999384 -1.22 0.221 -2.864686 .6630077 marrw24 | 0 (omitted) marrw25 | -1.714881 1.480971 -1.16 0.247 -4.61753 1.187768 marrw26 | 0 (omitted) radhlw2 | .0059255 .0058476 1.01 0.311 -.0055355 .0173866 bf4 | -.5647269 .1952268 -2.89 0.004 -.9473645 -.1820894 bf4m | .4033962 .174148 2.32 0.021 .0620723 .7447201 shfamw2 | .0021518 .0065354 0.33 0.742 -.0106573 .0149608 shrelaw2 | -.0127936 .0062133 -2.06 0.039 -.0249714 -.0006158 avgcumdosew2 | 1.042177 1.610695 0.65 0.518 -2.114727 4.199082 marrw21Xd3 | 1.083118 1.522783 0.71 0.477 -1.901481 4.067718 marrw22Xd3 | .3220928 1.102342 0.29 0.770 -1.838458 2.482644 marrw23Xd3 | .394904 .7698412 0.51 0.608 -1.113957 1.903765 marrw24Xd3 | 0 (omitted) marrw25Xd3 | .3165762 .9106048 0.35 0.728 -1.468176 2.101329 marrw26Xd3 | 0 (omitted) radhlw2Xd3 | .0024173 .0037617 0.64 0.520 -.0049555 .00979 bf4Xd3 | -.0361335 .046891 -0.77 0.441 -.1280381 .0557711 shfamw2Xd3 | -.0034656 .0052445 -0.66 0.509 -.0137447 .0068135 shrelaw2Xd3 | -.0162686 .0222527 -0.73 0.465 -.0598831 .0273459 _cons | -6.710013 2.18777 -3.07 0.002 -10.99796 -2.422062 ------------------------------------------------------------------------------ Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 54 19 | 73 - | 39 250 | 289 -----------+--------------------------+----------- Total | 93 269 | 362 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 58.06% Specificity Pr( -|~D) 92.94% Positive predictive value Pr( D| +) 73.97% Negative predictive value Pr(~D| -) 86.51% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 7.06% False - rate for true D Pr( -| D) 41.94% False + rate for classified + Pr(~D| +) 26.03% False - rate for classified - Pr( D| -) 13.49% -------------------------------------------------- Correctly classified 83.98% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 362 number of covariate patterns = 361 Pearson chi2(341) = 404.20 Prob > chi2 = 0.0104 Measures of Fit for logit of HP2sxlife Log-Lik Intercept Only: -206.266 Log-Lik Full Model: -137.424 D(338): 274.849 LR(19): 137.683 Prob > LR: 0.000 McFadden's R2: 0.334 McFadden's Adj R2: 0.217 Maximum Likelihood R2: 0.316 Cragg & Uhler's R2: 0.465 McKelvey and Zavoina's R2: 0.497 Efron's R2: 0.388 Variance of y*: 6.546 Variance of error: 3.290 Count R2: 0.840 Adj Count R2: 0.376 AIC: 0.892 AIC*n: 322.849 BIC: -1716.527 BIC': -25.742 831 . 832 . title "fully female moderator model of dose & HP2sxlife relationship in wv 3" > ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****fully female moderator model of dose & HP2sxlife relationship in wv 3***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:58 ***** ******************************************************************************* ******************************************************************************* 833 . title2 "Signif female dose main effect " ------------------------------------------------------------------------------- title2: Signif female dose main effect Date and time: 1 Jul 2012 15:04:58 Working directory: /Users/robertyaffee/ > Documents/data/research/chwk/phase3/Htests/H1tests/h1pt2 Stata data file: chwide1jul2012.dta ha > s 2406 variables and 703 observations Signif female dose main effect ------------------------------------------------------------------------------- 834 . 835 . * female models 836 . forvalues j=2/2 { 2. des bf4 bf4m shfamw2 shrelaw2 avgcumdosew2 3. title3 "trimmed HP2sexlife main effects models wave 1 for H1 part 2 with d > ose ns" 4. title "wave 2 dose HP2sexlife relationship but avgcumdosew`j': Dose not si > gnif" 5. logit HP2sxlife age radhlw`j' bf4 bf4m /// > shrelaw2 shfamw`j' avgcumdosew`j' /// > shrelaw2Xd3 if gender==2 6. estat class 7. estat gof 8. fitstat 9. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) shfamw2 double %8.0g Percentage of strains and hassles related to family in 1996 shrelaw2 double %8.0g Percentage of strains and hassles related to relationships in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 ------------------------------------------------------------------------------- title3 : trimmed HP2sexlife main effects models wave 1 for H1 part 2 with dose > ns 1 Jul 2012 15:04:58 computer Macintosh (Intel 64-bit) folder /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/ > h1pt2 Data file chwide1jul2012.dta currrently has 2406 variables and 703 observ > ations ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****wave 2 dose HP2sexlife relationship but avgcumdosew2: Dose not signif***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:58 ***** ******************************************************************************* ******************************************************************************* Iteration 0: log likelihood = -206.26609 Iteration 1: log likelihood = -145.82764 Iteration 2: log likelihood = -140.338 Iteration 3: log likelihood = -140.1844 Iteration 4: log likelihood = -140.1838 Iteration 5: log likelihood = -140.1838 Logistic regression Number of obs = 362 LR chi2(8) = 132.16 Prob > chi2 = 0.0000 Log likelihood = -140.1838 Pseudo R2 = 0.3204 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .086161 .0187529 4.59 0.000 .049406 .122916 radhlw2 | .0086327 .004785 1.80 0.071 -.0007457 .018011 bf4 | -.5633458 .1891523 -2.98 0.003 -.9340774 -.1926141 bf4m | .3743527 .1732499 2.16 0.031 .0347891 .7139163 shrelaw2 | -.0122309 .0059271 -2.06 0.039 -.0238478 -.000614 shfamw2 | -.0009006 .0051781 -0.17 0.862 -.0110495 .0092484 avgcumdosew2 | .9067215 .7875706 1.15 0.250 -.6368885 2.450332 shrelaw2Xd3 | -.0126027 .0132428 -0.95 0.341 -.0385582 .0133527 _cons | -7.277228 1.721919 -4.23 0.000 -10.65213 -3.902329 ------------------------------------------------------------------------------ Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 52 22 | 74 - | 41 247 | 288 -----------+--------------------------+----------- Total | 93 269 | 362 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 55.91% Specificity Pr( -|~D) 91.82% Positive predictive value Pr( D| +) 70.27% Negative predictive value Pr(~D| -) 85.76% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 8.18% False - rate for true D Pr( -| D) 44.09% False + rate for classified + Pr(~D| +) 29.73% False - rate for classified - Pr( D| -) 14.24% -------------------------------------------------- Correctly classified 82.60% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 362 number of covariate patterns = 361 Pearson chi2(352) = 405.41 Prob > chi2 = 0.0259 Measures of Fit for logit of HP2sxlife Log-Lik Intercept Only: -206.266 Log-Lik Full Model: -140.184 D(353): 280.368 LR(8): 132.165 Prob > LR: 0.000 McFadden's R2: 0.320 McFadden's Adj R2: 0.277 Maximum Likelihood R2: 0.306 Cragg & Uhler's R2: 0.450 McKelvey and Zavoina's R2: 0.479 Efron's R2: 0.373 Variance of y*: 6.317 Variance of error: 3.290 Count R2: 0.826 Adj Count R2: 0.323 AIC: 0.824 AIC*n: 298.368 BIC: -1799.383 BIC': -85.031 837 . * female models 838 . forvalues j=2/2 { 2. des bf4 bf4m shfamw2 shrelaw2 avgcumdosew2 3. title3 "trimmed HP2sexlife main effects models wave 1 for H1 part 2 with d > ose ns" 4. title "wave 2 dose HP2sexlife relationship but avgcumdosew`j': Dose not si > gnif" 5. logit HP2sxlife age radhlw`j' bf4 bf4m /// > shrelaw2 shfamw`j' /// > if gender==2 6. estat class 7. estat gof 8. fitstat 9. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) shfamw2 double %8.0g Percentage of strains and hassles related to family in 1996 shrelaw2 double %8.0g Percentage of strains and hassles related to relationships in 1996 avgcumdosew2 double %8.0g Average mean dose CS1337 in mGy for wave 2 ------------------------------------------------------------------------------- title3 : trimmed HP2sexlife main effects models wave 1 for H1 part 2 with dose > ns 1 Jul 2012 15:04:59 computer Macintosh (Intel 64-bit) folder /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/ > h1pt2 Data file chwide1jul2012.dta currrently has 2406 variables and 703 observ > ations ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****wave 2 dose HP2sexlife relationship but avgcumdosew2: Dose not signif***** ***** ***** ***** ***** ***** 1 Jul 2012 15:04:59 ***** ******************************************************************************* ******************************************************************************* Iteration 0: log likelihood = -206.26609 Iteration 1: log likelihood = -147.80582 Iteration 2: log likelihood = -142.56802 Iteration 3: log likelihood = -142.50325 Iteration 4: log likelihood = -142.50319 Iteration 5: log likelihood = -142.50319 Logistic regression Number of obs = 362 LR chi2(6) = 127.53 Prob > chi2 = 0.0000 Log likelihood = -142.50319 Pseudo R2 = 0.3091 ------------------------------------------------------------------------------ HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0773867 .0151879 5.10 0.000 .0476189 .1071545 radhlw2 | .0092803 .0047101 1.97 0.049 .0000487 .0185119 bf4 | -.5791652 .1890895 -3.06 0.002 -.9497739 -.2085566 bf4m | .3860275 .1731752 2.23 0.026 .0466104 .7254445 shrelaw2 | -.0111587 .0058224 -1.92 0.055 -.0225703 .000253 shfamw2 | -.0012219 .005072 -0.24 0.810 -.0111628 .008719 _cons | -6.703691 1.604696 -4.18 0.000 -9.848837 -3.558545 ------------------------------------------------------------------------------ Logistic model for HP2sxlife -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 49 23 | 72 - | 44 246 | 290 -----------+--------------------------+----------- Total | 93 269 | 362 Classified + if predicted Pr(D) >= .5 True D defined as HP2sxlife != 0 -------------------------------------------------- Sensitivity Pr( +| D) 52.69% Specificity Pr( -|~D) 91.45% Positive predictive value Pr( D| +) 68.06% Negative predictive value Pr(~D| -) 84.83% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 8.55% False - rate for true D Pr( -| D) 47.31% False + rate for classified + Pr(~D| +) 31.94% False - rate for classified - Pr( D| -) 15.17% -------------------------------------------------- Correctly classified 81.49% -------------------------------------------------- Logistic model for HP2sxlife, goodness-of-fit test number of observations = 362 number of covariate patterns = 357 Pearson chi2(350) = 401.39 Prob > chi2 = 0.0301 Measures of Fit for logit of HP2sxlife Log-Lik Intercept Only: -206.266 Log-Lik Full Model: -142.503 D(355): 285.006 LR(6): 127.526 Prob > LR: 0.000 McFadden's R2: 0.309 McFadden's Adj R2: 0.275 Maximum Likelihood R2: 0.297 Cragg & Uhler's R2: 0.437 McKelvey and Zavoina's R2: 0.465 Efron's R2: 0.360 Variance of y*: 6.146 Variance of error: 3.290 Count R2: 0.815 Adj Count R2: 0.280 AIC: 0.826 AIC*n: 299.006 BIC: -1806.527 BIC': -92.176 839 . 840 . scalar MainEffsxlifeFw2 = "age radhlw2 bf4 bf4m shrelaw2 shfamw2" 841 . scalar SigDoseSxlifeFw2="no" 842 . * xx female main effects model: no sign dose main effect 843 . * xx 6 signif main effects 844 . * xx no moderator effects significant 845 . 846 . *----------- mediator models for Dose => sexlife impact 847 . 848 . di as input "testing possible sex life mediator effects for males" testing possible sex life mediator effects for males 849 . 850 . * age is a mediating effect for males for Dose=> sex life for men 851 . des bf4 bf4m storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) 852 . glm age avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1330.6004 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 147.6853 Deviance = 49917.64009 (1/df) Deviance = 147.6853 Pearson = 49917.64009 (1/df) Pearson = 147.6853 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.838826 Log likelihood = -1330.6004 BIC = 47947.46 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .5832314 .2635871 2.21 0.027 .0666101 1.099853 _cons | 48.62133 .7061562 68.85 0.000 47.23729 50.00537 ------------------------------------------------------------------------------ 853 . glm HP2sxlife age if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 287.05 Iteration 2: deviance = 282.9134 Iteration 3: deviance = 282.8157 Iteration 4: deviance = 282.8156 Iteration 5: deviance = 282.8156 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 282.8156218 (1/df) Deviance = .8367326 Pearson = 327.5643783 (1/df) Pearson = .9691254 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1687.368 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0532907 .0067666 7.88 0.000 .0400283 .0665531 _cons | -3.620299 .3741324 -9.68 0.000 -4.353585 -2.887013 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 854 . 855 . * illness is a mediating effect for males = > sex life for men 856 . des illw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- illw2 double %8.0g Total number of illnesses experienced in time period 1987-1996 857 . glm illw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -303.59609 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = .3512988 Deviance = 118.7390083 (1/df) Deviance = .3512988 Pearson = 118.7390083 (1/df) Pearson = .3512988 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 1.797624 Log likelihood = -303.5960853 BIC = -1851.445 ------------------------------------------------------------------------------ | OIM illw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .0085423 .0128556 0.66 0.506 -.0166543 .0337389 _cons | .2741359 .0344406 7.96 0.000 .2066336 .3416382 ------------------------------------------------------------------------------ 858 . glm HP2sxlife illw2 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 327.4086 Iteration 2: deviance = 327.3702 Iteration 3: deviance = 327.3699 Iteration 4: deviance = 327.3699 Iteration 5: deviance = 327.3699 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 327.36992 (1/df) Deviance = .9685501 Pearson = 335.9738293 (1/df) Pearson = .9940054 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1642.814 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2 | .4754044 .1211711 3.92 0.000 .2379134 .7128953 _cons | -.9963884 .0886054 -11.25 0.000 -1.170052 -.822725 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 859 . 860 . des radhlw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- radhlw2 double %8.0g how much believed personal health is affected by radiation in 1996 861 . glm radhlw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1693.4076 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 1247.933 Deviance = 421801.4584 (1/df) Deviance = 1247.933 Pearson = 421801.4584 (1/df) Pearson = 1247.933 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.972986 Log likelihood = -1693.407647 BIC = 419831.3 ------------------------------------------------------------------------------ | OIM radhlw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.220373 .766216 1.59 0.111 -.2813831 2.722128 _cons | 45.63198 2.052711 22.23 0.000 41.60874 49.65522 ------------------------------------------------------------------------------ 862 . glm HP2sxlife radhlw2 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 299.0301 Iteration 2: deviance = 296.5897 Iteration 3: deviance = 296.5625 Iteration 4: deviance = 296.5625 Iteration 5: deviance = 296.5625 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 296.5625078 (1/df) Deviance = .8774039 Pearson = 336.752315 (1/df) Pearson = .9963086 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1673.621 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- radhlw2 | .0156386 .0022528 6.94 0.000 .0112232 .020054 _cons | -1.687381 .1543913 -10.93 0.000 -1.989983 -1.38478 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 863 . 864 . 865 . des bf4 storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) 866 . glm bf4 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1027.1225 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 24.7771 Deviance = 8374.659221 (1/df) Deviance = 24.7771 Pearson = 8374.659221 (1/df) Pearson = 24.7771 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.053662 Log likelihood = -1027.122509 BIC = 6404.476 ------------------------------------------------------------------------------ | OIM bf4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.0331637 .1079644 -0.31 0.759 -.2447701 .1784427 _cons | 12.52896 .2892393 43.32 0.000 11.96206 13.09586 ------------------------------------------------------------------------------ 867 . glm HP2sxlife bf4 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 265.0595 Iteration 2: deviance = 261.6959 Iteration 3: deviance = 261.6273 Iteration 4: deviance = 261.6271 Iteration 5: deviance = 261.6271 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 261.6270836 (1/df) Deviance = .7740446 Pearson = 301.9171445 (1/df) Pearson = .893246 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1708.557 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.1431725 .0149643 -9.57 0.000 -.172502 -.1138431 _cons | .7780696 .180124 4.32 0.000 .425033 1.131106 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 868 . 869 . des bf4m storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) 870 . glm bf4m avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1060.7791 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 30.20174 Deviance = 10208.18969 (1/df) Deviance = 30.20174 Pearson = 10208.18969 (1/df) Pearson = 30.20174 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.251642 Log likelihood = -1060.779096 BIC = 8238.006 ------------------------------------------------------------------------------ | OIM bf4m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.0266037 .1191987 -0.22 0.823 -.2602289 .2070215 _cons | 20.34324 .3193362 63.70 0.000 19.71735 20.96913 ------------------------------------------------------------------------------ 871 . glm HP2sxlife bf4m if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 266.1375 Iteration 2: deviance = 263.3205 Iteration 3: deviance = 263.273 Iteration 4: deviance = 263.2729 Iteration 5: deviance = 263.2729 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 263.2728985 (1/df) Deviance = .7789139 Pearson = 303.0162706 (1/df) Pearson = .8964978 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1706.911 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4m | -.1287243 .0140303 -9.17 0.000 -.1562231 -.1012255 _cons | 1.624944 .276779 5.87 0.000 1.082468 2.167421 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 872 . 873 . 874 . * age is a mediating effect for females for Dose=> sex life for women 875 . glm age avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1406.9403 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 136.9184 Deviance = 49427.52828 (1/df) Deviance = 136.9184 Pearson = 49427.52828 (1/df) Pearson = 136.9184 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.762756 Log likelihood = -1406.940271 BIC = 47299.65 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.502324 .4454009 3.37 0.001 .6293547 2.375294 _cons | 48.86944 .7323225 66.73 0.000 47.43412 50.30477 ------------------------------------------------------------------------------ 876 . glm HP2sxlife age if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 336.5251 Iteration 2: deviance = 333.7638 Iteration 3: deviance = 333.7326 Iteration 4: deviance = 333.7326 Iteration 5: deviance = 333.7326 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 333.7325644 (1/df) Deviance = .9244669 Pearson = 379.4384762 (1/df) Pearson = 1.051076 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1794.147 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0606116 .0071416 8.49 0.000 .0466144 .0746088 _cons | -3.838422 .3937027 -9.75 0.000 -4.610065 -3.066779 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 877 . 878 . * illness is a mediating effect for females = > sex life for men 879 . des illw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- illw2 double %8.0g Total number of illnesses experienced in time period 1987-1996 880 . glm illw2 avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -463.51524 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = .756881 Deviance = 273.2340487 (1/df) Deviance = .756881 Pearson = 273.2340487 (1/df) Pearson = .756881 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 2.564822 Log likelihood = -463.5152411 BIC = -1854.645 ------------------------------------------------------------------------------ | OIM illw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .1249912 .0331157 3.77 0.000 .0600856 .1898968 _cons | .301285 .0544484 5.53 0.000 .194568 .4080019 ------------------------------------------------------------------------------ 881 . glm HP2sxlife illw2 if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 388.6886 Iteration 2: deviance = 388.2329 Iteration 3: deviance = 388.2328 Iteration 4: deviance = 388.2328 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 388.2327887 (1/df) Deviance = 1.075437 Pearson = 359.7775997 (1/df) Pearson = .9966138 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1739.647 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2 | .4442672 .0949037 4.68 0.000 .2582594 .630275 _cons | -.8535276 .0872416 -9.78 0.000 -1.024518 -.6825372 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 882 . 883 . des radhlw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- radhlw2 double %8.0g how much believed personal health is affected by radiation in 1996 884 . glm radhlw2 avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1791.2233 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 1137.567 Deviance = 410661.5604 (1/df) Deviance = 1137.567 Pearson = 410661.5604 (1/df) Pearson = 1137.567 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.880018 Log likelihood = -1791.223306 BIC = 408533.7 ------------------------------------------------------------------------------ | OIM radhlw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 3.302288 1.283833 2.57 0.010 .7860214 5.818555 _cons | 56.95167 2.110863 26.98 0.000 52.81445 61.08888 ------------------------------------------------------------------------------ 885 . glm HP2sxlife radhlw2 if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 390.2214 Iteration 2: deviance = 389.9357 Iteration 3: deviance = 389.9356 Iteration 4: deviance = 389.9356 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 389.9355535 (1/df) Deviance = 1.080154 Pearson = 370.7850434 (1/df) Pearson = 1.027105 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1737.944 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- radhlw2 | .0108547 .0023275 4.66 0.000 .0062929 .0154166 _cons | -1.340315 .1718732 -7.80 0.000 -1.67718 -1.003449 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 886 . 887 . des bf4 // soma recentered storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) 888 . * bf4 is a mediting effect for females for Dose=> sex life for women 889 . glm bf4 avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1109.0983 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 26.53281 Deviance = 9578.344971 (1/df) Deviance = 26.53281 Pearson = 9578.344971 (1/df) Pearson = 26.53281 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.121754 Log likelihood = -1109.098281 BIC = 7450.466 ------------------------------------------------------------------------------ | OIM bf4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.595012 .1960703 -3.03 0.002 -.9793027 -.2107212 _cons | 11.02048 .3223763 34.19 0.000 10.38863 11.65232 ------------------------------------------------------------------------------ 890 . glm HP2sxlife bf4 if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 344.0399 Iteration 2: deviance = 342.6827 Iteration 3: deviance = 342.6686 Iteration 4: deviance = 342.6686 Iteration 5: deviance = 342.6686 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 342.6686152 (1/df) Deviance = .9492205 Pearson = 336.3540555 (1/df) Pearson = .9317287 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1785.211 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.1230087 .0148291 -8.30 0.000 -.1520732 -.0939442 _cons | .5134392 .1542351 3.33 0.001 .211144 .8157344 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 891 . 892 . des bf4m // soma recentered storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) 893 . * bf4m is a possible mediating effect for female sex life 894 . glm bf4m avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1140.8259 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 31.60104 Deviance = 11407.97484 (1/df) Deviance = 31.60104 Pearson = 11407.97484 (1/df) Pearson = 31.60104 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.296561 Log likelihood = -1140.825904 BIC = 9280.095 ------------------------------------------------------------------------------ | OIM bf4m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.599311 .2139789 -2.80 0.005 -1.018702 -.1799202 _cons | 18.83424 .3518214 53.53 0.000 18.14469 19.5238 ------------------------------------------------------------------------------ 895 . glm HP2sxlife bf4m if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 356.1451 Iteration 2: deviance = 355.6209 Iteration 3: deviance = 355.6178 Iteration 4: deviance = 355.6178 Iteration 5: deviance = 355.6178 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 355.6178202 (1/df) Deviance = .9850909 Pearson = 340.0032816 (1/df) Pearson = .9418373 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1772.262 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4m | -.0990193 .0135516 -7.31 0.000 -.12558 -.0724586 _cons | 1.063679 .244399 4.35 0.000 .584666 1.542693 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 896 . 897 . des shfamw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- shfamw2 double %8.0g Percentage of strains and hassles related to family in 1996 898 . glm shfamw2 avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1804.1821 Generalized linear models No. of obs = 362 Optimization : ML Residual df = 360 Scale parameter = 1255.79 Deviance = 452084.3956 (1/df) Deviance = 1255.79 Pearson = 452084.3956 (1/df) Pearson = 1255.79 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.978907 Log likelihood = -1804.182119 BIC = 449963.4 ------------------------------------------------------------------------------ | OIM shfamw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .6648412 1.349346 0.49 0.622 -1.979828 3.30951 _cons | 33.6876 2.218839 15.18 0.000 29.33876 38.03645 ------------------------------------------------------------------------------ 899 . glm HP2sxlife shfamw2 if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 411.7925 Iteration 2: deviance = 411.1829 Iteration 3: deviance = 411.1826 Iteration 4: deviance = 411.1826 Generalized linear models No. of obs = 362 Optimization : MQL Fisher scoring Residual df = 360 (IRLS EIM) Scale parameter = 1 Deviance = 411.1826472 (1/df) Deviance = 1.142174 Pearson = 361.8999919 (1/df) Pearson = 1.005278 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1709.809 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- shfamw2 | -.0023803 .0021918 -1.09 0.277 -.0066761 .0019156 _cons | -.5736929 .1047839 -5.48 0.000 -.7790656 -.3683202 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 900 . 901 . des shrelaw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- shrelaw2 double %8.0g Percentage of strains and hassles related to relationships in 1996 902 . glm shrelaw2 avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1767.2727 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 996.9369 Deviance = 359894.211 (1/df) Deviance = 996.9369 Pearson = 359894.211 (1/df) Pearson = 996.9369 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.748059 Log likelihood = -1767.272695 BIC = 357766.3 ------------------------------------------------------------------------------ | OIM shrelaw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.945314 1.20186 1.62 0.106 -.4102891 4.300917 _cons | 22.64351 1.976084 11.46 0.000 18.77046 26.51657 ------------------------------------------------------------------------------ 903 . glm HP2sxlife shrelaw2 if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 415.0412 Iteration 2: deviance = 414.406 Iteration 3: deviance = 414.4058 Iteration 4: deviance = 414.4058 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 414.4057989 (1/df) Deviance = 1.147939 Pearson = 362.9721983 (1/df) Pearson = 1.005463 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1713.474 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- shrelaw2 | -.0020918 .0024591 -0.85 0.395 -.0069114 .0027279 _cons | -.5970179 .0953785 -6.26 0.000 -.7839564 -.4100794 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 904 . 905 . 906 . 907 . *xx summary of mediating effects: age and illness mediate sex life for men 908 . * age illnesss radhlw2 bf4 bf4m (soma) media > te sex life for women 909 . 910 . scalar sxlifeMedMw2 = "age illw2" 911 . scalar sxlifeMedFw2 = "age illw2 radhlw2 bf4 bf4m" 912 . title4 "5. summary matrix contstruction for dose -> sexlife impact" ------------------------------------------------------------------------------- 5. summary matrix contstruction for dose -> sexlife impact ------------------------------------------------------------------------------- 913 . 914 . matrix define HP2sxlifeMw2 = J(1,8, 0) 915 . matrix define HP2sxlifeFw2 = J(1,8, 0) 916 . matrix colnames HP2sxlifeMw2 = hypnum ptnum wave gender medsig numMAsig numM > odsig numMed 917 . matrix colnames HP2sxlifeFw2 = hypnum ptnum wave gender medsig numMA > sig numModsig numMed 918 . matrix define HP2sxlifeMw2 = (1, 2, 3, 1, 0, 4, 0, 2 ) 919 . matrix define HP2sxlifeFw2 = (1, 2, 3, 2, 1, 6, 0, 5) 920 . matrix rowname HP2sxlifeMw2 = HP2sxlifeMw2 921 . matrix rowname HP2sxlifeFw2 = HP2sxlifeFw2 922 . matlist HP2sxlifeMw2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ HP2sxlifeMw2 | 1 2 3 1 0 4 > 0 | c8 -------------+----------- HP2sxlifeMw2 | 2 923 . matlist HP2sxlifeFw2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ HP2sxlifeFw2 | 1 2 3 2 1 6 > 0 | c8 -------------+----------- HP2sxlifeFw2 | 5 924 . matrix define H1pt2w2 = ( HP2wkMw2 \ HP2wkFw2 \ HP2hmcrMw2 \ HP2hmcrFw2 \ HP2 > spMw2 \ HP2spFw2 \ HP2prbfamMw2 \ HP2prbfamFw2 \ HP2sxlifeMw2 \ HP2sxlifeFw2 > ) 925 . 926 . matlist H1pt2w2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ r1 | 1 2 3 1 0 4 > 0 r1 | 1 2 3 2 0 1 > 0 r1 | 1 2 3 1 0 4 > 0 r1 | 1 2 3 2 0 2 > 0 HP2spMw2 | 1 2 3 1 0 2 > 0 HP2spFw2 | 1 2 3 2 1 5 > 5 HP2prbfamM | 1 2 3 1 0 5 > 0 HP2prbfamF | 1 2 3 2 0 2 > 2 HP2sxlifeMw2 | 1 2 3 1 0 4 > 0 HP2sxlifeFw2 | 1 2 3 2 1 6 > 0 | c8 -------------+----------- r1 | 4 r1 | 6 r1 | 2 r1 | 2 HP2spMw2 | 1 HP2spFw2 | 2 HP2prbfamM | 1 HP2prbfamF | 2 HP2sxlifeMw2 | 2 HP2sxlifeFw2 | 5 927 . matrix colnames H1pt2w2 = hypnum ptnum wave gender medsig numMAsig numMo > dsig numMed 928 . matrix rownames H1pt2w2 = HP2wkMw2 HP2wkFw2 HP2hmcrMw2 HP2hmcrFw2 HP2pr > bfhmMw2 HP2prbfhmFw2 929 . matlist H1pt2w2 | hypnum ptnum wave gender medsig numMAsig > numModsig -------------+----------------------------------------------------------------- ------------ HP2wkMw2 | 1 2 3 1 0 4 > 0 HP2wkFw2 | 1 2 3 2 0 1 > 0 HP2hmcrMw2 | 1 2 3 1 0 4 > 0 HP2hmcrFw2 | 1 2 3 2 0 2 > 0 HP2prbfhmMw2 | 1 2 3 1 0 2 > 0 HP2prbfhmFw2 | 1 2 3 2 1 5 > 5 HP2prbfhmFw2 | 1 2 3 1 0 5 > 0 HP2prbfhmFw2 | 1 2 3 2 0 2 > 2 HP2prbfhmFw2 | 1 2 3 1 0 4 > 0 HP2prbfhmFw2 | 1 2 3 2 1 6 > 0 | numMed -------------+----------- HP2wkMw2 | 4 HP2wkFw2 | 6 HP2hmcrMw2 | 2 HP2hmcrFw2 | 2 HP2prbfhmMw2 | 1 HP2prbfhmFw2 | 2 HP2prbfhmFw2 | 1 HP2prbfhmFw2 | 2 HP2prbfhmFw2 | 2 HP2prbfhmFw2 | 5 930 . 931 . 932 . 933 . 934 . *=================== Chunk 8 Dose => interests and Hobbies relationship 935 . * washes out for males 936 . * washes out for females in trimmed model 937 . 938 . title "6. Moderators and Mediators of the Dose = > interests and hobbies Imp > act" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** *****6. Moderators and Mediators of the Dose = > interests and hobbies Impact** > *** ***** ***** ***** ***** ***** 1 Jul 2012 15:05:35 ***** ******************************************************************************* ******************************************************************************* 939 . 940 . title4 " Hyp. 1. pt 2 wave 2 Main effects Dose=> Interests and Hobbies impact > identification" ------------------------------------------------------------------------------- Hyp. 1. pt 2 wave 2 Main effects Dose=> Interests and Hobbies impact identific > ation ------------------------------------------------------------------------------- 941 . 942 . * Chunk 8 ---Female full Interest and hobbies model 943 . forvalues j=2/2 { 2. set more off 3. 944 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 945 . foreach var in HP2inthob { 5. forvalues k=1/2 { 6. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 7. 946 . di as input "Full main model for `var' for wave= `j' " 8. di _skip(4) 9. di as input "chunk 8 H1 test:Gender= `k' model Wave = `j' for `e(depvar > )' " 10. di _skip(4) 11. title "Full Nottingham Part 2 subscale models for male and then females" 12. di as input "Model for gender==`k' and wave == `j'" 13. di _skip(2) 14. xi: logistic `var' age i.educ occ1w`j'-occ8w`j' /// > marrw`j'1- marrw`j'3 marrw`j'5-marrw`j'6 inc1w`j'-inc4w`j' // > / > radhlw`j' havmil avgcumdosew`j' `w`j'bf' /// > deaw`j' dvcew`j' sepaw`j' accdw`j' movew`j' /// > illw`j' shfamw`j' shhlw`j' shjobw`j' shrelaw`j' suprtw`j' suc > hrw`j' /// > havmilsq radhlw2 if gender==2, coef nolog difficult iterate > (50) 15. estat class 16. estat gof 17. fitstat 18. } 19. } 20. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Full main model for HP2inthob for wave= 2 chunk 8 H1 test:Gender= 1 model Wave = 2 for HP2sxlife ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for male and then females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:05:35 ***** ******************************************************************************* ******************************************************************************* Model for gender==1 and wave == 2 i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 7 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity note: radhlw2 omitted because of collinearity Logistic regression Number of obs = 343 LR chi2(50) = 122.71 Prob > chi2 = 0.0000 Log likelihood = -105.17277 Pseudo R2 = 0.3684 ------------------------------------------------------------------------------ HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0823123 .0222811 3.69 0.000 .0386422 .1259824 _Ieduc_2 | -11.49207 1090.325 -0.01 0.992 -2148.489 2125.505 _Ieduc_3 | -10.49189 1090.324 -0.01 0.992 -2147.489 2126.505 _Ieduc_4 | -9.521531 1090.325 -0.01 0.993 -2146.519 2127.476 _Ieduc_5 | -10.03097 1090.325 -0.01 0.993 -2147.028 2126.966 _Ieduc_6 | -10.65519 1090.325 -0.01 0.992 -2147.652 2126.342 _Ieduc_7 | -10.56771 1090.33 -0.01 0.992 -2147.575 2126.44 _Ieduc_8 | 0 (omitted) occ1w2 | -1.444251 1.786645 -0.81 0.419 -4.94601 2.057508 occ2w2 | -1.72162 1.86422 -0.92 0.356 -5.375425 1.932185 occ3w2 | -.6960307 1.813742 -0.38 0.701 -4.2509 2.858838 occ4w2 | -1.340236 1.971898 -0.68 0.497 -5.205085 2.524613 occ5w2 | -1.866674 2.177453 -0.86 0.391 -6.134403 2.401055 occ6w2 | -.6630026 2.009767 -0.33 0.741 -4.602073 3.276067 occ7w2 | -.3132082 1.768051 -0.18 0.859 -3.778525 3.152109 occ8w2 | -.4424864 1.980995 -0.22 0.823 -4.325166 3.440193 marrw21 | 1.198754 1.252366 0.96 0.338 -1.255838 3.653346 marrw22 | -.723147 1.45696 -0.50 0.620 -3.578736 2.132442 marrw23 | -.1412732 .8614541 -0.16 0.870 -1.829692 1.547146 marrw25 | .030411 1.273943 0.02 0.981 -2.466472 2.527294 marrw26 | 0 (omitted) inc1w2 | .7639278 1.820866 0.42 0.675 -2.804904 4.33276 inc2w2 | 1.088119 1.754837 0.62 0.535 -2.351299 4.527537 inc3w2 | .8549853 1.806256 0.47 0.636 -2.685212 4.395182 inc4w2 | 2.107171 2.102907 1.00 0.316 -2.014451 6.228794 radhlw2 | .0162662 .0092193 1.76 0.078 -.0018033 .0343356 havmil | .0028568 .0035632 0.80 0.423 -.004127 .0098406 avgcumdosew2 | .1191237 .1112746 1.07 0.284 -.0989705 .3372179 bf1 | -.0579291 .0444461 -1.30 0.192 -.1450419 .0291837 bf4 | -.5134212 .2911522 -1.76 0.078 -1.084069 .0572265 bf2 | .0000225 .0001357 0.17 0.868 -.0002436 .0002885 bf4m | .3813738 .2769085 1.38 0.168 -.1613568 .9241045 bf5m | -.0013491 .0018728 -0.72 0.471 -.0050196 .0023214 bf7m | -.0007653 .0006452 -1.19 0.236 -.0020299 .0004993 bf8 | .0000124 .0000428 0.29 0.772 -.0000716 .0000964 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | .0409398 .0388667 1.05 0.292 -.0352376 .1171172 bf22 | .0000496 .0001446 0.34 0.732 -.0002338 .0003329 bf29 | .0000147 .0000386 0.38 0.704 -.000061 .0000904 bf30 | .0005975 .0003487 1.71 0.087 -.0000859 .0012809 bf40 | -.092913 .1571517 -0.59 0.554 -.4009247 .2150987 deaw2 | .1650922 .2350487 0.70 0.482 -.2955947 .6257792 dvcew2 | -.44027 1.751166 -0.25 0.801 -3.872493 2.991953 sepaw2 | 0 (omitted) accdw2 | .3072193 .6191348 0.50 0.620 -.9062626 1.520701 movew2 | -.4208456 .8150343 -0.52 0.606 -2.018283 1.176592 illw2 | .146668 .2184808 0.67 0.502 -.2815466 .5748825 shfamw2 | .0028218 .0077742 0.36 0.717 -.0124154 .018059 shhlw2 | .0082475 .0076919 1.07 0.284 -.0068283 .0233233 shjobw2 | -.0093426 .0067931 -1.38 0.169 -.0226569 .0039716 shrelaw2 | -.0142034 .0081278 -1.75 0.081 -.0301336 .0017268 suprtw2 | -.0099735 .0060387 -1.65 0.099 -.021809 .0018621 suchrw2 | -.0045113 .0068989 -0.65 0.513 -.0180329 .0090102 havmilsq | -2.60e-06 4.34e-06 -0.60 0.550 -.0000111 5.92e-06 radhlw2 | 0 (omitted) _cons | 2.140849 1090.33 0.00 0.998 -2134.867 2139.148 ------------------------------------------------------------------------------ Note: 1 failure and 0 successes completely determined. Logistic model for HP2inthob -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 32 12 | 44 - | 33 266 | 299 -----------+--------------------------+----------- Total | 65 278 | 343 Classified + if predicted Pr(D) >= .5 True D defined as HP2inthob != 0 -------------------------------------------------- Sensitivity Pr( +| D) 49.23% Specificity Pr( -|~D) 95.68% Positive predictive value Pr( D| +) 72.73% Negative predictive value Pr(~D| -) 88.96% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 4.32% False - rate for true D Pr( -| D) 50.77% False + rate for classified + Pr(~D| +) 27.27% False - rate for classified - Pr( D| -) 11.04% -------------------------------------------------- Correctly classified 86.88% -------------------------------------------------- Logistic model for HP2inthob, goodness-of-fit test number of observations = 343 number of covariate patterns = 343 Pearson chi2(292) = 307.38 Prob > chi2 = 0.2568 Measures of Fit for logistic of HP2inthob Log-Lik Intercept Only: -166.528 Log-Lik Full Model: -105.173 D(286): 210.346 LR(50): 122.710 Prob > LR: 0.000 McFadden's R2: 0.368 McFadden's Adj R2: 0.026 Maximum Likelihood R2: 0.301 Cragg & Uhler's R2: 0.484 McKelvey and Zavoina's R2: 0.627 Efron's R2: 0.388 Variance of y*: 8.828 Variance of error: 3.290 Count R2: 0.869 Adj Count R2: 0.308 AIC: 0.946 AIC*n: 324.346 BIC: -1459.245 BIC': 169.177 Full main model for HP2inthob for wave= 2 chunk 8 H1 test:Gender= 2 model Wave = 2 for HP2inthob ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for male and then females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:05:37 ***** ******************************************************************************* ******************************************************************************* Model for gender==2 and wave == 2 i.educ _Ieduc_1-8 (naturally coded; _Ieduc_1 omitted) note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 7 obs not used note: _Ieduc_8 omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity note: radhlw2 omitted because of collinearity Logistic regression Number of obs = 343 LR chi2(50) = 122.71 Prob > chi2 = 0.0000 Log likelihood = -105.17277 Pseudo R2 = 0.3684 ------------------------------------------------------------------------------ HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0823123 .0222811 3.69 0.000 .0386422 .1259824 _Ieduc_2 | -11.49207 1090.325 -0.01 0.992 -2148.489 2125.505 _Ieduc_3 | -10.49189 1090.324 -0.01 0.992 -2147.489 2126.505 _Ieduc_4 | -9.521531 1090.325 -0.01 0.993 -2146.519 2127.476 _Ieduc_5 | -10.03097 1090.325 -0.01 0.993 -2147.028 2126.966 _Ieduc_6 | -10.65519 1090.325 -0.01 0.992 -2147.652 2126.342 _Ieduc_7 | -10.56771 1090.33 -0.01 0.992 -2147.575 2126.44 _Ieduc_8 | 0 (omitted) occ1w2 | -1.444251 1.786645 -0.81 0.419 -4.94601 2.057508 occ2w2 | -1.72162 1.86422 -0.92 0.356 -5.375425 1.932185 occ3w2 | -.6960307 1.813742 -0.38 0.701 -4.2509 2.858838 occ4w2 | -1.340236 1.971898 -0.68 0.497 -5.205085 2.524613 occ5w2 | -1.866674 2.177453 -0.86 0.391 -6.134403 2.401055 occ6w2 | -.6630026 2.009767 -0.33 0.741 -4.602073 3.276067 occ7w2 | -.3132082 1.768051 -0.18 0.859 -3.778525 3.152109 occ8w2 | -.4424864 1.980995 -0.22 0.823 -4.325166 3.440193 marrw21 | 1.198754 1.252366 0.96 0.338 -1.255838 3.653346 marrw22 | -.723147 1.45696 -0.50 0.620 -3.578736 2.132442 marrw23 | -.1412732 .8614541 -0.16 0.870 -1.829692 1.547146 marrw25 | .030411 1.273943 0.02 0.981 -2.466472 2.527294 marrw26 | 0 (omitted) inc1w2 | .7639278 1.820866 0.42 0.675 -2.804904 4.33276 inc2w2 | 1.088119 1.754837 0.62 0.535 -2.351299 4.527537 inc3w2 | .8549853 1.806256 0.47 0.636 -2.685212 4.395182 inc4w2 | 2.107171 2.102907 1.00 0.316 -2.014451 6.228794 radhlw2 | .0162662 .0092193 1.76 0.078 -.0018033 .0343356 havmil | .0028568 .0035632 0.80 0.423 -.004127 .0098406 avgcumdosew2 | .1191237 .1112746 1.07 0.284 -.0989705 .3372179 bf1 | -.0579291 .0444461 -1.30 0.192 -.1450419 .0291837 bf4 | -.5134212 .2911522 -1.76 0.078 -1.084069 .0572265 bf2 | .0000225 .0001357 0.17 0.868 -.0002436 .0002885 bf4m | .3813738 .2769085 1.38 0.168 -.1613568 .9241045 bf5m | -.0013491 .0018728 -0.72 0.471 -.0050196 .0023214 bf7m | -.0007653 .0006452 -1.19 0.236 -.0020299 .0004993 bf8 | .0000124 .0000428 0.29 0.772 -.0000716 .0000964 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | .0409398 .0388667 1.05 0.292 -.0352376 .1171172 bf22 | .0000496 .0001446 0.34 0.732 -.0002338 .0003329 bf29 | .0000147 .0000386 0.38 0.704 -.000061 .0000904 bf30 | .0005975 .0003487 1.71 0.087 -.0000859 .0012809 bf40 | -.092913 .1571517 -0.59 0.554 -.4009247 .2150987 deaw2 | .1650922 .2350487 0.70 0.482 -.2955947 .6257792 dvcew2 | -.44027 1.751166 -0.25 0.801 -3.872493 2.991953 sepaw2 | 0 (omitted) accdw2 | .3072193 .6191348 0.50 0.620 -.9062626 1.520701 movew2 | -.4208456 .8150343 -0.52 0.606 -2.018283 1.176592 illw2 | .146668 .2184808 0.67 0.502 -.2815466 .5748825 shfamw2 | .0028218 .0077742 0.36 0.717 -.0124154 .018059 shhlw2 | .0082475 .0076919 1.07 0.284 -.0068283 .0233233 shjobw2 | -.0093426 .0067931 -1.38 0.169 -.0226569 .0039716 shrelaw2 | -.0142034 .0081278 -1.75 0.081 -.0301336 .0017268 suprtw2 | -.0099735 .0060387 -1.65 0.099 -.021809 .0018621 suchrw2 | -.0045113 .0068989 -0.65 0.513 -.0180329 .0090102 havmilsq | -2.60e-06 4.34e-06 -0.60 0.550 -.0000111 5.92e-06 radhlw2 | 0 (omitted) _cons | 2.140849 1090.33 0.00 0.998 -2134.867 2139.148 ------------------------------------------------------------------------------ Note: 1 failure and 0 successes completely determined. Logistic model for HP2inthob -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 32 12 | 44 - | 33 266 | 299 -----------+--------------------------+----------- Total | 65 278 | 343 Classified + if predicted Pr(D) >= .5 True D defined as HP2inthob != 0 -------------------------------------------------- Sensitivity Pr( +| D) 49.23% Specificity Pr( -|~D) 95.68% Positive predictive value Pr( D| +) 72.73% Negative predictive value Pr(~D| -) 88.96% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 4.32% False - rate for true D Pr( -| D) 50.77% False + rate for classified + Pr(~D| +) 27.27% False - rate for classified - Pr( D| -) 11.04% -------------------------------------------------- Correctly classified 86.88% -------------------------------------------------- Logistic model for HP2inthob, goodness-of-fit test number of observations = 343 number of covariate patterns = 343 Pearson chi2(292) = 307.38 Prob > chi2 = 0.2568 Measures of Fit for logistic of HP2inthob Log-Lik Intercept Only: -166.528 Log-Lik Full Model: -105.173 D(286): 210.346 LR(50): 122.710 Prob > LR: 0.000 McFadden's R2: 0.368 McFadden's Adj R2: 0.026 Maximum Likelihood R2: 0.301 Cragg & Uhler's R2: 0.484 McKelvey and Zavoina's R2: 0.627 Efron's R2: 0.388 Variance of y*: 8.828 Variance of error: 3.290 Count R2: 0.869 Adj Count R2: 0.308 AIC: 0.946 AIC*n: 324.346 BIC: -1459.245 BIC': 169.177 947 . 948 . * Chunk 8 ---Female trimmed Interest and hobbies model 949 . forvalues j=2/2 { 2. set more off 3. 950 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 951 . foreach var in HP2inthob { 5. forvalues k=2/2 { 6. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 7. 952 . di as input "trimmed main model for `var' for wave= `j' " 8. di _skip(4) 9. di as input "chunk 8 H1 test:Gender= `k' model Wave = `j' for `e(depvar > )' " 10. di _skip(4) 11. title "Full Nottingham Part 2 subscale models for male and then females" 12. di as input "Model for gender==`k' and wave == `j'" 13. di _skip(2) 14. logit `var' age /// > avgcumdosew`j' bf4 bf30 /// > shrelaw`j' suprtw`j' /// > radhlw2 if gender==2, difficult iterate(50) nolog 15. } 16. } 17. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) trimmed main model for HP2inthob for wave= 2 chunk 8 H1 test:Gender= 2 model Wave = 2 for HP2inthob ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for male and then females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:05:39 ***** ******************************************************************************* ******************************************************************************* Model for gender==2 and wave == 2 Logistic regression Number of obs = 363 LR chi2(7) = 101.28 Prob > chi2 = 0.0000 Log likelihood = -121.47255 Pseudo R2 = 0.2942 ------------------------------------------------------------------------------ HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0729699 .016233 4.50 0.000 .0411537 .104786 avgcumdosew2 | .0713471 .0870475 0.82 0.412 -.0992629 .2419571 bf4 | -.12277 .0348875 -3.52 0.000 -.1911482 -.0543918 bf30 | .0005237 .0002568 2.04 0.041 .0000204 .001027 shrelaw2 | -.0121549 .0056037 -2.17 0.030 -.0231379 -.001172 suprtw2 | -.0103307 .0040941 -2.52 0.012 -.018355 -.0023065 radhlw2 | .0119091 .0056652 2.10 0.036 .0008055 .0230128 _cons | -4.716583 1.190104 -3.96 0.000 -7.049144 -2.384021 ------------------------------------------------------------------------------ 953 . 954 . 955 . 956 . // female interests and hobbies wash out 957 . 958 . 959 . *------------------------------------------------------------------------- 960 . 961 . 962 . 963 . 964 . 965 . 966 . 967 . 968 . label var radhlw2 "Self-perceived Chornobyl health threat in wave 2" 969 . 970 . title4 "6. trimmed Moderators of male Dose => Interests and Hobbies Impact" ------------------------------------------------------------------------------- 6. trimmed Moderators of male Dose => Interests and Hobbies Impact ------------------------------------------------------------------------------- 971 . 972 . di as input "wave 2 Main effects Dose=> Interests and Hobbies impact identifi > cation" wave 2 Main effects Dose=> Interests and Hobbies impact identification 973 . forvalues j=2/2 { 2. set more off 3. 974 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 975 . foreach var in HP2inthob { 5. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 6. di _skip(2) 7. di as input "Full main model for `var' for wave= `j' " 8. di _skip(4) 9. di as input "chunk 8 H1 test:Gender= male model Wave = `j' for `e(depva > r)' " 10. di _skip(4) 11. title "Full Nottingham Part 2 subscale models " "males on wave=`j'" 12. des bf5m shfamw2 radhlw2 bf4m 13. xi: logistic `var' age radhlw2 avgcumdosew2 shfamw2 /// > bf5m if gender==1, coef nolog difficult iterate(50) 14. estat class 15. estat gof 16. fitstat 17. di _skip(2) 18. di as input "Note: bf4m is necssary for bf5 but if bf4m is in model bf5 is > not signif." 19. di as input " Therefore, bf5 is not deemed significant." 20. } 21. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Full main model for HP2inthob for wave= 2 chunk 8 H1 test:Gender= male model Wave = 2 for HP2inthob ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models ***** ***** males on wave=2 ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:05:40 ***** ******************************************************************************* ******************************************************************************* storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m shfamw2 double %8.0g Percentage of strains and hassles related to family in 1996 radhlw2 double %8.0g Self-perceived Chornobyl health threat in wave 2 bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) Logistic regression Number of obs = 339 LR chi2(5) = 55.04 Prob > chi2 = 0.0000 Log likelihood = -91.424671 Pseudo R2 = 0.2314 ------------------------------------------------------------------------------ HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0431333 .0167168 2.58 0.010 .010369 .0758976 radhlw2 | .0348063 .0070847 4.91 0.000 .0209205 .048692 avgcumdosew2 | -.1062023 .1574615 -0.67 0.500 -.4148212 .2024166 shfamw2 | .0030601 .0048986 0.62 0.532 -.006541 .0126612 bf5m | -.0015801 .0010798 -1.46 0.143 -.0036965 .0005363 _cons | -6.400487 1.022095 -6.26 0.000 -8.403758 -4.397217 ------------------------------------------------------------------------------ Logistic model for HP2inthob -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 8 4 | 12 - | 30 297 | 327 -----------+--------------------------+----------- Total | 38 301 | 339 Classified + if predicted Pr(D) >= .5 True D defined as HP2inthob != 0 -------------------------------------------------- Sensitivity Pr( +| D) 21.05% Specificity Pr( -|~D) 98.67% Positive predictive value Pr( D| +) 66.67% Negative predictive value Pr(~D| -) 90.83% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 1.33% False - rate for true D Pr( -| D) 78.95% False + rate for classified + Pr(~D| +) 33.33% False - rate for classified - Pr( D| -) 9.17% -------------------------------------------------- Correctly classified 89.97% -------------------------------------------------- Logistic model for HP2inthob, goodness-of-fit test number of observations = 339 number of covariate patterns = 327 Pearson chi2(321) = 372.91 Prob > chi2 = 0.0242 Measures of Fit for logistic of HP2inthob Log-Lik Intercept Only: -118.946 Log-Lik Full Model: -91.425 D(333): 182.849 LR(5): 55.042 Prob > LR: 0.000 McFadden's R2: 0.231 McFadden's Adj R2: 0.181 Maximum Likelihood R2: 0.150 Cragg & Uhler's R2: 0.297 McKelvey and Zavoina's R2: 0.397 Efron's R2: 0.214 Variance of y*: 5.454 Variance of error: 3.290 Count R2: 0.900 Adj Count R2: 0.105 AIC: 0.575 AIC*n: 194.849 BIC: -1757.209 BIC': -25.912 Note: bf4m is necssary for bf5 but if bf4m is in model bf5 is not signif. Therefore, bf5 is not deemed significant. 976 . 977 . cap gen bf5mXd3 = bf5m*avgcumdosew2 978 . 979 . 980 . 981 . title3 "wave 2 Main effects Dose=> Interests and Hobbies impact identificatio > n" ------------------------------------------------------------------------------- title3 : wave 2 Main effects Dose=> Interests and Hobbies impact identification 1 Jul 2012 15:05:41 computer Macintosh (Intel 64-bit) folder /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/ > h1pt2 Data file chwide1jul2012.dta currrently has 2399 variables and 703 observ > ations 982 . forvalues j=2/2 { 2. set more off 3. 983 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 984 . foreach var in HP2inthob { 5. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 6. di as input "Full main model for `var' for wave= `j' " 7. di _skip(4) 8. di as input "chunk 8 H1 test:Gender= male model Wave = `j' for `e(depva > r)' " 9. di _skip(4) 10. title "Full Nottingham Part 2 subscale models for males in wave=`j'" 11. 985 . xi: logistic `var' age /// > radhlw`j' avgcumdosew`j' /// > shfamw`j' bf4m /// > bf5m bf5mXd3 if gender==1, coef nolog difficult iterate(50 > ) 12. estat class 13. estat gof 14. fitstat 15. } 16. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Full main model for HP2inthob for wave= 2 chunk 8 H1 test:Gender= male model Wave = 2 for HP2inthob ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for males in wave=2 ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:05:41 ***** ******************************************************************************* ******************************************************************************* Logistic regression Number of obs = 339 LR chi2(7) = 61.20 Prob > chi2 = 0.0000 Log likelihood = -88.347465 Pseudo R2 = 0.2572 ------------------------------------------------------------------------------ HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .030943 .0178289 1.74 0.083 -.0040009 .065887 radhlw2 | .0256859 .0076857 3.34 0.001 .0106222 .0407496 avgcumdosew2 | -.0706233 .347896 -0.20 0.839 -.752487 .6112404 shfamw2 | .0004942 .0051095 0.10 0.923 -.0095202 .0105087 bf4m | -.0869807 .0356099 -2.44 0.015 -.1567749 -.0171866 bf5m | -.0007397 .0014331 -0.52 0.606 -.0035486 .0020692 bf5mXd3 | -.0000719 .0008404 -0.09 0.932 -.0017191 .0015753 _cons | -3.59705 1.521825 -2.36 0.018 -6.579772 -.6143281 ------------------------------------------------------------------------------ Logistic model for HP2inthob -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 10 5 | 15 - | 28 296 | 324 -----------+--------------------------+----------- Total | 38 301 | 339 Classified + if predicted Pr(D) >= .5 True D defined as HP2inthob != 0 -------------------------------------------------- Sensitivity Pr( +| D) 26.32% Specificity Pr( -|~D) 98.34% Positive predictive value Pr( D| +) 66.67% Negative predictive value Pr(~D| -) 91.36% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 1.66% False - rate for true D Pr( -| D) 73.68% False + rate for classified + Pr(~D| +) 33.33% False - rate for classified - Pr( D| -) 8.64% -------------------------------------------------- Correctly classified 90.27% -------------------------------------------------- Logistic model for HP2inthob, goodness-of-fit test number of observations = 339 number of covariate patterns = 334 Pearson chi2(326) = 295.89 Prob > chi2 = 0.8832 Measures of Fit for logistic of HP2inthob Log-Lik Intercept Only: -118.946 Log-Lik Full Model: -88.347 D(331): 176.695 LR(7): 61.196 Prob > LR: 0.000 McFadden's R2: 0.257 McFadden's Adj R2: 0.190 Maximum Likelihood R2: 0.165 Cragg & Uhler's R2: 0.328 McKelvey and Zavoina's R2: 0.374 Efron's R2: 0.224 Variance of y*: 5.258 Variance of error: 3.290 Count R2: 0.903 Adj Count R2: 0.132 AIC: 0.568 AIC*n: 192.695 BIC: -1751.711 BIC': -20.414 986 . scalar SigDoseInthbMw2 = "no" 987 . scalar MainEffInthbMw2 = "age radhlw2 shfamw2" 988 . scalar InthbMw2 = "none" 989 . 990 . *------chunk 8 female moderator models 991 . title "trimmed Moderators of female Dose => Interests and Hobbies Impact" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** trimmed Moderators of female Dose => Interests and Hobbies Impact ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:05:43 ***** ******************************************************************************* ******************************************************************************* 992 . 993 . 994 . forvalues j=2/2 { 2. set more off 3. 995 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 996 . foreach var in HP2inthob { 5. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 6. di as input "Full main model for `var' for wave= `j' " 7. di _skip(4) 8. di as input "chunk 8 H1 test:Gender= male model Wave = `j' for `e(depva > r)' " 9. di _skip(4) 10. title "Full Nottingham Part 2 subscale models for females " 11. 997 . xi: logistic `var' age /// > radhlw`j' avgcumdosew`j' /// > bf4 /// > if gender==2, coef nolog difficult iterate(50) 12. estat class 13. estat gof 14. fitstat 15. } 16. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Full main model for HP2inthob for wave= 2 chunk 8 H1 test:Gender= male model Wave = 2 for HP2inthob ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for females ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:05:43 ***** ******************************************************************************* ******************************************************************************* Logistic regression Number of obs = 363 LR chi2(4) = 85.23 Prob > chi2 = 0.0000 Log likelihood = -129.49784 Pseudo R2 = 0.2476 ------------------------------------------------------------------------------ HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0747628 .0160244 4.67 0.000 .0433556 .10617 radhlw2 | .0174604 .0053569 3.26 0.001 .0069611 .0279597 avgcumdosew2 | .0556287 .0871446 0.64 0.523 -.1151717 .226429 bf4 | -.0957645 .0312145 -3.07 0.002 -.1569439 -.0345852 _cons | -5.962105 1.09169 -5.46 0.000 -8.101778 -3.822431 ------------------------------------------------------------------------------ Logistic model for HP2inthob -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 27 8 | 35 - | 39 289 | 328 -----------+--------------------------+----------- Total | 66 297 | 363 Classified + if predicted Pr(D) >= .5 True D defined as HP2inthob != 0 -------------------------------------------------- Sensitivity Pr( +| D) 40.91% Specificity Pr( -|~D) 97.31% Positive predictive value Pr( D| +) 77.14% Negative predictive value Pr(~D| -) 88.11% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 2.69% False - rate for true D Pr( -| D) 59.09% False + rate for classified + Pr(~D| +) 22.86% False - rate for classified - Pr( D| -) 11.89% -------------------------------------------------- Correctly classified 87.05% -------------------------------------------------- Logistic model for HP2inthob, goodness-of-fit test number of observations = 363 number of covariate patterns = 361 Pearson chi2(356) = 453.01 Prob > chi2 = 0.0004 Measures of Fit for logistic of HP2inthob Log-Lik Intercept Only: -172.113 Log-Lik Full Model: -129.498 D(358): 258.996 LR(4): 85.229 Prob > LR: 0.000 McFadden's R2: 0.248 McFadden's Adj R2: 0.219 Maximum Likelihood R2: 0.209 Cragg & Uhler's R2: 0.342 McKelvey and Zavoina's R2: 0.407 Efron's R2: 0.292 Variance of y*: 5.547 Variance of error: 3.290 Count R2: 0.871 Adj Count R2: 0.288 AIC: 0.741 AIC*n: 268.996 BIC: -1851.201 BIC': -61.652 998 . scalar SigdoseInthbFw2 = "no" 999 . scalar MainEffInthbFw2 = "age radhlw2 bf4" 1000 . 1001 . *------chunk 8 testing female moderators 1002 . title "trimmed Moderators of female Dose => Interests and Hobbies Impact" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** trimmed Moderators of female Dose => Interests and Hobbies Impact ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:05:44 ***** ******************************************************************************* ******************************************************************************* 1003 . 1004 . 1005 . forvalues j=2/2 { 2. set more off 3. 1006 . des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' /// > bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40 4. 1007 . foreach var in HP2inthob { 5. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 6. di as input "Full main model for `var' for wave= `j' " 7. di _skip(4) 8. di as input "chunk 8 H1 test:Gender= male model Wave = `j' for `e(depva > r)' " 9. di _skip(4) 10. title "Full Nottingham Part 2 subscale models for females in wave=`j' " 11. 1008 . xi: logistic `var' age /// > radhlw`j' avgcumdosew`j' /// > bf4 bf4Xd3 ageXd3 radhlw2Xd3 /// > if gender==2, coef nolog difficult iterate(50) 12. estat class 13. estat gof 14. fitstat 15. } 16. } storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age educ1 byte %8.0g educ==1. did not graduate high school educ2 byte %8.0g educ==2. graduated high school educ3 byte %8.0g educ==3. technical degree educ4 byte %8.0g educ==4. did not finish college/bachelor's educ5 byte %8.0g educ==5. graduated college/bachelor's educ6 byte %8.0g educ==6. finished specialist/master's degree educ7 byte %8.0g educ==7. doctor of science/phd marrw21 byte %8.0g marrw2==1. single marrw22 byte %8.0g marrw2==2. cohabitating marrw23 byte %8.0g marrw2==3. married marrw24 byte %8.0g marrw2==4. separated marrw25 byte %8.0g marrw2==5. divorced marrw26 byte %8.0g marrw2==6. widowed inc1w2 double %15.0g LABJ Income is not sufficient for basic neccessities in 1996 inc2w2 double %15.0g LABJ Income is just sufficient for basic neccessities in 1996 inc3w2 double %15.0g LABJ Income is sufficient for basics plus extra purchases/savings in 1996 inc4w2 double %15.0g LABJ Income allows to comfortably afford luxury items in 1996 bf1 float %9.0g bf1 = max(0, kzchorn - 40) bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) bf9 float %9.0g bf9= max(0, 30 - shhlw1) bf11 float %9.0g bf11= max(0, 20 - sufamw1) bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) bf15m float %9.0g bf15m= max(0, 1 - icdxcnt) * bf2 bf30 float %9.0g bf30 = max(0, neiw1 - 85) * bf20 bf40 float %9.0g bf40 = max(0, icdxcnt - 1.01635E-007) Full main model for HP2inthob for wave= 2 chunk 8 H1 test:Gender= male model Wave = 2 for HP2inthob ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Full Nottingham Part 2 subscale models for females in wave=2 ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:05:44 ***** ******************************************************************************* ******************************************************************************* Logistic regression Number of obs = 363 LR chi2(7) = 88.01 Prob > chi2 = 0.0000 Log likelihood = -128.11005 Pseudo R2 = 0.2557 ------------------------------------------------------------------------------ HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0684009 .0187006 3.66 0.000 .0317484 .1050534 radhlw2 | .0215957 .0064007 3.37 0.001 .0090506 .0341409 avgcumdosew2 | -.1117068 .8606863 -0.13 0.897 -1.798621 1.575207 bf4 | -.0939457 .0393671 -2.39 0.017 -.1711039 -.0167876 bf4Xd3 | -.0023725 .0194992 -0.12 0.903 -.0405902 .0358453 ageXd3 | .0062659 .0102599 0.61 0.541 -.0138431 .0263749 radhlw2Xd3 | -.0033605 .0032954 -1.02 0.308 -.0098193 .0030983 _cons | -5.94901 1.257396 -4.73 0.000 -8.41346 -3.484559 ------------------------------------------------------------------------------ Logistic model for HP2inthob -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 28 8 | 36 - | 38 289 | 327 -----------+--------------------------+----------- Total | 66 297 | 363 Classified + if predicted Pr(D) >= .5 True D defined as HP2inthob != 0 -------------------------------------------------- Sensitivity Pr( +| D) 42.42% Specificity Pr( -|~D) 97.31% Positive predictive value Pr( D| +) 77.78% Negative predictive value Pr(~D| -) 88.38% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 2.69% False - rate for true D Pr( -| D) 57.58% False + rate for classified + Pr(~D| +) 22.22% False - rate for classified - Pr( D| -) 11.62% -------------------------------------------------- Correctly classified 87.33% -------------------------------------------------- Logistic model for HP2inthob, goodness-of-fit test number of observations = 363 number of covariate patterns = 361 Pearson chi2(353) = 461.18 Prob > chi2 = 0.0001 Measures of Fit for logistic of HP2inthob Log-Lik Intercept Only: -172.113 Log-Lik Full Model: -128.110 D(355): 256.220 LR(7): 88.005 Prob > LR: 0.000 McFadden's R2: 0.256 McFadden's Adj R2: 0.209 Maximum Likelihood R2: 0.215 Cragg & Uhler's R2: 0.351 McKelvey and Zavoina's R2: 0.418 Efron's R2: 0.305 Variance of y*: 5.654 Variance of error: 3.290 Count R2: 0.873 Adj Count R2: 0.303 AIC: 0.750 AIC*n: 272.220 BIC: -1836.293 BIC': -46.744 1009 . scalar InthbModFw2 = "none" 1010 . 1011 . title4 "Chunk 8 interests and hobbies testing mediator effects " ------------------------------------------------------------------------------- Chunk 8 interests and hobbies testing mediator effects ------------------------------------------------------------------------------- 1012 . 1013 . * age is a mediating effect for males for Dose=> sex life for men 1014 . 1015 . glm age avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1330.6004 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 147.6853 Deviance = 49917.64009 (1/df) Deviance = 147.6853 Pearson = 49917.64009 (1/df) Pearson = 147.6853 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.838826 Log likelihood = -1330.6004 BIC = 47947.46 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .5832314 .2635871 2.21 0.027 .0666101 1.099853 _cons | 48.62133 .7061562 68.85 0.000 47.23729 50.00537 ------------------------------------------------------------------------------ 1016 . glm HP2inthob age if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 224.8585 Iteration 2: deviance = 219.8355 Iteration 3: deviance = 219.6997 Iteration 4: deviance = 219.6996 Iteration 5: deviance = 219.6996 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 219.6996277 (1/df) Deviance = .6499989 Pearson = 354.7105724 (1/df) Pearson = 1.04944 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1750.484 ------------------------------------------------------------------------------ | EIM HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .031726 .0062563 5.07 0.000 .0194638 .0439882 _cons | -2.867608 .344284 -8.33 0.000 -3.542392 -2.192824 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1017 . 1018 . * illness is a mediating effect for males = > sex life for men 1019 . des illw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- illw2 double %8.0g Total number of illnesses experienced in time period 1987-1996 1020 . glm illw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -303.59609 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = .3512988 Deviance = 118.7390083 (1/df) Deviance = .3512988 Pearson = 118.7390083 (1/df) Pearson = .3512988 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 1.797624 Log likelihood = -303.5960853 BIC = -1851.445 ------------------------------------------------------------------------------ | OIM illw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .0085423 .0128556 0.66 0.506 -.0166543 .0337389 _cons | .2741359 .0344406 7.96 0.000 .2066336 .3416382 ------------------------------------------------------------------------------ 1021 . glm HP2inthob illw2 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 237.2651 Iteration 2: deviance = 236.0196 Iteration 3: deviance = 236.0142 Iteration 4: deviance = 236.0142 Iteration 5: deviance = 236.0142 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 236.0141663 (1/df) Deviance = .6982668 Pearson = 338.89241 (1/df) Pearson = 1.00264 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1734.169 ------------------------------------------------------------------------------ | EIM HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2 | .2044103 .113873 1.80 0.073 -.0187766 .4275973 _cons | -1.284117 .0852302 -15.07 0.000 -1.451165 -1.117069 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1022 . 1023 . des radhlw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- radhlw2 double %8.0g Self-perceived Chornobyl health threat in wave 2 1024 . glm radhlw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1693.4076 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 1247.933 Deviance = 421801.4584 (1/df) Deviance = 1247.933 Pearson = 421801.4584 (1/df) Pearson = 1247.933 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.972986 Log likelihood = -1693.407647 BIC = 419831.3 ------------------------------------------------------------------------------ | OIM radhlw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.220373 .766216 1.59 0.111 -.2813831 2.722128 _cons | 45.63198 2.052711 22.23 0.000 41.60874 49.65522 ------------------------------------------------------------------------------ 1025 . glm HP2inthob radhlw2 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 207.4869 Iteration 2: deviance = 195.0786 Iteration 3: deviance = 193.7239 Iteration 4: deviance = 193.6942 Iteration 5: deviance = 193.6942 Iteration 6: deviance = 193.6942 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 193.694234 (1/df) Deviance = .5730599 Pearson = 330.3909814 (1/df) Pearson = .9774881 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1776.489 ------------------------------------------------------------------------------ | EIM HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- radhlw2 | .0187765 .0024049 7.81 0.000 .014063 .02349 _cons | -2.360804 .1846104 -12.79 0.000 -2.722634 -1.998974 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1026 . 1027 . 1028 . des shfamw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- shfamw2 double %8.0g Percentage of strains and hassles related to family in 1996 1029 . glm shfamw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1704.8947 Generalized linear models No. of obs = 339 Optimization : ML Residual df = 337 Scale parameter = 1375.284 Deviance = 463470.769 (1/df) Deviance = 1375.284 Pearson = 463470.769 (1/df) Pearson = 1375.284 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 10.07017 Log likelihood = -1704.894657 BIC = 461507.4 ------------------------------------------------------------------------------ | OIM shfamw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.7484378 .8043765 -0.93 0.352 -2.324987 .8281112 _cons | 34.76184 2.15791 16.11 0.000 30.53241 38.99127 ------------------------------------------------------------------------------ 1030 . glm HP2inthob shfamw2 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 236.0134 Iteration 2: deviance = 234.459 Iteration 3: deviance = 234.4495 Iteration 4: deviance = 234.4495 Generalized linear models No. of obs = 339 Optimization : MQL Fisher scoring Residual df = 337 (IRLS EIM) Scale parameter = 1 Deviance = 234.4495475 (1/df) Deviance = .695696 Pearson = 340.0883436 (1/df) Pearson = 1.009164 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1728.912 ------------------------------------------------------------------------------ | EIM HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- shfamw2 | .0043459 .0019602 2.22 0.027 .0005039 .0081879 _cons | -1.379269 .1084635 -12.72 0.000 -1.591853 -1.166684 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1031 . 1032 . des bf5m storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf5m float %9.0g bf5m = max(0, ecprw3 - 75) * bf4m 1033 . glm bf5m avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -2273.2523 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 37801.15 Deviance = 12776789.76 (1/df) Deviance = 37801.15 Pearson = 12776789.76 (1/df) Pearson = 37801.15 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 13.38384 Log likelihood = -2273.252279 BIC = 1.28e+07 ------------------------------------------------------------------------------ | OIM bf5m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 9.243905 4.217043 2.19 0.028 .9786526 17.50916 _cons | 104.6228 11.29756 9.26 0.000 82.47996 126.7656 ------------------------------------------------------------------------------ 1034 . glm HP2inthob bf5m if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 238.9021 Iteration 2: deviance = 238.0124 Iteration 3: deviance = 238.0098 Iteration 4: deviance = 238.0098 Iteration 5: deviance = 238.0098 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 238.0098185 (1/df) Deviance = .7041711 Pearson = 339.8073754 (1/df) Pearson = 1.005347 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1732.174 ------------------------------------------------------------------------------ | EIM HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf5m | .0001615 .000378 0.43 0.669 -.0005794 .0009024 _cons | -1.236147 .0878768 -14.07 0.000 -1.408382 -1.063912 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1035 . 1036 . glm bf4 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1027.1225 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 24.7771 Deviance = 8374.659221 (1/df) Deviance = 24.7771 Pearson = 8374.659221 (1/df) Pearson = 24.7771 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.053662 Log likelihood = -1027.122509 BIC = 6404.476 ------------------------------------------------------------------------------ | OIM bf4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.0331637 .1079644 -0.31 0.759 -.2447701 .1784427 _cons | 12.52896 .2892393 43.32 0.000 11.96206 13.09586 ------------------------------------------------------------------------------ 1037 . glm HP2inthob bf4 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 201.5914 Iteration 2: deviance = 191.8769 Iteration 3: deviance = 191.2682 Iteration 4: deviance = 191.2622 Iteration 5: deviance = 191.2622 Iteration 6: deviance = 191.2622 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 191.2622075 (1/df) Deviance = .5658645 Pearson = 305.593706 (1/df) Pearson = .9041234 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1778.921 ------------------------------------------------------------------------------ | EIM HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.1182178 .013496 -8.76 0.000 -.1446695 -.091766 _cons | .0441185 .1517879 0.29 0.771 -.2533804 .3416174 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1038 . 1039 . 1040 . * age is a mediating effect for females for Dose=> sex life for women 1041 . glm age avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1406.9403 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 136.9184 Deviance = 49427.52828 (1/df) Deviance = 136.9184 Pearson = 49427.52828 (1/df) Pearson = 136.9184 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.762756 Log likelihood = -1406.940271 BIC = 47299.65 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.502324 .4454009 3.37 0.001 .6293547 2.375294 _cons | 48.86944 .7323225 66.73 0.000 47.43412 50.30477 ------------------------------------------------------------------------------ 1042 . glm HP2inthob age if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 293.9671 Iteration 2: deviance = 289.27 Iteration 3: deviance = 289.1951 Iteration 4: deviance = 289.1951 Iteration 5: deviance = 289.1951 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 289.1950995 (1/df) Deviance = .8010945 Pearson = 415.3464621 (1/df) Pearson = 1.150544 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1838.684 ------------------------------------------------------------------------------ | EIM HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0515323 .0068567 7.52 0.000 .0380933 .0649713 _cons | -3.649661 .3847198 -9.49 0.000 -4.403698 -2.895624 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1043 . 1044 . des bf4 // soma recentered storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) 1045 . * bf4 is a mediting effect for females for Dose=> sex life for women 1046 . glm bf4 avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1109.0983 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 26.53281 Deviance = 9578.344971 (1/df) Deviance = 26.53281 Pearson = 9578.344971 (1/df) Pearson = 26.53281 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.121754 Log likelihood = -1109.098281 BIC = 7450.466 ------------------------------------------------------------------------------ | OIM bf4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.595012 .1960703 -3.03 0.002 -.9793027 -.2107212 _cons | 11.02048 .3223763 34.19 0.000 10.38863 11.65232 ------------------------------------------------------------------------------ 1047 . glm HP2inthob bf4 if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 304.5744 Iteration 2: deviance = 301.504 Iteration 3: deviance = 301.4488 Iteration 4: deviance = 301.4487 Iteration 5: deviance = 301.4487 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 301.4486672 (1/df) Deviance = .8350379 Pearson = 341.1451194 (1/df) Pearson = .9450003 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1826.431 ------------------------------------------------------------------------------ | EIM HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.0993749 .0142594 -6.97 0.000 -.1273229 -.071427 _cons | .0117358 .1447759 0.08 0.935 -.2720197 .2954914 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1048 . 1049 . des bf4m // soma recentered storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) 1050 . * bf4m is a possible mediating effect for female sex life 1051 . glm bf4m avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1140.8259 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 31.60104 Deviance = 11407.97484 (1/df) Deviance = 31.60104 Pearson = 11407.97484 (1/df) Pearson = 31.60104 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.296561 Log likelihood = -1140.825904 BIC = 9280.095 ------------------------------------------------------------------------------ | OIM bf4m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.599311 .2139789 -2.80 0.005 -1.018702 -.1799202 _cons | 18.83424 .3518214 53.53 0.000 18.14469 19.5238 ------------------------------------------------------------------------------ 1052 . glm HP2sxlife bf4m if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 356.1451 Iteration 2: deviance = 355.6209 Iteration 3: deviance = 355.6178 Iteration 4: deviance = 355.6178 Iteration 5: deviance = 355.6178 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 355.6178202 (1/df) Deviance = .9850909 Pearson = 340.0032816 (1/df) Pearson = .9418373 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1772.262 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4m | -.0990193 .0135516 -7.31 0.000 -.12558 -.0724586 _cons | 1.063679 .244399 4.35 0.000 .584666 1.542693 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1053 . 1054 . des shfamw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- shfamw2 double %8.0g Percentage of strains and hassles related to family in 1996 1055 . glm shfamw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1704.8947 Generalized linear models No. of obs = 339 Optimization : ML Residual df = 337 Scale parameter = 1375.284 Deviance = 463470.769 (1/df) Deviance = 1375.284 Pearson = 463470.769 (1/df) Pearson = 1375.284 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 10.07017 Log likelihood = -1704.894657 BIC = 461507.4 ------------------------------------------------------------------------------ | OIM shfamw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.7484378 .8043765 -0.93 0.352 -2.324987 .8281112 _cons | 34.76184 2.15791 16.11 0.000 30.53241 38.99127 ------------------------------------------------------------------------------ 1056 . glm HP2sxlife shfamw2 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 338.2157 Iteration 2: deviance = 338.0066 Iteration 3: deviance = 338.0065 Iteration 4: deviance = 338.0065 Generalized linear models No. of obs = 339 Optimization : MQL Fisher scoring Residual df = 337 (IRLS EIM) Scale parameter = 1 Deviance = 338.0064771 (1/df) Deviance = 1.002987 Pearson = 339.3157137 (1/df) Pearson = 1.006872 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1625.356 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- shfamw2 | .0027669 .0020626 1.34 0.180 -.0012757 .0068096 _cons | -.9380005 .1081399 -8.67 0.000 -1.149951 -.7260503 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1057 . 1058 . des bf4 storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4 float %9.0g bf4 = max(0, 24 - BSIsoma) 1059 . glm bf4 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1027.1225 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 24.7771 Deviance = 8374.659221 (1/df) Deviance = 24.7771 Pearson = 8374.659221 (1/df) Pearson = 24.7771 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.053662 Log likelihood = -1027.122509 BIC = 6404.476 ------------------------------------------------------------------------------ | OIM bf4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.0331637 .1079644 -0.31 0.759 -.2447701 .1784427 _cons | 12.52896 .2892393 43.32 0.000 11.96206 13.09586 ------------------------------------------------------------------------------ 1060 . glm HP2sxlife bf4 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 265.0595 Iteration 2: deviance = 261.6959 Iteration 3: deviance = 261.6273 Iteration 4: deviance = 261.6271 Iteration 5: deviance = 261.6271 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 261.6270836 (1/df) Deviance = .7740446 Pearson = 301.9171445 (1/df) Pearson = .893246 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1708.557 ------------------------------------------------------------------------------ | EIM HP2sxlife | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4 | -.1431725 .0149643 -9.57 0.000 -.172502 -.1138431 _cons | .7780696 .180124 4.32 0.000 .425033 1.131106 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1061 . 1062 . des bf4m // soma recentered storage display value variable name type format label variable label ------------------------------------------------------------------------------- bf4m float %9.0g bf4m = max(0, 32 - BSIsoma) 1063 . * bf4m is a possible mediating effect for female ints and hobbies 1064 . glm bf4m avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1140.8259 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 31.60104 Deviance = 11407.97484 (1/df) Deviance = 31.60104 Pearson = 11407.97484 (1/df) Pearson = 31.60104 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 6.296561 Log likelihood = -1140.825904 BIC = 9280.095 ------------------------------------------------------------------------------ | OIM bf4m | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | -.599311 .2139789 -2.80 0.005 -1.018702 -.1799202 _cons | 18.83424 .3518214 53.53 0.000 18.14469 19.5238 ------------------------------------------------------------------------------ 1065 . glm HP2inthob bf4m if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 310.9241 Iteration 2: deviance = 308.9696 Iteration 3: deviance = 308.9479 Iteration 4: deviance = 308.9478 Iteration 5: deviance = 308.9478 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 308.9478254 (1/df) Deviance = .8558112 Pearson = 336.1855337 (1/df) Pearson = .9312619 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1818.932 ------------------------------------------------------------------------------ | EIM HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- bf4m | -.0801416 .0127284 -6.30 0.000 -.1050887 -.0551944 _cons | .4618823 .2255505 2.05 0.041 .0198113 .9039532 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1066 . 1067 . des age // age is a possilbe mediating effect for interest and hobbies storage display value variable name type format label variable label ------------------------------------------------------------------------------- age double %8.0g * Respondent's age 1068 . glm age avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1330.6004 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 147.6853 Deviance = 49917.64009 (1/df) Deviance = 147.6853 Pearson = 49917.64009 (1/df) Pearson = 147.6853 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.838826 Log likelihood = -1330.6004 BIC = 47947.46 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .5832314 .2635871 2.21 0.027 .0666101 1.099853 _cons | 48.62133 .7061562 68.85 0.000 47.23729 50.00537 ------------------------------------------------------------------------------ 1069 . glm HP2inthob age if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 224.8585 Iteration 2: deviance = 219.8355 Iteration 3: deviance = 219.6997 Iteration 4: deviance = 219.6996 Iteration 5: deviance = 219.6996 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 219.6996277 (1/df) Deviance = .6499989 Pearson = 354.7105724 (1/df) Pearson = 1.04944 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1750.484 ------------------------------------------------------------------------------ | EIM HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .031726 .0062563 5.07 0.000 .0194638 .0439882 _cons | -2.867608 .344284 -8.33 0.000 -3.542392 -2.192824 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1070 . 1071 . des illw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- illw2 double %8.0g Total number of illnesses experienced in time period 1987-1996 1072 . glm illw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -303.59609 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = .3512988 Deviance = 118.7390083 (1/df) Deviance = .3512988 Pearson = 118.7390083 (1/df) Pearson = .3512988 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 1.797624 Log likelihood = -303.5960853 BIC = -1851.445 ------------------------------------------------------------------------------ | OIM illw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .0085423 .0128556 0.66 0.506 -.0166543 .0337389 _cons | .2741359 .0344406 7.96 0.000 .2066336 .3416382 ------------------------------------------------------------------------------ 1073 . glm HP2inthob illw2 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 237.2651 Iteration 2: deviance = 236.0196 Iteration 3: deviance = 236.0142 Iteration 4: deviance = 236.0142 Iteration 5: deviance = 236.0142 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 236.0141663 (1/df) Deviance = .6982668 Pearson = 338.89241 (1/df) Pearson = 1.00264 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1734.169 ------------------------------------------------------------------------------ | EIM HP2inthob | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2 | .2044103 .113873 1.80 0.073 -.0187766 .4275973 _cons | -1.284117 .0852302 -15.07 0.000 -1.451165 -1.117069 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1074 . 1075 . 1076 . *xx summary of mediating effects: males only age and illw2 2 1077 . *xx females: age 1078 . 1079 . 1080 . 1081 . * summary of sxlife moderator effects none 1082 . scalar sxLifeMedMw2 = "age illw2" 1083 . scalar sxLifeMedFw2 = "age bf4 bf4m" 1084 . 1085 . * no sign main dose effect for males 1086 . * no male moderators 1087 . * 3 signif main effects in male main effect model 1088 . 1089 . 1090 . * no signif dose main effect for females 1091 . * 3 main female effects 1092 . * no significant female moderators 1093 . matrix define HP2inthbMw2 = J(1,8, 0) 1094 . matrix define HP2inthbFw2 = J(1,8, 0) 1095 . matrix colnames HP2inthbMw2 = hypnum ptnum wave gender medsig numMAsig numMo > dsig numMed 1096 . matrix colnames HP2inthbFw2 = hypnum ptnum wave gender medsig numMAsig numMo > dsig numMed 1097 . matrix define HP2inthbMw2 = (1, 2, 3, 1, 0, 3, 0, 2 ) 1098 . matrix define HP2inthbFw2 = (1, 2, 3, 2, 0, 3, 0, 3 ) 1099 . matrix rowname HP2inthbMw2 = HP2inthbM 1100 . matrix rowname HP2inthbFw2 = HP2inthobF 1101 . matlist HP2inthbMw2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ HP2inthbM | 1 2 3 1 0 3 > 0 | c8 -------------+----------- HP2inthbM | 2 1102 . matlist HP2inthbFw2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ HP2inthobF | 1 2 3 2 0 3 > 0 | c8 -------------+----------- HP2inthobF | 3 1103 . matrix define H1pt2w2 = ( HP2wkMw2 \ HP2wkFw2 \ HP2hmcrMw2 \ HP2hmcrF > w2 \ HP2spMw2 /// > \ HP2spFw2 \ HP2prbfamMw2 \ HP2prbfamFw2 \ HP2inthbMw2 \ HP2inthb > Fw2 ) 1104 . matlist H1pt2w2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ r1 | 1 2 3 1 0 4 > 0 r1 | 1 2 3 2 0 1 > 0 r1 | 1 2 3 1 0 4 > 0 r1 | 1 2 3 2 0 2 > 0 HP2spMw2 | 1 2 3 1 0 2 > 0 HP2spFw2 | 1 2 3 2 1 5 > 5 HP2prbfamM | 1 2 3 1 0 5 > 0 HP2prbfamF | 1 2 3 2 0 2 > 2 HP2inthbM | 1 2 3 1 0 3 > 0 HP2inthobF | 1 2 3 2 0 3 > 0 | c8 -------------+----------- r1 | 4 r1 | 6 r1 | 2 r1 | 2 HP2spMw2 | 1 HP2spFw2 | 2 HP2prbfamM | 1 HP2prbfamF | 2 HP2inthbM | 2 HP2inthobF | 3 1105 . matrix colnames H1pt2w2 = hypnum ptnum wave gender medsig numMAsig numMo > dsig numMed 1106 . matrix rownames H1pt2w2 = HP2wkMw2 HP2wkFw2 HP2hmcrMw2 HP2hmcrFw2 HP2sp > Mw2 HP2spFw2 HP2prbfamMw2 HP2prbfamFw2 HP2inthbMw2 HP2inthbFw2 1107 . matlist H1pt2w2 | hypnum ptnum wave gender medsig numMAsig > numModsig -------------+----------------------------------------------------------------- ------------ HP2wkMw2 | 1 2 3 1 0 4 > 0 HP2wkFw2 | 1 2 3 2 0 1 > 0 HP2hmcrMw2 | 1 2 3 1 0 4 > 0 HP2hmcrFw2 | 1 2 3 2 0 2 > 0 HP2spMw2 | 1 2 3 1 0 2 > 0 HP2spFw2 | 1 2 3 2 1 5 > 5 HP2prbfamMw2 | 1 2 3 1 0 5 > 0 HP2prbfamFw2 | 1 2 3 2 0 2 > 2 HP2inthbMw2 | 1 2 3 1 0 3 > 0 HP2inthbFw2 | 1 2 3 2 0 3 > 0 | numMed -------------+----------- HP2wkMw2 | 4 HP2wkFw2 | 6 HP2hmcrMw2 | 2 HP2hmcrFw2 | 2 HP2spMw2 | 1 HP2spFw2 | 2 HP2prbfamMw2 | 1 HP2prbfamFw2 | 2 HP2inthbMw2 | 2 HP2inthbFw2 | 3 1108 . 1109 . *xxxxxxxxxxxxxxx chunk 9 Dose=> vacation plans impactxxxxxxxxxxxxxxxxxxxxxx > xxx 1110 . title "7. Hypothesis 1 pt 2 wave 2 dose vacation plans impact" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** 7. Hypothesis 1 pt 2 wave 2 dose vacation plans impact ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:06:27 ***** ******************************************************************************* ******************************************************************************* 1111 . 1112 . cap gen hp2vacatn = HP2vacatn 1113 . 1114 . forvalues j=2/2 { 2. title " wave 2 Dose = >hp2vacatn Female main effects models for H1 pt2" 3. set more off 4. local w1bf bf1 bf4 bf9 bf10 bf11 bf4m bf15m bf20 bf22 bf30 bf40 5. local w2bf bf1 bf4 bf6 bf7 bf14 bf15 bf40 6. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 b > f40 7. di _skip(3) 8. 1115 . di as input "Female model wave 2 dose-hp2vactn moderator model " 9. di _skip(4) 10. logit hp2vacatn age i.educ occ1w`j'-occ8w`j' /// > marrw`j'1- marrw`j'3 marrw`j'5-marrw`j'6 inc1w`j'-inc4w`j' /// > radhlw`j' havmil avgcumdosew`j' `w`j'bf' /// > deaw`j' dvcew`j' sepaw`j' accdw`j' movew`j' /// > illw`j' shfamw`j' shhlw`j' shjobw`j' shrelaw`j' suprtw`j' suchrw`j' havmils > q /// > radhlw`j' avgcumdosew`j' if gender==2, nolog 11. di _skip(4) 12. title3 "trimmed hp2hmcare main effects models for H1 no direct dose effec > t for male" 13. pwcorr hp2vacatn age deaw2 shjobw2 bf7m shjobw2 havmilsq /// > radhlw2 avgcumdosew2 if gender==2, sig obs sidak star(.05) listwise 14. di _skip(1) 15. di as input "For females trimmed vacation plans model on wave2 and d2 is n > ot signif " 16. di _skip(1) 17. logistic hp2vacatn age deaw2 shjobw2 bf7m havmil /// > radhlw2 avgcumdosew2 if /// > gender==2, coef nolog 18. } ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** wave 2 Dose = >hp2vacatn Female main effects models for H1 pt2 ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:06:27 ***** ******************************************************************************* ******************************************************************************* Female model wave 2 dose-hp2vactn moderator model note: 1.educ != 0 predicts success perfectly 1.educ dropped and 1 obs not used note: bf15m != 0 predicts failure perfectly bf15m dropped and 12 obs not used note: sepaw2 != 0 predicts failure perfectly sepaw2 dropped and 7 obs not used note: 7.educ omitted because of collinearity note: marrw26 omitted because of collinearity note: bf17 omitted because of collinearity note: radhlw2 omitted because of collinearity note: avgcumdosew2 omitted because of collinearity Logistic regression Number of obs = 342 LR chi2(49) = 109.68 Prob > chi2 = 0.0000 Log likelihood = -105.52458 Pseudo R2 = 0.3420 ------------------------------------------------------------------------------ hp2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0854768 .0223995 3.82 0.000 .0415747 .129379 | educ | 1 | 0 (empty) 2 | -.1593387 2.114879 -0.08 0.940 -4.304426 3.985748 3 | .0597641 2.056969 0.03 0.977 -3.971821 4.091349 4 | 1.938135 2.166629 0.89 0.371 -2.308379 6.184649 5 | .5746086 2.110158 0.27 0.785 -3.561224 4.710442 6 | .514209 2.047201 0.25 0.802 -3.498231 4.526649 7 | 0 (omitted) | occ1w2 | -1.136832 2.2792 -0.50 0.618 -5.603982 3.330319 occ2w2 | -.2856505 2.305307 -0.12 0.901 -4.803969 4.232668 occ3w2 | -.2169427 2.294811 -0.09 0.925 -4.714689 4.280804 occ4w2 | -.683475 2.371054 -0.29 0.773 -5.330655 3.963705 occ5w2 | -1.371472 2.630882 -0.52 0.602 -6.527906 3.784962 occ6w2 | .0514752 2.451671 0.02 0.983 -4.753711 4.856661 occ7w2 | -.1816542 2.247999 -0.08 0.936 -4.587651 4.224342 occ8w2 | .8447307 2.549827 0.33 0.740 -4.152839 5.8423 marrw21 | -.3962689 1.357581 -0.29 0.770 -3.057079 2.264541 marrw22 | -.7029234 1.344493 -0.52 0.601 -3.338081 1.932234 marrw23 | -.2946087 .8362371 -0.35 0.725 -1.933603 1.344386 marrw25 | -.3847095 1.312384 -0.29 0.769 -2.956935 2.187516 marrw26 | 0 (omitted) inc1w2 | -.1652011 2.308319 -0.07 0.943 -4.689424 4.359021 inc2w2 | .5715606 2.259621 0.25 0.800 -3.857216 5.000337 inc3w2 | .4239638 2.273454 0.19 0.852 -4.031925 4.879853 inc4w2 | -.1023039 2.63108 -0.04 0.969 -5.259126 5.054518 radhlw2 | .0311845 .0092957 3.35 0.001 .0129652 .0494037 havmil | .0003107 .0031022 0.10 0.920 -.0057695 .006391 avgcumdosew2 | .0845855 .1349954 0.63 0.531 -.1800007 .3491716 bf1 | -.0293284 .0365427 -0.80 0.422 -.1009508 .042294 bf4 | -.035894 .2239004 -0.16 0.873 -.4747308 .4029428 bf2 | .0000573 .0001382 0.41 0.678 -.0002135 .0003281 bf4m | -.0145711 .2099542 -0.07 0.945 -.4260738 .3969316 bf5m | -.0005728 .0016599 -0.35 0.730 -.0038262 .0026805 bf7m | -.0009478 .0006252 -1.52 0.130 -.0021731 .0002775 bf8 | -.0000327 .0000408 -0.80 0.422 -.0001126 .0000472 bf15m | 0 (omitted) bf17 | 0 (omitted) bf20 | .0039952 .030974 0.13 0.897 -.0567128 .0647032 bf22 | .0000865 .0001546 0.56 0.576 -.0002165 .0003895 bf29 | .0000586 .0000394 1.49 0.137 -.0000187 .000136 bf30 | -.0003297 .0003684 -0.89 0.371 -.0010518 .0003924 bf40 | -.1986581 .1714089 -1.16 0.246 -.5346134 .1372973 deaw2 | .2948255 .2283224 1.29 0.197 -.1526782 .7423292 dvcew2 | 1.55263 1.538911 1.01 0.313 -1.463579 4.56884 sepaw2 | 0 (omitted) accdw2 | -.6724545 .7843213 -0.86 0.391 -2.209696 .8647871 movew2 | -.2536562 .8260121 -0.31 0.759 -1.87261 1.365298 illw2 | .1944001 .2307879 0.84 0.400 -.2579358 .646736 shfamw2 | .010723 .0072538 1.48 0.139 -.0034942 .0249402 shhlw2 | .0100536 .0078834 1.28 0.202 -.0053977 .0255048 shjobw2 | -.0092745 .0068673 -1.35 0.177 -.0227342 .0041852 shrelaw2 | -.0224849 .0085804 -2.62 0.009 -.0393023 -.0056675 suprtw2 | -.0043936 .0060874 -0.72 0.470 -.0163246 .0075375 suchrw2 | .0021145 .0069953 0.30 0.762 -.0115961 .015825 havmilsq | -6.40e-07 2.42e-06 -0.26 0.792 -5.39e-06 4.11e-06 radhlw2 | 0 (omitted) avgcumdosew2 | 0 (omitted) _cons | -5.212149 3.615206 -1.44 0.149 -12.29782 1.873525 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------- title3 : trimmed hp2hmcare main effects models for H1 no direct dose effect for > male 1 Jul 2012 15:06:28 computer Macintosh (Intel 64-bit) folder /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/ > h1pt2 Data file chwide1jul2012.dta currrently has 2399 variables and 703 observ > ations | hp2vac~n age deaw2 shjobw2 bf7m shjobw2 havmilsq -------------+--------------------------------------------------------------- hp2vacatn | 1.0000 | | 363 | age | 0.3765* 1.0000 | 0.0000 | 363 363 | deaw2 | 0.1195 0.1404 1.0000 | 0.5641 0.2337 | 363 363 363 | shjobw2 | -0.0747 -0.0331 -0.0711 1.0000 | 0.9977 1.0000 0.9991 | 363 363 363 363 | bf7m | -0.0670 -0.0780 0.0244 -0.1112 1.0000 | 0.9997 0.9953 1.0000 0.7134 | 363 363 363 363 363 | shjobw2 | -0.0747 -0.0331 -0.0711 1.0000* -0.1112 1.0000 | 0.9977 1.0000 0.9991 0.0000 0.7134 | 363 363 363 363 363 363 | havmilsq | -0.0378 -0.0465 -0.0150 0.0080 0.0150 0.0080 1.0000 | 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 | 363 363 363 363 363 363 363 | radhlw2 | 0.2650* 0.2605* 0.1095 -0.0109 0.2974* -0.0109 -0.0284 | 0.0000 0.0000 0.7430 1.0000 0.0000 1.0000 1.0000 | 363 363 363 363 363 363 363 | avgcumdosew2 | 0.1224 0.1748* -0.0214 0.0826 -0.0270 0.0826 -0.0052 | 0.5104 0.0292 1.0000 0.9883 1.0000 0.9883 1.0000 | 363 363 363 363 363 363 363 | | radhlw2 avgcum~2 -------------+------------------ radhlw2 | 1.0000 | | 363 | avgcumdosew2 | 0.1342 1.0000 | 0.3162 | 363 363 | For females trimmed vacation plans model on wave2 and d2 is not signif Logistic regression Number of obs = 363 LR chi2(7) = 76.69 Prob > chi2 = 0.0000 Log likelihood = -129.17206 Pseudo R2 = 0.2289 ------------------------------------------------------------------------------ hp2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .079348 .0155749 5.09 0.000 .0488217 .1098742 deaw2 | .1646231 .1715933 0.96 0.337 -.1716935 .5009397 shjobw2 | -.0056137 .0041432 -1.35 0.175 -.0137341 .0025068 bf7m | -.0006133 .00025 -2.45 0.014 -.0011032 -.0001234 havmil | -.0001958 .0011139 -0.18 0.860 -.0023791 .0019875 radhlw2 | .0233367 .005861 3.98 0.000 .0118494 .034824 avgcumdosew2 | .0651649 .0909818 0.72 0.474 -.1131561 .243486 _cons | -6.606305 1.018983 -6.48 0.000 -8.603475 -4.609135 ------------------------------------------------------------------------------ 1116 . 1117 . // vacation plans washes out for females also 1118 . 1119 . 1120 . scalar SigDoseVactnMw2 = "no" 1121 . scalar MainEffVactnMw2 = "age bf7m radhlw2 " 1122 . 1123 . local cn7:colnames(e(b)) 1124 . di "`cn7'" age deaw2 shjobw2 bf7m havmil radhlw2 avgcumdosew2 _cons 1125 . local len7 = length("`cn7'") 1126 . di `len7' 56 1127 . local len7b = `len7' - 6 1128 . di `len7b' 50 1129 . local myvarlist = substr("`cn7'",1,`len7b') 1130 . di "`myvarlist'" age deaw2 shjobw2 bf7m havmil radhlw2 avgcumdosew2 1131 . 1132 . foreach var in `myvarlist' { 2. cap gen `var'Xd3 = `var'*avgcumdosew2 3. } 1133 . 1134 . title " Trimmed male main effects dose=> vacation plans model" ******************************************************************************* ******************************************************************************* ***** ***** ***** ***** ***** Trimmed male main effects dose=> vacation plans model ***** ***** ***** ***** ***** ***** 1 Jul 2012 15:06:30 ***** ******************************************************************************* ******************************************************************************* 1135 . di as input "No sig main male dose main effects model" No sig main male dose main effects model 1136 . sw, pr(.1): logit hp2vacatn `myvarlist' if gender==1 begin with full model p = 0.7095 >= 0.1000 removing deaw2 p = 0.6626 >= 0.1000 removing avgcumdosew2 p = 0.4924 >= 0.1000 removing shjobw2 p = 0.4544 >= 0.1000 removing havmil p = 0.1243 >= 0.1000 removing bf7m Logistic regression Number of obs = 340 LR chi2(2) = 36.09 Prob > chi2 = 0.0000 Log likelihood = -107.10862 Pseudo R2 = 0.1442 ------------------------------------------------------------------------------ hp2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0587352 .0156958 3.74 0.000 .027972 .0894984 radhlw2 | .0163217 .005398 3.02 0.002 .0057417 .0269016 _cons | -6.0454 .8953448 -6.75 0.000 -7.800244 -4.290557 ------------------------------------------------------------------------------ 1137 . 1138 . 1139 . local cn8:colnames(e(b)) 1140 . di "`cn8'" age radhlw2 _cons 1141 . local len8 = length("`cn8'") 1142 . di `len7' 56 1143 . local len8b = `len8' - 6 1144 . di `len8b' 11 1145 . local myvarlist = substr("`cn8'",1,`len8b') 1146 . di "`myvarlist'" age radhlw2 1147 . 1148 . 1149 . 1150 . title4 "Trimmed male interaction dose=> vacation plans model" ------------------------------------------------------------------------------- Trimmed male interaction dose=> vacation plans model ------------------------------------------------------------------------------- 1151 . logit hp2vacatn age radhlw2 ageXd3 bf7m avgcumdosew2 bf4m bf4mXd3 bf7mXd3 if > gender==1 Iteration 0: log likelihood = -125.15243 Iteration 1: log likelihood = -103.75295 Iteration 2: log likelihood = -95.975162 Iteration 3: log likelihood = -95.713486 Iteration 4: log likelihood = -95.674656 Iteration 5: log likelihood = -95.673437 Iteration 6: log likelihood = -95.673437 Logistic regression Number of obs = 340 LR chi2(8) = 58.96 Prob > chi2 = 0.0000 Log likelihood = -95.673437 Pseudo R2 = 0.2355 ------------------------------------------------------------------------------ hp2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0351266 .0232179 1.51 0.130 -.0103796 .0806328 radhlw2 | .0006782 .0066781 0.10 0.919 -.0124106 .0137669 ageXd3 | .0103149 .0213141 0.48 0.628 -.0314599 .0520897 bf7m | .0001809 .0004722 0.38 0.702 -.0007446 .0011065 avgcumdosew2 | -.5848691 1.717213 -0.34 0.733 -3.950545 2.780807 bf4m | -.1548807 .0718689 -2.16 0.031 -.2957412 -.0140202 bf4mXd3 | -.0235592 .0602635 -0.39 0.696 -.1416735 .094555 bf7mXd3 | .000194 .0003621 0.54 0.592 -.0005158 .0009038 _cons | -1.171738 1.695722 -0.69 0.490 -4.495293 2.151816 ------------------------------------------------------------------------------ 1152 . 1153 . scalar vactnModMw2 ="none" 1154 . 1155 . di as input "Trimmed Female model wave 2 main effects dose-hp2vacatn model " Trimmed Female model wave 2 main effects dose-hp2vacatn model 1156 . forvalues j=2/2 { 2. local w2bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 > bf40 3. 1157 . xi: logistic hp2vacatn age radhlw`j' avgcumdosew`j' /// > deaw`j' suchrw`j' /// > if gender==2, coef nolog difficult iterate(50) 4. 1158 . } Logistic regression Number of obs = 363 LR chi2(5) = 70.03 Prob > chi2 = 0.0000 Log likelihood = -132.50308 Pseudo R2 = 0.2090 ------------------------------------------------------------------------------ hp2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0851818 .0154813 5.50 0.000 .054839 .1155246 radhlw2 | .0170145 .0053804 3.16 0.002 .0064692 .0275599 avgcumdosew2 | .0786711 .0879844 0.89 0.371 -.0937752 .2511173 deaw2 | .201149 .1681372 1.20 0.232 -.1283938 .5306918 suchrw2 | -.0020193 .0038609 -0.52 0.601 -.0095866 .005548 _cons | -7.431055 .9998597 -7.43 0.000 -9.390744 -5.471366 ------------------------------------------------------------------------------ 1159 . 1160 . scalar SigDoseVactnMw2 = "no" 1161 . 1162 . * summary of male moderating effects: no sign main dose effect in main effec > ts model 1163 . * no signif male moderators 1164 . * 3 significant main effects in main effects model 1165 . 1166 . * summary of female moderation main effects: no signif main dose effect 1167 . * 3 signif main effects 1168 . 1169 . local cn9:colnames(e(b)) 1170 . di "`cn9'" age radhlw2 avgcumdosew2 deaw2 suchrw2 _cons 1171 . local len9 = length("`cn9'") 1172 . di `len9' 44 1173 . local len9b = `len9' - 6 1174 . di `len9b' 38 1175 . local myvarlist = substr("`cn9'",1,`len9b') 1176 . di "`myvarlist'" age radhlw2 avgcumdosew2 deaw2 suchrw2 1177 . 1178 . 1179 . foreach var in `myvarlist' { 2. cap gen `var'Xd3 = `var'*avgcumdosew2 3. } 1180 . 1181 . 1182 . * female dose vacatn w2 models 1183 . 1184 . title4 "trimmed hp2vacatn wave3 main effects models for H1" ------------------------------------------------------------------------------- trimmed hp2vacatn wave3 main effects models for H1 ------------------------------------------------------------------------------- 1185 . di as input "For females hp2vacatn on wave3" For females hp2vacatn on wave3 1186 . sw, pr(.1):logit hp2vacatn `myvarlist' if gender==2 begin with full model p = 0.6010 >= 0.1000 removing suchrw2 p = 0.4044 >= 0.1000 removing avgcumdosew2 p = 0.2500 >= 0.1000 removing deaw2 Logistic regression Number of obs = 363 LR chi2(2) = 67.78 Prob > chi2 = 0.0000 Log likelihood = -133.624 Pseudo R2 = 0.2023 ------------------------------------------------------------------------------ hp2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .087459 .0153711 5.69 0.000 .0573321 .1175858 radhlw2 | .0183043 .0051588 3.55 0.000 .0081933 .0284153 _cons | -7.552753 .9504531 -7.95 0.000 -9.415607 -5.689899 ------------------------------------------------------------------------------ 1187 . estat class Logistic model for hp2vacatn -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 18 3 | 21 - | 45 297 | 342 -----------+--------------------------+----------- Total | 63 300 | 363 Classified + if predicted Pr(D) >= .5 True D defined as hp2vacatn != 0 -------------------------------------------------- Sensitivity Pr( +| D) 28.57% Specificity Pr( -|~D) 99.00% Positive predictive value Pr( D| +) 85.71% Negative predictive value Pr(~D| -) 86.84% -------------------------------------------------- False + rate for true ~D Pr( +|~D) 1.00% False - rate for true D Pr( -| D) 71.43% False + rate for classified + Pr(~D| +) 14.29% False - rate for classified - Pr( D| -) 13.16% -------------------------------------------------- Correctly classified 86.78% -------------------------------------------------- 1188 . estat gof Logistic model for hp2vacatn, goodness-of-fit test number of observations = 363 number of covariate patterns = 219 Pearson chi2(216) = 284.26 Prob > chi2 = 0.0013 1189 . fitstat Measures of Fit for logit of hp2vacatn Log-Lik Intercept Only: -167.516 Log-Lik Full Model: -133.624 D(360): 267.248 LR(2): 67.784 Prob > LR: 0.000 McFadden's R2: 0.202 McFadden's Adj R2: 0.184 Maximum Likelihood R2: 0.170 Cragg & Uhler's R2: 0.283 McKelvey and Zavoina's R2: 0.354 Efron's R2: 0.237 Variance of y*: 5.091 Variance of error: 3.290 Count R2: 0.868 Adj Count R2: 0.238 AIC: 0.753 AIC*n: 273.248 BIC: -1854.737 BIC': -55.995 1190 . 1191 . scalar SigDoseVactnFw2 = "no" 1192 . scalar MainEffVactnFw2 = "age radhlw2 deaw2" 1193 . 1194 . di as result " Full moderator model for females for dose=> Vacation plans" Full moderator model for females for dose=> Vacation plans 1195 . logit hp2vacatn age radhlw2 deaw2 suchrw2 avgcumdosew2 ageXd3 radhlw2Xd3 deaw > 2Xd3 /// > suchrw2Xd3 havmilsq if gender==2 Iteration 0: log likelihood = -167.516 Iteration 1: log likelihood = -135.52597 Iteration 2: log likelihood = -131.52279 Iteration 3: log likelihood = -131.2764 Iteration 4: log likelihood = -131.27069 Iteration 5: log likelihood = -131.27061 Iteration 6: log likelihood = -131.27061 Logistic regression Number of obs = 363 LR chi2(10) = 72.49 Prob > chi2 = 0.0000 Log likelihood = -131.27061 Pseudo R2 = 0.2164 ------------------------------------------------------------------------------ hp2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0708879 .0190976 3.71 0.000 .0334572 .1083185 radhlw2 | .0165656 .0064315 2.58 0.010 .0039601 .0291712 deaw2 | .3060548 .241868 1.27 0.206 -.1679977 .7801073 suchrw2 | -.0009912 .0046331 -0.21 0.831 -.0100718 .0080894 avgcumdosew2 | -.9450174 1.222942 -0.77 0.440 -3.34194 1.451905 ageXd3 | .0137792 .0133034 1.04 0.300 -.012295 .0398534 radhlw2Xd3 | .001068 .0036864 0.29 0.772 -.0061572 .0082931 deaw2Xd3 | -.1266981 .2241693 -0.57 0.572 -.5660619 .3126657 suchrw2Xd3 | -.0004938 .0024641 -0.20 0.841 -.0053234 .0043359 havmilsq | -5.05e-07 1.35e-06 -0.37 0.708 -3.15e-06 2.14e-06 _cons | -6.701508 1.270732 -5.27 0.000 -9.192096 -4.210919 ------------------------------------------------------------------------------ 1196 . 1197 . di as result "trimmed female moderator model for dose=> vacation plans" trimmed female moderator model for dose=> vacation plans 1198 . logit hp2vacatn age avgcumdosew2 suchrw2 deaw2 deaw2Xd3 if gender==2 Iteration 0: log likelihood = -167.516 Iteration 1: log likelihood = -140.72298 Iteration 2: log likelihood = -137.85316 Iteration 3: log likelihood = -137.83165 Iteration 4: log likelihood = -137.83165 Logistic regression Number of obs = 363 LR chi2(5) = 59.37 Prob > chi2 = 0.0000 Log likelihood = -137.83165 Pseudo R2 = 0.1772 ------------------------------------------------------------------------------ hp2vacatn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0896607 .0149698 5.99 0.000 .0603204 .1190011 avgcumdosew2 | .1488443 .1025315 1.45 0.147 -.0521136 .3498023 suchrw2 | -.0054654 .0036554 -1.50 0.135 -.0126297 .001699 deaw2 | .2935759 .2210405 1.33 0.184 -.1396555 .7268074 deaw2Xd3 | -.0992117 .1869089 -0.53 0.596 -.4655464 .267123 _cons | -6.416219 .881865 -7.28 0.000 -8.144642 -4.687795 ------------------------------------------------------------------------------ 1199 . 1200 . scalar VacatnModFw2 = "none" 1201 . *xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx > xxx 1202 . cap gen hp2vactn = HP2vacatn 1203 . title4 "Chunk 9 HP2vacatn plans mediator models for males" ------------------------------------------------------------------------------- Chunk 9 HP2vacatn plans mediator models for males ------------------------------------------------------------------------------- 1204 . 1205 . * age is a mediator for males 1206 . glm age avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1330.6004 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 147.6853 Deviance = 49917.64009 (1/df) Deviance = 147.6853 Pearson = 49917.64009 (1/df) Pearson = 147.6853 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.838826 Log likelihood = -1330.6004 BIC = 47947.46 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .5832314 .2635871 2.21 0.027 .0666101 1.099853 _cons | 48.62133 .7061562 68.85 0.000 47.23729 50.00537 ------------------------------------------------------------------------------ 1207 . glm hp2vactn age if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 230.5483 Iteration 2: deviance = 224.6254 Iteration 3: deviance = 224.4149 Iteration 4: deviance = 224.4147 Iteration 5: deviance = 224.4147 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 224.4146771 (1/df) Deviance = .6639487 Pearson = 345.2370578 (1/df) Pearson = 1.021411 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1745.769 ------------------------------------------------------------------------------ | EIM hp2vactn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0377399 .0063833 5.91 0.000 .0252289 .0502509 _cons | -3.150096 .3549593 -8.87 0.000 -3.845803 -2.454388 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1208 . 1209 . * illness is a mediating effect for males = > vacatn for men 1210 . des illw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- illw2 double %8.0g Total number of illnesses experienced in time period 1987-1996 1211 . glm illw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -303.59609 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = .3512988 Deviance = 118.7390083 (1/df) Deviance = .3512988 Pearson = 118.7390083 (1/df) Pearson = .3512988 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 1.797624 Log likelihood = -303.5960853 BIC = -1851.445 ------------------------------------------------------------------------------ | OIM illw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .0085423 .0128556 0.66 0.506 -.0166543 .0337389 _cons | .2741359 .0344406 7.96 0.000 .2066336 .3416382 ------------------------------------------------------------------------------ 1212 . glm hp2vactn illw2 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 246.5506 Iteration 2: deviance = 245.3093 Iteration 3: deviance = 245.3029 Iteration 4: deviance = 245.3029 Iteration 5: deviance = 245.3029 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 245.3029322 (1/df) Deviance = .7257483 Pearson = 335.4909761 (1/df) Pearson = .9925769 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1724.881 ------------------------------------------------------------------------------ | EIM hp2vactn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2 | .3031606 .1114242 2.72 0.007 .0847731 .5215481 _cons | -1.277579 .0863769 -14.79 0.000 -1.446875 -1.108284 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1213 . 1214 . des radhlw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- radhlw2 double %8.0g Self-perceived Chornobyl health threat in wave 2 1215 . glm radhlw2 avgcumdosew2 if gender==1, fam(gaus) link(identity) Iteration 0: log likelihood = -1693.4076 Generalized linear models No. of obs = 340 Optimization : ML Residual df = 338 Scale parameter = 1247.933 Deviance = 421801.4584 (1/df) Deviance = 1247.933 Pearson = 421801.4584 (1/df) Pearson = 1247.933 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.972986 Log likelihood = -1693.407647 BIC = 419831.3 ------------------------------------------------------------------------------ | OIM radhlw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.220373 .766216 1.59 0.111 -.2813831 2.722128 _cons | 45.63198 2.052711 22.23 0.000 41.60874 49.65522 ------------------------------------------------------------------------------ 1216 . glm hp2vactn radhlw2 if gender==1, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 234.5448 Iteration 2: deviance = 229.6041 Iteration 3: deviance = 229.4465 Iteration 4: deviance = 229.4462 Iteration 5: deviance = 229.4462 Generalized linear models No. of obs = 340 Optimization : MQL Fisher scoring Residual df = 338 (IRLS EIM) Scale parameter = 1 Deviance = 229.4462113 (1/df) Deviance = .6788349 Pearson = 340.4683102 (1/df) Pearson = 1.007303 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1740.737 ------------------------------------------------------------------------------ | EIM hp2vactn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- radhlw2 | .0116586 .0021811 5.35 0.000 .0073837 .0159334 _cons | -1.817407 .1519393 -11.96 0.000 -2.115203 -1.519612 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1217 . 1218 . title4 "HP2vacatn plans mediator models for females" ------------------------------------------------------------------------------- HP2vacatn plans mediator models for females ------------------------------------------------------------------------------- 1219 . 1220 . * age is a mediator for females 1221 . glm age avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1406.9403 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 136.9184 Deviance = 49427.52828 (1/df) Deviance = 136.9184 Pearson = 49427.52828 (1/df) Pearson = 136.9184 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 7.762756 Log likelihood = -1406.940271 BIC = 47299.65 ------------------------------------------------------------------------------ | OIM age | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 1.502324 .4454009 3.37 0.001 .6293547 2.375294 _cons | 48.86944 .7323225 66.73 0.000 47.43412 50.30477 ------------------------------------------------------------------------------ 1222 . glm hp2vactn age if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 288.2228 Iteration 2: deviance = 282.9212 Iteration 3: deviance = 282.8 Iteration 4: deviance = 282.8 Iteration 5: deviance = 282.8 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 282.7999835 (1/df) Deviance = .7833795 Pearson = 396.9154874 (1/df) Pearson = 1.099489 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1845.079 ------------------------------------------------------------------------------ | EIM hp2vactn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0510238 .0068535 7.44 0.000 .0375912 .0644564 _cons | -3.660305 .3855366 -9.49 0.000 -4.415943 -2.904667 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1223 . 1224 . * illness is a mediating effect for females = > vacatn 1225 . des illw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- illw2 double %8.0g Total number of illnesses experienced in time period 1987-1996 1226 . glm illw2 avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -463.51524 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = .756881 Deviance = 273.2340487 (1/df) Deviance = .756881 Pearson = 273.2340487 (1/df) Pearson = .756881 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 2.564822 Log likelihood = -463.5152411 BIC = -1854.645 ------------------------------------------------------------------------------ | OIM illw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | .1249912 .0331157 3.77 0.000 .0600856 .1898968 _cons | .301285 .0544484 5.53 0.000 .194568 .4080019 ------------------------------------------------------------------------------ 1227 . glm hp2vactn illw2 if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 329.874 Iteration 2: deviance = 329.8007 Iteration 3: deviance = 329.8005 Iteration 4: deviance = 329.8005 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 329.8005369 (1/df) Deviance = .9135749 Pearson = 363.0908863 (1/df) Pearson = 1.005792 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1798.079 ------------------------------------------------------------------------------ | EIM hp2vactn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- illw2 | .1861547 .0776301 2.40 0.016 .0340026 .3383068 _cons | -1.027515 .0837109 -12.27 0.000 -1.191586 -.8634451 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1228 . 1229 . * radhlw2 is a mediating effect for females => vactn 1230 . des radhlw2 storage display value variable name type format label variable label ------------------------------------------------------------------------------- radhlw2 double %8.0g Self-perceived Chornobyl health threat in wave 2 1231 . glm radhlw2 avgcumdosew2 if gender==2, fam(gaus) link(identity) Iteration 0: log likelihood = -1791.2233 Generalized linear models No. of obs = 363 Optimization : ML Residual df = 361 Scale parameter = 1137.567 Deviance = 410661.5604 (1/df) Deviance = 1137.567 Pearson = 410661.5604 (1/df) Pearson = 1137.567 Variance function: V(u) = 1 [Gaussian] Link function : g(u) = u [Identity] AIC = 9.880018 Log likelihood = -1791.223306 BIC = 408533.7 ------------------------------------------------------------------------------ | OIM radhlw2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- avgcumdosew2 | 3.302288 1.283833 2.57 0.010 .7860214 5.818555 _cons | 56.95167 2.110863 26.98 0.000 52.81445 61.08888 ------------------------------------------------------------------------------ 1232 . glm hp2vactn radhlw2 if gender==2, fam(bin) irls scale(dev) link(probit) Iteration 1: deviance = 310.7426 Iteration 2: deviance = 307.9144 Iteration 3: deviance = 307.878 Iteration 4: deviance = 307.878 Iteration 5: deviance = 307.878 Generalized linear models No. of obs = 363 Optimization : MQL Fisher scoring Residual df = 361 (IRLS EIM) Scale parameter = 1 Deviance = 307.8780006 (1/df) Deviance = .8528476 Pearson = 369.926962 (1/df) Pearson = 1.024728 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = invnorm(u) [Probit] BIC = -1820.001 ------------------------------------------------------------------------------ | EIM hp2vactn | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- radhlw2 | .0127779 .0023866 5.35 0.000 .0081002 .0174555 _cons | -1.793767 .1848064 -9.71 0.000 -2.15598 -1.431553 ------------------------------------------------------------------------------ (Standard errors scaled using square root of deviance-based dispersion.) 1233 . 1234 . * summary of male moderating effects: no sign main dose effect in main effec > ts model 1235 . * no signif male moderators 1236 . * 3 significant main effects in main effects model 1237 . 1238 . scalar VactnMedMw2 = "age illw2" 1239 . scalar VactnMedFw2 = "age illw2 radhlw2" 1240 . 1241 . *xx summary of moderator effects for females: 1242 . * no signif main dose effect 1243 . * 3 signif main effects in main effect model 1244 . * 1 moderator: deaw2Xd3 1245 . title4 "7. SUMMARY MATRIX of dose - vacation plans impact" ------------------------------------------------------------------------------- 7. SUMMARY MATRIX of dose - vacation plans impact ------------------------------------------------------------------------------- 1246 . set more off 1247 . matrix define HP2vactnMw2 = J(1,8, 0) 1248 . matrix define HP2vactnFw2 = J(1,8, 0) 1249 . matrix colnames HP2vactnMw2= hypnum ptnum wave gender medsig numMAsig numMod > sig numMed 1250 . matrix colnames HP2vactnFw2= hypnum ptnum wave gender medsig numMAsig numMod > sig numMed 1251 . matrix define HP2vactnMw2= (1, 2, 3, 1, 0, 3, 0, 2 ) 1252 . matrix define HP2vactnFw2= (1, 2, 3, 2, 0, 3, 1, 3 ) 1253 . matrix rowname HP2vactnMw2 = HP2vactnM 1254 . matrix rowname HP2vactnFw2 = HP2vactnF 1255 . matlist HP2vactnMw2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ HP2vactnM | 1 2 3 1 0 3 > 0 | c8 -------------+----------- HP2vactnM | 2 1256 . matlist HP2vactnFw2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ HP2vactnF | 1 2 3 2 0 3 > 1 | c8 -------------+----------- HP2vactnF | 3 1257 . matrix define H1pt2w2=(HP2wkMw2 \ HP2wkFw2 \ HP2hmcrMw2 \ HP2hmcrFw2 \ HP2spM > w2 \ HP2spFw2 \ HP2prbfamMw2 \ HP2prbfamFw2 \ HP2inthbMw2 \ HP2inthbFw2 \ HP2 > vactnMw2 \ HP2vactnFw2 ) 1258 . 1259 . matlist H1pt2w2 | c1 c2 c3 c4 c5 c6 > c7 -------------+----------------------------------------------------------------- ------------ r1 | 1 2 3 1 0 4 > 0 r1 | 1 2 3 2 0 1 > 0 r1 | 1 2 3 1 0 4 > 0 r1 | 1 2 3 2 0 2 > 0 HP2spMw2 | 1 2 3 1 0 2 > 0 HP2spFw2 | 1 2 3 2 1 5 > 5 HP2prbfamM | 1 2 3 1 0 5 > 0 HP2prbfamF | 1 2 3 2 0 2 > 2 HP2inthbM | 1 2 3 1 0 3 > 0 HP2inthobF | 1 2 3 2 0 3 > 0 HP2vactnM | 1 2 3 1 0 3 > 0 HP2vactnF | 1 2 3 2 0 3 > 1 | c8 -------------+----------- r1 | 4 r1 | 6 r1 | 2 r1 | 2 HP2spMw2 | 1 HP2spFw2 | 2 HP2prbfamM | 1 HP2prbfamF | 2 HP2inthbM | 2 HP2inthobF | 3 HP2vactnM | 2 HP2vactnF | 3 1260 . matrix colnames H1pt2w2 = hypnum ptnum wave gender medsig numMAsig numMo > dsig numMed 1261 . matrix rownames H1pt2w2 =HP2wkMw2 HP2wkFw2 HP2hmcrMw2 HP2hmcrFw2 HP2socprbMw2 > HP2socprbFw2 HP2prbfamMw2 HP2prbfamFw2 HP2inthbMw2 HP2inthbFw2 HP2vacatnMw2 > HP2vacatnFw2 1262 . matlist H1pt2w2 | hypnum ptnum wave gender medsig numMAsig > numModsig -------------+----------------------------------------------------------------- ------------ HP2wkMw2 | 1 2 3 1 0 4 > 0 HP2wkFw2 | 1 2 3 2 0 1 > 0 HP2hmcrMw2 | 1 2 3 1 0 4 > 0 HP2hmcrFw2 | 1 2 3 2 0 2 > 0 HP2socprbMw2 | 1 2 3 1 0 2 > 0 HP2socprbFw2 | 1 2 3 2 1 5 > 5 HP2prbfamMw2 | 1 2 3 1 0 5 > 0 HP2prbfamFw2 | 1 2 3 2 0 2 > 2 HP2inthbMw2 | 1 2 3 1 0 3 > 0 HP2inthbFw2 | 1 2 3 2 0 3 > 0 HP2vacatnMw2 | 1 2 3 1 0 3 > 0 HP2vacatnFw2 | 1 2 3 2 0 3 > 1 | numMed -------------+----------- HP2wkMw2 | 4 HP2wkFw2 | 6 HP2hmcrMw2 | 2 HP2hmcrFw2 | 2 HP2socprbMw2 | 1 HP2socprbFw2 | 2 HP2prbfamMw2 | 1 HP2prbfamFw2 | 2 HP2inthbMw2 | 2 HP2inthbFw2 | 3 HP2vacatnMw2 | 2 HP2vacatnFw2 | 3 1263 . 1264 . 1265 . sjlog close, replace