. set seed 1010101

. set matsize 11000

.
. set linesize 80

. *******
.
. *   This script requires pre-installation of spost2 by J. Scott Long for fitstat command
. *   It also requires installation of title2.ado, title3.ado
. *
. *      for the fitstat command to function
. ******
.
. global User  "Robert Alan Yaffee"

. * 17 June 2012
.
. *------- Primary objective:  Hypothesis 1 tests for Part 2 of Nottingham health profile
.
. *********  Hypothesis tests      Robert A. Yaffee    10 June 2012 main effects and moderator identification approach
. *  protocol is to test Health profile subscales against potential confounders including socio-demog vars,
. *     major neg life events, stresses and hassles, social supports, perceived health, distance,
. *     threat and dose to see whether there is a dose-psych response
.
. *  We construct a full model, trimmed model (at .1 level), and then test interactions with dose if there is a
. *     significant main effect dose psych response
.
. *     Trimming is performed with backward elimination to the .1 level
. *
. *  This approach identifies the moderating variables for our path analysis.    We analyze our
. *     final models for congruence with regression assumptions with the rdiag program for a validity assessment
.
. *  In this section, we decompose the effects into direct and indirect and total with a structural equation model.
.
.
. *----------------------------------------------------------------------------------------------------------
.
. *----------- Organization of the program --------------------------------------------------------------------------
. *  main effects regresssions are run for all part 1 health profile subscales except that of emotional
. *    reaction for both males and females separately
. *  these models are trimmed with backward elimination to determine whether the main effect of avg cumulative dose
. *     is significant for that wave
. *   If the main effect of average cumulative dose for that wave is not statistically significant with the
. *     covariates controlled we do not go further along that path
. *   If the main effect of average cumulative dose for that wave is statistically significant, we trim the
. *     model to a p = .1
. *   If average cumulative dose is not significant, we stop
. *   If average cumulative dose is significant we perform a hierarchical regression with interactions between dose
. *      and other significant main effects
. *   We proceed to test for mediating effects of those other significant explanatory variable
.
. ***------------------------------------------------------------------------------------------------------------------
.
. * Part 2 Nottingham subscales
. *  1: hp2work
. *  2: hp2hmcare
. *  3: hp2probsoc
. *  4: hp2pbfhm
. *  5: hp2sexlife
. *  6: hp2inthob
. *  7: hp2vacatn
.
. local dvwhole HP2work-HP2vacatn

. local dv2 HP2work HP2hmcare HP2probsoc HP2pbfhm HP2sxlife HP2inthob HP2vacatn

.
.
.
. *************************************
. di "$User"
Robert Alan Yaffee

.
. title "Testing Hypothesis 1 part 2  wave 1"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****                Testing Hypothesis 1 part 2  wave 1                  *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:07  *****
*******************************************************************************
*******************************************************************************


.
. cd /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/h1pt2
/Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/h1pt2

. use chwide29june2012, clear
(Zero for missing on all icdx)

. cd /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/h1pt2
/Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/h1pt2

.
.
. di "{hline}"
-------------

. di "{hline}"
-------------

. title2 "Chunk 1 Hyp 1:radiation dose and Nottingham Health profile subscales"
-------------
title2: Chunk 1 Hyp 1:radiation dose and Nottingham Health profile subscales
                                       Date and time:   1 Jul 2012    06:17:07
                                       Working directory:  /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/h1pt2
                                       Stata data file:  chwide29june2012.dta  has  2373  variables and  703  observations

Chunk 1 Hyp 1:radiation dose and Nottingham Health profile subscales
-------------

.
.
. //  there is substantial intercorrelation among the items warranting a
. //  multivariate regression model
. cap dummies educ

. cap order educ1-educ8, after(educ)

.
. * These variables are substantially correlated
. bysort gender:tetrachoric HP2work HP2hmcare HP2probsoc HP2pbfhm HP2sxlife HP2inthob HP2vacatn

-------------
-> gender = 1. male
(obs=340)

   |  HP2work HP2hmc~e HP2pro~c HP2pbfhm HP2sxl~e HP2int~b HP2vac~n
-------------+---------------------------------------------------------------
     HP2work |   1.0000
   HP2hmcare |   0.7223   1.0000
  HP2probsoc |   0.6334   0.8318   1.0000
    HP2pbfhm |   0.4210   0.6204   0.6498   1.0000
   HP2sxlife |   0.6703   0.6703   0.7450   0.6703   1.0000
   HP2inthob |   0.5840   0.7559   0.8043   0.7155   0.7346   1.0000
   HP2vacatn |   0.6334   0.7643   0.8466   0.6498   0.6940   0.8701   1.0000

-------------
-> gender = 2. female
(obs=363)

   |  HP2work HP2hmc~e HP2pro~c HP2pbfhm HP2sxl~e HP2int~b HP2vac~n
-------------+---------------------------------------------------------------
     HP2work |   1.0000
   HP2hmcare |   0.7246   1.0000
  HP2probsoc |   0.7615   0.7918   1.0000
    HP2pbfhm |   0.5884   0.8081   0.8128   1.0000
   HP2sxlife |   0.7778   0.7015   0.8569   0.7553   1.0000
   HP2inthob |   0.6513   0.7441   0.8636   0.8291   0.8512   1.0000
   HP2vacatn |   0.6993   0.7441   0.8415   0.7654   0.8485   0.9266   1.0000

.
.
. cap gen havmilsq = havmil^2

.
. cap rename Havmil havmil

. //  controlling for potential confounders
. //  socio-demographics age gender educ income occp marstat children inc
. //  distance from accident side
. //  perceived Chornobyl related health threat to oneself
.
. local w1bf bf1 bf4 bf9 bf10 bf11 bf4m bf15m bf20 bf22 bf30 bf40

. local w2bf bf1 bf4 bf6 bf7 bf14 bf15 bf40

. local w3bf bf1 bf4 bf2 bf4m bf5m bf7m bf8 bf15m bf17 bf20 bf22 bf29 bf30 bf40

.
. *-------------------------------------------------------------------------------
. *  Hypothesis 1   Part 2 wave 1 tests    male and female
. * endogneous Nottingham pt 2 subscales: HP2work HP2hmcare HP2probsoc HP2pbfhm ///
> *                                        HP2sxlife HP2inthob HP2vacatn
. *  structure of models
. *    1. general models on all Pt 2 subscales with all potential confounders
. *    2. trimmed models on all Pt 2 subscales with from all potential confounders
. *    3. from trimmed models examination of possible moderator variables
. *    4. from trimmed models examination of possible mediator variables
. *    5. Summary analysis and model evaluation of final models only
. *   program is divided into 8 chunks one a general model and 1 for each
. *       endogenous variable
. *-------------------------------------------------------------------------------
.
. * Endogenous variable summarization
. set more off

. foreach var in HP2work HP2hmcare HP2pbfhm HP2probsoc HP2sxlife HP2inthob HP2vacatn {
  2.    tab gender `var' ,  row col exact
  3.       }

+-------------------+
| Key               |
+-------------------+
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                |   Nottingham Health
                |   profile subscale
                |      Part2: paid
   respondent's |      employment
        gender  |     0. no     1. yes |     Total
----------------+----------------------+----------
        1. male |       270         70 |       340
                |     79.41      20.59 |    100.00
                |     50.00      42.94 |     48.36
----------------+----------------------+----------
      2. female |       270         93 |       363
                |     74.38      25.62 |    100.00
                |     50.00      57.06 |     51.64
----------------+----------------------+----------
          Total |       540        163 |       703
                |     76.81      23.19 |    100.00
                |    100.00     100.00 |    100.00

           Fisher's exact =                 0.129
   1-sided Fisher's exact =                 0.068

+-------------------+
| Key               |
+-------------------+
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                |   Hlth profile Pt2:
                |    Home cleaning,
   respondent's |  cooking and repairs
        gender  |     0. no     1. yes |     Total
----------------+----------------------+----------
        1. male |       270         70 |       340
                |     79.41      20.59 |    100.00
                |     53.15      35.90 |     48.36
----------------+----------------------+----------
      2. female |       238        125 |       363
                |     65.56      34.44 |    100.00
                |     46.85      64.10 |     51.64
----------------+----------------------+----------
          Total |       508        195 |       703
                |     72.26      27.74 |    100.00
                |    100.00     100.00 |    100.00

           Fisher's exact =                 0.000
   1-sided Fisher's exact =                 0.000

+-------------------+
| Key               |
+-------------------+
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                |   Hlth profile Pt2:
                |  Hlth causing probs
                |  with family members
   respondent's |        at home
        gender  |     0. no     1. yes |     Total
----------------+----------------------+----------
        1. male |       318         22 |       340
                |     93.53       6.47 |    100.00
                |     50.16      31.88 |     48.36
----------------+----------------------+----------
      2. female |       316         47 |       363
                |     87.05      12.95 |    100.00
                |     49.84      68.12 |     51.64
----------------+----------------------+----------
          Total |       634         69 |       703
                |     90.18       9.82 |    100.00
                |    100.00     100.00 |    100.00

           Fisher's exact =                 0.005
   1-sided Fisher's exact =                 0.003

+-------------------+
| Key               |
+-------------------+
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                |   Hlth profile Pt2:
                |  Hlth causing probs
   respondent's |   with social life
        gender  |     0. no     1. yes |     Total
----------------+----------------------+----------
        1. male |       299         41 |       340
                |     87.94      12.06 |    100.00
                |     50.85      35.65 |     48.36
----------------+----------------------+----------
      2. female |       289         74 |       363
                |     79.61      20.39 |    100.00
                |     49.15      64.35 |     51.64
----------------+----------------------+----------
          Total |       588        115 |       703
                |     83.64      16.36 |    100.00
                |    100.00     100.00 |    100.00

           Fisher's exact =                 0.003
   1-sided Fisher's exact =                 0.002

+-------------------+
| Key               |
+-------------------+
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                |   Hlth profile Pt2:
                |  Hlth causing probs
   respondent's |     with sex life
        gender  |     0. no     1. yes |     Total
----------------+----------------------+----------
        1. male |       271         69 |       340
                |     79.71      20.29 |    100.00
                |     50.19      42.33 |     48.36
----------------+----------------------+----------
      2. female |       269         94 |       363
                |     74.10      25.90 |    100.00
                |     49.81      57.67 |     51.64
----------------+----------------------+----------
          Total |       540        163 |       703
                |     76.81      23.19 |    100.00
                |    100.00     100.00 |    100.00

           Fisher's exact =                 0.089
   1-sided Fisher's exact =                 0.047

+-------------------+
| Key               |
+-------------------+
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                |   Hlth profile Pt2:
                |      Hlth probs
                |   interfering with
   respondent's |  interests & hobbies
        gender  |     0. no     1. yes |     Total
----------------+----------------------+----------
        1. male |       302         38 |       340
                |     88.82      11.18 |    100.00
                |     50.42      36.54 |     48.36
----------------+----------------------+----------
      2. female |       297         66 |       363
                |     81.82      18.18 |    100.00
                |     49.58      63.46 |     51.64
----------------+----------------------+----------
          Total |       599        104 |       703
                |     85.21      14.79 |    100.00
                |    100.00     100.00 |    100.00

           Fisher's exact =                 0.011
   1-sided Fisher's exact =                 0.006

+-------------------+
| Key               |
+-------------------+
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                |   Hlth profile Pt2:
                |      Hlth probs
                |   interfering with
   respondent's |       vacations
        gender  |     0. no     1. yes |     Total
----------------+----------------------+----------
        1. male |       299         41 |       340
                |     87.94      12.06 |    100.00
                |     49.92      39.42 |     48.36
----------------+----------------------+----------
      2. female |       300         63 |       363
                |     82.64      17.36 |    100.00
                |     50.08      60.58 |     51.64
----------------+----------------------+----------
          Total |       599        104 |       703
                |     85.21      14.79 |    100.00
                |    100.00     100.00 |    100.00

           Fisher's exact =                 0.056
   1-sided Fisher's exact =                 0.030

.
.
.
.
. * Chunk 1  General models for all part 2 of Nottingham Health Profile
.
.
. forvalues j=1/1 {
  2. des age educ1-educ7 marrw`j'1-marrw`j'6 inc1w`j'-inc4w`j' ///
>   bf1 bf4 bf9 bf11 bf4m bf15m bf30 bf40
  3.   }

              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


marrw11           byte     %8.0g                        marrw1==1. single


marrw12           byte     %8.0g                        marrw1==2. cohabitating


marrw13           byte     %8.0g                        marrw1==3. married


marrw14           byte     %8.0g                        marrw1==4. separated


marrw15           byte     %8.0g                        marrw1==5. divorced


marrw16           byte     %8.0g                        marrw1==6. widowed


inc1w1            double   %15.0g        LABJ           Income is not sufficient for basic neccessities in 1986


inc2w1            double   %15.0g        LABJ           Income is just sufficient for basic neccessities in 1986


inc3w1            double   %15.0g        LABJ           Income is sufficient for basics plus extra purchases/savings in 1986


inc4w1            double   %15.0g        LABJ           Income allows to comfortably afford luxury items in 1986


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)


.
. title "H1: Zero order tests : "


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****                      H1: Zero order tests :                         *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:07  *****
*******************************************************************************
*******************************************************************************


.
. forvalues j=1/1 {
  2.   set more off
  3.   foreach var in HP2work HP2hmcare HP2probsoc HP2pbfhm HP2sxlife HP2inthob ///
>       HP2vacatn  {
  4.         forvalues k=1/2 {
  5.
.
.           logit `var' avgcumdosew`j' if gender==`k'
  6.
.           estat class
  7.           estat gof
  8.
.            }
  9.          }
 10.          }

Iteration 0:   log likelihood = -172.87291
Iteration 1:   log likelihood = -172.80738
Iteration 2:   log likelihood = -172.80504
Iteration 3:   log likelihood = -172.80504

Logistic regression                               Number of obs   =        340
                                                  LR chi2(1)      =       0.14
                                                  Prob > chi2     =     0.7126
Log likelihood = -172.80504                       Pseudo R2       =     0.0004

-------------+----------------------------------------------------------------
      HP2work |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |    .0267035    .0696365      0.38    0.701     -.1097815     .1631885
       _cons |   -1.361951    .1380833     -9.86    0.000     -1.632589    -1.091312
-------------+----------------------------------------------------------------

Logistic model for HP2work

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         0             0  |          0
     -     |        70           270  |        340
-----------+--------------------------+-----------
   Total   |        70           270  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2work != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    0.00%
Specificity                     Pr( -|~D)  100.00%
Positive predictive value       Pr( D| +)       .%
Negative predictive value       Pr(~D| -)   79.41%
--------------------------------------------------
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| -)   20.59%
--------------------------------------------------
Correctly classified                        79.41%
--------------------------------------------------


Logistic model for HP2work, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       212
            Pearson chi2(210) =       218.39
                  Prob > chi2 =         0.3312

Iteration 0:   log likelihood = -206.56255
Iteration 1:   log likelihood =  -203.2501
Iteration 2:   log likelihood = -203.17037
Iteration 3:   log likelihood = -203.16977
Iteration 4:   log likelihood = -203.16977

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =       6.79
                                                  Prob > chi2     =     0.0092
Log likelihood = -203.16977                       Pseudo R2       =     0.0164

-------------+----------------------------------------------------------------
      HP2work |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |    .5291654    .2193581      2.41    0.016      .0992315     .9590993
       _cons |   -1.253303    .1436065     -8.73    0.000     -1.534766    -.9718391
-------------+----------------------------------------------------------------

Logistic model for HP2work

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         4             2  |          6
     -     |        89           268  |        357
-----------+--------------------------+-----------
   Total   |        93           270  |        363

Classified + if predicted Pr(D) >= .5
True D defined as HP2work != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    4.30%
Specificity                     Pr( -|~D)   99.26%
Positive predictive value       Pr( D| +)   66.67%
Negative predictive value       Pr(~D| -)   75.07%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    0.74%
False - rate for true D         Pr( -| D)   95.70%
False + rate for classified +   Pr(~D| +)   33.33%
False - rate for classified -   Pr( D| -)   24.93%
--------------------------------------------------
Correctly classified                        74.93%
--------------------------------------------------


Logistic model for HP2work, goodness-of-fit test


       number of observations =       363
 number of covariate patterns =       253
            Pearson chi2(251) =       270.20
                  Prob > chi2 =         0.1934

Iteration 0:   log likelihood = -172.87291
Iteration 1:   log likelihood = -172.87274
Iteration 2:   log likelihood = -172.87274

Logistic regression                               Number of obs   =        340
                                                  LR chi2(1)      =       0.00
                                                  Prob > chi2     =     0.9849
Log likelihood = -172.87274                       Pseudo R2       =     0.0000

-------------+----------------------------------------------------------------
    HP2hmcare |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |     .001512     .079662      0.02    0.985     -.1546227     .1576466
       _cons |   -1.350578    .1384628     -9.75    0.000      -1.62196    -1.079195
-------------+----------------------------------------------------------------

Logistic model for HP2hmcare

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         0             0  |          0
     -     |        70           270  |        340
-----------+--------------------------+-----------
   Total   |        70           270  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2hmcare != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    0.00%
Specificity                     Pr( -|~D)  100.00%
Positive predictive value       Pr( D| +)       .%
Negative predictive value       Pr(~D| -)   79.41%
--------------------------------------------------
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| -)   20.59%
--------------------------------------------------
Correctly classified                        79.41%
--------------------------------------------------


Logistic model for HP2hmcare, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       212
            Pearson chi2(210) =       214.39
                  Prob > chi2 =         0.4031

Iteration 0:   log likelihood = -233.72859
Iteration 1:   log likelihood = -233.68175
Iteration 2:   log likelihood = -233.68169
Iteration 3:   log likelihood = -233.68169

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =       0.09
                                                  Prob > chi2     =     0.7594
Log likelihood = -233.68169                       Pseudo R2       =     0.0002

-------------+----------------------------------------------------------------
    HP2hmcare |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |   -.0632884    .2096625     -0.30    0.763     -.4742193     .3476424
       _cons |   -.6229243    .1302352     -4.78    0.000     -.8781805    -.3676681
-------------+----------------------------------------------------------------

Logistic model for HP2hmcare

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         0             0  |          0
     -     |       125           238  |        363
-----------+--------------------------+-----------
   Total   |       125           238  |        363

Classified + if predicted Pr(D) >= .5
True D defined as HP2hmcare != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    0.00%
Specificity                     Pr( -|~D)  100.00%
Positive predictive value       Pr( D| +)       .%
Negative predictive value       Pr(~D| -)   65.56%
--------------------------------------------------
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| -)   34.44%
--------------------------------------------------
Correctly classified                        65.56%
--------------------------------------------------


Logistic model for HP2hmcare, goodness-of-fit test


       number of observations =       363
 number of covariate patterns =       253
            Pearson chi2(251) =       278.20
                  Prob > chi2 =         0.1146

Iteration 0:   log likelihood = -125.15243
Iteration 1:   log likelihood = -123.88677
Iteration 2:   log likelihood = -123.70274
Iteration 3:   log likelihood = -123.43192
Iteration 4:   log likelihood =  -123.4306
Iteration 5:   log likelihood =  -123.4306

Logistic regression                               Number of obs   =        340
                                                  LR chi2(1)      =       3.44
                                                  Prob > chi2     =     0.0635
Log likelihood =  -123.4306                       Pseudo R2       =     0.0138

-------------+----------------------------------------------------------------
   HP2probsoc |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |    .1384454    .0891383      1.55    0.120     -.0362625     .3131532
       _cons |   -2.060115    .1739293    -11.84    0.000      -2.40101     -1.71922
-------------+----------------------------------------------------------------

Logistic model for HP2probsoc

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         1             0  |          1
     -     |        40           299  |        339
-----------+--------------------------+-----------
   Total   |        41           299  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2probsoc != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    2.44%
Specificity                     Pr( -|~D)  100.00%
Positive predictive value       Pr( D| +)  100.00%
Negative predictive value       Pr(~D| -)   88.20%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    0.00%
False - rate for true D         Pr( -| D)   97.56%
False + rate for classified +   Pr(~D| +)    0.00%
False - rate for classified -   Pr( D| -)   11.80%
--------------------------------------------------
Correctly classified                        88.24%
--------------------------------------------------


Logistic model for HP2probsoc, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       212
            Pearson chi2(210) =       236.36
                  Prob > chi2 =         0.1023

Iteration 0:   log likelihood =  -183.5701
Iteration 1:   log likelihood =  -174.2547
Iteration 2:   log likelihood = -174.21827
Iteration 3:   log likelihood =  -174.2181
Iteration 4:   log likelihood =  -174.2181

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =      18.70
                                                  Prob > chi2     =     0.0000
Log likelihood =  -174.2181                       Pseudo R2       =     0.0509

-------------+----------------------------------------------------------------
   HP2probsoc |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |    1.066471    .3458273      3.08    0.002      .3886619      1.74428
       _cons |   -1.742732    .1750064     -9.96    0.000     -2.085738    -1.399725
-------------+----------------------------------------------------------------

Logistic model for HP2probsoc

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         5             2  |          7
     -     |        69           287  |        356
-----------+--------------------------+-----------
   Total   |        74           289  |        363

Classified + if predicted Pr(D) >= .5
True D defined as HP2probsoc != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    6.76%
Specificity                     Pr( -|~D)   99.31%
Positive predictive value       Pr( D| +)   71.43%
Negative predictive value       Pr(~D| -)   80.62%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    0.69%
False - rate for true D         Pr( -| D)   93.24%
False + rate for classified +   Pr(~D| +)   28.57%
False - rate for classified -   Pr( D| -)   19.38%
--------------------------------------------------
Correctly classified                        80.44%
--------------------------------------------------


Logistic model for HP2probsoc, goodness-of-fit test


       number of observations =       363
 number of covariate patterns =       253
            Pearson chi2(251) =       275.26
                  Prob > chi2 =         0.1402

Iteration 0:   log likelihood = -81.506236
Iteration 1:   log likelihood = -81.410884
Iteration 2:   log likelihood = -81.408212
Iteration 3:   log likelihood = -81.408194
Iteration 4:   log likelihood = -81.408194

Logistic regression                               Number of obs   =        340
                                                  LR chi2(1)      =       0.20
                                                  Prob > chi2     =     0.6579
Log likelihood = -81.408194                       Pseudo R2       =     0.0012

-------------+----------------------------------------------------------------
     HP2pbfhm |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |   -.1107434    .3490104     -0.32    0.751     -.7947912     .5733044
       _cons |   -2.631874    .2460769    -10.70    0.000     -3.114176    -2.149572
-------------+----------------------------------------------------------------

Logistic model for HP2pbfhm

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         0             0  |          0
     -     |        22           318  |        340
-----------+--------------------------+-----------
   Total   |        22           318  |        340

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| -)   93.53%
--------------------------------------------------
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| -)    6.47%
--------------------------------------------------
Correctly classified                        93.53%
--------------------------------------------------


Logistic model for HP2pbfhm, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       212
            Pearson chi2(210) =       250.62
                  Prob > chi2 =         0.0288

Iteration 0:   log likelihood = -139.89675
Iteration 1:   log likelihood = -139.43271
Iteration 2:   log likelihood = -139.35378
Iteration 3:   log likelihood =  -139.3536
Iteration 4:   log likelihood =  -139.3536

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =       1.09
                                                  Prob > chi2     =     0.2973
Log likelihood =  -139.3536                       Pseudo R2       =     0.0039

-------------+----------------------------------------------------------------
     HP2pbfhm |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |    .2461605    .2198912      1.12    0.263     -.1848183     .6771394
       _cons |   -1.995488     .179138    -11.14    0.000     -2.346592    -1.644384
-------------+----------------------------------------------------------------

Logistic model for HP2pbfhm

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         0             0  |          0
     -     |        47           316  |        363
-----------+--------------------------+-----------
   Total   |        47           316  |        363

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| -)   87.05%
--------------------------------------------------
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| -)   12.95%
--------------------------------------------------
Correctly classified                        87.05%
--------------------------------------------------


Logistic model for HP2pbfhm, goodness-of-fit test


       number of observations =       363
 number of covariate patterns =       253
            Pearson chi2(251) =       271.27
                  Prob > chi2 =         0.1811

Iteration 0:   log likelihood = -171.51396
Iteration 1:   log likelihood = -171.01815
Iteration 2:   log likelihood =  -170.9595
Iteration 3:   log likelihood = -170.95837
Iteration 4:   log likelihood = -170.95837

Logistic regression                               Number of obs   =        340
                                                  LR chi2(1)      =       1.11
                                                  Prob > chi2     =     0.2918
Log likelihood = -170.95837                       Pseudo R2       =     0.0032

-------------+----------------------------------------------------------------
    HP2sxlife |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |    .0718092    .0688452      1.04    0.297     -.0631249     .2067434
       _cons |   -1.401935    .1391936    -10.07    0.000      -1.67475    -1.129121
-------------+----------------------------------------------------------------

Logistic model for HP2sxlife

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         0             1  |          1
     -     |        69           270  |        339
-----------+--------------------------+-----------
   Total   |        69           271  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2sxlife != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    0.00%
Specificity                     Pr( -|~D)   99.63%
Positive predictive value       Pr( D| +)    0.00%
Negative predictive value       Pr(~D| -)   79.65%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    0.37%
False - rate for true D         Pr( -| D)  100.00%
False + rate for classified +   Pr(~D| +)  100.00%
False - rate for classified -   Pr( D| -)   20.35%
--------------------------------------------------
Correctly classified                        79.41%
--------------------------------------------------


Logistic model for HP2sxlife, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       212
            Pearson chi2(210) =       249.01
                  Prob > chi2 =         0.0337

Iteration 0:   log likelihood = -207.62116
Iteration 1:   log likelihood = -201.39188
Iteration 2:   log likelihood = -201.38495
Iteration 3:   log likelihood = -201.38495

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =      12.47
                                                  Prob > chi2     =     0.0004
Log likelihood = -201.38495                       Pseudo R2       =     0.0300

-------------+----------------------------------------------------------------
    HP2sxlife |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |    .7954962    .2829573      2.81    0.005      .2409102     1.350082
       _cons |   -1.327626    .1524624     -8.71    0.000     -1.626446    -1.028805
-------------+----------------------------------------------------------------

Logistic model for HP2sxlife

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         5             2  |          7
     -     |        89           267  |        356
-----------+--------------------------+-----------
   Total   |        94           269  |        363

Classified + if predicted Pr(D) >= .5
True D defined as HP2sxlife != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    5.32%
Specificity                     Pr( -|~D)   99.26%
Positive predictive value       Pr( D| +)   71.43%
Negative predictive value       Pr(~D| -)   75.00%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    0.74%
False - rate for true D         Pr( -| D)   94.68%
False + rate for classified +   Pr(~D| +)   28.57%
False - rate for classified -   Pr( D| -)   25.00%
--------------------------------------------------
Correctly classified                        74.93%
--------------------------------------------------


Logistic model for HP2sxlife, goodness-of-fit test


       number of observations =       363
 number of covariate patterns =       253
            Pearson chi2(251) =       287.01
                  Prob > chi2 =         0.0587

Iteration 0:   log likelihood = -119.06428
Iteration 1:   log likelihood = -119.03888
Iteration 2:   log likelihood = -119.03876
Iteration 3:   log likelihood = -119.03876

Logistic regression                               Number of obs   =        340
                                                  LR chi2(1)      =       0.05
                                                  Prob > chi2     =     0.8213
Log likelihood = -119.03876                       Pseudo R2       =     0.0002

-------------+----------------------------------------------------------------
    HP2inthob |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |   -.0276429    .1341516     -0.21    0.837     -.2905751     .2352894
       _cons |   -2.061705    .1795207    -11.48    0.000     -2.413559    -1.709851
-------------+----------------------------------------------------------------

Logistic model for HP2inthob

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         0             0  |          0
     -     |        38           302  |        340
-----------+--------------------------+-----------
   Total   |        38           302  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2inthob != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    0.00%
Specificity                     Pr( -|~D)  100.00%
Positive predictive value       Pr( D| +)       .%
Negative predictive value       Pr(~D| -)   88.82%
--------------------------------------------------
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| -)   11.18%
--------------------------------------------------
Correctly classified                        88.82%
--------------------------------------------------


Logistic model for HP2inthob, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       212
            Pearson chi2(210) =       264.01
                  Prob > chi2 =         0.0068

Iteration 0:   log likelihood = -172.11257
Iteration 1:   log likelihood = -170.06689
Iteration 2:   log likelihood = -169.70579
Iteration 3:   log likelihood = -169.70055
Iteration 4:   log likelihood = -169.70055

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =       4.82
                                                  Prob > chi2     =     0.0281
Log likelihood = -169.70055                       Pseudo R2       =     0.0140

-------------+----------------------------------------------------------------
    HP2inthob |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |    .4479912    .2012295      2.23    0.026      .0535886     .8423938
       _cons |   -1.670514    .1581676    -10.56    0.000     -1.980517    -1.360512
-------------+----------------------------------------------------------------

Logistic model for HP2inthob

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         2             1  |          3
     -     |        64           296  |        360
-----------+--------------------------+-----------
   Total   |        66           297  |        363

Classified + if predicted Pr(D) >= .5
True D defined as HP2inthob != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    3.03%
Specificity                     Pr( -|~D)   99.66%
Positive predictive value       Pr( D| +)   66.67%
Negative predictive value       Pr(~D| -)   82.22%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    0.34%
False - rate for true D         Pr( -| D)   96.97%
False + rate for classified +   Pr(~D| +)   33.33%
False - rate for classified -   Pr( D| -)   17.78%
--------------------------------------------------
Correctly classified                        82.09%
--------------------------------------------------


Logistic model for HP2inthob, goodness-of-fit test


       number of observations =       363
 number of covariate patterns =       253
            Pearson chi2(251) =       272.21
                  Prob > chi2 =         0.1709

Iteration 0:   log likelihood = -125.15243
Iteration 1:   log likelihood = -125.15124
Iteration 2:   log likelihood = -125.15124

Logistic regression                               Number of obs   =        340
                                                  LR chi2(1)      =       0.00
                                                  Prob > chi2     =     0.9611
Log likelihood = -125.15124                       Pseudo R2       =     0.0000

-------------+----------------------------------------------------------------
    HP2vacatn |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |   -.0050166    .1045628     -0.05    0.962      -.209956     .1999228
       _cons |   -1.984744    .1722028    -11.53    0.000     -2.322256    -1.647233
-------------+----------------------------------------------------------------

Logistic model for HP2vacatn

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         0             0  |          0
     -     |        41           299  |        340
-----------+--------------------------+-----------
   Total   |        41           299  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2vacatn != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    0.00%
Specificity                     Pr( -|~D)  100.00%
Positive predictive value       Pr( D| +)       .%
Negative predictive value       Pr(~D| -)   87.94%
--------------------------------------------------
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| -)   12.06%
--------------------------------------------------
Correctly classified                        87.94%
--------------------------------------------------


Logistic model for HP2vacatn, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       212
            Pearson chi2(210) =       238.04
                  Prob > chi2 =         0.0895

Iteration 0:   log likelihood =   -167.516
Iteration 1:   log likelihood = -166.78704
Iteration 2:   log likelihood =  -166.7022
Iteration 3:   log likelihood = -166.70218
Iteration 4:   log likelihood = -166.70218

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =       1.63
                                                  Prob > chi2     =     0.2020
Log likelihood = -166.70218                       Pseudo R2       =     0.0049

-------------+----------------------------------------------------------------
    HP2vacatn |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew1 |    .2732774    .2032816      1.34    0.179     -.1251473     .6717021
       _cons |   -1.659547    .1595045    -10.40    0.000      -1.97217    -1.346924
-------------+----------------------------------------------------------------

Logistic model for HP2vacatn

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         0             0  |          0
     -     |        63           300  |        363
-----------+--------------------------+-----------
   Total   |        63           300  |        363

Classified + if predicted Pr(D) >= .5
True D defined as HP2vacatn != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    0.00%
Specificity                     Pr( -|~D)  100.00%
Positive predictive value       Pr( D| +)       .%
Negative predictive value       Pr(~D| -)   82.64%
--------------------------------------------------
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| -)   17.36%
--------------------------------------------------
Correctly classified                        82.64%
--------------------------------------------------


Logistic model for HP2vacatn, goodness-of-fit test


       number of observations =       363
 number of covariate patterns =       253
            Pearson chi2(251) =       259.83
                  Prob > chi2 =         0.3375

.
. * + *  dose affects women in work        (p = .529   se=.219 z=2.41  p=0.016)
. * + *  dose affects women in social life (b = 1.066  se=.345 z=3.08  p=.002
. * + *  dose affects women in sex life (b = .795  se=.283 z=2.81 p=.005
. * + *  dose affects women in interests and hobbies (b = .448 se=.201 z=2.23 p=.026
.
.
.
. *---------------------- Full or general model to be trimmed
. cap gen whpel = WHPel

. cap gen whpsleep = WHPsleep

. cap gen whppain = WHPpain

.
. title " H1: Test of hypothesis 1: Pt 2 wave 1 general models"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****        H1: Test of hypothesis 1: Pt 2 wave 1 general models         *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:25  *****
*******************************************************************************
*******************************************************************************


. forvalues j=1/1 {
  2. set more off
  3.   foreach var in HP2work HP2hmcare HP2probsoc HP2pbfhm HP2sxlife HP2inthob ///
>       HP2vacatn  {
  4.       forvalues k=1/2 {
  5. local w1bf bf1 bf4 bf9 bf10 bf11 bf4m bf15m bf20 bf22 bf30 bf40
  6. di _skip(4)
  7.
. title "Full main model for `var' for wave= `j' "
  8. di _skip(4)
  9. title "chunk 2 H1 test:Gender= `k'  model Wave =  `j' for `e(depvar)' "
 10. di _skip(4)
 11.
.
.         logit `var' age  ///
>                 radhlw`j' radfmw`j' radchw`j' bf1 bf20   ///
>                  deaw`j'     ///
>          shhlw`j' shjobw`j' shrelaw`j' suprtw`j'  ///
>          icdxcnt illw`j'                         ///
>           whpel whppain                         ///
>                  avgcumdosew`j' if gender==`k',  difficult iterate(500) nolog
 12.                         estat class
 13.                         estat gof
 14.                         fitstat
 15. }
 16. }
 17. }


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Full main model for HP2work for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:25  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****     chunk 2 H1 test:Gender= 1  model Wave =  1 for HP2vacatn        *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:25  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        340
                                                  LR chi2(16)     =      71.69
                                                  Prob > chi2     =     0.0000
Log likelihood = -137.02704                       Pseudo R2       =     0.2074

-------------+----------------------------------------------------------------
      HP2work |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0044711    .0147919      0.30    0.762     -.0245204     .0334626
     radhlw1 |    .0055036    .0093169      0.59    0.555     -.0127573     .0237645
     radfmw1 |   -.0044257    .0098833     -0.45    0.654     -.0237966     .0149453
     radchw1 |   -.0000483    .0046926     -0.01    0.992     -.0092456     .0091491
         bf1 |    -.018817    .0510829     -0.37    0.713     -.1189376     .0813035
        bf20 |    .0273587     .047912      0.57    0.568     -.0665471     .1212644
       deaw1 |   -.2405105    .3359175     -0.72    0.474     -.8988968     .4178758
      shhlw1 |    .0006665    .0062215      0.11    0.915     -.0115273     .0128603
     shjobw1 |   -.0004676    .0057551     -0.08    0.935     -.0117473     .0108122
    shrelaw1 |    .0003096    .0048879      0.06    0.949     -.0092704     .0098896
     suprtw1 |   -.0004976    .0063567     -0.08    0.938     -.0129564     .0119612
     icdxcnt |    .2773482    .1040751      2.66    0.008      .0733648     .4813316
       illw1 |    -.401163    .4018596     -1.00    0.318     -1.188793     .3864674
       whpel |    .0172129     .005632      3.06    0.002      .0061745     .0282514
     whppain |    .0270562    .0098111      2.76    0.006      .0078268     .0462856
avgcumdosew1 |   -.0305113     .080402     -0.38    0.704     -.1880963     .1270736
       _cons |   -4.466927      1.9708     -2.27    0.023     -8.329623    -.6042303
-------------+----------------------------------------------------------------

Logistic model for HP2work

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        20            19  |         39
     -     |        50           251  |        301
-----------+--------------------------+-----------
   Total   |        70           270  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2work != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   28.57%
Specificity                     Pr( -|~D)   92.96%
Positive predictive value       Pr( D| +)   51.28%
Negative predictive value       Pr(~D| -)   83.39%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    7.04%
False - rate for true D         Pr( -| D)   71.43%
False + rate for classified +   Pr(~D| +)   48.72%
False - rate for classified -   Pr( D| -)   16.61%
--------------------------------------------------
Correctly classified                        79.71%
--------------------------------------------------


Logistic model for HP2work, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       338
            Pearson chi2(321) =       294.56
                  Prob > chi2 =         0.8525

Measures of Fit for logit of HP2work

Log-Lik Intercept Only:     -172.873     Log-Lik Full Model:         -137.027
D(323):                      274.054     LR(16):                       71.692
                                         Prob > LR:                     0.000
McFadden's R2:                 0.207     McFadden's Adj R2:             0.109
Maximum Likelihood R2:         0.190     Cragg & Uhler's R2:            0.298
McKelvey and Zavoina's R2:     0.318     Efron's R2:                    0.199
Variance of y*:                4.826     Variance of error:             3.290
Count R2:                      0.797     Adj Count R2:                  0.014
AIC:                           0.906     AIC*n:                       308.054
BIC:                       -1608.695     BIC':                         21.571


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Full main model for HP2work for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:26  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****      chunk 2 H1 test:Gender= 2  model Wave =  1 for HP2work         *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:26  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        361
                                                  LR chi2(16)     =      81.74
                                                  Prob > chi2     =     0.0000
Log likelihood = -165.09989                       Pseudo R2       =     0.1984

-------------+----------------------------------------------------------------
      HP2work |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0436643    .0136282      3.20    0.001      .0169536      .070375
     radhlw1 |    .0134741     .010114      1.33    0.183      -.006349     .0332972
     radfmw1 |   -.0072429    .0101908     -0.71    0.477     -.0272166     .0127308
     radchw1 |   -.0000885    .0043108     -0.02    0.984     -.0085376     .0083605
         bf1 |   -.0117953    .0274232     -0.43    0.667     -.0655439     .0419532
        bf20 |    .0142668    .0238684      0.60    0.550     -.0325145      .061048
       deaw1 |   -.1363375    .2080881     -0.66    0.512     -.5441827     .2715076
      shhlw1 |    .0053101    .0058328      0.91    0.363     -.0061219     .0167421
     shjobw1 |   -.0075903    .0055664     -1.36    0.173     -.0185003     .0033197
    shrelaw1 |   -.0051051    .0046819     -1.09    0.276     -.0142814     .0040712
     suprtw1 |   -.0030402    .0043383     -0.70    0.483     -.0115432     .0054628
     icdxcnt |    .0827126    .0664875      1.24    0.213     -.0476005     .2130256
       illw1 |    .2786013    .2660618      1.05    0.295     -.2428702     .8000728
       whpel |    .0152336    .0048159      3.16    0.002      .0057945     .0246726
     whppain |     .014168    .0074264      1.91    0.056     -.0003875     .0287235
avgcumdosew1 |     .233446    .2206198      1.06    0.290     -.1989608     .6658528
       _cons |   -5.245473    1.125739     -4.66    0.000     -7.451882    -3.039065
-------------+----------------------------------------------------------------

Logistic model for HP2work

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        34            22  |         56
     -     |        59           246  |        305
-----------+--------------------------+-----------
   Total   |        93           268  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2work != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   36.56%
Specificity                     Pr( -|~D)   91.79%
Positive predictive value       Pr( D| +)   60.71%
Negative predictive value       Pr(~D| -)   80.66%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    8.21%
False - rate for true D         Pr( -| D)   63.44%
False + rate for classified +   Pr(~D| +)   39.29%
False - rate for classified -   Pr( D| -)   19.34%
--------------------------------------------------
Correctly classified                        77.56%
--------------------------------------------------


Logistic model for HP2work, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       361
            Pearson chi2(344) =       341.33
                  Prob > chi2 =         0.5306

Measures of Fit for logit of HP2work

Log-Lik Intercept Only:     -205.969     Log-Lik Full Model:         -165.100
D(344):                      330.200     LR(16):                       81.738
                                         Prob > LR:                     0.000
McFadden's R2:                 0.198     McFadden's Adj R2:             0.116
Maximum Likelihood R2:         0.203     Cragg & Uhler's R2:            0.298
McKelvey and Zavoina's R2:     0.309     Efron's R2:                    0.218
Variance of y*:                4.760     Variance of error:             3.290
Count R2:                      0.776     Adj Count R2:                  0.129
AIC:                           1.009     AIC*n:                       364.200
BIC:                       -1695.574     BIC':                         12.484


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Full main model for HP2hmcare for wave= 1               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:28  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****      chunk 2 H1 test:Gender= 1  model Wave =  1 for HP2work         *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:28  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        340
                                                  LR chi2(16)     =     114.24
                                                  Prob > chi2     =     0.0000
Log likelihood = -115.75339                       Pseudo R2       =     0.3304

-------------+----------------------------------------------------------------
    HP2hmcare |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0348269    .0159888      2.18    0.029      .0034895     .0661644
     radhlw1 |    .0091934     .010032      0.92    0.359      -.010469     .0288558
     radfmw1 |   -.0015339    .0107213     -0.14    0.886     -.0225473     .0194795
     radchw1 |   -.0025479    .0053133     -0.48    0.632     -.0129618      .007866
         bf1 |    .0044125    .0386976      0.11    0.909     -.0714334     .0802585
        bf20 |   -.0093947    .0344293     -0.27    0.785     -.0768749     .0580856
       deaw1 |   -.3454294    .3628049     -0.95    0.341     -1.056514     .3656551
      shhlw1 |    .0058564    .0067948      0.86    0.389     -.0074612     .0191739
     shjobw1 |    .0016209     .006774      0.24    0.811     -.0116559     .0148977
    shrelaw1 |   -.0082332     .005585     -1.47    0.140     -.0191796     .0027133
     suprtw1 |   -.0214791    .0088177     -2.44    0.015     -.0387614    -.0041967
     icdxcnt |    .1074769     .119853      0.90    0.370     -.1274308     .3423845
       illw1 |   -.0671769    .4683637     -0.14    0.886     -.9851529     .8507991
       whpel |    .0301155    .0062474      4.82    0.000      .0178707     .0423602
     whppain |    .0447413    .0111559      4.01    0.000      .0228761     .0666065
avgcumdosew1 |   -.1789909    .1692386     -1.06    0.290     -.5106924     .1527106
       _cons |   -4.458808     1.53434     -2.91    0.004     -7.466059    -1.451556
-------------+----------------------------------------------------------------

Logistic model for HP2hmcare

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        29            13  |         42
     -     |        41           257  |        298
-----------+--------------------------+-----------
   Total   |        70           270  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2hmcare != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   41.43%
Specificity                     Pr( -|~D)   95.19%
Positive predictive value       Pr( D| +)   69.05%
Negative predictive value       Pr(~D| -)   86.24%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    4.81%
False - rate for true D         Pr( -| D)   58.57%
False + rate for classified +   Pr(~D| +)   30.95%
False - rate for classified -   Pr( D| -)   13.76%
--------------------------------------------------
Correctly classified                        84.12%
--------------------------------------------------


Logistic model for HP2hmcare, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       338
            Pearson chi2(321) =       328.90
                  Prob > chi2 =         0.3686

Measures of Fit for logit of HP2hmcare

Log-Lik Intercept Only:     -172.873     Log-Lik Full Model:         -115.753
D(323):                      231.507     LR(16):                      114.239
                                         Prob > LR:                     0.000
McFadden's R2:                 0.330     McFadden's Adj R2:             0.232
Maximum Likelihood R2:         0.285     Cragg & Uhler's R2:            0.447
McKelvey and Zavoina's R2:     0.488     Efron's R2:                    0.356
Variance of y*:                6.421     Variance of error:             3.290
Count R2:                      0.841     Adj Count R2:                  0.229
AIC:                           0.781     AIC*n:                       265.507
BIC:                       -1651.243     BIC':                        -20.976


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Full main model for HP2hmcare for wave= 1               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:29  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****     chunk 2 H1 test:Gender= 2  model Wave =  1 for HP2hmcare        *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:29  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        361
                                                  LR chi2(16)     =     158.32
                                                  Prob > chi2     =     0.0000
Log likelihood = -153.72247                       Pseudo R2       =     0.3399

-------------+----------------------------------------------------------------
    HP2hmcare |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0766875    .0145106      5.28    0.000      .0482473     .1051276
     radhlw1 |   -.0081045    .0095764     -0.85    0.397     -.0268738     .0106649
     radfmw1 |    .0114943    .0098792      1.16    0.245     -.0078686     .0308571
     radchw1 |    .0085273    .0045394      1.88    0.060     -.0003697     .0174243
         bf1 |   -.0315413    .0252931     -1.25    0.212     -.0811149     .0180324
        bf20 |    .0145893    .0216082      0.68    0.500     -.0277619     .0569406
       deaw1 |   -.0186371    .2272069     -0.08    0.935     -.4639545     .4266802
      shhlw1 |   -.0008315    .0064215     -0.13    0.897     -.0134173     .0117544
     shjobw1 |    .0005999    .0059499      0.10    0.920     -.0110617     .0122616
    shrelaw1 |   -.0077666    .0050737     -1.53    0.126     -.0177109     .0021777
     suprtw1 |    -.007335    .0045994     -1.59    0.111     -.0163497     .0016797
     icdxcnt |    .0247886    .0693901      0.36    0.721     -.1112135     .1607907
       illw1 |    .8730775    .3079516      2.84    0.005      .2695035     1.476652
       whpel |    .0163122    .0050673      3.22    0.001      .0063804      .026244
     whppain |    .0361659    .0082941      4.36    0.000      .0199098     .0524221
avgcumdosew1 |    -.884003    .3553339     -2.49    0.013     -1.580445    -.1875613
       _cons |   -6.169687    1.080938     -5.71    0.000     -8.288288    -4.051087
-------------+----------------------------------------------------------------

Logistic model for HP2hmcare

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        80            27  |        107
     -     |        45           209  |        254
-----------+--------------------------+-----------
   Total   |       125           236  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2hmcare != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   64.00%
Specificity                     Pr( -|~D)   88.56%
Positive predictive value       Pr( D| +)   74.77%
Negative predictive value       Pr(~D| -)   82.28%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   11.44%
False - rate for true D         Pr( -| D)   36.00%
False + rate for classified +   Pr(~D| +)   25.23%
False - rate for classified -   Pr( D| -)   17.72%
--------------------------------------------------
Correctly classified                        80.06%
--------------------------------------------------


Logistic model for HP2hmcare, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       361
            Pearson chi2(344) =       347.20
                  Prob > chi2 =         0.4416

Measures of Fit for logit of HP2hmcare

Log-Lik Intercept Only:     -232.881     Log-Lik Full Model:         -153.722
D(344):                      307.445     LR(16):                      158.318
                                         Prob > LR:                     0.000
McFadden's R2:                 0.340     McFadden's Adj R2:             0.267
Maximum Likelihood R2:         0.355     Cragg & Uhler's R2:            0.490
McKelvey and Zavoina's R2:     0.515     Efron's R2:                    0.394
Variance of y*:                6.783     Variance of error:             3.290
Count R2:                      0.801     Adj Count R2:                  0.424
AIC:                           0.946     AIC*n:                       341.445
BIC:                       -1718.329     BIC':                        -64.096


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****            Full main model for HP2probsoc for wave= 1               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:30  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****     chunk 2 H1 test:Gender= 1  model Wave =  1 for HP2hmcare        *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:30  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        340
                                                  LR chi2(16)     =     111.41
                                                  Prob > chi2     =     0.0000
Log likelihood = -69.447614                       Pseudo R2       =     0.4451

-------------+----------------------------------------------------------------
   HP2probsoc |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0355468    .0218913      1.62    0.104     -.0073594      .078453
     radhlw1 |    .0098411    .0137267      0.72    0.473     -.0170627     .0367449
     radfmw1 |   -.0073717     .014503     -0.51    0.611      -.035797     .0210537
     radchw1 |   -.0179932    .0075766     -2.37    0.018     -.0328431    -.0031433
         bf1 |     .038297    .0507166      0.76    0.450     -.0611058     .1376997
        bf20 |    -.029096    .0442065     -0.66    0.510     -.1157391     .0575471
       deaw1 |   -.4626441    .5566802     -0.83    0.406     -1.553717      .628429
      shhlw1 |    .0104714    .0090187      1.16    0.246     -.0072049     .0281477
     shjobw1 |    .0182305    .0092576      1.97    0.049       .000086     .0363751
    shrelaw1 |    -.019084    .0074454     -2.56    0.010     -.0336767    -.0044912
     suprtw1 |   -.0037761    .0093483     -0.40    0.686     -.0220983     .0145462
     icdxcnt |   -.0955168    .1718698     -0.56    0.578     -.4323754     .2413418
       illw1 |    .1004301    .5556925      0.18    0.857     -.9887072     1.189567
       whpel |    .0313698    .0088021      3.56    0.000       .014118     .0486216
     whppain |    .0425959    .0139177      3.06    0.002      .0153177      .069874
avgcumdosew1 |    .0182844     .076719      0.24    0.812     -.1320821     .1686509
       _cons |    -5.01396    1.937006     -2.59    0.010     -8.810422    -1.217498
-------------+----------------------------------------------------------------

Logistic model for HP2probsoc

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        23             4  |         27
     -     |        18           295  |        313
-----------+--------------------------+-----------
   Total   |        41           299  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2probsoc != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   56.10%
Specificity                     Pr( -|~D)   98.66%
Positive predictive value       Pr( D| +)   85.19%
Negative predictive value       Pr(~D| -)   94.25%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    1.34%
False - rate for true D         Pr( -| D)   43.90%
False + rate for classified +   Pr(~D| +)   14.81%
False - rate for classified -   Pr( D| -)    5.75%
--------------------------------------------------
Correctly classified                        93.53%
--------------------------------------------------


Logistic model for HP2probsoc, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       338
            Pearson chi2(321) =       263.41
                  Prob > chi2 =         0.9918

Measures of Fit for logit of HP2probsoc

Log-Lik Intercept Only:     -125.152     Log-Lik Full Model:          -69.448
D(323):                      138.895     LR(16):                      111.410
                                         Prob > LR:                     0.000
McFadden's R2:                 0.445     McFadden's Adj R2:             0.309
Maximum Likelihood R2:         0.279     Cragg & Uhler's R2:            0.536
McKelvey and Zavoina's R2:     0.599     Efron's R2:                    0.442
Variance of y*:                8.194     Variance of error:             3.290
Count R2:                      0.935     Adj Count R2:                  0.463
AIC:                           0.509     AIC*n:                       172.895
BIC:                       -1743.854     BIC':                        -18.146


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****            Full main model for HP2probsoc for wave= 1               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:32  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****     chunk 2 H1 test:Gender= 2  model Wave =  1 for HP2probsoc       *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:32  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        361
                                                  LR chi2(16)     =     155.15
                                                  Prob > chi2     =     0.0000
Log likelihood = -105.53677                       Pseudo R2       =     0.4237

-------------+----------------------------------------------------------------
   HP2probsoc |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0958525    .0197132      4.86    0.000      .0572154     .1344896
     radhlw1 |   -.0144168    .0110303     -1.31    0.191     -.0360359     .0072022
     radfmw1 |    .0292909    .0118033      2.48    0.013      .0061568     .0524251
     radchw1 |    .0004482    .0055681      0.08    0.936      -.010465     .0113614
         bf1 |      .01482    .0347884      0.43    0.670     -.0533639     .0830039
        bf20 |     -.00763     .029457     -0.26    0.796     -.0653647     .0501047
       deaw1 |   -.0892195    .2476466     -0.36    0.719     -.5745978     .3961589
      shhlw1 |    .0039318    .0075022      0.52    0.600     -.0107722     .0186358
     shjobw1 |   -.0049536    .0072469     -0.68    0.494     -.0191573     .0092501
    shrelaw1 |   -.0073538    .0061315     -1.20    0.230     -.0193712     .0046637
     suprtw1 |   -.0014009    .0053466     -0.26    0.793     -.0118799     .0090782
     icdxcnt |     .004018    .0848625      0.05    0.962     -.1623094     .1703454
       illw1 |   -.3003166    .3810263     -0.79    0.431     -1.047114     .4464812
       whpel |    .0108751    .0059007      1.84    0.065     -.0006901     .0224403
     whppain |    .0394765     .009769      4.04    0.000      .0203295     .0586234
avgcumdosew1 |    .7290518    .3238033      2.25    0.024      .0944091     1.363695
       _cons |   -9.019722     1.57892     -5.71    0.000     -12.11435    -5.925096
-------------+----------------------------------------------------------------

Logistic model for HP2probsoc

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        41            11  |         52
     -     |        33           276  |        309
-----------+--------------------------+-----------
   Total   |        74           287  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2probsoc != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   55.41%
Specificity                     Pr( -|~D)   96.17%
Positive predictive value       Pr( D| +)   78.85%
Negative predictive value       Pr(~D| -)   89.32%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    3.83%
False - rate for true D         Pr( -| D)   44.59%
False + rate for classified +   Pr(~D| +)   21.15%
False - rate for classified -   Pr( D| -)   10.68%
--------------------------------------------------
Correctly classified                        87.81%
--------------------------------------------------


Logistic model for HP2probsoc, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       361
            Pearson chi2(344) =       340.95
                  Prob > chi2 =         0.5364

Measures of Fit for logit of HP2probsoc

Log-Lik Intercept Only:     -183.113     Log-Lik Full Model:         -105.537
D(344):                      211.074     LR(16):                      155.152
                                         Prob > LR:                     0.000
McFadden's R2:                 0.424     McFadden's Adj R2:             0.331
Maximum Likelihood R2:         0.349     Cragg & Uhler's R2:            0.548
McKelvey and Zavoina's R2:     0.597     Efron's R2:                    0.457
Variance of y*:                8.172     Variance of error:             3.290
Count R2:                      0.878     Adj Count R2:                  0.405
AIC:                           0.679     AIC*n:                       245.074
BIC:                       -1814.700     BIC':                        -60.930


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Full main model for HP2pbfhm for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:33  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****     chunk 2 H1 test:Gender= 1  model Wave =  1 for HP2probsoc       *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:33  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        340
                                                  LR chi2(16)     =      48.79
                                                  Prob > chi2     =     0.0000
Log likelihood = -57.112403                       Pseudo R2       =     0.2993

-------------+----------------------------------------------------------------
     HP2pbfhm |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0340114    .0244533      1.39    0.164     -.0139163      .081939
     radhlw1 |    .0363986    .0174833      2.08    0.037      .0021319     .0706654
     radfmw1 |   -.0015313    .0178247     -0.09    0.932      -.036467     .0334045
     radchw1 |    .0009915    .0075779      0.13    0.896     -.0138609     .0158438
         bf1 |    .0206116    .0579697      0.36    0.722      -.093007     .1342302
        bf20 |   -.0291487    .0520408     -0.56    0.575     -.1311468     .0728495
       deaw1 |   -.1920532    .5370629     -0.36    0.721     -1.244677     .8605707
      shhlw1 |    .0059078    .0124198      0.48    0.634     -.0184346     .0302502
     shjobw1 |   -.0320847    .0132185     -2.43    0.015     -.0579926    -.0061768
    shrelaw1 |    .0003818    .0085053      0.04    0.964     -.0162882     .0170518
     suprtw1 |   -.0429566    .0159915     -2.69    0.007     -.0742993    -.0116139
     icdxcnt |    .0038445    .1941372      0.02    0.984     -.3766574     .3843463
       illw1 |    1.272118    .6632283      1.92    0.055      -.027785     2.572022
       whpel |    .0269973    .0096145      2.81    0.005      .0081531     .0458414
     whppain |     .037165    .0179982      2.06    0.039      .0018892     .0724407
avgcumdosew1 |   -1.078232    .6877671     -1.57    0.117      -2.42623     .2697671
       _cons |    -4.86226    2.280387     -2.13    0.033     -9.331737    -.3927841
-------------+----------------------------------------------------------------


Note: 1 failure and 0 successes completely determined.


Logistic model for HP2pbfhm

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         4             3  |          7
     -     |        18           315  |        333
-----------+--------------------------+-----------
   Total   |        22           318  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2pbfhm != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   18.18%
Specificity                     Pr( -|~D)   99.06%
Positive predictive value       Pr( D| +)   57.14%
Negative predictive value       Pr(~D| -)   94.59%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    0.94%
False - rate for true D         Pr( -| D)   81.82%
False + rate for classified +   Pr(~D| +)   42.86%
False - rate for classified -   Pr( D| -)    5.41%
--------------------------------------------------
Correctly classified                        93.82%
--------------------------------------------------


Logistic model for HP2pbfhm, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       338
            Pearson chi2(321) =       275.35
                  Prob > chi2 =         0.9691

Measures of Fit for logit of HP2pbfhm

Log-Lik Intercept Only:      -81.506     Log-Lik Full Model:          -57.112
D(323):                      114.225     LR(16):                       48.788
                                         Prob > LR:                     0.000
McFadden's R2:                 0.299     McFadden's Adj R2:             0.091
Maximum Likelihood R2:         0.134     Cragg & Uhler's R2:            0.351
McKelvey and Zavoina's R2:     0.665     Efron's R2:                    0.217
Variance of y*:                9.830     Variance of error:             3.290
Count R2:                      0.938     Adj Count R2:                  0.045
AIC:                           0.436     AIC*n:                       148.225
BIC:                       -1768.525     BIC':                         44.475


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Full main model for HP2pbfhm for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:34  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****      chunk 2 H1 test:Gender= 2  model Wave =  1 for HP2pbfhm        *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:34  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        361
                                                  LR chi2(16)     =      96.83
                                                  Prob > chi2     =     0.0000
Log likelihood = -91.203197                       Pseudo R2       =     0.3468

-------------+----------------------------------------------------------------
     HP2pbfhm |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0502271    .0202018      2.49    0.013      .0106323     .0898219
     radhlw1 |    .0188112    .0160509      1.17    0.241     -.0126479     .0502703
     radfmw1 |   -.0042512    .0163854     -0.26    0.795     -.0363659     .0278636
     radchw1 |    .0153696    .0070188      2.19    0.029       .001613     .0291263
         bf1 |   -.0206127     .038919     -0.53    0.596     -.0968925      .055667
        bf20 |    .0185232    .0326257      0.57    0.570      -.045422     .0824684
       deaw1 |   -.5449908    .3113187     -1.75    0.080     -1.155164     .0651827
      shhlw1 |    .0064889    .0077309      0.84    0.401     -.0086635     .0216412
     shjobw1 |    .0017017     .007982      0.21    0.831     -.0139428     .0173462
    shrelaw1 |   -.0030227    .0063437     -0.48    0.634     -.0154561     .0094108
     suprtw1 |   -.0056802    .0063823     -0.89    0.373     -.0181892     .0068288
     icdxcnt |   -.0897183     .099014     -0.91    0.365     -.2837821     .1043455
       illw1 |    .3636938    .3638178      1.00    0.317      -.349376     1.076764
       whpel |    .0235691    .0067551      3.49    0.000      .0103293     .0368089
     whppain |    .0227418    .0098549      2.31    0.021      .0034266      .042057
avgcumdosew1 |   -.1610235    .3017311     -0.53    0.594     -.7524056     .4303586
       _cons |   -8.739278    1.807919     -4.83    0.000     -12.28273    -5.195822
-------------+----------------------------------------------------------------

Logistic model for HP2pbfhm

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        22            10  |         32
     -     |        25           304  |        329
-----------+--------------------------+-----------
   Total   |        47           314  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2pbfhm != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   46.81%
Specificity                     Pr( -|~D)   96.82%
Positive predictive value       Pr( D| +)   68.75%
Negative predictive value       Pr(~D| -)   92.40%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    3.18%
False - rate for true D         Pr( -| D)   53.19%
False + rate for classified +   Pr(~D| +)   31.25%
False - rate for classified -   Pr( D| -)    7.60%
--------------------------------------------------
Correctly classified                        90.30%
--------------------------------------------------


Logistic model for HP2pbfhm, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       361
            Pearson chi2(344) =       451.49
                  Prob > chi2 =         0.0001

Measures of Fit for logit of HP2pbfhm

Log-Lik Intercept Only:     -139.619     Log-Lik Full Model:          -91.203
D(344):                      182.406     LR(16):                       96.831
                                         Prob > LR:                     0.000
McFadden's R2:                 0.347     McFadden's Adj R2:             0.225
Maximum Likelihood R2:         0.235     Cragg & Uhler's R2:            0.437
McKelvey and Zavoina's R2:     0.516     Efron's R2:                    0.364
Variance of y*:                6.796     Variance of error:             3.290
Count R2:                      0.903     Adj Count R2:                  0.255
AIC:                           0.599     AIC*n:                       216.406
BIC:                       -1843.368     BIC':                         -2.609


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Full main model for HP2sxlife for wave= 1               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:35  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****      chunk 2 H1 test:Gender= 1  model Wave =  1 for HP2pbfhm        *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:35  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        340
                                                  LR chi2(16)     =     114.99
                                                  Prob > chi2     =     0.0000
Log likelihood = -114.01992                       Pseudo R2       =     0.3352

-------------+----------------------------------------------------------------
    HP2sxlife |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0590029    .0159634      3.70    0.000      .0277153     .0902906
     radhlw1 |    .0029312    .0094494      0.31    0.756     -.0155894     .0214517
     radfmw1 |    .0127523    .0103995      1.23    0.220     -.0076304     .0331349
     radchw1 |    .0010049    .0051864      0.19    0.846     -.0091602       .01117
         bf1 |    .0441999    .0336142      1.31    0.189     -.0216827     .1100825
        bf20 |   -.0395218    .0288279     -1.37    0.170     -.0960233     .0169798
       deaw1 |    .2670954    .2996511      0.89    0.373       -.32021     .8544008
      shhlw1 |    .0041926    .0065732      0.64    0.524     -.0086906     .0170758
     shjobw1 |    .0026749    .0064224      0.42    0.677     -.0099127     .0152625
    shrelaw1 |   -.0050533    .0054035     -0.94    0.350     -.0156441     .0055374
     suprtw1 |   -.0106082    .0074706     -1.42    0.156     -.0252504      .004034
     icdxcnt |     .296098    .1179759      2.51    0.012      .0648695     .5273264
       illw1 |   -.2958477    .4634184     -0.64    0.523     -1.204131     .6124357
       whpel |    .0120514    .0063449      1.90    0.058     -.0003844     .0244872
     whppain |    .0336666    .0114387      2.94    0.003      .0112471     .0560861
avgcumdosew1 |   -.0106642    .0694896     -0.15    0.878     -.1468612     .1255329
       _cons |   -5.801405    1.365674     -4.25    0.000     -8.478076    -3.124734
-------------+----------------------------------------------------------------

Logistic model for HP2sxlife

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        30            14  |         44
     -     |        39           257  |        296
-----------+--------------------------+-----------
   Total   |        69           271  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2sxlife != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   43.48%
Specificity                     Pr( -|~D)   94.83%
Positive predictive value       Pr( D| +)   68.18%
Negative predictive value       Pr(~D| -)   86.82%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    5.17%
False - rate for true D         Pr( -| D)   56.52%
False + rate for classified +   Pr(~D| +)   31.82%
False - rate for classified -   Pr( D| -)   13.18%
--------------------------------------------------
Correctly classified                        84.41%
--------------------------------------------------


Logistic model for HP2sxlife, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       338
            Pearson chi2(321) =       275.73
                  Prob > chi2 =         0.9680

Measures of Fit for logit of HP2sxlife

Log-Lik Intercept Only:     -171.514     Log-Lik Full Model:         -114.020
D(323):                      228.040     LR(16):                      114.988
                                         Prob > LR:                     0.000
McFadden's R2:                 0.335     McFadden's Adj R2:             0.236
Maximum Likelihood R2:         0.287     Cragg & Uhler's R2:            0.452
McKelvey and Zavoina's R2:     0.507     Efron's R2:                    0.337
Variance of y*:                6.674     Variance of error:             3.290
Count R2:                      0.844     Adj Count R2:                  0.232
AIC:                           0.771     AIC*n:                       262.040
BIC:                       -1654.710     BIC':                        -21.725


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Full main model for HP2sxlife for wave= 1               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:37  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****     chunk 2 H1 test:Gender= 2  model Wave =  1 for HP2sxlife        *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:37  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        361
                                                  LR chi2(16)     =     129.30
                                                  Prob > chi2     =     0.0000
Log likelihood = -142.36769                       Pseudo R2       =     0.3123

-------------+----------------------------------------------------------------
    HP2sxlife |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0752426    .0153753      4.89    0.000      .0451077     .1053776
     radhlw1 |    .0092055     .011296      0.81    0.415     -.0129343     .0313452
     radfmw1 |   -.0020864    .0113956     -0.18    0.855     -.0244214     .0202487
     radchw1 |    .0020443    .0048206      0.42    0.672     -.0074039     .0114924
         bf1 |   -.0058128    .0313589     -0.19    0.853     -.0672751     .0556495
        bf20 |    .0144978    .0272214      0.53    0.594     -.0388551     .0678507
       deaw1 |    .0318637    .1661748      0.19    0.848      -.293833     .3575604
      shhlw1 |   -.0053469     .006256     -0.85    0.393     -.0176085     .0069146
     shjobw1 |    .0073481    .0058944      1.25    0.213     -.0042048      .018901
    shrelaw1 |    -.000888    .0049189     -0.18    0.857     -.0105288     .0087528
     suprtw1 |   -.0014398    .0045581     -0.32    0.752     -.0103734     .0074938
     icdxcnt |    .0149569    .0731981      0.20    0.838     -.1285087     .1584226
       illw1 |    .0490087    .2878774      0.17    0.865     -.5152206      .613238
       whpel |    .0156843    .0052032      3.01    0.003      .0054862     .0258824
     whppain |    .0164051    .0080757      2.03    0.042      .0005769     .0322332
avgcumdosew1 |    .3037096    .2538654      1.20    0.232     -.1938575     .8012766
       _cons |   -7.814904    1.357311     -5.76    0.000     -10.47518    -5.154624
-------------+----------------------------------------------------------------

Logistic model for HP2sxlife

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        50            21  |         71
     -     |        44           246  |        290
-----------+--------------------------+-----------
   Total   |        94           267  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2sxlife != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   53.19%
Specificity                     Pr( -|~D)   92.13%
Positive predictive value       Pr( D| +)   70.42%
Negative predictive value       Pr(~D| -)   84.83%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    7.87%
False - rate for true D         Pr( -| D)   46.81%
False + rate for classified +   Pr(~D| +)   29.58%
False - rate for classified -   Pr( D| -)   15.17%
--------------------------------------------------
Correctly classified                        81.99%
--------------------------------------------------


Logistic model for HP2sxlife, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       361
            Pearson chi2(344) =       337.28
                  Prob > chi2 =         0.5919

Measures of Fit for logit of HP2sxlife

Log-Lik Intercept Only:     -207.020     Log-Lik Full Model:         -142.368
D(344):                      284.735     LR(16):                      129.304
                                         Prob > LR:                     0.000
McFadden's R2:                 0.312     McFadden's Adj R2:             0.230
Maximum Likelihood R2:         0.301     Cragg & Uhler's R2:            0.441
McKelvey and Zavoina's R2:     0.466     Efron's R2:                    0.349
Variance of y*:                6.156     Variance of error:             3.290
Count R2:                      0.820     Adj Count R2:                  0.309
AIC:                           0.883     AIC*n:                       318.735
BIC:                       -1741.039     BIC':                        -35.082


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Full main model for HP2inthob for wave= 1               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:38  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****     chunk 2 H1 test:Gender= 1  model Wave =  1 for HP2sxlife        *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:38  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        340
                                                  LR chi2(16)     =      91.98
                                                  Prob > chi2     =     0.0000
Log likelihood = -73.074474                       Pseudo R2       =     0.3863

-------------+----------------------------------------------------------------
    HP2inthob |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0148994    .0203155      0.73    0.463     -.0249182      .054717
     radhlw1 |    .0286139    .0136564      2.10    0.036      .0018479     .0553799
     radfmw1 |   -.0044824    .0143771     -0.31    0.755      -.032661     .0236962
     radchw1 |   -.0050595    .0066433     -0.76    0.446     -.0180802     .0079611
         bf1 |   -1.418867    121.2417     -0.01    0.991     -239.0481     236.2104
        bf20 |    1.418704    121.2416      0.01    0.991     -236.2105     239.0479
       deaw1 |   -1.999749    .8944348     -2.24    0.025     -3.752809    -.2466886
      shhlw1 |    .0080065    .0083096      0.96    0.335     -.0082799      .024293
     shjobw1 |    .0001383    .0082403      0.02    0.987     -.0160124      .016289
    shrelaw1 |   -.0131905    .0071071     -1.86    0.063     -.0271201     .0007391
     suprtw1 |    .0056189     .009045      0.62    0.534     -.0121089     .0233467
     icdxcnt |    .2290065    .1519945      1.51    0.132     -.0688972     .5269103
       illw1 |    .0860647    .5894849      0.15    0.884     -1.069305     1.241434
       whpel |    .0216709    .0084653      2.56    0.010      .0050793     .0382625
     whppain |    .0370937    .0138627      2.68    0.007      .0099234     .0642641
avgcumdosew1 |    -.588666     .521679     -1.13    0.259     -1.611138     .4338061
       _cons |   -62.36568    4849.665     -0.01    0.990     -9567.534     9442.803
-------------+----------------------------------------------------------------


Note: 28 failures and 0 successes completely determined.


Logistic model for HP2inthob

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        14             7  |         21
     -     |        24           295  |        319
-----------+--------------------------+-----------
   Total   |        38           302  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2inthob != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   36.84%
Specificity                     Pr( -|~D)   97.68%
Positive predictive value       Pr( D| +)   66.67%
Negative predictive value       Pr(~D| -)   92.48%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    2.32%
False - rate for true D         Pr( -| D)   63.16%
False + rate for classified +   Pr(~D| +)   33.33%
False - rate for classified -   Pr( D| -)    7.52%
--------------------------------------------------
Correctly classified                        90.88%
--------------------------------------------------


Logistic model for HP2inthob, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       338
            Pearson chi2(321) =       247.06
                  Prob > chi2 =         0.9992

Measures of Fit for logit of HP2inthob

Log-Lik Intercept Only:     -119.064     Log-Lik Full Model:          -73.074
D(323):                      146.149     LR(16):                       91.980
                                         Prob > LR:                     0.000
McFadden's R2:                 0.386     McFadden's Adj R2:             0.243
Maximum Likelihood R2:         0.237     Cragg & Uhler's R2:            0.471
McKelvey and Zavoina's R2:     0.969     Efron's R2:                    0.345
Variance of y*:              106.254     Variance of error:             3.290
Count R2:                      0.909     Adj Count R2:                  0.184
AIC:                           0.530     AIC*n:                       180.149
BIC:                       -1736.600     BIC':                          1.284


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Full main model for HP2inthob for wave= 1               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:39  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****     chunk 2 H1 test:Gender= 2  model Wave =  1 for HP2inthob        *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:39  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        361
                                                  LR chi2(16)     =     107.06
                                                  Prob > chi2     =     0.0000
Log likelihood = -118.18124                       Pseudo R2       =     0.3117

-------------+----------------------------------------------------------------
    HP2inthob |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0699624    .0173466      4.03    0.000      .0359636     .1039611
     radhlw1 |    .0286584    .0137075      2.09    0.037      .0017921     .0555247
     radfmw1 |   -.0091301    .0134976     -0.68    0.499     -.0355848     .0173246
     radchw1 |    -.002594     .005339     -0.49    0.627     -.0130582     .0078702
         bf1 |   -.0501488    .0429551     -1.17    0.243     -.1343392     .0340416
        bf20 |    .0487177    .0389536      1.25    0.211     -.0276299     .1250654
       deaw1 |   -.0351925    .2003939     -0.18    0.861     -.4279574     .3575723
      shhlw1 |    .0097488    .0069887      1.39    0.163     -.0039487     .0234463
     shjobw1 |   -.0083677    .0069365     -1.21    0.228     -.0219629     .0052276
    shrelaw1 |    .0000442    .0054396      0.01    0.994     -.0106173     .0107057
     suprtw1 |   -.0015489    .0051663     -0.30    0.764     -.0116747     .0085768
     icdxcnt |    -.012606    .0820109     -0.15    0.878     -.1733443     .1481324
       illw1 |   -.1247965    .3437396     -0.36    0.717     -.7985136     .5489207
       whpel |    .0192658    .0057633      3.34    0.001        .00797     .0305616
     whppain |     .012087    .0089516      1.35    0.177     -.0054578     .0296317
avgcumdosew1 |    .2321163    .2262873      1.03    0.305     -.2113986     .6756312
       _cons |   -9.276591    1.854246     -5.00    0.000     -12.91085    -5.642336
-------------+----------------------------------------------------------------

Logistic model for HP2inthob

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        34             9  |         43
     -     |        32           286  |        318
-----------+--------------------------+-----------
   Total   |        66           295  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2inthob != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   51.52%
Specificity                     Pr( -|~D)   96.95%
Positive predictive value       Pr( D| +)   79.07%
Negative predictive value       Pr(~D| -)   89.94%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    3.05%
False - rate for true D         Pr( -| D)   48.48%
False + rate for classified +   Pr(~D| +)   20.93%
False - rate for classified -   Pr( D| -)   10.06%
--------------------------------------------------
Correctly classified                        88.64%
--------------------------------------------------


Logistic model for HP2inthob, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       361
            Pearson chi2(344) =       359.30
                  Prob > chi2 =         0.2742

Measures of Fit for logit of HP2inthob

Log-Lik Intercept Only:     -171.710     Log-Lik Full Model:         -118.181
D(344):                      236.362     LR(16):                      107.058
                                         Prob > LR:                     0.000
McFadden's R2:                 0.312     McFadden's Adj R2:             0.213
Maximum Likelihood R2:         0.257     Cragg & Uhler's R2:            0.418
McKelvey and Zavoina's R2:     0.488     Efron's R2:                    0.350
Variance of y*:                6.429     Variance of error:             3.290
Count R2:                      0.886     Adj Count R2:                  0.379
AIC:                           0.749     AIC*n:                       270.362
BIC:                       -1789.412     BIC':                        -12.835


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Full main model for HP2vacatn for wave= 1               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:40  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****     chunk 2 H1 test:Gender= 1  model Wave =  1 for HP2inthob        *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:40  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        340
                                                  LR chi2(16)     =      98.70
                                                  Prob > chi2     =     0.0000
Log likelihood = -75.801382                       Pseudo R2       =     0.3943

-------------+----------------------------------------------------------------
    HP2vacatn |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0398301    .0197634      2.02    0.044      .0010946     .0785657
     radhlw1 |    .0047366    .0118205      0.40    0.689     -.0184313     .0279044
     radfmw1 |     .015829    .0130489      1.21    0.225     -.0097463     .0414043
     radchw1 |    .0072518    .0068171      1.06    0.287     -.0061096     .0206131
         bf1 |    -.090948    .1013342     -0.90    0.369     -.2895594     .1076635
        bf20 |    .0753999      .09773      0.77    0.440     -.1161474     .2669473
       deaw1 |   -1.809128     .750665     -2.41    0.016     -3.280404    -.3378512
      shhlw1 |     .013514    .0080537      1.68    0.093      -.002271     .0292989
     shjobw1 |    .0150221    .0086051      1.75    0.081     -.0018435     .0318877
    shrelaw1 |   -.0214143     .007497     -2.86    0.004     -.0361083    -.0067204
     suprtw1 |    .0028908    .0084025      0.34    0.731     -.0135777     .0193594
     icdxcnt |    .4435615    .1508178      2.94    0.003      .1479641      .739159
       illw1 |   -1.026194    .5937076     -1.73    0.084     -2.189839     .1374516
       whpel |    .0237366    .0083486      2.84    0.004      .0073736     .0400996
     whppain |    .0251755    .0140614      1.79    0.073     -.0023843     .0527353
avgcumdosew1 |   -.1311446    .2327744     -0.56    0.573     -.5873741     .3250849
       _cons |   -11.20463    4.009778     -2.79    0.005     -19.06366    -3.345614
-------------+----------------------------------------------------------------

Logistic model for HP2vacatn

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        20             5  |         25
     -     |        21           294  |        315
-----------+--------------------------+-----------
   Total   |        41           299  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2vacatn != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   48.78%
Specificity                     Pr( -|~D)   98.33%
Positive predictive value       Pr( D| +)   80.00%
Negative predictive value       Pr(~D| -)   93.33%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    1.67%
False - rate for true D         Pr( -| D)   51.22%
False + rate for classified +   Pr(~D| +)   20.00%
False - rate for classified -   Pr( D| -)    6.67%
--------------------------------------------------
Correctly classified                        92.35%
--------------------------------------------------


Logistic model for HP2vacatn, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       338
            Pearson chi2(321) =       306.80
                  Prob > chi2 =         0.7062

Measures of Fit for logit of HP2vacatn

Log-Lik Intercept Only:     -125.152     Log-Lik Full Model:          -75.801
D(323):                      151.603     LR(16):                       98.702
                                         Prob > LR:                     0.000
McFadden's R2:                 0.394     McFadden's Adj R2:             0.258
Maximum Likelihood R2:         0.252     Cragg & Uhler's R2:            0.484
McKelvey and Zavoina's R2:     0.653     Efron's R2:                    0.385
Variance of y*:                9.479     Variance of error:             3.290
Count R2:                      0.924     Adj Count R2:                  0.366
AIC:                           0.546     AIC*n:                       185.603
BIC:                       -1731.147     BIC':                         -5.439


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Full main model for HP2vacatn for wave= 1               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:42  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****     chunk 2 H1 test:Gender= 2  model Wave =  1 for HP2vacatn        *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:42  *****
*******************************************************************************
*******************************************************************************


Logistic regression                               Number of obs   =        361
                                                  LR chi2(16)     =     104.18
                                                  Prob > chi2     =     0.0000
Log likelihood = -115.04362                       Pseudo R2       =     0.3117

-------------+----------------------------------------------------------------
    HP2vacatn |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0623094    .0174604      3.57    0.000      .0280876     .0965313
     radhlw1 |    .0132309    .0137994      0.96    0.338     -.0138154     .0402773
     radfmw1 |    .0131893    .0144763      0.91    0.362     -.0151838     .0415623
     radchw1 |    .0047628    .0056439      0.84    0.399      -.006299     .0158246
         bf1 |   -.0565421    .0358938     -1.58    0.115     -.1268928     .0138085
        bf20 |    .0378606    .0310299      1.22    0.222     -.0229568     .0986781
       deaw1 |   -.0382725    .2214393     -0.17    0.863     -.4722855     .3957406
      shhlw1 |   -.0005756    .0069591     -0.08    0.934     -.0142153     .0130641
     shjobw1 |    .0004867    .0068619      0.07    0.943     -.0129624     .0139357
    shrelaw1 |   -.0017939    .0056066     -0.32    0.749     -.0127825     .0091948
     suprtw1 |    .0069702    .0049845      1.40    0.162     -.0027992     .0167396
     icdxcnt |   -.0064093    .0849315     -0.08    0.940      -.172872     .1600534
       illw1 |    .0924944    .3377854      0.27    0.784     -.5695528     .7545416
       whpel |    .0093917    .0057464      1.63    0.102     -.0018712     .0206545
     whppain |    .0238505    .0092573      2.58    0.010      .0057066     .0419944
avgcumdosew1 |   -.0242345    .2692162     -0.09    0.928     -.5518885     .5034194
       _cons |   -8.784324    1.645252     -5.34    0.000     -12.00896     -5.55969
-------------+----------------------------------------------------------------

Logistic model for HP2vacatn

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        31            11  |         42
     -     |        32           287  |        319
-----------+--------------------------+-----------
   Total   |        63           298  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2vacatn != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   49.21%
Specificity                     Pr( -|~D)   96.31%
Positive predictive value       Pr( D| +)   73.81%
Negative predictive value       Pr(~D| -)   89.97%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    3.69%
False - rate for true D         Pr( -| D)   50.79%
False + rate for classified +   Pr(~D| +)   26.19%
False - rate for classified -   Pr( D| -)   10.03%
--------------------------------------------------
Correctly classified                        88.09%
--------------------------------------------------


Logistic model for HP2vacatn, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       361
            Pearson chi2(344) =       393.10
                  Prob > chi2 =         0.0348

Measures of Fit for logit of HP2vacatn

Log-Lik Intercept Only:     -167.134     Log-Lik Full Model:         -115.044
D(344):                      230.087     LR(16):                      104.180
                                         Prob > LR:                     0.000
McFadden's R2:                 0.312     McFadden's Adj R2:             0.210
Maximum Likelihood R2:         0.251     Cragg & Uhler's R2:            0.415
McKelvey and Zavoina's R2:     0.483     Efron's R2:                    0.350
Variance of y*:                6.365     Variance of error:             3.290
Count R2:                      0.881     Adj Count R2:                  0.317
AIC:                           0.732     AIC*n:                       264.087
BIC:                       -1795.687     BIC':                         -9.958

.
. * Notes from analysis
. * + *    dose almost affects women in home care  w1(b = - 5.512, se=.27, -1.87, p=.062)
. * + *    dose affects women in their social life (b = .7561, se=.310, z=2.42 p=.015)
. * + *    dose does not affect men in anything
. * + *
. title " H1: Test of hypothesis 1: Pt 2 wave 1 stepwise backward trimming models"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
***** H1: Test of hypothesis 1: Pt 2 wave 1 stepwise backward trimming models*****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:43  *****
*******************************************************************************
*******************************************************************************


. forvalues j=1/1 {
  2. set more off
  3.   foreach var in HP2work HP2hmcare HP2probsoc HP2pbfhm HP2sxlife HP2inthob ///
>       HP2vacatn  {
  4.       forvalues k=1/2 {
  5. local w1bf bf1 bf4 bf9 bf10 bf11 bf4m bf15m bf20 bf22 bf30 bf40
  6. di _skip(4)
  7.
. title "Trimmed model for `var' for wave= `j' "
  8. di _skip(4)
  9. title "Gender= `k'  model Wave =  `j' for `e(depvar)' "
 10. di _skip(4)
 11.
.
.        sw, pr(.1): logit `var' age  ///
>                 radhlw`j' radfmw`j' radchw`j' bf1 bf20   ///
>                  deaw`j'     ///
>          shhlw`j' shjobw`j' shrelaw`j' suprtw`j'  ///
>          icdxcnt illw`j'                         ///
>           whpel whppain                         ///
>                  avgcumdosew`j' if gender==`k',  difficult iterate(500) nolog
 12.                         estat class
 13.                         estat gof
 14.                         fitstat
 15. }
 16. }
 17. }


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****               Trimmed model for HP2work for wave= 1                 *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:43  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Gender= 1  model Wave =  1 for HP2vacatn                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:17:43  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.9918 >= 0.1000  removing radchw1


p = 0.9496 >= 0.1000  removing shrelaw1


p = 0.9409 >= 0.1000  removing shjobw1


p = 0.9381 >= 0.1000  removing suprtw1


p = 0.8744 >= 0.1000  removing shhlw1


p = 0.7664 >= 0.1000  removing age


p = 0.7363 >= 0.1000  removing bf1


p = 0.6904 >= 0.1000  removing avgcumdosew1


p = 0.6691 >= 0.1000  removing radfmw1


p = 0.6541 >= 0.1000  removing radhlw1


p = 0.4299 >= 0.1000  removing deaw1


p = 0.2426 >= 0.1000  removing illw1


Logistic regression                               Number of obs   =        340
                                                  LR chi2(4)      =      68.79
                                                  Prob > chi2     =     0.0000
Log likelihood = -138.47644                       Pseudo R2       =     0.1990

-------------+----------------------------------------------------------------
      HP2work |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
     whppain |    .0241253    .0090481      2.67    0.008      .0063914     .0418593
        bf20 |    .0113133    .0066979      1.69    0.091     -.0018144      .024441
     icdxcnt |    .2676172    .0895948      2.99    0.003      .0920146     .4432198
       whpel |    .0177362    .0053835      3.29    0.001      .0071848     .0282876
       _cons |   -3.724149    .5667296     -6.57    0.000     -4.834919     -2.61338
-------------+----------------------------------------------------------------

Logistic model for HP2work

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        20            16  |         36
     -     |        50           254  |        304
-----------+--------------------------+-----------
   Total   |        70           270  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2work != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   28.57%
Specificity                     Pr( -|~D)   94.07%
Positive predictive value       Pr( D| +)   55.56%
Negative predictive value       Pr(~D| -)   83.55%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    5.93%
False - rate for true D         Pr( -| D)   71.43%
False + rate for classified +   Pr(~D| +)   44.44%
False - rate for classified -   Pr( D| -)   16.45%
--------------------------------------------------
Correctly classified                        80.59%
--------------------------------------------------


Logistic model for HP2work, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       214
            Pearson chi2(209) =       221.23
                  Prob > chi2 =         0.2678

Measures of Fit for logit of HP2work

Log-Lik Intercept Only:     -172.873     Log-Lik Full Model:         -138.476
D(335):                      276.953     LR(4):                        68.793
                                         Prob > LR:                     0.000
McFadden's R2:                 0.199     McFadden's Adj R2:             0.170
Maximum Likelihood R2:         0.183     Cragg & Uhler's R2:            0.287
McKelvey and Zavoina's R2:     0.298     Efron's R2:                    0.189
Variance of y*:                4.687     Variance of error:             3.290
Count R2:                      0.806     Adj Count R2:                  0.057
AIC:                           0.844     AIC*n:                       286.953
BIC:                       -1675.744     BIC':                        -45.477


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****               Trimmed model for HP2work for wave= 1                 *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:18:01  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Gender= 2  model Wave =  1 for HP2work                 *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:18:01  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.9836 >= 0.1000  removing radchw1


p = 0.6639 >= 0.1000  removing bf1


p = 0.4919 >= 0.1000  removing deaw1


p = 0.5001 >= 0.1000  removing suprtw1


p = 0.5449 >= 0.1000  removing bf20


p = 0.4975 >= 0.1000  removing radfmw1


p = 0.3522 >= 0.1000  removing shhlw1


p = 0.3784 >= 0.1000  removing shrelaw1


p = 0.3095 >= 0.1000  removing illw1


p = 0.2665 >= 0.1000  removing avgcumdosew1


p = 0.1557 >= 0.1000  removing shjobw1


p = 0.1825 >= 0.1000  removing icdxcnt


Logistic regression                               Number of obs   =        361
                                                  LR chi2(4)      =      71.92
                                                  Prob > chi2     =     0.0000
Log likelihood = -170.01046                       Pseudo R2       =     0.1746

-------------+----------------------------------------------------------------
      HP2work |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0430616    .0128257      3.36    0.001      .0179237     .0681995
     radhlw1 |    .0062995    .0037665      1.67    0.094     -.0010827     .0136818
     whppain |    .0144246    .0069414      2.08    0.038      .0008197     .0280294
       whpel |    .0138666    .0045881      3.02    0.003      .0048741     .0228591
       _cons |   -4.521239    .6749507     -6.70    0.000     -5.844118    -3.198359
-------------+----------------------------------------------------------------

Logistic model for HP2work

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        32            22  |         54
     -     |        61           246  |        307
-----------+--------------------------+-----------
   Total   |        93           268  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2work != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   34.41%
Specificity                     Pr( -|~D)   91.79%
Positive predictive value       Pr( D| +)   59.26%
Negative predictive value       Pr(~D| -)   80.13%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    8.21%
False - rate for true D         Pr( -| D)   65.59%
False + rate for classified +   Pr(~D| +)   40.74%
False - rate for classified -   Pr( D| -)   19.87%
--------------------------------------------------
Correctly classified                        77.01%
--------------------------------------------------


Logistic model for HP2work, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       337
            Pearson chi2(332) =       352.69
                  Prob > chi2 =         0.2083

Measures of Fit for logit of HP2work

Log-Lik Intercept Only:     -205.969     Log-Lik Full Model:         -170.010
D(356):                      340.021     LR(4):                        71.916
                                         Prob > LR:                     0.000
McFadden's R2:                 0.175     McFadden's Adj R2:             0.150
Maximum Likelihood R2:         0.181     Cragg & Uhler's R2:            0.265
McKelvey and Zavoina's R2:     0.268     Efron's R2:                    0.200
Variance of y*:                4.493     Variance of error:             3.290
Count R2:                      0.770     Adj Count R2:                  0.108
AIC:                           0.970     AIC*n:                       350.021
BIC:                       -1756.420     BIC':                        -48.361


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Trimmed model for HP2hmcare for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:18:31  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Gender= 1  model Wave =  1 for HP2work                 *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:18:31  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.9092 >= 0.1000  removing bf1


p = 0.8941 >= 0.1000  removing radfmw1


p = 0.8830 >= 0.1000  removing illw1


p = 0.8165 >= 0.1000  removing shjobw1


p = 0.5944 >= 0.1000  removing radchw1


p = 0.5334 >= 0.1000  removing bf20


p = 0.3202 >= 0.1000  removing icdxcnt


p = 0.3052 >= 0.1000  removing avgcumdosew1


p = 0.2942 >= 0.1000  removing deaw1


p = 0.2428 >= 0.1000  removing radhlw1


p = 0.1677 >= 0.1000  removing shrelaw1


p = 0.1744 >= 0.1000  removing shhlw1


Logistic regression                               Number of obs   =        340
                                                  LR chi2(4)      =     103.68
                                                  Prob > chi2     =     0.0000
Log likelihood = -121.03484                       Pseudo R2       =     0.2999

-------------+----------------------------------------------------------------
    HP2hmcare |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0421012    .0142713      2.95    0.003      .0141299     .0700725
       whpel |    .0306134    .0057198      5.35    0.000      .0194028     .0418239
     whppain |    .0416801    .0100067      4.17    0.000      .0220674     .0612928
     suprtw1 |   -.0219742    .0077148     -2.85    0.004     -.0370949    -.0068534
       _cons |     -4.8777    .7901314     -6.17    0.000     -6.426329    -3.329071
-------------+----------------------------------------------------------------

Logistic model for HP2hmcare

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        29            12  |         41
     -     |        41           258  |        299
-----------+--------------------------+-----------
   Total   |        70           270  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2hmcare != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   41.43%
Specificity                     Pr( -|~D)   95.56%
Positive predictive value       Pr( D| +)   70.73%
Negative predictive value       Pr(~D| -)   86.29%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    4.44%
False - rate for true D         Pr( -| D)   58.57%
False + rate for classified +   Pr(~D| +)   29.27%
False - rate for classified -   Pr( D| -)   13.71%
--------------------------------------------------
Correctly classified                        84.41%
--------------------------------------------------


Logistic model for HP2hmcare, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       222
            Pearson chi2(217) =       256.04
                  Prob > chi2 =         0.0356

Measures of Fit for logit of HP2hmcare

Log-Lik Intercept Only:     -172.873     Log-Lik Full Model:         -121.035
D(335):                      242.070     LR(4):                       103.676
                                         Prob > LR:                     0.000
McFadden's R2:                 0.300     McFadden's Adj R2:             0.271
Maximum Likelihood R2:         0.263     Cragg & Uhler's R2:            0.412
McKelvey and Zavoina's R2:     0.440     Efron's R2:                    0.318
Variance of y*:                5.880     Variance of error:             3.290
Count R2:                      0.844     Adj Count R2:                  0.243
AIC:                           0.741     AIC*n:                       252.070
BIC:                       -1710.627     BIC':                        -80.360


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Trimmed model for HP2hmcare for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:18:50  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Gender= 2  model Wave =  1 for HP2hmcare                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:18:50  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.9346 >= 0.1000  removing deaw1


p = 0.9274 >= 0.1000  removing shjobw1


p = 0.9250 >= 0.1000  removing shhlw1


p = 0.7220 >= 0.1000  removing icdxcnt


p = 0.4740 >= 0.1000  removing bf20


p = 0.3654 >= 0.1000  removing radhlw1


p = 0.4280 >= 0.1000  removing radfmw1


Logistic regression                               Number of obs   =        361
                                                  LR chi2(9)      =     156.21
                                                  Prob > chi2     =     0.0000
Log likelihood = -154.77824                       Pseudo R2       =     0.3354

-------------+----------------------------------------------------------------
    HP2hmcare |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0784185    .0141629      5.54    0.000      .0506596     .1061773
     suprtw1 |   -.0074615    .0044903     -1.66    0.097     -.0162624     .0013393
    shrelaw1 |   -.0074666    .0044295     -1.69    0.092     -.0161484     .0012151
     radchw1 |    .0091431    .0041111      2.22    0.026      .0010854     .0172007
         bf1 |   -.0134929    .0066636     -2.02    0.043     -.0265533    -.0004326
       illw1 |    .8286649    .2951482      2.81    0.005      .2501851     1.407145
avgcumdosew1 |   -.8828153    .3523719     -2.51    0.012     -1.573451    -.1921792
       whpel |    .0166275    .0049499      3.36    0.001      .0069259      .026329
     whppain |    .0354236    .0080733      4.39    0.000      .0196003     .0512469
       _cons |   -5.557274    .7545551     -7.36    0.000     -7.036174    -4.078373
-------------+----------------------------------------------------------------

Logistic model for HP2hmcare

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        78            28  |        106
     -     |        47           208  |        255
-----------+--------------------------+-----------
   Total   |       125           236  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2hmcare != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   62.40%
Specificity                     Pr( -|~D)   88.14%
Positive predictive value       Pr( D| +)   73.58%
Negative predictive value       Pr(~D| -)   81.57%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   11.86%
False - rate for true D         Pr( -| D)   37.60%
False + rate for classified +   Pr(~D| +)   26.42%
False - rate for classified -   Pr( D| -)   18.43%
--------------------------------------------------
Correctly classified                        79.22%
--------------------------------------------------


Logistic model for HP2hmcare, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       361
            Pearson chi2(351) =       336.37
                  Prob > chi2 =         0.7036

Measures of Fit for logit of HP2hmcare

Log-Lik Intercept Only:     -232.881     Log-Lik Full Model:         -154.778
D(351):                      309.556     LR(9):                       156.206
                                         Prob > LR:                     0.000
McFadden's R2:                 0.335     McFadden's Adj R2:             0.292
Maximum Likelihood R2:         0.351     Cragg & Uhler's R2:            0.485
McKelvey and Zavoina's R2:     0.507     Efron's R2:                    0.385
Variance of y*:                6.668     Variance of error:             3.290
Count R2:                      0.792     Adj Count R2:                  0.400
AIC:                           0.913     AIC*n:                       329.556
BIC:                       -1757.440     BIC':                       -103.206


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Trimmed model for HP2probsoc for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:19:02  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Gender= 1  model Wave =  1 for HP2hmcare                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:19:02  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.8566 >= 0.1000  removing illw1


p = 0.8048 >= 0.1000  removing avgcumdosew1


p = 0.7122 >= 0.1000  removing suprtw1


p = 0.5307 >= 0.1000  removing radfmw1


p = 0.6029 >= 0.1000  removing icdxcnt


p = 0.4983 >= 0.1000  removing radhlw1


p = 0.5103 >= 0.1000  removing bf20


p = 0.5641 >= 0.1000  removing bf1


p = 0.3364 >= 0.1000  removing deaw1


Logistic regression                               Number of obs   =        340
                                                  LR chi2(7)      =     108.25
                                                  Prob > chi2     =     0.0000
Log likelihood = -71.027401                       Pseudo R2       =     0.4325

-------------+----------------------------------------------------------------
   HP2probsoc |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0333706    .0199055      1.68    0.094     -.0056435     .0723848
       whpel |    .0291626    .0079784      3.66    0.000      .0135252        .0448
     whppain |    .0409491    .0127699      3.21    0.001      .0159204     .0659777
     radchw1 |   -.0213396    .0069551     -3.07    0.002     -.0349713     -.007708
     shjobw1 |    .0190523    .0085805      2.22    0.026      .0022347     .0358699
    shrelaw1 |    -.020721    .0070404     -2.94    0.003     -.0345199    -.0069222
      shhlw1 |    .0133953    .0077253      1.73    0.083      -.001746     .0285366
       _cons |   -5.786632     1.24788     -4.64    0.000     -8.232431    -3.340832
-------------+----------------------------------------------------------------

Logistic model for HP2probsoc

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        23             6  |         29
     -     |        18           293  |        311
-----------+--------------------------+-----------
   Total   |        41           299  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2probsoc != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   56.10%
Specificity                     Pr( -|~D)   97.99%
Positive predictive value       Pr( D| +)   79.31%
Negative predictive value       Pr(~D| -)   94.21%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    2.01%
False - rate for true D         Pr( -| D)   43.90%
False + rate for classified +   Pr(~D| +)   20.69%
False - rate for classified -   Pr( D| -)    5.79%
--------------------------------------------------
Correctly classified                        92.94%
--------------------------------------------------


Logistic model for HP2probsoc, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       330
            Pearson chi2(322) =       229.07
                  Prob > chi2 =         1.0000

Measures of Fit for logit of HP2probsoc

Log-Lik Intercept Only:     -125.152     Log-Lik Full Model:          -71.027
D(332):                      142.055     LR(7):                       108.250
                                         Prob > LR:                     0.000
McFadden's R2:                 0.432     McFadden's Adj R2:             0.369
Maximum Likelihood R2:         0.273     Cragg & Uhler's R2:            0.523
McKelvey and Zavoina's R2:     0.592     Efron's R2:                    0.424
Variance of y*:                8.072     Variance of error:             3.290
Count R2:                      0.929     Adj Count R2:                  0.415
AIC:                           0.465     AIC*n:                       158.055
BIC:                       -1793.155     BIC':                        -67.447


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Trimmed model for HP2probsoc for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:19:16  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Gender= 2  model Wave =  1 for HP2probsoc               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:19:16  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.9622 >= 0.1000  removing icdxcnt


p = 0.9410 >= 0.1000  removing radchw1


p = 0.7898 >= 0.1000  removing bf20


p = 0.7774 >= 0.1000  removing suprtw1


p = 0.7270 >= 0.1000  removing deaw1


p = 0.6009 >= 0.1000  removing shhlw1


p = 0.6240 >= 0.1000  removing shjobw1


p = 0.5408 >= 0.1000  removing bf1


p = 0.4711 >= 0.1000  removing illw1


p = 0.2037 >= 0.1000  removing radhlw1


p = 0.1024 >= 0.1000  removing shrelaw1


Logistic regression                               Number of obs   =        361
                                                  LR chi2(5)      =     149.14
                                                  Prob > chi2     =     0.0000
Log likelihood = -108.54318                       Pseudo R2       =     0.4072

-------------+----------------------------------------------------------------
   HP2probsoc |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0896167    .0185234      4.84    0.000      .0533114      .125922
avgcumdosew1 |    .6947235    .3138168      2.21    0.027      .0796539     1.309793
     radfmw1 |    .0187784    .0052399      3.58    0.000      .0085082     .0290485
     whppain |    .0339469    .0088357      3.84    0.000      .0166292     .0512645
       whpel |    .0113771    .0055886      2.04    0.042      .0004237     .0223305
       _cons |   -9.114844    1.143245     -7.97    0.000     -11.35556    -6.874125
-------------+----------------------------------------------------------------

Logistic model for HP2probsoc

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        45            12  |         57
     -     |        29           275  |        304
-----------+--------------------------+-----------
   Total   |        74           287  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2probsoc != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   60.81%
Specificity                     Pr( -|~D)   95.82%
Positive predictive value       Pr( D| +)   78.95%
Negative predictive value       Pr(~D| -)   90.46%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    4.18%
False - rate for true D         Pr( -| D)   39.19%
False + rate for classified +   Pr(~D| +)   21.05%
False - rate for classified -   Pr( D| -)    9.54%
--------------------------------------------------
Correctly classified                        88.64%
--------------------------------------------------


Logistic model for HP2probsoc, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       358
            Pearson chi2(352) =       348.12
                  Prob > chi2 =         0.5484

Measures of Fit for logit of HP2probsoc

Log-Lik Intercept Only:     -183.113     Log-Lik Full Model:         -108.543
D(355):                      217.086     LR(5):                       149.139
                                         Prob > LR:                     0.000
McFadden's R2:                 0.407     McFadden's Adj R2:             0.374
Maximum Likelihood R2:         0.338     Cragg & Uhler's R2:            0.531
McKelvey and Zavoina's R2:     0.579     Efron's R2:                    0.441
Variance of y*:                7.806     Variance of error:             3.290
Count R2:                      0.886     Adj Count R2:                  0.446
AIC:                           0.635     AIC*n:                       229.086
BIC:                       -1873.465     BIC':                       -119.695


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Trimmed model for HP2pbfhm for wave= 1                 *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:19:32  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Gender= 1  model Wave =  1 for HP2probsoc               *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:19:32  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.9842 >= 0.1000  removing icdxcnt


p = 0.9610 >= 0.1000  removing shrelaw1


p = 0.9276 >= 0.1000  removing radfmw1


p = 0.9070 >= 0.1000  removing radchw1


p = 0.7422 >= 0.1000  removing bf1


p = 0.7524 >= 0.1000  removing deaw1


p = 0.6097 >= 0.1000  removing shhlw1


p = 0.4148 >= 0.1000  removing bf20


p = 0.1413 >= 0.1000  removing age


p = 0.1190 >= 0.1000  removing avgcumdosew1


p = 0.2896 >= 0.1000  removing illw1


Logistic regression                               Number of obs   =        340
                                                  LR chi2(5)      =      40.42
                                                  Prob > chi2     =     0.0000
Log likelihood = -61.295528                       Pseudo R2       =     0.2480

-------------+----------------------------------------------------------------
     HP2pbfhm |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
     whppain |    .0395558    .0147437      2.68    0.007      .0106587      .068453
     radhlw1 |    .0298374    .0080615      3.70    0.000      .0140371     .0456377
       whpel |    .0259162    .0086694      2.99    0.003      .0089246     .0429079
     shjobw1 |   -.0253531    .0091549     -2.77    0.006     -.0432963    -.0074098
     suprtw1 |   -.0336087    .0147083     -2.29    0.022     -.0624364     -.004781
       _cons |   -4.488377    .6533291     -6.87    0.000     -5.768878    -3.207875
-------------+----------------------------------------------------------------

Logistic model for HP2pbfhm

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         3             3  |          6
     -     |        19           315  |        334
-----------+--------------------------+-----------
   Total   |        22           318  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2pbfhm != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   13.64%
Specificity                     Pr( -|~D)   99.06%
Positive predictive value       Pr( D| +)   50.00%
Negative predictive value       Pr(~D| -)   94.31%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    0.94%
False - rate for true D         Pr( -| D)   86.36%
False + rate for classified +   Pr(~D| +)   50.00%
False - rate for classified -   Pr( D| -)    5.69%
--------------------------------------------------
Correctly classified                        93.53%
--------------------------------------------------


Logistic model for HP2pbfhm, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       254
            Pearson chi2(248) =       217.38
                  Prob > chi2 =         0.9201

Measures of Fit for logit of HP2pbfhm

Log-Lik Intercept Only:      -81.506     Log-Lik Full Model:          -61.296
D(334):                      122.591     LR(5):                        40.421
                                         Prob > LR:                     0.000
McFadden's R2:                 0.248     McFadden's Adj R2:             0.174
Maximum Likelihood R2:         0.112     Cragg & Uhler's R2:            0.294
McKelvey and Zavoina's R2:     0.479     Efron's R2:                    0.165
Variance of y*:                6.309     Variance of error:             3.290
Count R2:                      0.935     Adj Count R2:                  0.000
AIC:                           0.396     AIC*n:                       134.591
BIC:                       -1824.277     BIC':                        -11.277


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Trimmed model for HP2pbfhm for wave= 1                 *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:19:48  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Gender= 2  model Wave =  1 for HP2pbfhm                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:19:48  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.8312 >= 0.1000  removing shjobw1


p = 0.7770 >= 0.1000  removing radfmw1


p = 0.6601 >= 0.1000  removing shrelaw1


p = 0.6022 >= 0.1000  removing avgcumdosew1


p = 0.5932 >= 0.1000  removing bf1


p = 0.8053 >= 0.1000  removing bf20


p = 0.3787 >= 0.1000  removing suprtw1


p = 0.4458 >= 0.1000  removing illw1


p = 0.3360 >= 0.1000  removing icdxcnt


p = 0.2987 >= 0.1000  removing shhlw1


p = 0.1307 >= 0.1000  removing deaw1


Logistic regression                               Number of obs   =        361
                                                  LR chi2(5)      =      89.96
                                                  Prob > chi2     =     0.0000
Log likelihood = -94.639572                       Pseudo R2       =     0.3222

-------------+----------------------------------------------------------------
     HP2pbfhm |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0398856    .0186257      2.14    0.032        .00338     .0763913
     radhlw1 |    .0134802    .0064286      2.10    0.036      .0008804       .02608
     whppain |    .0232758      .00891      2.61    0.009      .0058126     .0407391
     radchw1 |    .0144111     .006637      2.17    0.030      .0014028     .0274195
       whpel |    .0205992    .0062919      3.27    0.001      .0082674     .0329311
       _cons |   -7.543832    1.147753     -6.57    0.000     -9.793387    -5.294278
-------------+----------------------------------------------------------------

Logistic model for HP2pbfhm

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        20            10  |         30
     -     |        27           304  |        331
-----------+--------------------------+-----------
   Total   |        47           314  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2pbfhm != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   42.55%
Specificity                     Pr( -|~D)   96.82%
Positive predictive value       Pr( D| +)   66.67%
Negative predictive value       Pr(~D| -)   91.84%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    3.18%
False - rate for true D         Pr( -| D)   57.45%
False + rate for classified +   Pr(~D| +)   33.33%
False - rate for classified -   Pr( D| -)    8.16%
--------------------------------------------------
Correctly classified                        89.75%
--------------------------------------------------


Logistic model for HP2pbfhm, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       353
            Pearson chi2(347) =       414.17
                  Prob > chi2 =         0.0076

Measures of Fit for logit of HP2pbfhm

Log-Lik Intercept Only:     -139.619     Log-Lik Full Model:          -94.640
D(355):                      189.279     LR(5):                        89.958
                                         Prob > LR:                     0.000
McFadden's R2:                 0.322     McFadden's Adj R2:             0.279
Maximum Likelihood R2:         0.221     Cragg & Uhler's R2:            0.410
McKelvey and Zavoina's R2:     0.467     Efron's R2:                    0.325
Variance of y*:                6.170     Variance of error:             3.290
Count R2:                      0.898     Adj Count R2:                  0.213
AIC:                           0.558     AIC*n:                       201.279
BIC:                       -1901.273     BIC':                        -60.514


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Trimmed model for HP2sxlife for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:20:04  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Gender= 1  model Wave =  1 for HP2pbfhm                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:20:04  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.8780 >= 0.1000  removing avgcumdosew1


p = 0.8410 >= 0.1000  removing radchw1


p = 0.7825 >= 0.1000  removing radhlw1


p = 0.6989 >= 0.1000  removing shjobw1


p = 0.5131 >= 0.1000  removing illw1


p = 0.4364 >= 0.1000  removing shhlw1


p = 0.5320 >= 0.1000  removing shrelaw1


p = 0.3028 >= 0.1000  removing deaw1


p = 0.1398 >= 0.1000  removing bf1


p = 0.9063 >= 0.1000  removing bf20


Logistic regression                               Number of obs   =        340
                                                  LR chi2(6)      =     110.24
                                                  Prob > chi2     =     0.0000
Log likelihood = -116.39626                       Pseudo R2       =     0.3214

-------------+----------------------------------------------------------------
    HP2sxlife |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0652945    .0153243      4.26    0.000      .0352595     .0953295
       whpel |    .0109418    .0060737      1.80    0.072     -.0009625     .0228462
     radfmw1 |    .0162283    .0051519      3.15    0.002      .0061308     .0263257
     whppain |    .0348445    .0102768      3.39    0.001      .0147023     .0549867
     suprtw1 |   -.0134728     .006621     -2.03    0.042     -.0264497    -.0004959
     icdxcnt |    .2922161    .1033651      2.83    0.005      .0896242      .494808
       _cons |   -7.208937    .9075371     -7.94    0.000     -8.987677    -5.430197
-------------+----------------------------------------------------------------

Logistic model for HP2sxlife

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        30            15  |         45
     -     |        39           256  |        295
-----------+--------------------------+-----------
   Total   |        69           271  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2sxlife != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   43.48%
Specificity                     Pr( -|~D)   94.46%
Positive predictive value       Pr( D| +)   66.67%
Negative predictive value       Pr(~D| -)   86.78%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    5.54%
False - rate for true D         Pr( -| D)   56.52%
False + rate for classified +   Pr(~D| +)   33.33%
False - rate for classified -   Pr( D| -)   13.22%
--------------------------------------------------
Correctly classified                        84.12%
--------------------------------------------------


Logistic model for HP2sxlife, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       326
            Pearson chi2(319) =       264.88
                  Prob > chi2 =         0.9878

Measures of Fit for logit of HP2sxlife

Log-Lik Intercept Only:     -171.514     Log-Lik Full Model:         -116.396
D(333):                      232.793     LR(6):                       110.235
                                         Prob > LR:                     0.000
McFadden's R2:                 0.321     McFadden's Adj R2:             0.281
Maximum Likelihood R2:         0.277     Cragg & Uhler's R2:            0.436
McKelvey and Zavoina's R2:     0.490     Efron's R2:                    0.323
Variance of y*:                6.448     Variance of error:             3.290
Count R2:                      0.841     Adj Count R2:                  0.217
AIC:                           0.726     AIC*n:                       246.793
BIC:                       -1708.246     BIC':                        -75.262


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Trimmed model for HP2sxlife for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:20:22  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Gender= 2  model Wave =  1 for HP2sxlife                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:20:22  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.8648 >= 0.1000  removing illw1


p = 0.8681 >= 0.1000  removing shrelaw1


p = 0.8586 >= 0.1000  removing bf1


p = 0.8608 >= 0.1000  removing deaw1


p = 0.8515 >= 0.1000  removing radfmw1


p = 0.8467 >= 0.1000  removing icdxcnt


p = 0.8004 >= 0.1000  removing suprtw1


p = 0.6968 >= 0.1000  removing radchw1


p = 0.4180 >= 0.1000  removing shhlw1


p = 0.2983 >= 0.1000  removing shjobw1


p = 0.1737 >= 0.1000  removing avgcumdosew1


p = 0.1578 >= 0.1000  removing radhlw1


Logistic regression                               Number of obs   =        361
                                                  LR chi2(4)      =     122.93
                                                  Prob > chi2     =     0.0000
Log likelihood = -145.55661                       Pseudo R2       =     0.2969

-------------+----------------------------------------------------------------
    HP2sxlife |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |       .0811     .014608      5.55    0.000      .0524687     .1097312
       whpel |    .0159603     .004917      3.25    0.001      .0063231     .0255975
     whppain |    .0188975    .0074141      2.55    0.011      .0043662     .0334289
        bf20 |     .012052    .0060759      1.98    0.047      .0001434     .0239605
       _cons |   -7.375375    .9398372     -7.85    0.000     -9.217422    -5.533328
-------------+----------------------------------------------------------------

Logistic model for HP2sxlife

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        52            20  |         72
     -     |        42           247  |        289
-----------+--------------------------+-----------
   Total   |        94           267  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2sxlife != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   55.32%
Specificity                     Pr( -|~D)   92.51%
Positive predictive value       Pr( D| +)   72.22%
Negative predictive value       Pr(~D| -)   85.47%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    7.49%
False - rate for true D         Pr( -| D)   44.68%
False + rate for classified +   Pr(~D| +)   27.78%
False - rate for classified -   Pr( D| -)   14.53%
--------------------------------------------------
Correctly classified                        82.83%
--------------------------------------------------


Logistic model for HP2sxlife, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       332
            Pearson chi2(327) =       363.89
                  Prob > chi2 =         0.0782

Measures of Fit for logit of HP2sxlife

Log-Lik Intercept Only:     -207.020     Log-Lik Full Model:         -145.557
D(356):                      291.113     LR(4):                       122.926
                                         Prob > LR:                     0.000
McFadden's R2:                 0.297     McFadden's Adj R2:             0.273
Maximum Likelihood R2:         0.289     Cragg & Uhler's R2:            0.423
McKelvey and Zavoina's R2:     0.446     Efron's R2:                    0.343
Variance of y*:                5.941     Variance of error:             3.290
Count R2:                      0.828     Adj Count R2:                  0.340
AIC:                           0.834     AIC*n:                       301.113
BIC:                       -1805.327     BIC':                        -99.371


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Trimmed model for HP2inthob for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:20:40  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Gender= 1  model Wave =  1 for HP2sxlife                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:20:40  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.9907 >= 0.1000  removing bf20


p = 0.9302 >= 0.1000  removing shjobw1


p = 0.8432 >= 0.1000  removing illw1


p = 0.7673 >= 0.1000  removing radfmw1


p = 0.7010 >= 0.1000  removing bf1


p = 0.5830 >= 0.1000  removing suprtw1


p = 0.4880 >= 0.1000  removing shhlw1


p = 0.4285 >= 0.1000  removing age


p = 0.2101 >= 0.1000  removing avgcumdosew1


p = 0.1717 >= 0.1000  removing radchw1


p = 0.1740 >= 0.1000  removing shrelaw1


Logistic regression                               Number of obs   =        340
                                                  LR chi2(5)      =      80.74
                                                  Prob > chi2     =     0.0000
Log likelihood = -78.692502                       Pseudo R2       =     0.3391

-------------+----------------------------------------------------------------
    HP2inthob |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
     whppain |    .0369298    .0116633      3.17    0.002      .0140702     .0597895
     radhlw1 |    .0279188    .0064639      4.32    0.000      .0152498     .0405878
       whpel |     .019931    .0076767      2.60    0.009       .004885      .034977
       deaw1 |   -1.667302    .8004389     -2.08    0.037     -3.236134    -.0984709
     icdxcnt |    .2498247    .1207088      2.07    0.038      .0132398     .4864096
       _cons |   -5.613683    .7338306     -7.65    0.000     -7.051964    -4.175401
-------------+----------------------------------------------------------------

Logistic model for HP2inthob

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        12             6  |         18
     -     |        26           296  |        322
-----------+--------------------------+-----------
   Total   |        38           302  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2inthob != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   31.58%
Specificity                     Pr( -|~D)   98.01%
Positive predictive value       Pr( D| +)   66.67%
Negative predictive value       Pr(~D| -)   91.93%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    1.99%
False - rate for true D         Pr( -| D)   68.42%
False + rate for classified +   Pr(~D| +)   33.33%
False - rate for classified -   Pr( D| -)    8.07%
--------------------------------------------------
Correctly classified                        90.59%
--------------------------------------------------


Logistic model for HP2inthob, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       230
            Pearson chi2(224) =       168.13
                  Prob > chi2 =         0.9979

Measures of Fit for logit of HP2inthob

Log-Lik Intercept Only:     -119.064     Log-Lik Full Model:          -78.693
D(334):                      157.385     LR(5):                        80.744
                                         Prob > LR:                     0.000
McFadden's R2:                 0.339     McFadden's Adj R2:             0.289
Maximum Likelihood R2:         0.211     Cragg & Uhler's R2:            0.420
McKelvey and Zavoina's R2:     0.536     Efron's R2:                    0.283
Variance of y*:                7.089     Variance of error:             3.290
Count R2:                      0.906     Adj Count R2:                  0.158
AIC:                           0.498     AIC*n:                       169.385
BIC:                       -1789.483     BIC':                        -51.599


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Trimmed model for HP2inthob for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:20:56  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Gender= 2  model Wave =  1 for HP2inthob                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:20:56  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.9935 >= 0.1000  removing shrelaw1


p = 0.8780 >= 0.1000  removing icdxcnt


p = 0.8689 >= 0.1000  removing deaw1


p = 0.7643 >= 0.1000  removing suprtw1


p = 0.6717 >= 0.1000  removing illw1


p = 0.6244 >= 0.1000  removing radchw1


p = 0.4921 >= 0.1000  removing radfmw1


p = 0.3091 >= 0.1000  removing avgcumdosew1


p = 0.2613 >= 0.1000  removing shjobw1


p = 0.4289 >= 0.1000  removing shhlw1


p = 0.1822 >= 0.1000  removing bf1


p = 0.4734 >= 0.1000  removing bf20


p = 0.1579 >= 0.1000  removing whppain


Logistic regression                               Number of obs   =        361
                                                  LR chi2(3)      =      98.51
                                                  Prob > chi2     =     0.0000
Log likelihood = -122.45637                       Pseudo R2       =     0.2868

-------------+----------------------------------------------------------------
    HP2inthob |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0696936    .0159107      4.38    0.000      .0385093      .100878
     radhlw1 |    .0210747    .0050179      4.20    0.000      .0112399     .0309095
       whpel |    .0209588    .0046339      4.52    0.000      .0118765      .030041
       _cons |   -7.529659    .9609077     -7.84    0.000     -9.413003    -5.646314
-------------+----------------------------------------------------------------

Logistic model for HP2inthob

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        33             9  |         42
     -     |        33           286  |        319
-----------+--------------------------+-----------
   Total   |        66           295  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2inthob != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   50.00%
Specificity                     Pr( -|~D)   96.95%
Positive predictive value       Pr( D| +)   78.57%
Negative predictive value       Pr(~D| -)   89.66%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    3.05%
False - rate for true D         Pr( -| D)   50.00%
False + rate for classified +   Pr(~D| +)   21.43%
False - rate for classified -   Pr( D| -)   10.34%
--------------------------------------------------
Correctly classified                        88.37%
--------------------------------------------------


Logistic model for HP2inthob, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       311
            Pearson chi2(307) =       333.54
                  Prob > chi2 =         0.1427

Measures of Fit for logit of HP2inthob

Log-Lik Intercept Only:     -171.710     Log-Lik Full Model:         -122.456
D(357):                      244.913     LR(3):                        98.507
                                         Prob > LR:                     0.000
McFadden's R2:                 0.287     McFadden's Adj R2:             0.264
Maximum Likelihood R2:         0.239     Cragg & Uhler's R2:            0.389
McKelvey and Zavoina's R2:     0.442     Efron's R2:                    0.323
Variance of y*:                5.893     Variance of error:             3.290
Count R2:                      0.884     Adj Count R2:                  0.364
AIC:                           0.701     AIC*n:                       252.913
BIC:                       -1857.417     BIC':                        -80.841


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Trimmed model for HP2vacatn for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:21:16  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Gender= 1  model Wave =  1 for HP2inthob                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:21:16  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.7308 >= 0.1000  removing suprtw1


p = 0.7188 >= 0.1000  removing radhlw1


p = 0.5817 >= 0.1000  removing avgcumdosew1


p = 0.3549 >= 0.1000  removing bf20


p = 0.3694 >= 0.1000  removing bf1


p = 0.1833 >= 0.1000  removing radchw1


p = 0.2099 >= 0.1000  removing shhlw1


Logistic regression                               Number of obs   =        340
                                                  LR chi2(9)      =      92.20
                                                  Prob > chi2     =     0.0000
Log likelihood = -79.053284                       Pseudo R2       =     0.3683

-------------+----------------------------------------------------------------
    HP2vacatn |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0321464    .0188438      1.71    0.088     -.0047868     .0690796
     whppain |    .0260025    .0131305      1.98    0.048      .0002672     .0517378
     radfmw1 |    .0197654    .0067422      2.93    0.003       .006551     .0329799
       whpel |    .0211036    .0075827      2.78    0.005      .0062419     .0359654
     icdxcnt |    .4575473    .1422501      3.22    0.001      .1787422     .7363524
       illw1 |   -.9064967    .5059662     -1.79    0.073     -1.898172     .0851788
       deaw1 |   -1.672145    .7293212     -2.29    0.022     -3.101588    -.2427017
    shrelaw1 |   -.0153622    .0064155     -2.39    0.017     -.0279363    -.0027881
     shjobw1 |    .0153928    .0074732      2.06    0.039      .0007457     .0300399
       _cons |   -7.615507    1.211011     -6.29    0.000     -9.989045    -5.241968
-------------+----------------------------------------------------------------

Logistic model for HP2vacatn

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        17             4  |         21
     -     |        24           295  |        319
-----------+--------------------------+-----------
   Total   |        41           299  |        340

Classified + if predicted Pr(D) >= .5
True D defined as HP2vacatn != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   41.46%
Specificity                     Pr( -|~D)   98.66%
Positive predictive value       Pr( D| +)   80.95%
Negative predictive value       Pr(~D| -)   92.48%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    1.34%
False - rate for true D         Pr( -| D)   58.54%
False + rate for classified +   Pr(~D| +)   19.05%
False - rate for classified -   Pr( D| -)    7.52%
--------------------------------------------------
Correctly classified                        91.76%
--------------------------------------------------


Logistic model for HP2vacatn, goodness-of-fit test


       number of observations =       340
 number of covariate patterns =       336
            Pearson chi2(326) =       273.22
                  Prob > chi2 =         0.9848

Measures of Fit for logit of HP2vacatn

Log-Lik Intercept Only:     -125.152     Log-Lik Full Model:          -79.053
D(330):                      158.107     LR(9):                        92.198
                                         Prob > LR:                     0.000
McFadden's R2:                 0.368     McFadden's Adj R2:             0.288
Maximum Likelihood R2:         0.238     Cragg & Uhler's R2:            0.456
McKelvey and Zavoina's R2:     0.594     Efron's R2:                    0.352
Variance of y*:                8.095     Variance of error:             3.290
Count R2:                      0.918     Adj Count R2:                  0.317
AIC:                           0.524     AIC*n:                       178.107
BIC:                       -1765.445     BIC':                        -39.738


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****              Trimmed model for HP2vacatn for wave= 1                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:21:28  *****
*******************************************************************************
*******************************************************************************


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****             Gender= 2  model Wave =  1 for HP2vacatn                *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:21:28  *****
*******************************************************************************
*******************************************************************************


  begin with full model


p = 0.9435 >= 0.1000  removing shjobw1


p = 0.9572 >= 0.1000  removing shhlw1


p = 0.9359 >= 0.1000  removing icdxcnt


p = 0.9258 >= 0.1000  removing avgcumdosew1


p = 0.8616 >= 0.1000  removing deaw1


p = 0.7940 >= 0.1000  removing illw1


p = 0.7231 >= 0.1000  removing shrelaw1


p = 0.3899 >= 0.1000  removing radchw1


p = 0.3413 >= 0.1000  removing radfmw1


p = 0.2692 >= 0.1000  removing bf20


p = 0.1152 >= 0.1000  removing suprtw1


p = 0.1651 >= 0.1000  removing bf1


Logistic regression                               Number of obs   =        361
                                                  LR chi2(4)      =      96.63
                                                  Prob > chi2     =     0.0000
Log likelihood = -118.81948                       Pseudo R2       =     0.2891

-------------+----------------------------------------------------------------
    HP2vacatn |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0615732    .0163503      3.77    0.000      .0295271     .0936192
     radhlw1 |    .0228501    .0051886      4.40    0.000      .0126806     .0330197
     whppain |    .0193205    .0082832      2.33    0.020      .0030858     .0355553
       whpel |    .0112173    .0054324      2.06    0.039      .0005699     .0218647
       _cons |   -7.351247    .9723341     -7.56    0.000     -9.256987    -5.445508
-------------+----------------------------------------------------------------

Logistic model for HP2vacatn

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        28            11  |         39
     -     |        35           287  |        322
-----------+--------------------------+-----------
   Total   |        63           298  |        361

Classified + if predicted Pr(D) >= .5
True D defined as HP2vacatn != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   44.44%
Specificity                     Pr( -|~D)   96.31%
Positive predictive value       Pr( D| +)   71.79%
Negative predictive value       Pr(~D| -)   89.13%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    3.69%
False - rate for true D         Pr( -| D)   55.56%
False + rate for classified +   Pr(~D| +)   28.21%
False - rate for classified -   Pr( D| -)   10.87%
--------------------------------------------------
Correctly classified                        87.26%
--------------------------------------------------


Logistic model for HP2vacatn, goodness-of-fit test


       number of observations =       361
 number of covariate patterns =       337
            Pearson chi2(332) =       363.63
                  Prob > chi2 =         0.1119

Measures of Fit for logit of HP2vacatn

Log-Lik Intercept Only:     -167.134     Log-Lik Full Model:         -118.819
D(356):                      237.639     LR(4):                        96.628
                                         Prob > LR:                     0.000
McFadden's R2:                 0.289     McFadden's Adj R2:             0.259
Maximum Likelihood R2:         0.235     Cragg & Uhler's R2:            0.389
McKelvey and Zavoina's R2:     0.442     Efron's R2:                    0.323
Variance of y*:                5.901     Variance of error:             3.290
Count R2:                      0.873     Adj Count R2:                  0.270
AIC:                           0.686     AIC*n:                       247.639
BIC:                       -1858.802     BIC':                        -73.073

.
.
. *-----------------   Trimmed models --------------------------------------------
. *   We use simultaneous trimming  for models that exhibited a dose -reponse effect
. *   They are female homcare and social life models
.
. cap gen hp2prbsoc = HP2probsoc

. title "Trimmed Home care dose relationship for women in wave one"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****     Trimmed Home care dose relationship for women in wave one       *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:21:47  *****
*******************************************************************************
*******************************************************************************


.
. logit hp2hmcare age    ///
>            illw1 medcow1  ///
>             radtlw1  ///
>            avgcumdosew1 ///
>           whpel whppain  ///
>           bf1 if gender==2, nolog

Logistic regression                               Number of obs   =        363
                                                  LR chi2(8)      =     163.60
                                                  Prob > chi2     =     0.0000
Log likelihood = -151.92971                       Pseudo R2       =     0.3500

-------------+----------------------------------------------------------------
    hp2hmcare |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0737808    .0140738      5.24    0.000      .0461966     .1013651
       illw1 |     .673119    .3021353      2.23    0.026      .0809448     1.265293
     medcow1 |    .0909715    .0393274      2.31    0.021      .0138912     .1680518
     radtlw1 |    .0111965    .0042122      2.66    0.008      .0029407     .0194524
avgcumdosew1 |    -1.03656    .3644478     -2.84    0.004     -1.750865    -.3222558
       whpel |    .0162255     .004942      3.28    0.001      .0065393     .0259116
     whppain |    .0376997    .0081058      4.65    0.000      .0218126     .0535869
         bf1 |   -.0169775    .0067166     -2.53    0.011     -.0301419    -.0038131
       _cons |   -5.817551    .7805859     -7.45    0.000     -7.347471    -4.287631
-------------+----------------------------------------------------------------

.
. scalar prbsocnumMAsig = 8

. scalar medsigFw1=1

.
.
.
.
. // Moderators for female home care in wave 1
.
.
.
. foreach var in age illw1 medcow1 radtlw1 whpel whppain bf1 {
  2. gen `var'Xd1 = `var'*avgcumdosew1
  3. }
(1 missing value generated)
(1 missing value generated)

.
. logit hp2hmcare age    ///
>            illw1 medcow1  ///
>             radtlw1  ///
>            avgcumdosew1 ///
>           whpel whppain  ///
>           bf1 ageXd1-bf1Xd1 if gender==2, nolog

Logistic regression                               Number of obs   =        363
                                                  LR chi2(15)     =     176.78
                                                  Prob > chi2     =     0.0000
Log likelihood = -145.33747                       Pseudo R2       =     0.3782

-------------+----------------------------------------------------------------
    hp2hmcare |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0938562    .0243056      3.86    0.000      .0462181     .1414942
       illw1 |    1.431197    .5530394      2.59    0.010      .3472596     2.515134
     medcow1 |   -.0236874    .0781116     -0.30    0.762     -.1767832     .1294085
     radtlw1 |    .0117074     .005978      1.96    0.050     -9.22e-06      .023424
avgcumdosew1 |    2.565947    4.682064      0.55    0.584     -6.610729     11.74262
       whpel |     .014443    .0083436      1.73    0.083     -.0019102     .0307963
     whppain |    .0477532    .0108825      4.39    0.000       .026424     .0690825
         bf1 |   -.0314154    .0142983     -2.20    0.028     -.0594395    -.0033913
      ageXd1 |   -.0922441    .0802468     -1.15    0.250      -.249525     .0650369
    illw1Xd1 |   -2.738378    1.838287     -1.49    0.136     -6.341355     .8645994
  medcow1Xd1 |    .4207286    .2604068      1.62    0.106     -.0896593     .9311164
  radtlw1Xd1 |   -.0032717    .0139165     -0.24    0.814     -.0305476     .0240041
    whpelXd1 |    .0102044    .0234197      0.44    0.663     -.0356974     .0561061
  whppainXd1 |   -.0278112    .0233101     -1.19    0.233     -.0734981     .0178757
      bf1Xd1 |    .0422653    .0421814      1.00    0.316     -.0404087     .1249392
       _cons |   -6.452066    1.357862     -4.75    0.000     -9.113427    -3.790706
-------------+----------------------------------------------------------------


Note: 1 failure and 1 success completely determined.


.
. * there are no significant moderators for this logistic regression above
.
.  logit hp2hmcare age    ///
>            illw1 medcow1  ///
>             radtlw1  ///
>            avgcumdosew1 ///
>           whpel whppain  ///
>           bf1 illw1Xd1-bf1Xd1 if gender==2, nolog

Logistic regression                               Number of obs   =        363
                                                  LR chi2(14)     =     175.47
                                                  Prob > chi2     =     0.0000
Log likelihood = -145.99205                       Pseudo R2       =     0.3754

-------------+----------------------------------------------------------------
    hp2hmcare |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0725065    .0147382      4.92    0.000        .04362     .1013929
       illw1 |    1.446639    .5460396      2.65    0.008      .3764213     2.516857
     medcow1 |   -.0246873    .0767048     -0.32    0.748      -.175026     .1256514
     radtlw1 |    .0119389    .0059247      2.02    0.044      .0003266     .0235511
avgcumdosew1 |   -1.955522    2.612535     -0.75    0.454     -7.075996     3.164952
       whpel |    .0149738    .0082874      1.81    0.071     -.0012692     .0312167
     whppain |    .0430492    .0105426      4.08    0.000       .022386     .0637123
         bf1 |   -.0266245    .0131941     -2.02    0.044     -.0524846    -.0007645
    illw1Xd1 |   -2.850958     1.86127     -1.53    0.126     -6.498981     .7970651
  medcow1Xd1 |    .4154341    .2584046      1.61    0.108     -.0910296     .9218979
  radtlw1Xd1 |   -.0029316     .014288     -0.21    0.837     -.0309356     .0250723
    whpelXd1 |    .0079383    .0237936      0.33    0.739     -.0386964     .0545729
  whppainXd1 |   -.0128189    .0247803     -0.52    0.605     -.0613873     .0357495
      bf1Xd1 |    .0245603    .0380102      0.65    0.518     -.0499383     .0990589
       _cons |   -5.452065    1.000834     -5.45    0.000     -7.413664    -3.490466
-------------+----------------------------------------------------------------


Note: 0 failures and 1 success completely determined.


.
.
.
. title "Trimmed hp2prbsoc relationship for women wave one "


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****         Trimmed hp2prbsoc relationship for women wave one           *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    06:21:51  *****
*******************************************************************************
*******************************************************************************


.
. cap gen hp2prbsoc = HP2probsoc

. title4 "Trimmed Home care dose relationship for women in wave one"
-------------
Trimmed Home care dose relationship for women in wave one
-------------

.
. logit hp2prbsoc age    ///
>             medcow1  ///
>             radfmw1  ///
>            avgcumdosew1 ///
>           whpel whppain ///
>           PTSDw1 ///
>          if gender==2, nolog

Logistic regression                               Number of obs   =        363
                                                  LR chi2(7)      =     162.68
                                                  Prob > chi2     =     0.0000
Log likelihood = -102.23234                       Pseudo R2       =     0.4431

-------------+----------------------------------------------------------------
    hp2prbsoc |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0841671    .0185985      4.53    0.000      .0477148     .1206194
     medcow1 |    .1037151    .0455399      2.28    0.023      .0144586     .1929715
     radfmw1 |    .0170613     .005462      3.12    0.002      .0063559     .0277667
avgcumdosew1 |     .908049    .3390227      2.68    0.007      .2435767     1.572521
       whpel |    .0146083    .0059656      2.45    0.014       .002916     .0263006
     whppain |    .0374859    .0092242      4.06    0.000      .0194069     .0555649
      PTSDw1 |   -.0172753    .0063709     -2.71    0.007     -.0297621    -.0047886
       _cons |   -8.997383    1.142711     -7.87    0.000     -11.23706    -6.757711
-------------+----------------------------------------------------------------

.
. foreach var in  medcow1 radfmw1 whpel whppain PTSDw1 {
  2.  cap gen `var'Xd1 = `var'*avgcumdosew1
  3.   }

.
.  logit hp2prbsoc age    ///
>             medcow1  ///
>             radfmw1  ///
>            avgcumdosew1 ///
>           whpel whppain ///
>           PTSDw1 ///
>           ageXd1 -PTSDw1Xd1  ///
>          if gender==2, nolog

Logistic regression                               Number of obs   =        363
                                                  LR chi2(16)     =     174.97
                                                  Prob > chi2     =     0.0000
Log likelihood =  -96.08734                       Pseudo R2       =     0.4766

-------------+----------------------------------------------------------------
    hp2prbsoc |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0962173    .0331953      2.90    0.004      .0311556     .1612789
     medcow1 |   -.0493166    .0911684     -0.54    0.589     -.2280035     .1293702
     radfmw1 |    .0097557     .007835      1.25    0.213     -.0056007     .0251121
avgcumdosew1 |    1.416399    6.697739      0.21    0.833     -11.71093     14.54373
       whpel |    .0091003    .0101715      0.89    0.371     -.0108354     .0290359
     whppain |     .026248    .0151345      1.73    0.083      -.003415      .055911
      PTSDw1 |   -.0131613    .0092295     -1.43    0.154     -.0312508     .0049282
      ageXd1 |   -.0685531    .1093182     -0.63    0.531     -.2828129     .1457066
    illw1Xd1 |   -1.231817    1.107379     -1.11    0.266     -3.402239     .9386064
  medcow1Xd1 |    .6427061    .3090544      2.08    0.038      .0369707     1.248442
  radtlw1Xd1 |   -.0197839    .0156583     -1.26    0.206     -.0504736     .0109058
    whpelXd1 |     .020316    .0272917      0.74    0.457     -.0331748     .0738068
  whppainXd1 |    .0488321    .0473175      1.03    0.302     -.0439084     .1415727
      bf1Xd1 |     .033552    .0243734      1.38    0.169      -.014219     .0813231
  radfmw1Xd1 |    .0214179    .0192623      1.11    0.266     -.0163356     .0591714
   PTSDw1Xd1 |    -.017654    .0192942     -0.91    0.360     -.0554699     .0201619
       _cons |   -8.728893    2.053325     -4.25    0.000     -12.75333     -4.70445
-------------+----------------------------------------------------------------


Note: 0 failures and 1 success completely determined.


.
.  * there is only one moderator and that is bf1Xd1 but this is not robust
.  * enough to remain significant when the others are removed so we discard it.
.
.   logit hp2prbsoc age    ///
>             medcow1  ///
>             radfmw1  ///
>            avgcumdosew1 ///
>           whpel whppain ///
>           PTSDw1 ///
>           bf1Xd1 ///
>          if gender==2, nolog

Logistic regression                               Number of obs   =        363
                                                  LR chi2(8)      =     164.92
                                                  Prob > chi2     =     0.0000
Log likelihood =  -101.1114                       Pseudo R2       =     0.4492

-------------+----------------------------------------------------------------
    hp2prbsoc |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .0835944    .0188465      4.44    0.000      .0466559     .1205329
     medcow1 |    .1020847    .0460658      2.22    0.027      .0117974     .1923721
     radfmw1 |    .0163655    .0054536      3.00    0.003      .0056766     .0270545
avgcumdosew1 |    .0406541    .5634741      0.07    0.942     -1.063735     1.145043
       whpel |    .0147761    .0060068      2.46    0.014       .003003     .0265492
     whppain |    .0378791    .0092891      4.08    0.000      .0196728     .0560854
      PTSDw1 |   -.0166667    .0064651     -2.58    0.010     -.0293381    -.0039953
      bf1Xd1 |     .019981    .0128067      1.56    0.119     -.0051197     .0450816
       _cons |   -8.947676    1.148842     -7.79    0.000     -11.19936    -6.695988
-------------+----------------------------------------------------------------

.
.
. scalar prbsocnumMAsig = 8

. scalar medsigFw1=1

.
.