-------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  c:\acdbookrevision\stata_final_programs_2013\racd04.txt
  log type:  text
 opened on:  14 Jan 2013, 20:37:54

. 
. ********** OVERVIEW OF racd04.do **********
. 
. * STATA Program 
. * copyright C 2013 by A. Colin Cameron and Pravin K. Trivedi 
. * used for "Regression Analyis of Count Data" SECOND EDITION
. * by A. Colin Cameron and Pravin K. Trivedi (2013)
. * Cambridge University Press
. 
. * This STATA program analyzes docor visits data for chapter 4
. *   4.2   TWO CROSSINGS THEOREM
. *   4.8.1 MIXTURE OF 2 POISSON
. *   4.8.8 EXAMPLE: PATENTS
. 
. * To run you need file
. *   racd09data.dta
. * and user-written Stata addon
. *   fmm
. * in your directory
. 
. ********** SETUP **********
. 
. set more off

. version 12

. clear all

. * set linesize 82
. set scheme s1mono  /* Graphics scheme */

. 
. ********** DATA DESCRIPTION
. 
. *  The original data is from 
. *  Bronwyn Hall, Zvi Griliches, and Jerry Hausman (1986), 
. *  "Patents and R&D: Is There a Lag?", 
. *  International Economic Review, 27, 265-283.
. *  See this article for more detailed discussion 
. *  Also see racd09makedata.do for further details  
. *  NOTE: Here we use just 1979 data 
. 
. ********** 4.2 TWO CROSSINGS THEOREM
. 
. * Poisson with mean mu = 10 
. * Negative binomial with mean mu = 10 and alpha = 0.2
. * so 1/alpha = 1/.2 = 5 and 1/(1+alpha*mu) = 1/(1+0.2*10) = 1/3
. * and alpha/(1+alpha*mu) = 0.2*10/(1+0.2*10) = 2/3
. 
. * ASIDE: The following gave unexpected wrong result
. * Stata function nbinomialp(n,k,p) returns the probability of 
. * observing k or fewer failures before the nth success
. * So  p = a*mu/(1+a*mu) = 2/3 = 0.666 and n = 1/a = 1/.2 = 5
. * But generate ynegbin = nbinomialp(5,k,0.6666666) gave mean 2.5, var = 3.75
. 
. set obs 21
obs was 0, now 21

. generate k = _n - 1

. generate prob_poisson = exp(-10)*(10^k)/exp(lngamma(k+1))

. generate check_poisson = poissonp(10,k)

. generate prob_nb = exp(lngamma(k+5)-lngamma(k+1)-lngamma(5))*((1/3)^5)*((2/3)^k)

. generate badprob_nb = nbinomialp(5,k,0.6666666)

. list, clean

        k   prob_p~n   check_~n    prob_nb   badpro~b  
  1.    0   .0000454   .0000454   .0041152   .1316872  
  2.    1    .000454    .000454   .0137174   .2194787  
  3.    2     .00227     .00227   .0274348   .2194787  
  4.    3   .0075667   .0075667   .0426764   .1707057  
  5.    4   .0189166   .0189166   .0569019   .1138038  
  6.    5   .0378333   .0378333   .0682823   .0682823  
  7.    6   .0630555   .0630555   .0758692   .0379346  
  8.    7   .0900792   .0900792    .079482   .0198705  
  9.    8    .112599    .112599    .079482   .0099353  
 10.    9     .12511     .12511   .0765382   .0047836  
 11.   10     .12511     .12511   .0714357   .0022324  
 12.   11   .1137364   .1137364   .0649415   .0010147  
 13.   12   .0947803   .0947803   .0577258    .000451  
 14.   13   .0729079   .0729079   .0503251   .0001966  
 15.   14   .0520771   .0520771   .0431358   .0000842  
 16.   15   .0347181   .0347181   .0364258   .0000356  
 17.   16   .0216988   .0216988   .0303548   .0000148  
 18.   17    .012764    .012764   .0249981   6.10e-06  
 19.   18   .0070911   .0070911   .0203688   2.49e-06  
 20.   19   .0037322   .0037322    .016438   1.00e-06  
 21.   20   .0018661   .0018661   .0131504   4.01e-07  

. 
. graph twoway (line prob_nb k, connect(stairstep))                         ///
>   (line prob_poisson k, connect(stairstep) lstyle(p3)), scale(1.2)        ///
>   legend( ring(0) rows(2) pos(1) label(1 "NB {&mu} = 10 {&alpha} = .2")   ///
>   label(2 "Poisson {&mu} = 10")) ytitle("Probability that Y = y") xtitle("y")

. 
. *** FIGURE 4.1: TWO CROSSINGS THEOREM
. 
. graph export racd04fig1.eps, replace
(file racd04fig1.eps written in EPS format)

. graph export racd04fig1.wmf, replace
(file c:\acdbookrevision\stata_final_programs_2013\racd04fig1.wmf written in Windows Metafile format)

. 
. **********   4.8.1 MIXTURE OF 2 POISSON
. 
. clear

. set obs 100000
obs was 0, now 100000

. set seed 10101

. generate xpmix = rpoisson(0.2)

. replace xpmix = rpoisson(6) if runiform() > 0.5
(49857 real changes made)

. label variable xpmix "X is .5xP[0.2] + .5xP[6.0]"

. tabulate xpmix

       X is |
.5xP[0.2] + |
  .5xP[6.0] |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     40,949       40.95       40.95
          1 |      8,945        8.95       49.89
          2 |      3,048        3.05       52.94
          3 |      4,498        4.50       57.44
          4 |      6,818        6.82       64.26
          5 |      8,112        8.11       72.37
          6 |      8,084        8.08       80.45
          7 |      6,780        6.78       87.23
          8 |      5,174        5.17       92.41
          9 |      3,418        3.42       95.83
         10 |      1,997        2.00       97.82
         11 |      1,146        1.15       98.97
         12 |        560        0.56       99.53
         13 |        278        0.28       99.81
         14 |        127        0.13       99.93
         15 |         42        0.04       99.98
         16 |         13        0.01       99.99
         17 |          6        0.01      100.00
         18 |          4        0.00      100.00
         22 |          1        0.00      100.00
------------+-----------------------------------
      Total |    100,000      100.00

. 
. * For appearance drop a few of the largest values
. histogram xpmix if xpmix < 17, discrete scale(1.2)
(start=0, width=1)

. 
. *** FIGURE 4.2: 50/50 MIXTURE OF POISSONS
. 
. graph export racd04fig2.eps, replace
(file racd04fig2.eps written in EPS format)

. graph export racd04fig2.wmf, replace
(file c:\acdbookrevision\stata_final_programs_2013\racd04fig2.wmf written in Windows Metafile format)

. 
. ********** 4.8.8 EXAMPLE: PATENTS
. 
. use racd09data.dta, clear

. 
. * Create log of total R&D over five years
. generate LOGRandD = ln(exp(LOGR)+exp(LOGR1)+exp(LOGR2)+exp(LOGR3)+exp(LOGR4)+exp(LOGR5))

. 
. * Use only 1979 data
. keep if YEAR==5
(1384 observations deleted)

. 
. * Regressor list
. global XLIST LOGRandD LOGK SCISECT

. 
. * Variable descriptions and summary statistics
. describe PAT $XLIST

              storage  display     value
variable name   type   format      label      variable label
-------------------------------------------------------------------------------------------------------------------------------
PAT             float  %9.0g                  Number of (successful) patents applied for this year
LOGRandD        float  %9.0g                  
LOGK            float  %9.0g                  Logarithm of the book value of capital in 1972
SCISECT         float  %9.0g                  Equals 1 if firm in the scientific sector

. summarize PAT $XLIST

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         PAT |       346    32.10116    66.36197          0        515
    LOGRandD |       346    3.071489    1.965798  -1.543733   8.723914
        LOGK |       346    3.921063    2.095542   -1.76965    9.66626
     SCISECT |       346    .4248555    .4950369          0          1

. 
. *** TABLE 4.2: FREQUENCIES OF PAT (with grouping)
. 
. recode PAT (0=0) (1=1) (2/5=2) (6/20=6) (21/50=31) (51/100=51)  ///
>    (101/200=101) (201/300=201) (301/600=301), gen(PATgrouped)
(194 differences between PAT and PATgrouped)

. tabulate PATgrouped

  RECODE of |
PAT (Number |
         of |
(successful |
  ) patents |
applied for |
 this year) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         76       21.97       21.97
          1 |         41       11.85       33.82
          2 |         74       21.39       55.20
          6 |         51       14.74       69.94
         31 |         43       12.43       82.37
         51 |         30        8.67       91.04
        101 |         19        5.49       96.53
        201 |          8        2.31       98.84
        301 |          4        1.16      100.00
------------+-----------------------------------
      Total |        346      100.00

. 
. * Poisson
. poisson PAT $XLIST, vce(robust)

Iteration 0:   log pseudolikelihood = -3366.1483  
Iteration 1:   log pseudolikelihood = -3365.8303  
Iteration 2:   log pseudolikelihood = -3365.8303  

Poisson regression                                Number of obs   =        346
                                                  Wald chi2(3)    =     641.60
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3365.8303                 Pseudo R2       =     0.7632

------------------------------------------------------------------------------
             |               Robust
         PAT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    LOGRandD |   .4753631    .064298     7.39   0.000     .3493413     .601385
        LOGK |   .2708473   .0552599     4.90   0.000     .1625398    .3791547
     SCISECT |   .4672292   .1549081     3.02   0.003      .163615    .7708435
       _cons |  -.3402801   .1900477    -1.79   0.073    -.7127667    .0322066
------------------------------------------------------------------------------

. estimates store POISS

. 
. * NB models: NB1 and NB2
. nbreg PAT $XLIST, dispersion(constant) vce(robust)

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -3366.1483  
Iteration 1:   log pseudolikelihood = -3365.8303  
Iteration 2:   log pseudolikelihood = -3365.8303  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -7036.5891  
Iteration 1:   log pseudolikelihood = -3372.0324  
Iteration 2:   log pseudolikelihood = -1554.1264  (backed up)
Iteration 3:   log pseudolikelihood = -1346.9519  
Iteration 4:   log pseudolikelihood = -1346.8822  
Iteration 5:   log pseudolikelihood = -1346.8822  

Fitting full model:

Iteration 0:   log pseudolikelihood = -1346.8822  
Iteration 1:   log pseudolikelihood = -1224.7323  
Iteration 2:   log pseudolikelihood = -1180.7271  
Iteration 3:   log pseudolikelihood =  -1148.109  
Iteration 4:   log pseudolikelihood = -1147.9317  
Iteration 5:   log pseudolikelihood = -1147.9316  

Negative binomial regression                      Number of obs   =        346
Dispersion           = constant                   Wald chi2(3)    =     714.08
Log pseudolikelihood = -1147.9316                 Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |               Robust
         PAT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    LOGRandD |   .4895402   .0484591    10.10   0.000     .3945622    .5845183
        LOGK |   .1727126   .0434001     3.98   0.000       .08765    .2577752
     SCISECT |   .3958147   .1096853     3.61   0.000     .1808356    .6107939
       _cons |   .2449866   .1689932     1.45   0.147     -.086234    .5762072
-------------+----------------------------------------------------------------
    /lndelta |   3.101643   .1525779                      2.802596     3.40069
-------------+----------------------------------------------------------------
       delta |   22.23446   3.392486                      16.48739    29.98479
------------------------------------------------------------------------------

. estimates store NB1

. nbreg PAT $XLIST, vce(robust)

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -3366.1483  
Iteration 1:   log pseudolikelihood = -3365.8303  
Iteration 2:   log pseudolikelihood = -3365.8303  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -1551.5708  
Iteration 1:   log pseudolikelihood = -1347.2714  
Iteration 2:   log pseudolikelihood = -1346.8824  
Iteration 3:   log pseudolikelihood = -1346.8822  
Iteration 4:   log pseudolikelihood = -1346.8822  

Fitting full model:

Iteration 0:   log pseudolikelihood = -1277.5529  (not concave)
Iteration 1:   log pseudolikelihood = -1196.6667  
Iteration 2:   log pseudolikelihood = -1137.5362  
Iteration 3:   log pseudolikelihood = -1127.8388  
Iteration 4:   log pseudolikelihood = -1127.5664  
Iteration 5:   log pseudolikelihood = -1127.5662  
Iteration 6:   log pseudolikelihood = -1127.5662  

Negative binomial regression                      Number of obs   =        346
Dispersion           = mean                       Wald chi2(3)    =     616.24
Log pseudolikelihood = -1127.5662                 Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |               Robust
         PAT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    LOGRandD |   .8159469   .0827754     9.86   0.000     .6537101    .9781837
        LOGK |    .114636   .0687745     1.67   0.096    -.0201595    .2494315
     SCISECT |  -.0897712   .1381794    -0.65   0.516    -.3605979    .1810555
       _cons |  -.8106295    .180078    -4.50   0.000    -1.163576   -.4576832
-------------+----------------------------------------------------------------
    /lnalpha |  -.1224513   .1134868                     -.3448814    .0999788
-------------+----------------------------------------------------------------
       alpha |    .884749   .1004074                      .7083043    1.105148
------------------------------------------------------------------------------

. estimates store NB2

. 
. * PIG model
. * Not included though ideally would be included.
. 
. * Finite mixture models: NB1 and NB2
. fmm PAT $XLIST, components(2) mixtureof(negbin1) vce(robust)

Fitting Negative Binomial-1 model:

Iteration 0:   log likelihood = -3366.1483  
Iteration 1:   log likelihood = -3365.8303  
Iteration 2:   log likelihood = -3365.8303  

Iteration 0:   log likelihood = -7036.5891  
Iteration 1:   log likelihood = -3372.0324  
Iteration 2:   log likelihood = -1554.1264  (backed up)
Iteration 3:   log likelihood = -1346.9519  
Iteration 4:   log likelihood = -1346.8822  
Iteration 5:   log likelihood = -1346.8822  

Iteration 0:   log likelihood = -1346.8822  
Iteration 1:   log likelihood = -1224.7323  
Iteration 2:   log likelihood = -1180.7271  
Iteration 3:   log likelihood =  -1148.109  
Iteration 4:   log likelihood = -1147.9317  
Iteration 5:   log likelihood = -1147.9316  

Fitting 2 component Negative Binomial-1 model:

Iteration 0:   log pseudolikelihood = -1147.9298  (not concave)
Iteration 1:   log pseudolikelihood = -1146.4074  (not concave)
Iteration 2:   log pseudolikelihood = -1139.7786  (not concave)
Iteration 3:   log pseudolikelihood = -1137.6292  (not concave)
Iteration 4:   log pseudolikelihood = -1136.5663  
Iteration 5:   log pseudolikelihood = -1135.2088  
Iteration 6:   log pseudolikelihood = -1133.2384  
Iteration 7:   log pseudolikelihood = -1132.8083  
Iteration 8:   log pseudolikelihood = -1132.8081  
Iteration 9:   log pseudolikelihood = -1132.8081  

2 component Negative Binomial-1 regression        Number of obs   =        346
                                                  Wald chi2(6)    =    1038.98
Log pseudolikelihood = -1132.8081                 Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |               Robust
         PAT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
component1   |
    LOGRandD |    .525363   .0642356     8.18   0.000     .3994636    .6512624
        LOGK |    .188418   .0692903     2.72   0.007     .0526116    .3242244
     SCISECT |   .6013736   .1455978     4.13   0.000     .3160073      .88674
       _cons |  -.4335583   .2665898    -1.63   0.104    -.9560647    .0889482
-------------+----------------------------------------------------------------
component2   |
    LOGRandD |   .6806978   .0996255     6.83   0.000     .4854354    .8759602
        LOGK |   .1690618   .0905431     1.87   0.062    -.0083994    .3465231
     SCISECT |  -.2368876   .2142517    -1.11   0.269    -.6568131    .1830379
       _cons |   .1118132   .2601031     0.43   0.667    -.3979795    .6216059
-------------+----------------------------------------------------------------
 /imlogitpi1 |   .8732938   .5833803     1.50   0.134    -.2701105    2.016698
   /lndelta1 |   2.504354   .2737051     9.15   0.000     1.967902    3.040806
   /lndelta2 |   2.734771   .4449584     6.15   0.000     1.862668    3.606873
------------------------------------------------------------------------------
      delta1 |   12.23565    3.34896                      7.155645     20.9221
      delta2 |   15.40621   6.855124                        6.4409    36.85065
         pi1 |   .7054306   .1212254                        .43288    .8825392
         pi2 |   .2945694   .1212254                      .1174608      .56712
------------------------------------------------------------------------------

. estimates store FMMNB1

. 
. fmm PAT $XLIST, components(2) mixtureof(negbin2) vce(robust)

Fitting Negative Binomial-2 model:

Iteration 0:   log likelihood = -3366.1483  
Iteration 1:   log likelihood = -3365.8303  
Iteration 2:   log likelihood = -3365.8303  

Iteration 0:   log likelihood = -1551.5708  
Iteration 1:   log likelihood = -1347.2714  
Iteration 2:   log likelihood = -1346.8824  
Iteration 3:   log likelihood = -1346.8822  
Iteration 4:   log likelihood = -1346.8822  

Iteration 0:   log likelihood = -1277.5529  (not concave)
Iteration 1:   log likelihood = -1196.6667  
Iteration 2:   log likelihood = -1137.5362  
Iteration 3:   log likelihood = -1127.8388  
Iteration 4:   log likelihood = -1127.5664  
Iteration 5:   log likelihood = -1127.5662  
Iteration 6:   log likelihood = -1127.5662  

Fitting 2 component Negative Binomial-2 model:

Iteration 0:   log pseudolikelihood = -1127.5701  (not concave)
Iteration 1:   log pseudolikelihood = -1127.5046  (not concave)
Iteration 2:   log pseudolikelihood = -1118.0772  (not concave)
Iteration 3:   log pseudolikelihood = -1115.7068  
Iteration 4:   log pseudolikelihood = -1115.4488  (not concave)
Iteration 5:   log pseudolikelihood = -1109.4996  (not concave)
Iteration 6:   log pseudolikelihood = -1107.1628  
Iteration 7:   log pseudolikelihood = -1106.4365  
Iteration 8:   log pseudolikelihood = -1106.1363  
Iteration 9:   log pseudolikelihood = -1106.1329  
Iteration 10:  log pseudolikelihood = -1106.1329  

2 component Negative Binomial-2 regression        Number of obs   =        346
                                                  Wald chi2(6)    =    1496.35
Log pseudolikelihood = -1106.1329                 Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |               Robust
         PAT |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
component1   |
    LOGRandD |   .9181075   .0634763    14.46   0.000     .7936963    1.042519
        LOGK |   .0653257    .056885     1.15   0.251    -.0461668    .1768182
     SCISECT |   .1028518   .1416338     0.73   0.468    -.1747454     .380449
       _cons |  -1.273755   .1607671    -7.92   0.000    -1.588853   -.9586572
-------------+----------------------------------------------------------------
component2   |
    LOGRandD |   .3519998   .0896255     3.93   0.000     .1763371    .5276625
        LOGK |   .2771243   .0617372     4.49   0.000     .1561216     .398127
     SCISECT |  -.5731597   .3459274    -1.66   0.098    -1.251165    .1048455
       _cons |   1.516435   .1911198     7.93   0.000     1.141847    1.891023
-------------+----------------------------------------------------------------
 /imlogitpi1 |   2.571207   .3377545     7.61   0.000      1.90922    3.233194
   /lnalpha1 |  -.4287745   .1222261    -3.51   0.000    -.6683332   -.1892157
   /lnalpha2 |   -2.77605   .7533299    -3.69   0.000    -4.252549    -1.29955
------------------------------------------------------------------------------
      alpha1 |   .6513068   .0796067                      .5125622     .827608
      alpha2 |   .0622841   .0469204                      .0142279    .2726544
         pi1 |   .9289854   .0222822                      .8709315    .9620645
         pi2 |   .0710146   .0222822                      .0379355    .1290685
------------------------------------------------------------------------------

. estimates store FMMNB2

. predict mu1, equation(component1)

. predict mu2, equation(component2)

. summarize mu*

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         mu1 |       346      39.611    132.7543   .0689383   1511.818
         mu2 |       346     67.0761    117.1558   1.166299   1176.551

. 
. * estimates table (including NB1 and FMNB2)
. estimates table POISS NB1 NB2 FMMNB1 FMMNB2, b(%9.3f) se stats(ll aic bic N k) equations(1)

--------------------------------------------------------------------------
    Variable |   POISS        NB1         NB2       FMMNB1      FMMNB2    
-------------+------------------------------------------------------------
#1           |
    LOGRandD |     0.475       0.490       0.816       0.525       0.918  
             |     0.064       0.048       0.083       0.064       0.063  
        LOGK |     0.271       0.173       0.115       0.188       0.065  
             |     0.055       0.043       0.069       0.069       0.057  
     SCISECT |     0.467       0.396      -0.090       0.601       0.103  
             |     0.155       0.110       0.138       0.146       0.142  
       _cons |    -0.340       0.245      -0.811      -0.434      -1.274  
             |     0.190       0.169       0.180       0.267       0.161  
-------------+------------------------------------------------------------
lndelta      |
       _cons |                 3.102                                      
             |                 0.153                                      
-------------+------------------------------------------------------------
lnalpha      |
       _cons |                            -0.122                          
             |                             0.113                          
-------------+------------------------------------------------------------
component2   |
    LOGRandD |                                         0.681       0.352  
             |                                         0.100       0.090  
        LOGK |                                         0.169       0.277  
             |                                         0.091       0.062  
     SCISECT |                                        -0.237      -0.573  
             |                                         0.214       0.346  
       _cons |                                         0.112       1.516  
             |                                         0.260       0.191  
-------------+------------------------------------------------------------
imlogitpi1   |
       _cons |                                         0.873       2.571  
             |                                         0.583       0.338  
-------------+------------------------------------------------------------
lndelta1     |
       _cons |                                         2.504              
             |                                         0.274              
-------------+------------------------------------------------------------
lndelta2     |
       _cons |                                         2.735              
             |                                         0.445              
-------------+------------------------------------------------------------
lnalpha1     |
       _cons |                                                    -0.429  
             |                                                     0.122  
-------------+------------------------------------------------------------
lnalpha2     |
       _cons |                                                    -2.776  
             |                                                     0.753  
-------------+------------------------------------------------------------
Statistics   |                                                            
          ll | -3365.830   -1147.932   -1127.566   -1132.808   -1106.133  
         aic |  6739.661    2305.863    2265.132    2287.616    2234.266  
         bic |  6755.046    2325.095    2284.365    2329.927    2276.577  
           N |       346         346         346         346         346  
           k |     4.000       5.000       5.000      11.000      11.000  
--------------------------------------------------------------------------
                                                              legend: b/se

. 
. *** TABLE 4.3: MODEL ESTIMATES
. 
. estimates table POISS NB2 FMMNB2, b(%9.3f) se stats(ll aic bic N k) equations(1)

--------------------------------------------------
    Variable |   POISS        NB2       FMMNB2    
-------------+------------------------------------
#1           |
    LOGRandD |     0.475       0.816       0.918  
             |     0.064       0.083       0.063  
        LOGK |     0.271       0.115       0.065  
             |     0.055       0.069       0.057  
     SCISECT |     0.467      -0.090       0.103  
             |     0.155       0.138       0.142  
       _cons |    -0.340      -0.811      -1.274  
             |     0.190       0.180       0.161  
-------------+------------------------------------
lnalpha      |
       _cons |                -0.122              
             |                 0.113              
-------------+------------------------------------
component2   |
    LOGRandD |                             0.352  
             |                             0.090  
        LOGK |                             0.277  
             |                             0.062  
     SCISECT |                            -0.573  
             |                             0.346  
       _cons |                             1.516  
             |                             0.191  
-------------+------------------------------------
imlogitpi1   |
       _cons |                             2.571  
             |                             0.338  
-------------+------------------------------------
lnalpha1     |
       _cons |                            -0.429  
             |                             0.122  
-------------+------------------------------------
lnalpha2     |
       _cons |                            -2.776  
             |                             0.753  
-------------+------------------------------------
Statistics   |                                    
          ll | -3365.830   -1127.566   -1106.133  
         aic |  6739.661    2265.132    2234.266  
         bic |  6755.046    2284.365    2276.577  
           N |       346         346         346  
           k |     4.000       5.000      11.000  
--------------------------------------------------
                                      legend: b/se

. 
. ********** CLOSE OUTPUT
. 
. * log close
. * clear
. * exit
. 
end of do-file

. exit, clear
