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 log: C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2003\clas
> s 4.smcl
 log type: smcl
 opened on:  8 Oct 2003, 11:29:26
 
. set linesize 79
 
. edit
(3 vars, 16 obs pasted into editor)
- preserve
 
. poisson count
 
Iteration 0: log likelihood = -221501.22 
Iteration 1: log likelihood = -221501.22 
 
Poisson regression  Number of obs =  16
 LR chi2(0) =  -0.00
 Prob > chi2 =  .
Log likelihood = -221501.22 Pseudo R2  =  -0.0000
 
------------------------------------------------------------------------------
 count | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
 _cons |  10.39578 .0013821 7521.99 0.000 10.39308 10.39849
------------------------------------------------------------------------------
 
. poisgof
 
 Goodness-of-fit chi2 =  442816.4
 Prob > chi2(15) = 0.0000
 
. predict const_only
(option n assumed; predicted number of events)
 
. table hed wed, contents (sum const_only) row col
 
------------------------------------------------------------
 | wed  
 hed | 1 2 3 4 Total
----------+-------------------------------------------------
 1 | 32721.38 32721.38 32721.38 32721.38 130885.5
 2 | 32721.38 32721.38 32721.38 32721.38 130885.5
 3 | 32721.38 32721.38 32721.38 32721.38 130885.5
 4 | 32721.38 32721.38 32721.38 32721.38 130885.5
 | 
 Total | 130885.5 130885.5 130885.5 130885.5 523542
------------------------------------------------------------
 
. desmat: poisson count hed wed
-----------------------------------------------------------------------------------
 Poisson regression
-----------------------------------------------------------------------------------
 Dependent variable  count
 Optimization:  ml
 Number of observations:  16
 Initial log likelihood:   -221501.223
 Log likelihood:  -113882.425
 LR chi square:  215237.595
 Model degrees of freedom:    6
 Pseudo R-squared:  0.486
 Prob:  0.000
-----------------------------------------------------------------------------------
nr Effect Coeff s.e.
----------------------------------------------------------------------------------
 count
 hed
1 2   1.072** 0.004
2 3 0.595** 0.005
3 4 0.235** 0.005
 wed
4  2 1.229** 0.004
5 3 0.733** 0.005
6 4 0.142** 0.005
7 _cons 9.187** 0.005
-----------------------------------------------------------------------------------
* p < .05
** p < .01
 
. poisgof
 
 Goodness-of-fit chi2 =  227578.9
  Prob > chi2(9) = 0.0000
 
. predict indep_model
(option n assumed; predicted number of events)





. table hed wed, contents (sum indep_model) row col
 
------------------------------------------------------------
 | wed 
 hed | 1 2 3 4 Total
----------+-------------------------------------------------
 1 | 9773.551 33398.43 20349.32 11263.7 74785
 2 | 28552.2 97569.33 59447.98 32905.5 218475
 3 | 17727.26 60578.06 36909.58 20430.1 135645
 4 | 12367.98 42264.19 25751.13 14253.7 94637
 | 
 Total | 68421 233810 142458 78853 523542
------------------------------------------------------------
 
. table hed wed, contents (sum count) row col
 
--------------------------------------------------
 | wed 
 hed | 1 2 3 4 Total
----------+---------------------------------------
 1 | 32016 33374 8407 988 74785
 2 | 28370 137876 43783 8446 218475
 3 | 7051 48766 61633 18195 135645
 4 | 984 13794 28635 51224 94637
 | 
 Total | 68421 233810 142458 78853 523542
--------------------------------------------------
 
. *The independence model fits the actual data perfectly on the marginals, but 
> not very well at all in the interior
. *let's introduce a single term that accounts for educational endogamy.
. gen ed_endogamy_simple=0
 
. replace ed_endogamy_simple=1 if hed==wed
(4 real changes made)
 
. table hed wed, contents (mean ed_endogamy_simple)
 
----------------------------------
 | wed 
 hed | 1 2 3 4
----------+-----------------------
  1 | 1 0 0 0
 2 | 0 1 0 0
 3 | 0 0 1 0
 4 | 0 0 0 1
----------------------------------
 
. desmat: poisson count hed wed ed_endogamy_simple
-----------------------------------------------------------------------------------
 Poisson regression
-----------------------------------------------------------------------------------
 Dependent variable count
 Optimization: ml
 Number of observations: 16
 Initial log likelihood:  -221501.223
 Log likelihood:  -41944.565
 LR chi square:  359113.316
 Model degrees of freedom:  7
  Pseudo R-squared:  0.811
 Prob:  0.000
-----------------------------------------------------------------------------------
nr Effect Coeff s.e.
----------------------------------------------------------------------------------
 count
 hed
1 2  0.740** 0.005
2 3 0.414** 0.005
3 4 0.216** 0.005
 wed
4 2  0.979** 0.005
5 3 0.608** 0.005
6 4 0.081** 0.005
 ed_endogamy_simple
7 1  1.115** 0.003
8 _cons 9.067** 0.005
----------------------------------------------------------------------------------
* p < .05
** p < .01
 
. display exp(1.115)
3.0495682
 
. poisgof
 
 Goodness-of-fit chi2 =  83703.13
 Prob > chi2(8) = 0.0000
 
. predict simple_endogamy
(option n assumed; predicted number of events)
 
. table hed wed, contents (sum simple_endogamy) row col
 
------------------------------------------------------------
 | wed 
 hed | 1 2 3 4 Total
----------+-------------------------------------------------
 1 | 26426.32 23047.51 15915.36 9395.808 74785
 2 | 18145.71 147304.7 33341.21 19683.35 218475
 3 | 13104.12 34867.67 73458.66 14214.54 135645
 4 | 10744.85 28590.09 19742.76 35559.3 94637
 | 
 Total | 68421 233810 142458 78853 523542
------------------------------------------------------------
 
. table hed wed, contents (sum count) row col
 
--------------------------------------------------
 | wed 
 hed | 1 2 3 4 Total
----------+---------------------------------------
 1 | 32016 33374 8407 988 74785
 2 | 28370 137876 43783 8446 218475
 3 | 7051 48766 61633 18195 135645
 4 | 984 13794 28635 51224 94637
 | 
 Total | 68421 233810 142458 78853 523542
--------------------------------------------------
 
. gen ed_endogamy_particular=0
 
. replace ed_endogamy_particular=hed if hed==wed
(4 real changes made)
 
. table hed wed, contents (mean ed_endogamy_particular)
 
----------------------------------
 | wed 
 hed | 1 2 3 4
----------+-----------------------
 1 | 1 0 0 0
 2 | 0 2 0 0
 3 | 0 0 3 0
 4 | 0 0 0 4
----------------------------------
 
. desmat: poisson count hed wed ed_endogamy_particular
---------------------------------------------------------------------------------
 Poisson regression
----------------------------------------------------------------------------------
 Dependent variable  count
 Optimization:  ml
 Number of observations:  16
 Initial log likelihood:   -221501.223
 Log likelihood:  -24059.274
 LR chi square:  394883.898
 Model degrees of freedom:    10
 Pseudo R-squared:  0.891
 Prob:  0.000
----------------------------------------------------------------------------------
nr Effect Coeff s.e.
---------------------------------------------------------------------------------
 count
 hed
1 2   1.134** 0.007
2 3 0.819** 0.006
3 4 -0.017* 0.007
 wed
4 2  1.372** 0.007
5 3 1.020** 0.007
6 4 -0.278** 0.008
 ed_endogamy_particular
7 1 1.722** 0.009
8 2 0.676** 0.007
9 3  0.537** 0.008
10 4 2.487** 0.009
11 _cons 8.652** 0.008
-------------------------------------------------------------------------------
----
* p < .05
** p < .01
 
. poisgof
 
 Goodness-of-fit chi2 =  47932.55
 Prob > chi2(5) = 0.0000
 
. predict P_full_endogamy
(option n assumed; predicted number of events)
 
. table hed wed, contents (sum P_full_endogamy) row col
 
------------------------------------------------------------
 | wed 
 hed | 1 2 3 4 Total
----------+-------------------------------------------------
 1 | 32016 22561.17 15875.39 4332.443 74785
 2 | 17790.29 137876 49342.89 13465.83 218475
 3 | 12987.8 51193.47 61633 9830.73 135645
 4 | 5626.913 22179.36 15606.73 51224 94637
 | 
 Total | 68421 233810 142458 78853 523542
------------------------------------------------------------
 
. table hed wed, contents (sum count) row col
 
--------------------------------------------------
 | wed 
 hed | 1 2 3 4 Total
----------+---------------------------------------
 1 | 32016 33374 8407 988 74785
 2 | 28370 137876 43783 8446 218475
 3 | 7051 48766 61633 18195 135645
 4 | 984 13794 28635 51224 94637
 | 
 Total | 68421 233810 142458 78853 523542
--------------------------------------------------
 
. *We have 4 interaction terms for educational endogamy for the different educational groups.
 The likelihood ratio test can confirm that this model fits better than the model which had 
uniform educational endogamy across groups.
. *If we want to ask questions about which kinds of endogamy are significantly 
> stronger than which other kinds, we need to test the parameters of the model 
> directly.
. desmat: poisson count hed wed ed_endogamy_particular
---------------------------------------------------------------------------------
 Poisson regression
---------------------------------------------------------------------------------
 Dependent variable  count
 Optimization: ml
 Number of observations:  16
 Initial log likelihood:   -221501.223
 Log likelihood:  -24059.274
 LR chi square:  394883.898
 Model degrees of freedom:    10
 Pseudo R-squared:  0.891
 Prob:  0.000
----------------------------------------------------------------------------------
nr Effect Coeff s.e.
---------------------------------------------------------------------------------
 count
 hed
1 2   1.134** 0.007
2 3 0.819** 0.006
3 4 -0.017* 0.007
 wed
4 2  1.372** 0.007
5 3 1.020** 0.007
6 4 -0.278** 0.008
 ed_endogamy_particular
7 1 1.722** 0.009
8 2 0.676** 0.007
9 3 0.537** 0.008
10 4 2.487** 0.009
11 _cons 8.652** 0.008
---------------------------------------------------------------------------------
* p < .05
** p < .01
 
. *If we want to ask whether the force of educational endogamy is significantly
> stronger for group 4 than group 1, we test:
. test _x_10-_x_7=0
 
 ( 1) - [count]_x_7 + [count]_x_10 = 0
 
 chi2( 1) = 3300.64
  Prob > chi2 =  0.0000
 
. *
. *This tells us that category 4 and category 1 are very significantly different in terms of educational endogamy.
. *Categories 2 and 3 are not so far apart, it might be more interesting to test whether that difference is statistically significant.
. test _x_8-_x_9=0
 
 ( 1) [count]_x_8 - [count]_x_9 = 0
 
 chi2( 1) = 129.03
 Prob > chi2 =  0.0000
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