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log: C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2007\sixth_class_log.log
log type: text
opened on: 11 Oct 2007, 11:02:15
. use "C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2007\ed_intermar.dta", clear
. display chi2tail(3,10)
.01856614
. *let's run through, briefly, how to calculate BIC and ID from the models.
. *First thing we need to know is N.
. table hed wed, contents(sum count) row col
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husband's | wife's education
education | <HS HS Some Col BA+ Total
----------+-------------------------------------------------
<HS | 32016 33374 8407 988 74785
HS | 28370 137876 43783 8446 218475
Some Col | 7051 48766 61633 18195 135645
BA+ | 984 13794 28635 51224 94637
|
Total | 68421 233810 142458 78853 523542
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. *Our total N here is 523542.
. *Now let's run a model.
. *let's run model 5.
. desmat: poisson count hed wed ed_endog_full ed_diff_3
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Poisson regression
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Dependent variable count
Optimization: ml
Number of observations: 16
Initial log likelihood: -221501.223
Log likelihood: -17940.195
LR chi square: 407122.056
Model degrees of freedom: 11
Pseudo R-squared: 0.919
Prob: 0.000
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nr Effect Coeff s.e.
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count
hed
1 HS 0.942** 0.007
2 Some Col 0.667** 0.007
3 BA+ 0.009 0.007
wed
4 HS 1.132** 0.007
5 Some Col 0.815** 0.007
6 BA+ -0.276** 0.008
ed_endog_full
7 1 1.410** 0.010
8 2 0.796** 0.007
9 3 0.583** 0.007
10 4 2.147** 0.010
ed_diff_3
11 1 -1.947** 0.023
12 _cons 8.964** 0.008
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* p < .05
** p < .01
. *let me run it again with smaller line size
. set linesize 75
. desmat: poisson count hed wed ed_endog_full ed_diff_3
---------------------------------------------------------------------------
Poisson regression
---------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 16
Initial log likelihood: -221501.223
Log likelihood: -17940.195
LR chi square: 407122.056
Model degrees of freedom: 11
Pseudo R-squared: 0.919
Prob: 0.000
---------------------------------------------------------------------------
nr Effect Coeff s.e.
---------------------------------------------------------------------------
count
hed
1 HS 0.942** 0.007
2 Some Col 0.667** 0.007
3 BA+ 0.009 0.007
wed
4 HS 1.132** 0.007
5 Some Col 0.815** 0.007
6 BA+ -0.276** 0.008
ed_endog_full
7 1 1.410** 0.010
8 2 0.796** 0.007
9 3 0.583** 0.007
10 4 2.147** 0.010
ed_diff_3
11 1 -1.947** 0.023
12 _cons 8.964** 0.008
---------------------------------------------------------------------------
* p < .05
** p < .01
. poisgof
Goodness-of-fit chi2 = 35694.39
Prob > chi2(4) = 0.0000
. *that's our M5
. predict P_M5
(option n assumed; predicted number of events)
. display 35694-4*(ln(523542))
35641.327
. *So BIC is easy to calculate after poisgof.
. gen M5_ID= 50*abs(( P_M5/523542)-(count/523542))
. table hed wed, contents(sum M5_ID) row col
------------------------------------------------------------
husband's | wife's education
education | <HS HS Some Col BA+ Total
----------+-------------------------------------------------
<HS | 0 .8714777 .8849629 .0134853 1.769926
HS | .7943684 0 .147196 .6471726 1.588737
Some Col | .7808831 .147196 0 .6336873 1.561766
BA+ | .0134853 1.018674 1.032159 0 2.064318
|
Total | 1.588737 2.037347 2.064318 1.294345 6.984747
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. *in order to calculate ID, you need to generate the cell by cell statistic, and then find some way to sum over all cells, I used table to calculate the sum, which in this case is 6.98
. save "C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2007\ed_intermar.dta", replace
file C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2007\ed_intermar.dta saved
. exit, clear