fifth class log

log type: text

opened on: 10 Oct 2005, 11:01:55

. table wed hed, contents(mean endogdm)

----------------------------------

| hed

wed | 1 2 3 4

----------+-----------------------

1 | 1 0 0 0

2 | 0 1 0 0

3 | 0 0 1 0

4 | 0 0 0 1

----------------------------------

. table wed hed, contents (mean endog)

----------------------------------

| hed

wed | 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 wed hed endogdm

------------------------------------------------------------------------------------------

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

wed

1 2 0.979** 0.005

2 3 0.608** 0.005

3 4 0.081** 0.005

hed

4 2 0.740** 0.005

5 3 0.414** 0.005

6 4 0.216** 0.005

endogdm

7 1 1.115** 0.003

8 _cons 9.067** 0.005

------------------------------------------------------------------------------------------

* p < .05

** p < .01

. set linesize 79

. desmat: poisson count wed hed endog

-------------------------------------------------------------------------------

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

wed

1 2 1.372** 0.007

2 3 1.020** 0.007

3 4 -0.278** 0.008

hed

4 2 1.134** 0.007

5 3 0.819** 0.006

6 4 -0.017* 0.007

endog

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

. display 83703-47932

35771

. display chi2tail(3,35771)

. display chi2tail(3,100)

1.554e-21

. lincom _x_7-_x_9

( 1) [count]_x_7 - [count]_x_9 = 0

------------------------------------------------------------------------------

count | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

(1) | 1.184561 .0123912 95.60 0.000 1.160275 1.208847

------------------------------------------------------------------------------

. *Note: We know because the model with 4 endogamy terms fits much better than

the model with 1 endogamy term, that educational endogamy varies across educational groups.

. *In order to say which level of educational endogamy is the highest, we can compare the coefficients, and test them.

. table hed wed, contents (mean eddiff3)

----------------------------------

| wed

hed | 1 2 3 4

----------+-----------------------

1 | 0 0 0 1

2 | 0 0 0 0

3 | 0 0 0 0

4 | 1 0 0 0

----------------------------------

. desmat: poisson count wed hed endog eddiff3

-------------------------------------------------------------------------------

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

wed

1 2 1.132** 0.007

2 3 0.815** 0.007

3 4 -0.276** 0.008

hed

4 2 0.942** 0.007

5 3 0.667** 0.007

6 4 0.009 0.007

endog

7 1 1.410** 0.010

8 2 0.796** 0.007

9 3 0.583** 0.007

10 4 2.147** 0.010

eddiff3

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 an improvement in the LRT goodness of fit on 1 df from the

. *previous model, but it is still far, far away from the actual data

. desmat: poisson count wed hed endog eddiff3 eddiff2

-------------------------------------------------------------------------------

Poisson regression

-------------------------------------------------------------------------------

Dependent variable count

Optimization: ml

Number of observations: 16

Initial log likelihood: -221501.223

Log likelihood: -145.628

LR chi square: 442711.189

Model degrees of freedom: 12

Pseudo R-squared: 0.999

Prob: 0.000

-------------------------------------------------------------------------------

nr Effect Coeff s.e.

-------------------------------------------------------------------------------

count

wed

1 2 0.817** 0.008

2 3 0.461** 0.007

3 4 -0.142** 0.009

hed

4 2 0.627** 0.008

5 3 0.355** 0.007

6 4 0.180** 0.008

endog

7 1 0.763** 0.011

8 2 0.779** 0.007

9 3 0.601** 0.008

10 4 1.195** 0.011

eddiff3

11 1 -2.749** 0.024

eddiff2

12 1 -1.068** 0.006

13 _cons 9.611** 0.009

-------------------------------------------------------------------------------

* p < .05

** p < .01

. poisgof

Goodness-of-fit chi2 = 105.2568

Prob > chi2(3) = 0.0000

. table hed wed, contents (mean eddiff2)

----------------------------------

| wed

hed | 1 2 3 4

----------+-----------------------

1 | 0 0 1 0

2 | 0 0 0 1

3 | 1 0 0 0

4 | 0 1 0 0

----------------------------------

. *let's take a look at how this model fits.

. predict M6

(option n assumed; predicted number of events)

. table hed wed, contents (sum count sum M6) row col

------------------------------------------------------------

| wed

hed | 1 2 3 4 Total

----------+-------------------------------------------------

1 | 32016 33374 8407 988 74785

| 32016 33801.72 8138.919 828.3573 74785

2 | 28370 137876 43783 8446 218475

| 27942.28 137876 44327.54 8329.189 218475

3 | 7051 48766 61633 18195 135645

| 7319.081 48221.46 61633 18471.45 135645

4 | 984 13794 28635 51224 94637

| 1143.643 13910.81 28358.55 51224 94637

Total | 68421 233810 142458 78853 523542

| 68421 233810 142458 78853 523542

------------------------------------------------------------

. *One way to look at this is the simple residuals

. gen M6_resid=M6-count

. table hed wed, contents (sum M6_resid)

------------------------------------------------------

| wed

hed | 1 2 3 4

----------+-------------------------------------------

1 | 0 427.7227 -268.0806 -159.6427

2 | -427.7227 0 544.5352 -116.8105

3 | 268.0806 -544.5352 0 276.4531

4 | 159.6427 116.8105 -276.4531 0

------------------------------------------------------

. table hed wed, contents (sum M6_resid) row col

-----------------------------------------------------------------

| wed

hed | 1 2 3 4 Total

----------+------------------------------------------------------

1 | 0 427.7227 -268.0806 -159.6427 -.0006104

2 | -427.7227 0 544.5352 -116.8105 .0019531

3 | 268.0806 -544.5352 0 276.4531 -.0014648

4 | 159.6427 116.8105 -276.4531 0 .0001221

Total | .0006104 -.0019531 .0014648 -.0001221 0

-----------------------------------------------------------------

. *one simple way to standardize the residuals is to use the Pearson residuals

. poisgof, pearson

Goodness-of-fit chi2 = 105.9502

Prob > chi2(3) = 0.0000

. gen M6_pearson_resid= (M6_resid^2)/ M6

. table hed wed, contents (sum M6_pearson_resid) row col

------------------------------------------------------------

| wed

hed | 1 2 3 4 Total

----------+-------------------------------------------------

1 | 0 5.412347 8.830066 30.76666 45.00908

2 | 6.547307 0 6.689263 1.638179 14.87475

3 | 9.819156 6.149098 0 4.137537 20.10579

4 | 22.28475 .9808705 2.695002 0 25.96062

Total | 38.65121 12.54232 18.21433 36.54238 105.9502

------------------------------------------------------------

. * Here is a clue to where we might try to apply another degree of freedom into our model- the two furthest out cells, where education =(1,4) and (4,1) fit poorly and contribute a lot to our pearson chisquare test. One additional term would fit both exactly, because we already have one term for these two cells (eddiff3) in the model.

. log close

log type: text

closed on: 10 Oct 2005, 11:56:25

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