--------------------------------------------------------------------------------------------
log: C:\AAA Miker
Files\newer web pages\soc_388_notes\soc_388_2003\class 5.log
log type: text
opened
on:
. use "C:\AAA Miker
Files\current class files\methods tabular arrays\ed intermar data, updat
> ed.dta", clear
. 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
--------------------------------------------------
. *Let's generate, a gender specific interaction for the
most unlikely combination
. gen husb4wife1=0
. replace husb4wife1=1 if hed==4
& wed==1
(1 real change made)
. edit
- preserve
. desmat: poisson count hed
wed husb4wife1
------------------------------------------------------------------------------------------
Poisson
regression
------------------------------------------------------------------------------------------
Dependent
variable
count
Optimization: ml
Number of
observations:
16
Initial log
likelihood:
-221501.223
Log
likelihood: -102975.137
LR chi
square:
237052.171
Model degrees of
freedom:
7
Pseudo
R-squared: 0.535
Prob:
0.000
------------------------------------------------------------------------------------------
nr Effect
Coeff s.e.
------------------------------------------------------------------------------------------
count
hed
1 2
1.072** 0.004
2 3
0.595** 0.005
3 4
0.396** 0.005
wed
4 2 1.013** 0.004
5 3
0.517** 0.005
6 4
-0.074** 0.005
husb4wife1
7 1
-2.877** 0.032
8 _cons
9.372** 0.005
------------------------------------------------------------------------------------------
*
p < .05
** p < .01
. display exp(-2.877)
.05630342
. *desmat can do lots of different things with comparison
categories for indicator variables.
. poisgof
Goodness-of-fit chi2
= 205764.3
Prob > chi2(8) = 0.0000
. desmat: poisson count hed=
------------------------------------------------------------------------------------------
Poisson
regression
------------------------------------------------------------------------------------------
Dependent
variable
count
Optimization:
ml
Number of
observations: 16
Initial log
likelihood:
-221501.223
Log
likelihood:
-102975.137
LR chi square:
237052.171
Model degrees of
freedom:
7
Pseudo
R-squared:
0.535
Prob:
0.000
------------------------------------------------------------------------------------------
nr Effect
Coeff s.e.
------------------------------------------------------------------------------------------
count
hed
1 1
-0.595** 0.005
2 2 0.477** 0.003
3 4
-0.199** 0.004
wed
4 1
-1.013** 0.004
5 3
-0.495** 0.003
6 4
-1.087** 0.004
husb4wife1
7 0
2.877** 0.032
8 _cons
8.104** 0.033
------------------------------------------------------------------------------------------
*
p < .05
** p < .01
. poisgof
Goodness-of-fit chi2 =
205764.3
Prob > chi2(8) = 0.0000
. desmat: poisson count hed
wed husb4wife1
------------------------------------------------------------------------------------------
Poisson
regression
------------------------------------------------------------------------------------------
Dependent
variable
count
Optimization:
ml
Number of
observations:
16
Initial log
likelihood:
-221501.223
Log
likelihood:
-102975.137
LR chi
square:
237052.171
Model degrees of
freedom:
7
Pseudo
R-squared: 0.535
Prob:
0.000
------------------------------------------------------------------------------------------
nr Effect Coeff s.e.
------------------------------------------------------------------------------------------
count
hed
1 2
1.072** 0.004
2 3
0.595** 0.005
3 4
0.396** 0.005
wed
4 2
1.013** 0.004
5 3
0.517** 0.005
6 4
-0.074** 0.005
husb4wife1
7 1 -2.877** 0.032
8 _cons
9.372** 0.005
------------------------------------------------------------------------------------------
*
p < .05
** p < .01
. *That's back to our original model.
. *Now let's add the diagonal cells and see how that
effects the off-diagonal interaction.
. set linesize 79
. desmat: poisson count hed wed
full_endog husb4wife1
-------------------------------------------------------------------------------
Poisson
regression
-------------------------------------------------------------------------------
Dependent
variable
count
Optimization: ml
Number of
observations:
16
Initial log
likelihood:
-221501.223
Log
likelihood: -20410.686
LR chi
square:
402181.075
Model degrees of
freedom:
11
Pseudo
R-squared:
0.908
Prob:
0.000
-------------------------------------------------------------------------------
nr Effect
Coeff s.e.
-------------------------------------------------------------------------------
count
hed
1 2
1.079** 0.007
2 3
0.788** 0.006
3 4
0.103** 0.007
wed
4 2
1.173** 0.007
5 3
0.843** 0.007
6 4
-0.426** 0.008
full_endog
7 1
1.536** 0.010
8 2
0.744** 0.007
9 3
0.561** 0.007
10 4
2.329** 0.009
husb4wife1
11 1
-2.049** 0.033
12 _cons
8.838** 0.008
-------------------------------------------------------------------------------
*
p < .05
** p < .01
. *Where does this model fit the data?
. *It should fit everywhere we have a single term fitting
a single cell
. predict P_ourmodel
(option n assumed; predicted
number of events)
. table hed wed, contents (sum
P_ourmodel) row col
------------------------------------------------------------
| wed
hed | 1 2 3 4
Total
----------+-------------------------------------------------
1 | 32016 22270.84 15999.74
4498.421 74785
2 | 20273.21 137876 47087.01 13238.79
218475
3 | 15147.79 48972.41 61633
9891.794 135645
4 | 984 24690.75 17738.25
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
--------------------------------------------------
. *What would the coefficent for 4-1 intermarriage look
like if we used only 1
> term rather than 4 terms to
describe endogamy diagonal?
. desmat: poisson count hed
wed simp_endog husb4wife1
-------------------------------------------------------------------------------
Poisson
regression
-------------------------------------------------------------------------------
Dependent
variable
count
Optimization:
ml
Number of
observations:
16
Initial log
likelihood:
-221501.223
Log
likelihood:
-32870.555
LR chi
square:
377261.337
Model degrees of
freedom: 8
Pseudo
R-squared:
0.852
Prob:
0.000
-------------------------------------------------------------------------------
nr Effect
Coeff s.e.
-------------------------------------------------------------------------------
count
hed
1 2 0.800** 0.005
2 3
0.470** 0.005
3 4
0.413** 0.005
wed
4 2 0.788** 0.005
5 3
0.414** 0.005
6 4
-0.159** 0.006
simp_endog
7 1 1.087** 0.003
husb4wife1
8 1
-2.725** 0.032
9 _cons
9.204** 0.005
-------------------------------------------------------------------------------
*
p < .05
** p < .01
. *just to remember what the two endogamy codings do.
. tabulate hed wed, contents
(mean simp_endog)
option contents() not allowed
r(198);
. table hed wed, contents
(mean simp_endog)
----------------------------------
| 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
----------------------------------
. table hed wed, contents
(mean full_endog)
----------------------------------
| 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
----------------------------------
. display chi2tail(50,100)
.00003455
. exit, clear