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log: C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2003\class 3.smcl
log type: smcl
opened on: 1 Oct 2003, 11:32:46
. set linesize 79
. edit
(3 vars, 4 obs pasted into editor)
- preserve
. describe
Contains data
obs: 4
vars: 3
size: 60 (99.9% of memory free)
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storage display value
variable name type format label variable label
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color str5 %9s Color
live str5 %9s
count byte %8.0g
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Sorted by:
Note: dataset has changed since last saved
. table live color, contents(sum count) row col
-------------------------------
| Color
live | Blue Green Total
----------+--------------------
Lilly | 23 10 33
Water | 27 15 42
|
Total | 50 25 75
-------------------------------
. desmat: poisson count color live
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Poisson regression
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Dependent variable count
Optimization: ml
Number of observations: 4
Initial log likelihood: -14.328
Log likelihood: -9.540
LR chi square: 9.578
Model degrees of freedom: 2
Pseudo R-squared: 0.334
Prob: 0.008
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nr Effect Coeff s.e.
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count
color
1 Green -0.693** 0.245
live
2 Water 0.241 0.233
3 _cons 3.091** 0.192
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* p < .05
** p < .01
. poisgof
Goodness-of-fit chi2 = .2445188
Prob > chi2(1) = 0.6210
. poisgof, pearson
Goodness-of-fit chi2 = .2435065
Prob > chi2(1) = 0.6217
. table live color, contents (mean _x_1)
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| Color
live | Blue Green
----------+-------------
Lilly | 0 1
Water | 0 1
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. table live color, contents (mean _x_2)
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| Color
live | Blue Green
----------+-------------
Lilly | 0 0
Water | 1 1
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. *Those above two tables are the dummy variables that desmat creates for the direct effects of color and the direct effects of live.
. desmat: poisson count color*live
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Poisson regression
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Dependent variable count
Optimization: ml
Number of observations: 4
Initial log likelihood: -14.328
Log likelihood: -9.417
LR chi square: 9.822
Model degrees of freedom: 3
Pseudo R-squared: 0.343
Prob: 0.020
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nr Effect Coeff s.e.
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count
color
1 Green -0.833* 0.379
live
2 Water 0.160 0.284
color.live
3 Green.Water 0.245 0.497
4 _cons 3.135** 0.209
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* p < .05
** p < .01
. poisgof
Goodness-of-fit chi2 = 7.95e-06
Prob > chi2(0) = .
. desmat: poisson count color*live, verbose
Desmat generated the following design matrix:
nr Variables Term Parameterization First Last
1 _x_1 color ind(1)
2 _x_2 live ind(1)
3 _x_3 color.live ind(1).ind(1)
Iteration 0: log likelihood = -9.417463
Iteration 1: log likelihood = -9.4173319
Iteration 2: log likelihood = -9.4173319
Poisson regression Number of obs = 4
LR chi2(3) = 9.82
Prob > chi2 = 0.0201
Log likelihood = -9.4173319 Pseudo R2 = 0.3427
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count | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_x_1 | -.8329091 .3787852 -2.20 0.028 -1.575315 -.0905037
_x_2 | .1603427 .2837522 0.57 0.572 -.3958014 .7164867
_x_3 | .2451225 .497174 0.49 0.622 -.7293206 1.219566
_cons | 3.135494 .2085144 15.04 0.000 2.726813 3.544175
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Poisson regression
-----------------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 4
Initial log likelihood: -14.328
Log likelihood: -9.417
LR chi square: 9.822
Model degrees of freedom: 3
Pseudo R-squared: 0.343
Prob: 0.020
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nr Effect Coeff s.e.
-----------------------------------------------------------------------------------------
count
color
1 Green -0.833* 0.379
live
2 Water 0.160 0.284
color.live
3 Green.Water 0.245 0.497
4 _cons 3.135** 0.209
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* p < .05
** p < .01
. table live color, contents (mean _x_3)
------------------------
| Color
live | Blue Green
----------+-------------
Lilly | 0 0
Water | 0 1
------------------------
. help desmat
. *I'm going to try to use desmat to generate the indicator variables different ly.
. desmat: poisson count color*live=ind(0)*ind(1)
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Poisson regression
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Dependent variable count
Optimization: ml
Number of observations: 4
Initial log likelihood: -14.328
Log likelihood: -9.417
LR chi square: 9.822
Model degrees of freedom: 3
Pseudo R-squared: 0.343
Prob: 0.020
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nr Effect Coeff s.e.
-----------------------------------------------------------------------------------------
count
color
1 Green -0.833* 0.379
live
2 Water 0.160 0.284
color.live
3 Green.Water 0.245 0.497
4 _cons 3.135** 0.209
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* p < .05
** p < .01
. desmat: poisson count color*live=ind(1)*ind(2)
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Poisson regression
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Dependent variable count
Optimization: ml
Number of observations: 4
Initial log likelihood: -14.328
Log likelihood: -9.417
LR chi square: 9.822
Model degrees of freedom: 3
Pseudo R-squared: 0.343
Prob: 0.020
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nr Effect Coeff s.e.
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count
color
1 Green -0.588 0.322
live
2 Lilly -0.160 0.284
color.live
3 Green.Lilly -0.245 0.497
4 _cons 3.296** 0.192
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* p < .05
** p < .01
. display exp(-.245)
.78270454
. log close
log: C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2003\clas s 3.smcl
log type: smcl
closed on: 1 Oct 2003, 12:22:44
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