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From: Jeremy Miles on 10 Dec 2009 13:43
Hello everyone,

I'm calculating predicted probabilities for a logistic regression
model in GLIMMIX, and there's something I don't understand (and can't
work out). My predicted probabilities (based either on my calculation
from the parameter estimates, or on saved predicted probabilities from
glimmix) are all much too high (or too low).

Here's a simple example to show what I mean. There are two variables
- an id variable, and an outcome, which is dichotomous, 50% of scores
are 0, 50% are 1.

data test;
input
id outcome ;
cards;
1 0
1 0
1 0
1 0
1 0
1 1
1 0
1 0
1 0
1 1
2 0
2 0
2 1
2 1
3 0
3 1
3 1
3 1
4 1
4 1
4 0
4 0
5 0
5 0
5 0
5 0
6 1
6 1
6 1
6 1
6 1
6 1
6 1
6 1
;
RUN;



If I run an intercept only model with either proc logistic or proc
glimmix, with no random effects, I get wha I expect, a parameter
estimate of zero:

proc logistic data=test;
model outcome = /;
run;

Analysis of Maximum Likelihood Estimates

Standard Wald
Parameter DF Estimate Error Chi-Square
Pr > ChiSq

Intercept 1 0 0.3430 0.0000
1.0000


proc glimmix data=test method=quad;
model outcome = / dist=binary solution;
run;



Parameter Estimates

Standard
Effect Estimate Error DF t Value
Pr > |t|

Intercept -205E-19 0.3430 33 -0.00
1.0000


proc glimmix data=test method=quad;
class id;
model outcome = /dist=binary solution ;
random intercept / subject=id ;
run;


When I run it with the random effect, the parameter estimate isn't
zero any more.


Solutions for Fixed Effects

Standard
Effect Estimate Error DF t Value
Pr > |t|

Intercept -0.03590 0.8643 5 -0.04
0.9685



Can anyone explain what I'm missing here?

Thanks,

Jeremy






--
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com
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