Re: st: Conditional probabilities with one fixed effect at a constant

[Michael Ingre <Michael.Ingre@ipm.ki.se>]

Buzz Burhans have made several suggestions to help me with my problem. 
I appreciate it a lot. It has inspired me to think in new ways and come 
closer to a solution.

I now think I have the solution. The one presented earlier is probably 
completely wrong. I will not go into much detail but, there might be 
other gllammers out there that might be interested.

If I want to predict probabilities with a fixed effect removed (or at a 
constant) this is what I believe needs to be done:

1. estimate the gllamm logit model
2. predict the random effects with -gllapred u , u-
4. set the random effect of the effect that should be constant to 0 - 
replace um3 = 0 -
3. predict conditional probabilities with -gllapred mu , mu us(um)-
4. calculate how much the fixed effect that should be removed adds to 
the model.
5. remove the probabilities (at 4) from the predicted probabilities in 
-mu-

If anyone has any comments I appreciate it.

In particular I'm not quite sure how to calculate the probabilities to 
remove. If it was a linear regression model it would be a pice of cake. 
But I'm not used to work with logit estimates. I guess I have to 
convert everything to log-odds before adding and/or subtracting and 
then convert back to probabilities.

A comment on the previous solution:

My best guess is that -gllapred- does not use subject specific 
information from the estimation sample at all when predicting responses 
from observations outside the estimation sample. Instead it seems like 
-gllapred- uses the estimated variances and covariances of the random 
effects as well as the responses in the new sample to simulate a 
plausible solution with subject specific probabilities.

This is quite different from -predict-, -gllapred- also uses the 
responses when predicting in a new sample. And if I'm not mistaken, it 
completely invalidates the solution suggested earlier. It makes no 
sense at all.

Michael

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