I'm using FC4 and R 2.3.1 to fit a mixed effects logistic regression.
The response is 0/1 and both the response and the age are the same for
each pair of observations for each subject (some observations are not
paired). For example:
id response age
1 0 30
1 0 30
2 1 55
2 1 55
3 0 37
4 1 52
5 0 39
5 0 39
etc.
I get the following error:
> (lmer(response~(1|id)+age,data=gdx,family=binomial))
Error in deviance(.Call("mer_coefGets", x, pars, 2, PACKAGE =
"Matrix")) :
Leading minor of order 2 in downdated X'X is not positive
definite
Similar problem if I use quasibinomial.
If I use glm, of course it thinks I have roughly twice the number of
subjects so the standard errors would be smaller than they should be.
I used SAS's NLMIXED and it converged without problems giving me
parameter estimates close to what glm gives, but with larger standard
errors. glmmPQL(MASS) gives very different parameter estimates.
Is it reasonable to fit a mixed effects model in this situation?
Is there some way to give starting values for lmer and/or glmmPQL?
Rick B.
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
