[R] lmer and mixed effects logistic regression

From: Rick Bilonick <rab45+_at_pitt.edu>
Date: Wed 14 Jun 2006 - 23:59:34 EST

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


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.

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