>>>>> "Rick" == Rick Bilonick <[EMAIL PROTECTED]>
>>>>> on Wed, 30 Nov 2005 23:11:07 -0500 writes:
Rick> I'm trying to fit a generalized mixed effects model to a data set
where
Rick> each subject has paired categorical responses y (so I'm trying to use
a
Rick> binomial logit link). There are about 183 observations and one
Rick> explanatory factor x. I'm trying to fit something like:
Rick> (lmer(y~x+(1|subject)))
If you want binomial you have to give ' family = binomial ' to lmer !
Further, always using 'data = ..' is generally recommended practice,
and I'd rather assign the result of lmer(.) than just print it,
i.e. (if you want to print too):
(model1 <- lmer(y ~ x + (1|subject), data = <your DFrame>, family = binomial))
and in your case y should be the 2-column matrix
cbind(successes, failures)
Martin Maechler, ETH Zurich
Rick> I also tried fitting the same type of model using glmmPQL from MASS.
In
Rick> both cases, I get a t-statistic that is huge (in the thousands) and a
Rick> tiny p-value. (Just for comparison, if I use lrm ignoring the
clustering
Rick> I get a t-statistic around 3 or so and what appears to be a reasonable
Rick> estimated coefficient which is very close to the estimated coefficient
Rick> using just one observation from each subject.
Rick> Most of the subjects have two responses and in almost all cases the
Rick> responses are identical although the explantory factor values are not
Rick> always identical for each subject.
Rick> If I use geeglm from geepack, I get reasonable estimates close to the
Rick> naive model results.
Rick> I also tried using the SAS glimmix macro to fit a generalized mixed
Rick> model and the routine does not converge.
Rick> Why does geeglm appear to work but not lmer and glmmPQL? Is this
likely
Rick> to be due to my particular data set?
Rick> Rick B.
Rick> ______________________________________________
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Rick> PLEASE do read the posting guide!
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