I'm trying to figure out how to carry out a Poisson regression fit to
longitudinal data with a gamma distribution with unknown shape and
scale parameters.
I've tried the 'lmer4' package's glmer() function, which fits the
Poisson regression using:
library('lme4')
fit5<- glmer(seizures ~ time + progabide + timeXprog +
offset(lnPeriod) + (1|id),
data=pdata, nAGQ=1, family=poisson) #note: can't use nAGQ>1, not
yet implemented
summary(fit5)
Here 'seizures' is a count and 'id' is the subject number.
This fit works, but uses the Poisson distribution with the gamma heterogeneity.
Based on the example in the help for glmer(), I tried
fit6<- glmer(seizures ~ time + progabide + timeXprog + offset(lnPeriod) +
(1|pgamma(id, shap, scal)), data=pdata, nAGQ=1, start=c(shap=1, scal=1),
family=poisson) #note: can't use nAGQ>1, not yet implemented
summary(fit6)
but this ends up with "Error in pgamma(id, shap, scal) : object
"shap" not found".
My questions are:
1. Can this be done?
2. Am I using the right package and function?
3. What am I doing wrong?
Any help would be appreciated.
Thanks.
================================================================
Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: [EMAIL PROTECTED]
Least Cost Formulations, Ltd. URL: http://lcfltd.com/
824 Timberlake Drive Tel: 757-467-0954
Virginia Beach, VA 23464-3239 Fax: 757-467-2947
"Vere scire est per causas scire"
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