R help - Aug 2008 - Specifying random effects distribution in glmer()

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: ral at lcfltd.com
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"

Are you trying to fit a Poisson GLMM with Gamma random effects? I don't
think you can do that using (g)lmer, which assumes a Gaussian
distribution for the random effects. You might have a look at the hnlmix
function in Jim Lindsey's repeated package. Or you could use Bayesian
methods in JAGS, BUGS etc. Usually Gamma random effects are multipliers,
not additive. So it makes sense to set the mean =1, unlike Gaussian
random effects with mean=0. This will place a restriction on the shape
and scale parameters.

HTH,

Simon.

On Sun, 2008-08-24 at 22:10 -0400, Robert A. LaBudde
wrote:
> 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: ral at lcfltd.com
> 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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-- 
Simon Blomberg, BSc (Hons), PhD, MAppStat. 
Lecturer and Consultant Statistician 
Faculty of Biological and Chemical Sciences 
The University of Queensland 
St. Lucia Queensland 4072 
Australia
Room 320 Goddard Building (8)
T: +61 7 3365 2506
http://www.uq.edu.au/~uqsblomb
email: S.Blomberg1_at_uq.edu.au

Policies:
1.  I will NOT analyse your data for you.
2.  Your deadline is your problem.

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an answer does not ensure that a reasonable answer can 
be extracted from a given body of data. - John Tukey.