[R] Poisson Regression

[Bill.Venables at csiro.au]

One possible way to treat parameters as "nuisance parameters" is to model them as random.  This gives allows them to have a reduced parametric load.  

There are many packages with funcitons to fit glmms.  One you may wish to look at is lme4, which has the lmer fitting function

library(lme4)
fm <- glmer(Y ~ A + B + (1|Subject), family = poisson, data = pData)

for example, may be a useful alternative to a fully fixed effects approach.

W. 

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of David Winsemius
Sent: Thursday, 14 October 2010 10:22 AM
To: Antonio Paredes
Cc: r-help at r-project.org
Subject: Re: [R] Poisson Regression


On Oct 13, 2010, at 4:50 PM, Antonio Paredes wrote:

> Hello everyone,
>
> I wanted to ask if there is an R-package to fit the following Poisson
> regression model
>
> log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k}
> i=1,\cdots,N (subjects)
> j=0,1 (two levels)
> k=0,1 (two levels)
>
> treating the \phi_{i} as nuinsance parameters.

If I am reading this piece correctly there should be no difference  
between a conditional treatment of phi_i in  that model and results  
from the unconditional model one would get from fitting with

glm(lambda ~ phi + alpha + beta  ,family="poisson").

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.6.9679&rep=rep1&type=pdf

(But I am always looking for corrections to my errors.)

-- 
David Winsemius, MD
West Hartford, CT

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