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-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of David Winsemius Sent: Thursday, 14 October 2010 10:22 AM To: Antonio Paredes Cc: r-help@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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
