You can use the lmer function in the Matrix package or glmmPQL in the MASS package. The former would be used like this:
p1 <- lmer(count ~ treatment + (1|subject), family=poisson) Dave On 20/09/06, Mark Jankowski <[EMAIL PROTECTED]> wrote: > Hello, > > I am trying to formulate a glm model with repeated measures of viral > blood counts where animal weight is a covariate. Animal treatment > group is the fixed effect and subject is the random effect. I'm > thinking this situation calls for a mixed model in the Poisson family > with data correlated to days post inoculation (DPI) where animal > weight is factored out to not influence goodness of fit tests. > > How might I alter the following code to reflect this situation? > > glm(count~treatment,family=poisson) > > If this is too much to ask, I understand! I realize I've got a ways > to go in writing this... > > Many thanks! > Mark > > ______________________________________________ > [email protected] 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. > -- ================================= David Barron Said Business School University of Oxford Park End Street Oxford OX1 1HP ______________________________________________ [email protected] 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.
