Dear List, I would appreciate help on the following matter:
I am aware that higher dimensional contingency tables can be analysed using either log-linear models or as a poisson regression using a generalized linear model: log-linear: loglm(~Age+Site, data=xtabs(~Age+Site, data=SSites.Rev, drop.unused.levels=T)) GLM: glm.table <- as.data.frame(xtabs(~Age+Site, data=SSites.Rev, drop.unused.levels=T)) glm(Freq ~ Age + Site, data=glm.table, family='poisson') where Site is a factor and Age is cast as a factor by xtabs() and treated as such. **Question**: Is it acceptable to step away from contingency table analysis by recasting Age as a numerical variable, and redoing the analysis as: glm(Freq ~ as.numeric(Age) + Site, data=glm.table, family='poisson') My reasons for wanting to do this are to be able to include non-linear terms in the model, using say restricted or natural cubic splines. Thank you in advance for your help. Regards, Mark Difford. --------------------------------------------------------------- Mark Difford Ph.D. candidate, Botany Department, Nelson Mandela Metropolitan University, Port Elizabeth, SA. ______________________________________________ R-help@stat.math.ethz.ch 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.
