Please excuse me for having posted a similar question on ecolog, but thus far I have received few useful answers there.
I am looking for some advice concerning techniques in R that are appropriate for correlated count data. Specifically, I have some "freezing days" data, which is a count of the number of days each spring that were below freezing. The counts were taken at the same location over a period of years. The data set is highly zero inflated and over-dispersed; glm with a quasipoisson error structure would seem to be appropriate, except that there is a high degree of correlation at lags of 1 making something like a corAR1 structure appropriate. My difficulty is that glm() does not take an argument for correlation. I could use lmer() to fit a model like: freezing days~years+(1|years), family=quasipoisson, correlation=corAR1 but lmer (and glmer) don't seem to be operating on quasi families anymore; I've found plenty of old posts here where lmer seems to have accepted quasi families in the past, but I get an error message that indicates lmer does not in fact accept quasi families. I should note that I have run the following model: freeze.glmmPQL3<-glmmPQL(num.freeze.days~years, random= ~1|years, family=quasipoisson,correlation=corAR1()) My gut says this is not the correct approach and I am unconvinced by the tiny p values that have been returned, especially as specification of poisson vs quasipoisson and the specification of corAR1() seem to make no difference to parameter estimation or p vals for said pars--it would seem that the random term for varying intercept by year is dominant. Maybe this is OK, but my above glm models return non-significant results and I expected handling the correlation to increase my p vals rather than decrease them. Perhaps an incorrect assumption. Therefore I need some alternative to look at trends in this data over time that allows for quasipoisson error and something along the lines of a corAR1() structure (or a mixed model that handles temporal pseudo-replication, but I am hesitant here). Thank you in advance, Lee [[alternative HTML version deleted]] ______________________________________________ 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.