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
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