Thierry,

I agree that the data is not actually zero-inflated and so I haven't
worried with something like a ZIP. I also have no desire to use a mixed
model for the very reason you state-that the measures were made at one
location.

As for using temperature rather than a derived variable--as much as I may
agree, that one's not my call.

What would your opinion be one the use of geeglm() for this data?

Perhaps it may be more appropriate to move this thread to the general help
list.

Thank you,

Lee



---------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 9 Jan 2012 09:07:21 +0000
> From: "ONKELINX, Thierry" <Thierry.ONKELINX@inbo.be>
> To: Lee Davis <m.lee.davis@gmail.com>,
>        "r-sig-mixed-models@r-project.org"      <
> r-sig-mixed-models@r-project.org>
> Subject: Re: [R-sig-ME] Correlated count data technique advice
> Message-ID:
>        <AA818EAD2576BC488B4F623941DA742757324440@inbomail.inbo.be>
> Content-Type: text/plain; charset="us-ascii"
>
> Dear Lee,
>
> A large numbers of zero do not imply zero-inflation. E.g.
> > mean(rpois(10000, 0.01) == 0)
> [1] 0.9902
> This simulation has 99% zero's and is not zero-inflated.
>
> Since you have a timeserie at only one location and one measurement per
> year there is no point in using a mixed model.
>
> Wouldn't it be more relevant to look directly at the temperature than
> using a derived variable?
>
> Best regards,
>
> Thierry
>
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
> Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
> + 32 2 525 02 51
> + 32 54 43 61 85
> Thierry.Onkelinx@inbo.be
> www.inbo.be
>
> To call in the statistician after the experiment is done may be no more
> than asking him to perform a post-mortem examination: he may be able to say
> what the experiment died of.
> ~ Sir Ronald Aylmer Fisher
>
> The plural of anecdote is not data.
> ~ Roger Brinner
>
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
> -----Oorspronkelijk bericht-----
> Van: r-sig-mixed-models-bounces@r-project.org [mailto:
> r-sig-mixed-models-bounces@r-project.org] Namens Lee Davis
> Verzonden: vrijdag 6 januari 2012 19:54
> Aan: r-sig-mixed-models@r-project.org
> Onderwerp: [R-sig-ME] Correlated count data technique advice
>
> 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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