[R] p-value for fixed effect in generalized linear mixed model

[Douglas Bates]

The lme4 package is still in development.  I can't guarantee the
evaluation of the deviance for the quasipoisson family.  There is a
certain amount of mystery about the role of the dispersion parameter
and the null deviance in families like quasipoisson and quasibinomial.
 For the time being don't base tests on a value of the deviance for
such models.

On Feb 20, 2008 8:19 AM, Martijn Vandegehuchte
<martijn.vandegehuchte at ugent.be> wrote:
> Dear R-users,
>
> I am currently trying to switch from SAS to R, and am not very familiar with R yet, so forgive me if this question is irrelevant.
>
> If I try to find the significance of the fixed factor "spikes" in a generalized linear mixed model, with "site" nested within "zone" as a random factor, I compare following two models with the anova function:
>
> model1<-lmer(aphids~spikes+(1|zone:site), method="ML", family=quasipoisson)
> model2<-lmer(aphids~(1|zone:site), method="ML", family=quasipoisson)
> anova(model1,model2)
>
> This gives me a p< 2.2e-16 ***, concluding that "spikes" has a highly significant effect on "aphids". However, when I look at the summary of model1:
>
> summary(model1)
>
> I find a t-value for "spikes" of  -0.1166 which is really insignificant...
>
> When I try model1 in SAS with proc glimmix, corrected for overdispersion with "random _residual_", it also gives a p-value for "spikes" of 0,985. So if "spikes" is not having a significant effect on "aphids", then why the above mentionned p-value generated by anova in R?
>
> Can anyone explain this?
>
> Please find the dataset in the attachment.
>
> Many thanks beforehand,
>
> Martijn.
>
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> --
> Martijn Vandegehuchte
> Ghent University
> Department Biology
> Terrestrial Ecology Unit
> K.L.Ledeganckstraat 35
> B-9000 Ghent
> telephone: +32 (0)9/264 50 84
> e-mail: martijn.vandegehuchte at ugent.be
>
> website TEREC: www.ecology.ugent.be/terec
>
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