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

[Martijn Vandegehuchte]

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.









-- 
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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