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

Martijn Vandegehuchte martijn.vandegehuchte at ugent.be
Wed Feb 20 15:19:19 CET 2008

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)

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:


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 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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