Martijn Vandegehuchte wrote:
First of all, thanks a lot for the info. I know the differences seem small, but most ecological journals still let their opinion about ecological relevance of predictors depend completely on p-values... So I think I'll stick to lmer because of the Laplace approximation. But I don't really know what you mean by: "If you are happy with the df given by lme you can use them ... this corresponds to the "between-within" option in SAS, Satterthwaite et al. are not available in R." I'm familiar to Satterthwaite's correction for the ddfm, I use it in SAS proc glimmix, but then I'm stuck with PQL again... But for my data the degrees of freedom should be large enough that it doesn't make that much of a difference. I just tested the same models in SAS proc glimmix, with and without Satterthwaite, and there's no difference. So if there is a way of getting the df to obtain a p-value in lmer, I would do so.
Then (maybe stupid) question is: how do I get the df? You mention lme, but can I make the same models in lme? Thanks again,
Sorry, I meant glmmPQL -- glmmPQL basically calls lme as a back end, so "df from lme" is the same as "df from lme". You can either take those df (I believe in your case it was 120 (total samples) - 6 (sites) - 6 (est. fixed parameters) = 108, or run SAS with Satterthwaite and see what it says df should be. If you then have a t statistic you can estimate its (two-tailed) p value with pt(tstat,df,lower.tail=FALSE)*2 [you can try this on the lme values and see if you get the right answers] Ben Bolker