[R] hierarchical linear models, mixed models and lme

[Dieter Menne]

Nicolas Ris <Nicolas.Ris <at> sophia.inra.fr> writes:

> I am trying to analyse the data of the box 10.5 in the Biometry from 
> Sokal and Rohlf (2001) using R. This is a three-level nested anova with 
> equal sample size : 3 different treatments are compared ; 2 rats (coded 
> 1 or 2) / treatment are studied ; 3 preparations (coded 1, 2 or 3) / 
> rats are available ; 2 readings of the glycogen content  / preparations 
> are realised. Treatment is fixed whereas Rats (nested in Treatment)  and 
> Prep (nested in Rats) are random effects.
> 
> According to a previous discussion found in the R-help archives (January 
> 2007), I have tried the following formula :
> >  box105.lme<-lme(content~treatment, box105.gd, random=~1|rats/prep)
> However,  the formula summary(box105.lme) gives wrong estimates for the 

Since your factors are codes as numbers, the first thing I would check if these
are really factors in your data frame. If these are numeric, you will get a
regression and degrees of freedom are totally off. Try

box105.gd$rats = as.factor(box105.gd$rats)
box105.gd$prep = as.factor(box105.gd$prep)

I am sure there will be some differences to Sokal/Rohlf afterwards, but you
should come closer. Please post a complete, self-running examples if you have
more questions.

Dieter