Hello,
I'm using aov() to analyse changes in brain volume between males and
females. For every subject (there are 331 in total) I have 8 volume
measurements (4 different brain lobes and 2 different tissues
(grey/white matter)). The data looks like this:
Subject Sex Lobe Tissue Volume
subect1 1 F g 262374
subect1 1 F w 173758
subect1 1 O g 67155
subect1 1 O w 30067
subect1 1 P g 117981
subect1 1 P w 85441
subect1 1 T g 185241
subect1 1 T w 83183
subect2 1 F g 255309
subect2 1 F w 164335
subect2 1 O g 71769
subect2 1 O w 31879
subect2 1 P g 120518
subect2 1 P w 90334
subect2 1 T g 168413
subect2 1 T w 75790
subect3 0 F g 243621
subect3 0 F w 167025
subect3 0 O g 65998
subect3 0 O w 29758
subect3 0 P g 118026
subect3 0 P w 91903
subect3 0 T g 156279
subect3 0 T w 82349
....
I'm trying to see if there is an interaction Sex*Lobe*Tissue. This is
the command I use with aov():
mod1<-aov(Volume~Sex*Lobe*Tissue+Error(Subject/(Lobe*Tissue)),data.vslt)
Subject is a random effect, Sex, Lobe and Tissue are fixed effects;
Sex is an outer factor (between subjects), and Lobe and Tissue are
inner factors (within-subjects); and there is indeed a significant
3-way interaction.
I was told, however, that the results reported by aov() may depend on
the order of the factors
(type I anova), and that is better to use lme() or lmer() with type
II, but I'm struggling to find the right syntaxis...
To begin, how should I write the model using lme() or lmer()??
I tried this with lme():
gvslt<-groupedData(Volume~1|Subject,outer=~Val,inner=list(~Lobe,~Tissue),data=vslt)
mod2<-lme(Volume~Val*Lobe*Tissue,random=~1|Subject,data=gvslt)
but I have interaction terms for every level of Lobe and Tissue, and 8
times the number of DF I should have... (around 331*8 instead of
~331).
Using lmer(), the specification of Subject as a random effect is
straightforward:
mod2<-lmer(Volume~Sex*Lobe*Tissue+(1|Subject),data.vslt)
but I can't figure out the /(Lobe*Tissue) part...
Thank you very much in advance!
roberto
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