Hi Roberto,
The other thing you can do --- if you don't wish to step across to lmer(),
where you will be able to exactly replicate the crossed-factor error
structure --- is stay with aov(... + Error()), but fit the factor you are
interested in last. Assume it is Sex. Then fit your model as
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 LobeTissue Volume
Hi Roberto,
but I can't figure out the /(Lobe*Tissue) part...
This type of nesting is easier to do using lmer(). To do it using lme() you
have to generate the crossed factor yourself. Do something like this:
##
tfac - with(vslt, interaction(Lobe, Tissue, drop=T))
str(tfac); head(tfac)
Thanks for answering Mark!
I tried with the coding of the interaction you suggested:
tfac-with(vlt,interaction(Lobe,Tissue,drop=T))
mod-lme(Volume~Sex*Lobe*Tissue,random=~1|Subject/tfac,data=vlt)
But is it normal that the DF are 2303? DF is 2303 even for the estimate of
LobeO that has only
Hi Roberto,
It's difficult to comment further on specifics without access to your data
set. A general point is that the output from summary(aov.object) is not
directly comparable with summary(lme.object). The latter gives you a summary
of a fitted linear regression model, not an analysis of
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