Dear All,
I would like to do a repeated measures analyis of a 4x6x2 factorial design with subject as a random variable.
According to the lme documetation, this is the way to do it: > anova(exp2.lme<-lme(RTs~rot*fu*fig, random = ~1|sub, data=exp2))
Yet, this gives a table in which all denDFs are identical. This does not seem right.
When I specify the test as
> anova(exp2.lme<-lme(RTs~rot*fu*fig, random = ~1|sub/rot, data=exp2))
I get the right denDF value for the factor 'rot' only, theoutput looks like this:
numDF denDF F-value p-value (Intercept) 1 8582 224052069 <.0001 rot 3 45 3 0.0356 fu 5 8582 81 <.0001 fig 1 8582 630 <.0001 rot:fu 15 8582 0 0.9565 rot:fig 3 8582 1 0.3089 fu:fig 5 8582 32 <.0001 rot:fu:fig 15 8582 1 0.8884
Is there a way to specify that subjects are a random factor over all variables?
i.e. the lme equivalent of exp2.aov<-aov(RTs~fig*rot*fu+Error(sub/(fig+rot+fu)), data=exp2)
I am new to R and hope this question is not too much of a 'newby' one...
Sincerely,
Gijs Plomp
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