Hi Everyone,

I’m trying to figure out how to get R to analyze this experiment properly.  I have a series of subjects each with two legs.  Within each leg there are two bones that I am interested in.  There are also two treatments that I am interested in.  That results in four different combinations of treatments.  Obviously, since the subjects only have two legs, they can’t receive each combination of treatment.  The groups are unbalanced so I can’t use aov.  As I understand it, lme should work but I am having a tough time figuring out what to use as a random term.  Is this correct?

x.lme <- lme(effect~bone*treatment1*treatment2, random= ~1|subject, data=x)

anova(x.lme)

I’ve tried a number of different random terms and get very similar results (and even get similar results running aov) but I’d like to know what the proper way of doing it is.

My next question is how to do post hoc tests on this.  One website recommends something like this from multcomp: summary(glht(am2,linfct=mcp(myfactor="Tukey")))

But apparently this doesn’t work with interactions.  With my data, I get a significant three-way interaction.  If I run TukeyHSD after an aov (ignoring the repeated measures) I would get a series of p values for all the different combinations of treatments and bones.  Is there a way to do this with lme?

Thank you in advance for any help.

Doug Bourne
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