1

If I use lme for mixed ANOVA as follow

lme_model=lme(dv ~ between*within, data frame, random=~1|ID, correlation=corCompSymm(form=~1|ID))

how can I perform between group comparison for every level of within factor?

This great answer advice to use anova with L argument. But I can't understand how.

Thanks for the help

Community
  • 1
sviter
  • 131
  • 1
  • 10
  • 1
    Did you try the `lsmeans` and `multcomp` packages ? – Stéphane Laurent Feb 11 '13 at 21:55
  • Thank you. `Lsmeans` is what I am looking for. `multcomp` with `summary(glht(lme_model, linfct=mcp(between = "Tukey")), test = adjusted(type = "fdr"))` lead to warning `In mcp2matrix(model, linfct = linfct) : covariate interactions found -- default contrast might be inappropriate`. Is `lsmeans` concerned about covariate interactions? – sviter Feb 12 '13 at 08:35
  • I think there are significant interactions in your fitting and then you get a warning about comparing main effects in such a situation because this comparison is possibly meaningless. – Stéphane Laurent Feb 12 '13 at 09:23
  • Sorry, but I don't understand your last thought. I decided to use `lsmeans(lme_model, pairwise ~ between | within)`. – sviter Feb 12 '13 at 09:36
  • 1
    When you have a model with two factors, say A and B, it is not pertinent to assess the effect of these factors when the interaction A:B is significant. I think `multcomp`and/or `lsmeans` returns a warning in such situations. – Stéphane Laurent Feb 12 '13 at 09:40

1 Answers1

1

If you are looking for post-hoc tests you should find additional functions in R such as TukeyHSD for example.

If you want to perform planned comparisons with your anova, I am used to enter directly contrast codes in the linear model.

I found a simple example here

Cheatboy2
  • 33
  • 2