[R-sig-ME] post-hoc comparison on interaction term in lme using contrasts

[jersa at centrum.cz]

Dear R experts,

I have a significant interaction term in my lme model and I was searching for a way, how to perform post hoc test.
I was told that direct Tukey test of interaction using glht within lme gives unreliable results and should be avoided.
I have searched for solutions and found out only a recomendation to build up a contrast matrix using function contrast.

I have 7 plant species and 4 treatments which significantly interact and I used folowing syntax to build the matrix

> cm<-contrast(MY MODEL, a=list(species=c("A","B","C","D","E","F","G"), treat=c("A","B","C")),
b=list( species=c("A","B","C","D","E","F","G"), treat=c("D","D","D")))

> cmtrx <- cm$X
> ttgl<-glht( MY MODEL,lin=cmtrx)
> confint(ttgl)

the outcome is 21 rows numbered from 1 to 21 (corresponds to 7 species * 3 treatments), and it is not clear to me, how the combinations are ordered?
i.e. 1 == 0  is for species A : treat A against species A : treat D
2 == 0   is for species A : treat B against species A : treat D

the outcome is estimated values plus confidence intervals such as
               Estimate       lwr            upr       
1 == 0  -0.2935212 -0.4847410 -0.1023014
2 == 0  -0.4448065 -0.6360263 -0.2535867
..........

I suppose this needs to be further digested by some function from multcomp package to estimate significance of these tests.
I tried to extend ttgl to ttgl<-glht( MY MODEL,lin=cmtrx(tension = "Tukey")) but that does not work.
summary(ttgl)$test$pvalues also has not yield what I need.
Can you please advise me how to go on?

Or is there easier way how to deal with interactions in lme?

Thank you very much for any help.
Best from Jana