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

[jersa at centrum.cz]

Dear Russ, that was very helpful! Works perfect. Thank you very much. Best from Jana

______________________________________________________________
> Od: "Lenth, Russell V" <russell-lenth at uiowa.edu>
> Komu: "r-sig-mixed-models at r-project.org" <r-sig-mixed-models at r-project.org>
> Datum: 15.01.2014 15:30
> Předmět: Re: [R-sig-ME] post-hoc comparison on interaction term in lme, using contrasts
>
>The lsmeans package might be helpful here.
>
>You can visualize the predictions via an interaction-plot:
>
>    library(lsmeans)
>    lsmip(MY_MODEL, treat ~ species)
>
>It looks like you want the treatment comparisons A-D, B-D, C-D for each
>species -- is that right? If so, it can be done using
>
>    lsmeans(MY_MODEL, trt.vs.ctrl ~ treat | species, ref = 4)
>
>Russ
>
>Russell V. Lenth  -  Professor Emeritus
>Department of Statistics and Actuarial Science   
>The University of Iowa  -  Iowa City, IA 52242  USA   
>Voice (319)335-0712 (Dept. office)  -  FAX (319)335-3017
>
>On 1/15/2014 5:02 AM, r-sig-mixed-models-request at r-project.org wrote:
>> 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
>
>