I am quite new to R so apologies if I fail to ask properly. I have done a test comparing bat species richness in five habitats as assessed by three methods. I used a linear mixed model in lme4 and got habitat, method and the interaction between the two as significant, with the random effects explaining little variation.
I then ran Tukey's post hoc tests as pairwise comparisons in three ways:
Firstly in lsmeans:
lsmeans(LMM.richness, pairwise~Habitat*Method, adjust="tukey")
Then in ‘agricolae’:
tx <- with(diversity, interaction(Method, Habitat)) amod <- aov(Richness ~ tx, data=diversity) library(agricolae) interaction <-HSD.test(amod, "tx", group=TRUE) interaction
Then in ghlt 'multcomp':
summary(glht(LMM.richness, linfct=mcp(Habitat="Tukey"))) summary(glht(LMM.richness, linfct=mcp(Method="Tukey"))) tuk <- glht(amod, linfct = mcp(tx = "Tukey")) summary(tuk) # standard display tuk.cld <- cld(tuk) # letter-based display opar <- par(mai=c(1,1,1.5,1)) par(mfrow=c(1,1)) plot(tuk.cld) par(opar)
I got somewhat different levels of significance from each method, with ghlt giving me the greatest number of significant results and lsmeans the least. When I ran this on a generalized linear model of abundance data from the same study, ghlt was then the most conservative. All the results from all packages make sense based on the graphs of the data.
Can anyone tell me if there are underlying reasons why these tests might be more or less conservative, whether in any case I have failed to specify anything correctly or whether any of these tests are not suitable for linear mixed models? It seems from the code to me that the 'agricolae' and 'multcomp' approaches fit their own model rather than the one I have specified, but I found these codes as suggested post-hoc tests on forums.