Dear all,
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. 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 post-hoc tests are not suitable for
linear mixed models?
Thankyou for your time,
Claire
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