Thanks Bert, Will post on r-sig-mixed-models list. Can't help it being in html though as i sent the query via -email.
Cheers Claire > Date: Thu, 22 May 2014 09:29:44 -0700 > Subject: Re: [R] Post-hoc tests on linear mixed model give mixed results. > From: gunter.ber...@gene.com > To: c.word...@live.com > CC: r-help@r-project.org > > Wrong list! This does not concern R programming. > > Post on the r-sig-mixed-models list instead in **PLAIN TEXT** rather than > html. > > Cheers, > Bert > > Bert Gunter > Genentech Nonclinical Biostatistics > (650) 467-7374 > > "Data is not information. Information is not knowledge. And knowledge > is certainly not wisdom." > H. Gilbert Welch > > > > > On Thu, May 22, 2014 at 6:52 AM, Claire <c.word...@live.com> wrote: > > 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 > > > > [[alternative HTML version deleted]] > > > > > > ______________________________________________ > > R-help@r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > [[alternative HTML version deleted]]
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
