Comparison of crossed ranom effects: lmer vs. MCMCglmm
Thanks Jarrod. Just to be on the safe side, MCMCglmm indeed fits two independent random effects in the "mcmc"-specification? The different results emerge because the MCMC-Approach treat the variance components as random variables that capture more of the skewness? It is often claimed that mixed models fitted via Maximum Likelihood underestimate the random effect variance. Best regards, Linus Holtermann Hamburgisches WeltWirtschaftsInstitut gemeinn?tzige GmbH (HWWI) Heimhuder Stra?e 71 20148 Hamburg Tel +49-(0)40-340576-336 Fax+49-(0)40-340576-776 Internet: www.hwwi.org Email: holtermann at hwwi.org Amtsgericht Hamburg HRB 94303 Gesch?ftsf?hrer: PD Dr. Christian Growitsch | Prof. Dr. Henning V?pel Prokura: Dipl. Kauffrau Alexis Malchin Umsatzsteuer-ID: DE 241849425
Von: Jarrod Hadfield [j.hadfield at ed.ac.uk]
Gesendet: Montag, 19. Januar 2015 19:25 An: Linus Holtermann Cc: r-sig-mixed-models at r-project.org Betreff: Re: [R-sig-ME] Comparison of crossed ranom effects: lmer vs. MCMCglmm Hi Linus, The point estimates are almost identical if the posterior mode is used: hist(mcmc$VCV[,"plate"], breaks=30) abline(v=VarCorr(ml)[["plate"]][1], col="red") The posterior mean (which is reported in the summary) is often not a good measure of central tendency for variance components because of the skew. Posterior modes have high Monte Carlo error though. Cheers, Jarrod Quoting Linus Holtermann <holtermann at hwwi.org> on Mon, 19 Jan 2015 18:39:52 +0100: > Hello, > > I read that lmer can handle independent (often labelled as crossed) > random effets in mixed models. It seems to be possible with MCMCglmm > as long as groups for the random effects are uniquely labelled. I > use the "Penicllin" data in the lme4-package to compare both > approaches: > > library(lme4) > library(MCMCglmm) > > str(Penicillin) > attach(Penicillin) > > ml <- lmer(diameter~ 1 + (1|plate)+ (1|sample)) > summary(ml) > > mcmc <- MCMCglmm(diameter~ 1, random=~ plate + sample,verbose=F, > nitt=110000,burn=10000,thin=10,data=Penicillin) > summary(mcmc) > > Why are the result for the plate-variance differ by a large amount? > Is it because MCMCglmm applies Gibbs sampling? Or is MCMCglmm doing > something else here, instead of fitting independent random effects? > > > Best regards, > > > Linus Holtermann > Hamburgisches WeltWirtschaftsInstitut gemeinn?tzige GmbH (HWWI) > Heimhuder Stra?e 71 > 20148 Hamburg > Tel +49-(0)40-340576-336 > Fax+49-(0)40-340576-776 > Internet: www.hwwi.org > Email: holtermann at hwwi.org > > Amtsgericht Hamburg HRB 94303 > Gesch?ftsf?hrer: PD Dr. Christian Growitsch | Prof. Dr. Henning V?pel > Prokura: Dipl. Kauffrau Alexis Malchin > Umsatzsteuer-ID: DE 241849425 > _______________________________________________ > R-sig-mixed-models at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models > > -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.