ZIP MCMCglmm - how to increase effective sample size?
Thanks, Pierre! Will do that, too! Best, DNM
From: Pierre de Villemereuil <pierre.de.villemereuil at mailoo.org>
Sent: Wednesday, November 1, 2017 1:25 PM
To: dani
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] ZIP MCMCglmm - how to increase effective sample size?
Sent: Wednesday, November 1, 2017 1:25 PM
To: dani
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] ZIP MCMCglmm - how to increase effective sample size?
You can look at the auto-correlation to guess how many more iterations are needed to increase your sample size. Something like: library(coda) autocorr.diag(mj$Sol) autocorr.diag(mj$VCV) Cheers, Pierre On Wednesday, 1 November 2017 20:14:15 NZDT dani wrote: > Hello Pierre and list members, > > > Thank you so much! The analysis with the new prior worked:) However, the effective samples are still small, so I am trying again the new prior with more iterations - will report back how my effective samples change. > > > Best regards, everyone! > > DNM > > > ________________________________ > From: Pierre de Villemereuil <pierre.de.villemereuil at mailoo.org> > Sent: Tuesday, October 31, 2017 10:11 PM > To: dani > Cc: r-sig-mixed-models at r-project.org > Subject: Re: [R-sig-ME] ZIP MCMCglmm - how to increase effective sample size? > > Just some parentheses issue: > priori <- list(R=list(V=diag(2), nu=1, fix=2), > G=list(G1=list(V=diag(2)/2, nu=2, alpha.mu=c(0,0), alpha.V=diag(2)*1000), > G2=list(V=diag(2)/2, nu=2, alpha.mu=c(0,0), alpha.V=diag(2)*1000))) > > Cheers, > Pierre > > Le mercredi 1 novembre 2017, 17:18:05 NZDT dani a ?crit : > > Hi Pierre, > > > > I tried using the new prior you suggested and I got this error: > > > > Error in MCMCglmm(y ~ trait - 1 + at.level(trait,1):(x1+: > > prior list should contain elements R, G, and/or B only > > > > I am not sure what to do about this:) > > Any advice would be very much appreciated. > > > > Thanks, > > DaniNM > > <http://aka.ms/weboutlook> > > > > > > ________________________________ > > From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> on behalf of Pierre de Villemereuil <pierre.de.villemereuil at mailoo.org> > > Sent: Tuesday, October 31, 2017 2:08 PM > > To: r-sig-mixed-models at r-project.org > > Subject: Re: [R-sig-ME] ZIP MCMCglmm - how to increase effective sample size? > > > > Hi, > > > > There are about three way to increase effective sample size: > > - increase the number of iterations > > - use a prior with better properties > > - change your model somehow (you might not always want to use that one...) > > > > In your case, using a slightly more informative prior and the extended parameters prior might help? Something like: > > > > priori <- list(R=list(V=diag(2), nu=1, fix=2), > > G=list(G1=list(V=diag(2)/2, nu=2, alpha.mu=c(0,0), alpha.V=diag(2)*1000)), > > G2=list(V=diag(2)/2, nu=2, alpha.mu=c(0,0), alpha.V=diag(2)*1000)))) > > > > Hope this helps, > > Pierre. > > > > On Wednesday, 25 October 2017 18:45:46 NZDT dani wrote: > > > Dear list members, > > > > > > I need some advice regarding this ZIP MCMCglmm model: > > > > > > library(MCMCglmm) > > > > > > priori <- list(R=list(V=diag(2), nu=0.002,fix=2), > > > G=list(G1=list(V=diag(2), n=2),G2=list(V=diag(2), n=2))) > > > > > > mj <- MCMCglmm(y ~ trait - 1 + at.level(trait,1):(x1+x2+x3+x4+ x5 +x6+x7+ offset), > > > random = ~idh(trait):group1 + idh(trait):group2, > > > family = "zipoisson", > > > prior = priori, > > > rcov = ~idh(trait):units, > > > verbose=FALSE, > > > thin = 100, > > > burnin = 3000, > > > nitt = 103000, > > > saveX=TRUE, saveZ=TRUE, saveXL=TRUE, pr=TRUE, pl=FALSE, > > > data = s25h) > > > > > > summary(mj) > > > > > > # Iterations = 3001:102901 > > > # Thinning interval = 100 > > > # Sample size = 1000 > > > # > > > # DIC: 4811.791 > > > # > > > # G-structure: ~idh(trait):group1 > > > # > > > # post.mean l-95% CI u-95% CI eff.samp > > > # traity.group1 0.4307 0.1351 0.9281 10.17 > > > # traitzi_y. group1 4.3196 2.1216 7.4310 31.26 > > > # > > > # ~idh(trait):group2 > > > # > > > # post.mean l-95% CI u-95% CI eff.samp > > > # traity. group2 0.4233 0.2341 0.6781 30.81 > > > # traitzi_y. group2 3.5497 1.2365 6.1525 26.39 > > > # > > > # R-structure: ~idh(trait):units > > > # > > > # post.mean l-95% CI u-95% CI eff.samp > > > # traity.units 0.02393 0.002833 0.06621 10.58 > > > # traitzi_y.units 1.00000 1.000000 1.00000 0.00 > > > # > > > # Location effects: y ~ trait - 1 + at.level(trait, 1):(x1 + x2 + x3 + x4 + x5 + x6 + x7 + offset) > > > # > > > # post.mean l-95% CI u-95% CI eff.samp pMCMC > > > # traity -4.3823820 -6.1496186 -2.6424402 23.592 <0.001 *** > > > # traitzi_y 3.4696204 2.6430476 4.1392235 1.922 <0.001 *** > > > # at.level(trait, 1):x1 -0.0498043 -0.2192051 0.1097667 16.979 0.522 > > > # at.level(trait, 1):x2M -0.2088408 -0.4535085 0.0440055 8.727 0.088 . > > > # at.level(trait, 1):x31 0.1422342 -0.1473884 0.4199985 11.521 0.288 > > > # at.level(trait, 1):x4 0.0007054 -0.0030953 0.0043456 24.299 0.680 > > > # at.level(trait, 1):x5 0.1131704 0.0647184 0.1676469 26.621 <0.001 *** > > > # at.level(trait, 1):x6 -0.0128734 -0.0483344 0.0306350 13.599 0.588 > > > # at.level(trait, 1):x7 0.0102356 -0.0276141 0.0540893 40.746 0.680 > > > # at.level(trait, 1):offset 1.3511873 0.6963525 2.1299075 13.216 <0.001 *** > > > # --- > > > # Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 > > > > > > I would like to increase my effective samples, but I am not sure which way to go. I tried increasing the NITT to 503000, but the effective samples actually got worse. Is there anything else I could do? I plan on dropping some variables from the model, but if I were to proceed with the model above, what could I have done better? > > > > > > Thanks in advance! > > > DNM > > > <http://aka.ms/weboutlook> > > > > > > [[alternative HTML version deleted]] > > > > > > > _______________________________________________ > > R-sig-mixed-models at r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models R-sig-mixed-models Info Page - stat.ethz.ch<https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models> stat.ethz.ch Your email address: Your name (optional): You may enter a privacy password below. 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