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>
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