[R-sig-ME] effective sample size in MCMCglmm

dani orchidn at live.com
Tue Oct 10 19:44:37 CEST 2017


Hello everyone,


My question is:

do the effective samples I obtain in my MCMCglmm output (attached below) make sense?


I understand that the rule of thumb is to get effective samples of at least 100-1000. How should I tweak the thin, burnin, and the nitt specifications? My computer reaches its memory limit fast and I have barely been able to run the model below.


I have the following model:

k<-12 # number of fixed effects

prior2<-list(B=list(V=diag(k)*1e4, mu=rep(0,k)),
             R=list(V=1, nu=0),
             G=list(G1=list(V=1, nu=0),
                    G2=list(V=1, nu=0),
                    G3=list(V=1, nu=0)))

prior2$B$mu[k]<-1
prior2$B$V[k,k]<-1e-4

m3new <- MCMCglmm(y ~ f_newage_c+x2n+x8n+x9n+x5n+l_lfvcspo+x3n+x4n+x6n+x7n+offset,
                  random =~ studyid+class+idv(l_lfvcspn),
                  data   = wo1,
                  family = "poisson", prior=prior2,
                  verbose=FALSE,
                  thin   = 10,
                  burnin = 2000,
                  nitt   = 200000,
                  saveX=TRUE, saveZ=TRUE, saveXL=TRUE, pr=TRUE, pl=TRUE)


Iterations = 2001:199991
Thinning interval  = 10
Sample size  = 19800
DIC: 2930.006

G-structure:
~studyid
           post.mean  l-95% CI u-95% CI eff.samp
studyid    0.1053 1.814e-11   0.5757    81.12
~class
          post.mean l-95% CI u-95% CI eff.samp
class    0.7008  0.07577    1.207    382.1

~idv(l_lfvcspn)
                 post.mean l-95% CI u-95% CI eff.samp
l_lfvcspn.     705.7    37.33     2044    11852

R-structure:
~units
           post.mean l-95% CI u-95% CI eff.samp
units     2.516    1.809     3.23    336.6

Location effects: y ~ f_newage_c + x2n + x8n + x9n + x5n + l_lfvcspo + x3n + x4n + x6n + x7n + offset
                       post.mean   l-95% CI   u-95% CI eff.samp  pMCMC
(Intercept) -7.0395427 -7.5590206 -6.5187847    865.6 <5e-05 ***
f_newage_c   0.0099703 -0.0222981  0.0448880   3324.7 0.5615
x2nM        -0.1068377 -0.3782251  0.1678528   3760.2 0.4462
x8n1         0.4103047  0.0920875  0.7179638   3884.3 0.0099 **
x9n1        -0.2784715 -0.5975232  0.0495615   3337.9 0.0901 .
x5n         -0.0009378 -0.0064175  0.0044266   3528.4 0.7283
l_lfvcspo    0.4018810 -0.8271349  1.4468375  14536.6 0.4080
x3n          0.0789652 -0.0018683  0.1523108   3726.0 0.0438 *
x4n          0.0602655 -0.0643903  0.1859443   2711.9 0.3356
x6n         -0.0137132 -0.0728385  0.0449804   3489.7 0.6453

Thank you all so much,
Dani


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