MCMCglmm and phylogeny - huge variance problem
Hi, There seems to be something amiss with the tree. Do you expect there to be branch lengths? Do you get warning messages outside of MCMCglmm if you use compute.brlen on the phylogeny? Jarrod
On Wed, 2011-07-06 at 16:45 +0200, Szymek Drobniak wrote:
Dear mixed modellers,
I'm trying to fit a comparative meta-analysis and everything's fine
but I'm getting several warnings (some of which I don't quite
understand and can't tell if they're causing the problem) and a weird
result with huge animal (ergo phylogeny-related) variance. Below is
the model and the prior, and of course the output with summary. Any
idea if this huge variance might be due to these warnings (relating
to the tree I guess) or due to the data (which has has high variance
anyway). Could prior expansion fix these problems?
Szymek
#########(code)
prior.fil <- list(R=list(V=1,n=0.002),
G=list(G1=list(V=1,n=0.002),G2=list(V=1,n=0.002),G3=list(V=1,n=0.002)))
meta.fil <- MCMCglmm(Zr~1, random=~ref+repeat+animal, prior=prior.fil,
pedigree=tree, mev=data$mev, data=data, verbose=F,
nitt=3000000, burnin=1000000, thin=800)
###########(summary of the model)
Iterations = 100001:4999501
Thinning interval = 500
Sample size = 9800
DIC: 256.8579
G-structure: ~ref
post.mean l-95% CI u-95% CI eff.samp
ref 0.03544 0.0002057 0.1017 9800
~repeat.
post.mean l-95% CI u-95% CI eff.samp
repeat. 0.01121 0.0001536 0.04053 9800
~animal
post.mean l-95% CI u-95% CI eff.samp
animal 3.765e+15 1.450e+15 4.504e+15 9507
R-structure: ~units
post.mean l-95% CI u-95% CI eff.samp
units 0.2345 0.1703 0.3107 9502
Location effects: Zr ~ 1
post.mean l-95% CI u-95% CI eff.samp pMCMC
(Intercept) -740.7 -453419.4 464404.3 9800 0.988
############(warnings)
Warning messages:
1: In inverseA(pedigree, nodes = nodes, scale = scale) :
no branch lengths: compute.brlen from ape has been used
2: In `levels<-`(`*tmp*`, value = c("JJJ", "DDD", "III", "GGG", "", :
duplicated levels will not be allowed in factors anymore
3: In MCMCglmm(Zr ~ 1, random = ~ref + repeat. + animal, prior = prior.fil, :
some combinations in animal do not exist and 50 missing records have
been generated
4: In `[<-.data.frame`(`*tmp*`, dim(data)[1] -
(dim(missing.combinations)[1] - :
provided 50 variables to replace 2 variables
5: In `[<-.factor`(`*tmp*`, iseq, value = c("JJJ", "1", "JJJ", "1", :
invalid factor level, NAs generated
6: In `levels<-`(`*tmp*`, value = c("JJJ", "DDD", "III", "GGG", "", :
duplicated levels will not be allowed in factors anymore
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