[R-sig-ME] MCMCglmm and phylogeny - huge variance problem

[Jarrod Hadfield]

Hi, 

There definitely seems to be a problem but its hard to figure out what
it is. Could you attach the tree (and data)?


Jarrod


On Wed, 2011-07-06 at 17:27 +0200, Szymek Drobniak wrote:
> Hi,
> 
> I tried compute.brlen but this works fine and computes branch lengths
> without any problem. I've used such trees before (without branch
> lengths, just topology) and they worked. Is it possible that this
> "duplicated levels" thing messes with everything?
> 
> sz.
> 
> On 6 July 2011 17:11, Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:
> > 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
> >>
> >
> >
> >
> > --
> > The University of Edinburgh is a charitable body, registered in
> > Scotland, with registration number SC005336.
> >
> >
> 
> 
> 



-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.