[R-sig-ME] Phylogenetic Logistic Regression + MCMCglmm

Jarrod Hadfield j.hadfield at ed.ac.uk
Sat Sep 1 21:06:22 CEST 2012


The most likely reason is that you have not fixed the non-identified  
residual variance, although there are other possibilities. Here is an  
example with a simulated tree and binary data:

l<-rbv(tree, 1, nodes="TIPS")+x+rnorm(100)
y<-rbinom(100, 1, plogis(l))

dat<-data.frame(y=y, x=x, species=tree$tip.label)

prior1=list(R=list(V=1, fix=1), G=list(G1=list(V=1, nu=1, alpha.mu=0,  

# residual variance fixed at 1.

m1<-MCMCglmm(y~x, random=~species, ginverse=list(species=Ainv),  
family="categorical", prior=prior1, data=dat)

# fixed effects should be zero and one


# phylogenetic ICC should be 1/(2+pi^2/3)=0.189. Note wdie credible  
intervals - you need v.large phylogenies to get precise estimates with  
binary data.



Quoting Joanna L Baker <J.L.Baker at 2008.hull.ac.uk> on Fri, 31 Aug 2012  
11:39:08 +0100:

> Hello all,
> I am currently trying to use MCMCglmm to carry out a phylogenetic
> logistic regression, with a single binary response and one or more
> continuous or categorical predictors. However, I am having issues with
> the phylogenetic component, 'animal', converging. This occurs when I use
> different datasets, with high, moderate and low phylogenetic signal.
> I have tried improper (with the expected issues), proper, and
> alpha-expanded priors but these do not seem to have much effect on the
> results. I should note that I have no issues with convergence when my
> response is a continuous variable.
> Does anybody have a worked example with data and tree that I could use
> to get started with phylogenetic logistic regression or could somebody
> point me in the direction of any published work that has used the
> package for such an analysis successfully? I'll much appreciate any help
> with this.
> Thanks again,
> Joanna Baker
> University of Hull
> Cottingham Road
> Hull
> East Yorkshire
> j.l.baker at 2008.hull.ac.uk

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