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

[Joanna L Baker]

Dear Jarrod,

Thank you very much for your response. Unfortunately I still have issues
with convergence even when the residual variance is fixed. Part of the
problem I was having was the specification for visualizing the posterior
distributions of the phylogenetic portion of the analysis. 

This may be a silly question, but what is the difference between:

plot(my2$VCV[,1])

plot(my2$VCV[,1]/(rowSums(my2$VCV)+pi^2/3)).

The two produce very obviously different graphs, and I was wondering
what exactly the second part of the latter plot is related to?

Even looking at the correct graph for the phylogenetic part of the
analysis, it seems I am having trouble with 'animal' converging, and
have massive issues with the variance. I have attached the posterior
distributions for both your example (PhyLogRegSIM.pdf) and my own data
(PhyLogRegDAT.pdf) to illustrate the problem.  This occurs even when
running the models for a longer time.
 
I much appreciate your time and advice on this matter. 

Thanks,
Joanna Baker


Below is the code for my model specification:

prior1=list(R=list(V=1, fix=1), G=list(G1=list(V=1, nu=1, alpha.mu=0,
alpha.V=1000)))

myAinv2<-inverseA(mytree)$Ainv
my2<-MCMCglmm(DV~IV, random=~animal, ginverse=list(animal=myAinv2), 
	family="categorical", prior=prior1, data=mydata,
	nitt=10000000,thin=1000,burnin=100000)

plot(my2$Sol)
plot(my2$VCV[,1]/(rowSums(my2$VCV)+pi^2/3))


c2<-((16*sqrt(3))/(15*pi))^2
my2.int<-my2$Sol/(sqrt(1+c2))
my2.int
posterior.mode(my2.int)
summary(my2.int)

-----Original Message-----
From: Jarrod Hadfield [mailto:j.hadfield at ed.ac.uk] 
Sent: 01 September 2012 20:06
To: Joanna L Baker
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Phylogenetic Logistic Regression + MCMCglmm

Hi,

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:

tree<-rcoal(100)
x<-rnorm(100)
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,
alpha.V=1000)))

# residual variance fixed at 1.


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

plot(m1$Sol)
# fixed effects should be zero and one

plot(m1$VCV[,1]/(rowSums(m1$VCV)+pi^2/3))

# 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.

Cheers,

Jarrod






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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