Hi All,
I am trying to look at the correlated evolution of traits using the
threshold model as implemented in phytools::threshBayes (Revell 2014) and
MCMCglmmRAM (Hadfield 2015). My understanding from Hadfield 2015 is that
the reduced animal models should yeild equivalent results, yet having run
both I am having trouble reconciling the results. I have a tree covering
~600 species of sharks. skates, and rays and am interested in testing for
the correlated evolution between reproductive mode (egg-laying and
live-bearing) with depth. When I test for this correlation using
phytools:threshBayes there is a clear result with a high correlation
coefficient (-0.986) as I would predict. When I run the same analysis using
MCMCglmmRAM I get a very different result, with a low degree of covariation
and CI crossing zero (-0.374; -3.638 - 3.163 95%CI). Both models are run
for 10,000,000 generations with the same bunr-in and sampling period. I
have looked at the trace plots for each model using coda and parameter
estimates and likelihodd . I'm not sure how to reconcile the differences in
these results and any advice would be greatly appreciated. I have appended
the code and outputs below.


#######################
#phytools::threshBayes#
#######################
>X<-shark.data[c("parity","log.med.depth")]
>X<-as.matrix(X)
>
>#mcmc paramters (also see control options)
>ngen<-10000000
>burnin<-0.2*ngen
>sample<-500
>
>thresh<-threshBayes(shark.tree,
>                    X,
>                    types=c("discrete","continuous"),
>                    ngen=ngen,
>                    control = list(sample=sample))

The return correlation is -0.986 (-0.99 - -0.976  95%HPD)


#############################
#MCMCglmm bivariate-gaussian#
#############################

>prior2=list(R=list(V=diag(2)*1e-15, fix=1), G=list(G1=list(V=diag(2),
nu=0.002, fix=2)))
>
>ellb.log.dep<-MCMCglmm(cbind(log.med.depth,parity)~trait-1,
>                       random=~us(trait):animal,
>                       rcov=~us(trait):units,
>                       pedigree=shark.tree,
>                       reduced=TRUE,
>                       data=traits,
>                       prior=prior2,
>                       pr=TRUE,
>                       pl=TRUE,
>                       family=c("gaussian", "threshold"),
>                       thin=500,
>                       burnin = 1000000,
>                       nitt = 10000000)
>
>summary(ellb.log.dep)

Iterations = 1000001:9999501
Thinning interval  = 500
Sample size  = 18000
DIC: 2930.751
G-structure:  ~us(trait):animal
                                        post.mean l-95% CI u-95% CI eff.samp
traitscale.depth:traitscale.depth.animal    16.965   15.092   18.864
 18000
traitparity:traitscale.depth.animal         -0.374   -3.638    3.163
1132
traitscale.depth:traitparity.animal         -0.374   -3.638    3.163
1132
traitparity:traitparity.animal               1.000    1.000    1.000
 0
R-structure:  ~us(trait):units
                                       post.mean l-95% CI u-95% CI eff.samp
traitscale.depth:traitscale.depth.units    16.965   15.092   18.864    18000
traitparity:traitscale.depth.units         -0.374   -3.638    3.163     1132
traitscale.depth:traitparity.units         -0.374   -3.638    3.163     1132
traitparity:traitparity.units               1.000    1.000    1.000        0
Location effects: cbind(scale.depth, parity) ~ trait - 1
                post.mean l-95% CI u-95% CI eff.samp pMCMC
traitscale.depth   0.12297 -3.63655  4.02005    18000 0.949
traitparity       -0.02212 -1.00727  0.93387    17058 0.971

Thanks for any and all input.

Cheers,
Chris

-- 
Christopher Mull
PhD Candidate, Shark Biology and Evolutionary Neuroecology
Dulvy Lab
Simon Fraser University
Burnaby,B.C.
V5A 1S6
Canada
(778) 782-3989
twitter: @mrsharkbrain
e-mail:cm...@sfu.ca
www.christophermull.weebly.com
www.earth2ocean.org

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