Hi Chris,

`I think MCMCglmm is probably giving you the right answer. There are huge`

`chunks of the phylogeny that are either egg-laying and live-bearing. The`

`non-phylogenetic model shows a strong relationship between reproductive`

`mode and depth, and that might be causal or it might just be because`

`certain taxa tend to live at greater depths and *happen to have* the`

`same reproductive mode. There's not much information in the phylogenetic`

`spread of reproductive modes to distinguish between these hypotheses,`

`hence the wide confidence intervals. Just to be sure can you`

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`a) just perform independent contrasts (not really suitable for binary`

`data, but probably won't give you an answer far off the truth and is a`

`nice simple sanity check).`

b) using MCMCglmm (not MCMCglmmRAM) fit

`prior.dep2<-=list(R=list(V=diag(1), fix=1), G=list(G1=list(V=diag(1),`

`nu=0.002)))`

dep2<-MCMCglmm(parity~log.med.depth, random=~animal, rcov=~units, pedigree=shark.tree, data=traits, prior=prior.dep2, pr=TRUE, pl=TRUE, family="threshold") an send me the summary and hist(dep2$Liab) Cheers, Jarrod On 16/12/2016 07:02, Jarrod Hadfield wrote:

Hi Chris,I think ngen in threshbayes is not the number of full iterations (i.e.a full update of all parameters), but the number of full iterationsmultiplied by the number of nodes (2n-1). With n=600 species thismeans threshbayes has only really done about 8,000 iterations (i.e.about 1000X less than MCMCglmm). Simulations suggest threshbayes isabout half as efficient per full iteration as MCMCglmm which meansthat it may have only collected 0.5*1132/1200 = 0.47 effective samplesfrom the posterior. The very extreme value and the surprisingly tightcredible intervals (+/-0.007) also suggest a problem.*However*, the low effective sample size for the covariance in theMCMglmm model is also worrying given the length of chain, and maypoint to potential problems. Are egg-laying/live-bearing scatteredthroughout the tree, or do they tend to aggregate a lot? Can you sendme the output from:prior.dep<-=list(R=list(V=diag(1)*1e-15, fix=1),G=list(G1=list(V=diag(1), fix=1)))dep<-MCMCglmm(parity~log.med.depth, random=~animal, rcov=~units, pedigree=shark.tree, reduced=TRUE, data=traits, prior=prior2, pr=TRUE, pl=TRUE, family="threshold") summary(dep)summary(glm(parity~log.med.depth, data=traits,family=binomial(link=probit)))Cheers, Jarrod On 15/12/2016 20:59, Chris Mull wrote: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

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