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 iterations`

`multiplied by the number of nodes (2n-1). With n=600 species this means`

`threshbayes has only really done about 8,000 iterations (i.e. about`

`1000X less than MCMCglmm). Simulations suggest threshbayes is about half`

`as efficient per full iteration as MCMCglmm which means that it may have`

`only collected 0.5*1132/1200 = 0.47 effective samples from the`

`posterior. The very extreme value and the surprisingly tight credible`

`intervals (+/-0.007) also suggest a problem.`

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`*However*, the low effective sample size for the covariance in the`

`MCMglmm model is also worrying given the length of chain, and may point`

`to potential problems. Are egg-laying/live-bearing scattered throughout`

`the tree, or do they tend to aggregate a lot? Can you send me 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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