Hi Chris,
OK - stick with the RAM model, the h2 is so high you will run into
numerical issues otherwise. In the two-trait model you might want to add
in us(at.level(trait,1)):units into the random effects (make sure it is
not the last term in the random formula) in case log.dep has a h2
substantially less than 1. Having a multi-level response will help with
power so I would go for it. threshBayes does handle ordinal responses
but you would probably have to run it for a VERY long time to sample the
posterior adequately.
Cheers,
Jarrod
On 16/12/2016 07:11, Chris Mull wrote:
Hi Jarrod,
I hadn't appreciated that the clustering of reproductive modes on the
tree might limit out ability to detect some of these relationships.
This is in fact a step in testing reproduction as an ordinal variable
(egg-laying, lecithotrophic live-bearing, and matrotrophic
live-bearing) which follows gradients of depth and latitude, and
threshBayes cannot handle ordinal variables. Perhaps treating the data
this way will help given more transitions. I have run the model in
MCMCglmm and have appended the summary and attached the histogram of
the liabilities. Thanks so much for your help with this...
summary(dep2)
Iterations = 3001:12991
Thinning interval = 10
Sample size = 1000
DIC: 31.2585
G-structure: ~animal
post.mean l-95% CI u-95% CI eff.samp
animal 82.18 35.88 140.1 6.266
R-structure: ~units
post.mean l-95% CI u-95% CI eff.samp
units 1 1 1 0
Location effects: parity ~ log.med.depth
post.mean l-95% CI u-95% CI eff.samp pMCMC
(Intercept) 0.4250 -13.5697 13.7913 28.54 0.946
log.med.depth -0.3601 -4.4399 3.8022 16.48 0.862
On Thu, Dec 15, 2016 at 11:10 PM, Jarrod Hadfield <j.hadfi...@ed.ac.uk
<mailto:j.hadfi...@ed.ac.uk>> wrote:
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
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
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.
**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
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
--
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 <mailto:e-mail%3acm...@sfu.ca>
www.christophermull.weebly.com <http://www.christophermull.weebly.com>
www.earth2ocean.org <http://www.earth2ocean.org>
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
_______________________________________________
R-sig-phylo mailing list - R-sig-phylo@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/
