View source: R/utilities-misc.R
load.rjmcmc | R Documentation |
generating a pooled and thinned sample of posterior estimates from rjmcmc
load.rjmcmc(x, phy = NULL, burnin = NULL, thin = NULL, ...)
x |
a vector of directory names (character strings) in which samples generated by geiger are found |
phy |
a phylogenetic tree of class 'phylo' for which to compile results; if NULL, the tree stored within each generated |
burnin |
the proportion of each chain to be removed as burnin |
thin |
an integer specifying the thinning interval for chains |
... |
arguments to be passed to other functions (has no effect in the present context) |
This function provides a means to compile results from at least a single MCMC run, and is especially useful in pooling of multiple independent runs. In cases where MCMC chains were sampled
using a set of (different) trees, the argument phy
can be used to summarize compiled results against a single summary tree. Branchwise samples are stored with unique edge identifiers
(character strings) that are more reliable than the use of numeric node-identifiers (see digest
for the function used to generate these unique edge labels).
If it is desired to run analyses for the same trait data across a set of trees (see Examples), it is strongly recommended that the set of trees be sent to rjmcmc.bm
as a multiPhylo
object (see read.tree
).
an object of class rjmcmc
or rjmcmcmc
JM Eastman
rjmcmc.bm
sal=get(data(caudata))
a<-sim<-sal$phy
bl=c(386,387,388,183,184,185,186)
mod=match(bl, sim$edge[,2])
sim$edge.length[mod]=sim$edge.length[mod]*64
dat=rTraitCont(sim)
while(1){
b=a
b$tip.label[183:186]=sample(b$tip.label[183:186])
if(!all(a$tip.label==b$tip.label)) break()
}
trees=list(a=a,b=b, c=ladderize(a, right=TRUE), d=ladderize(a, right=FALSE))
class(trees)="multiPhylo"
rjmcmc.bm(trees, dat, ngen=1e3, type="rbm")
res=load.rjmcmc(paste("relaxedBM", names(trees), sep="."), phy=trees$d, burnin=0.25)
plot(res, par="shifts", show.tip=FALSE, edge.width=2.5)
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