output.tree: Load location data

Description Usage Arguments Value Author(s) Examples

View source: R/bpec.R

Description

Posterior output for the tree model.

Usage

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output.tree(bpecout)
## S3 method for class 'bpec'
output.tree(bpecout)

Arguments

bpecout

R object from bpec.mcmc run

Value

clado

The MAP adjacency matrix for the tree in vectorised format: this means that for two haplotypes i,j, the (i,j)th entry of the matrix is 1 if the haplotypes are connected in the network and 0 otherwise.

levels

Starting from the root (level 0) all the way to the tips, the discrete depth for the Maximum A Posteriori tree plot.

edgeTotalProb

Posterior probabilities of each edge being present, i.e. corresponding to a mutation which occurred.

rootProbs

The posterior probability per chain that each haplotype was the root of the tree.

treeEdges

The set of edges (from and to haplotypes) of the Maximum A Posteriori haplotype tree (could be used in another program if needed).

rootLocProbs

Vector of posterior probabilities of each sampling location being the ancestral location.

migProbs

The posterior probability of 0...maxMig migrations.

Author(s)

Ioanna Manolopoulou & Axel Hille

Examples

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## if you want to load the `mini' example Brown Frog dataset
data(MacrocnemisRawSeqs)
data(MacrocnemisCoordsLocsMini)
rawSeqs <- MacrocnemisRawSeqs
coordsLocs <- MacrocnemisCoordsLocsMini

dims <- 3 #this is 2 if you only have geographical longitude/latitude. 
#(add 1 for each environmental or phenotypic covariate)
maxMig <- 2 #you will need a higher maximum number of migrations, suggest 7
ds <- 0 #start with ds=0 and increase to 1 and then to 2
iter <- 1000 #you will need far more iterations for convergence, start with 100,000
postSamples <- 100 #you will need at least 100 saved posterior samples

#run the Markov chain Monte Carlo sampler
bpecout <- bpec.mcmc(rawSeqs,coordsLocs,maxMig,iter,ds,postSamples,dims)
output.tree(bpecout)

BPEC documentation built on Aug. 30, 2018, 1:03 a.m.