Description Usage Arguments Value Author(s) Examples
Posterior output for the tree model.
1 2 3 | output.tree(bpecout)
## S3 method for class 'bpec'
output.tree(bpecout)
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bpecout |
R object from |
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. |
Ioanna Manolopoulou & Axel Hille
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## 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)
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