Description Usage Arguments Value Author(s) Examples
Shows all input files and settings used in a BPEC run.
1 2 3 4 |
bpecout |
R object from |
seqCountOrig |
The number of input sequences. |
seqLengthOrig |
The length of the input sequences. |
iter |
The number of MCMC iterations. |
ds |
The parsimony relaxation parameter. |
coordsLocs |
The input coordinate and observation file. |
coordsDims |
The input dimension (2 for purely geographical data). |
locNo |
The number of distinct sampling locations. |
locData |
The list of coordinates of each observation. |
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)
input(bpecout)
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