Description Usage Arguments Value Author(s) References See Also Examples
Performs a bayesian two-dimensional ancestral state estimation with single values as input (contrast with rase
) according to a Brownian Motion model of trait evolution (or dispersal for phylogeography). It uses Gibbs sampling to approximate the posterior distribution.
1 2 |
tree |
phylogenetic tree of class |
values |
2-dimensional trait values (e.g., coordinates for phylogeography). Should be a |
niter |
number of MCMC iterations. By default |
logevery |
iteration cycle to print current iteration. By default |
sigma2_scale |
optional. Window proposal for sigma2x & sigma2y. |
screenlog |
if |
params0 |
optional. A vector of initial parameter values in the following order: x ancestors, y ancestors, sigma2x and sigma2y. If |
returns a matrix where every column represents one parameter. The first columns (i.e., nX_x
) give the ancestral locations for trait x in the order of nodes in the tree (see the phylo
class for details), followed by the ancestral locations of trait y (i.e., nX_y
), and the rate parameter in x (sigma2x
) and y (sigma2y
).
Forrest Crawford, Ignacio Quintero
Quintero, I., Keil, P., Jetz, W., Crawford, F. W. 2015 Historical Biogeography Using Species Geographical Ranges. Systematic Biology. doi: 10.1093/sysbio/syv057
For the maximum likelihood version see point.like.bm
; for the incorporation of polygon uncertainty see rase
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ### Create some data to be analyzed
# Number of taxa
ntaxa <- 10
# Known parameters
mean_x <- 0
mean_y <- 0
sigma_x <- 1
sigma_y <- 1
# Create a random tree
tree <- ape::rtree(n = ntaxa)
# Create random data according to tree structure
x_locs <- as.numeric(mvtnorm::rmvnorm(1, rep(mean_x,ntaxa), sigma=sigma_x*vcv(tree)))
y_locs <- as.numeric(mvtnorm::rmvnorm(1, rep(mean_y,ntaxa), sigma=sigma_y*vcv(tree)))
values = list(x = x_locs, y = y_locs)
## Not run:
# run bm_ase for 10 iterations
bm_results = bm_ase(tree, values, niter = 10)
## End(Not run)
|
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