PCMMean | R Documentation |
Expected mean vector at each tip conditioned on a trait-value vector at the root
PCMMean( tree, model, X0 = model$X0, metaI = PCMInfo(NULL, tree, model, verbose = verbose), internal = FALSE, verbose = FALSE )
tree |
a phylo object with N tips. |
model |
an S3 object specifying both, the model type (class, e.g. "OU") as well as the concrete model parameter values at which the likelihood is to be calculated (see also Details). |
X0 |
a k-vector denoting the root trait |
metaI |
a list returned from a call to |
internal |
a logical indicating ig the per-node mean vectors should be returned (see Value). Default FALSE. |
verbose |
logical indicating if some debug-messages should printed. |
If internal is FALSE (default), then a k x N matrix Mu, such that Mu[, i]
equals the expected mean k-vector
at tip i, conditioned on X0
and the tree. Otherwise, a k x M matrix Mu containing the mean vector for each node.
# a Brownian motion model with one regime modelBM <- PCM(model = "BM", k = 2) # print the model modelBM # assign the model parameters at random: this will use uniform distribution # with boundaries specified by PCMParamLowerLimit and PCMParamUpperLimit # We do this in two steps: # 1. First we generate a random vector. Note the length of the vector equals PCMParamCount(modelBM) randomParams <- PCMParamRandomVecParams(modelBM, PCMNumTraits(modelBM), PCMNumRegimes(modelBM)) randomParams # 2. Then we load this random vector into the model. PCMParamLoadOrStore(modelBM, randomParams, 0, PCMNumTraits(modelBM), PCMNumRegimes(modelBM), TRUE) # create a random tree of 10 tips tree <- ape::rtree(10) PCMMean(tree, modelBM)
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