PCMBase (version 1.2.10)

PCMVar: Expected variance-covariance matrix for each couple of tips (i,j)

Description

Expected variance-covariance matrix for each couple of tips (i,j)

Usage

PCMVar(tree, model, W0 = matrix(0, PCMNumTraits(model),
  PCMNumTraits(model)), SE = matrix(0, PCMNumTraits(model),
  PCMTreeNumTips(tree)), metaI = PCMInfo(NULL, tree, model, verbose =
  verbose), internal = FALSE, verbose = FALSE)

Arguments

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).

W0

a numeric matrix denoting the initial k x k variance covariance matrix at the root (default is the k x k zero matrix).

SE

a k x N matrix specifying the standard error for each measurement in X. Alternatively, a k x k x N cube specifying an upper triangular k x k factor of the variance covariance matrix for the measurement error for each node i=1, ..., N. Default: matrix(0.0, PCMNumTraits(model), PCMTreeNumTips(tree)).

metaI

a list returned from a call to PCMInfo(X, tree, model, SE), containing meta-data such as N, M and k. Alternatively, this can be a function object that returns such a list, e.g. the functionPCMInfo or the function PCMInfoCpp from the PCMBaseCpp package.

internal

a logical indicating if the per-node variance-covariances matrices for the internal nodes should be returned (see Value). Default FALSE.

verbose

logical indicating if some debug-messages should printed.

Value

If internal is FALSE, a (k x N) x (k x N) matrix W, such that k x k block W[((i-1)*k)+(1:k), ((j-1)*k)+(1:k)] equals the expected covariance matrix between tips i and j. Otherwise, a list with an element 'W' as described above and a k x M matrix element 'Wii' containing the per-node variance covariance matrix for each node: The k x k block Wii[, (i-1)*k + (1:k)] represents the variance covariance matrix for node i.

Examples

# NOT RUN {
# 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)
covMat <- PCMVar(tree, modelBM)
# }