PCMAbCdEf {PCMBase}

Quadratic polynomial parameters A, b, C, d, E, f for each node

Description

An S3 generic function that has to be implemented for every model class. This function is called by PCMLik.

Usage

PCMAbCdEf(
  tree,
  model,
  SE = matrix(0, PCMNumTraits(model), PCMTreeNumTips(tree)),
  metaI = PCMInfo(NULL, tree, model, verbose = verbose),
  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).

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 tip i=1, ..., N. When SE is a matrix, the k x k measurement error variance matrix for a tip i is calculated as VE[, , i] <- diag(SE[, i] * SE[, i], nrow = k). When SE is a cube, the way how the measurement variance matrix for a tip i is calculated depends on the runtime option PCMBase.Transpose.Sigma_x as follows:

if getOption("PCMBase.Transpose.Sigma_x", FALSE) == FALSE (default):

VE[, , i] <- SE[, , i] %*% t(SE[, , i])

if getOption("PCMBase.Transpose.Sigma_x", FALSE) == TRUE:

VE[, , i] <- t(SE[, , i]) %*% SE[, , i]

Note that the above behavior is consistent with the treatment of the model parameters Sigma_x, Sigmae_x and Sigmaj_x, which are also specified as upper triangular factors. 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 character string naming a function or a function object that returns such a list, e.g. the functionPCMInfo or the function PCMInfoCpp from the PCMBaseCpp package.

verbose

logical indicating if some debug-messages should printed.