PCMBase (version 1.2.10)

PCMVarAtTime: Calculate the variance covariance k x k matrix at time t, under a PCM model

Description

Calculate the variance covariance k x k matrix at time t, under a PCM model

Usage

PCMVarAtTime(t, model, W0 = matrix(0, PCMNumTraits(model),
  PCMNumTraits(model)), SE = matrix(0, PCMNumTraits(model),
  PCMNumTraits(model)), regime = PCMRegimes(model)[1L],
  verbose = FALSE)

Arguments

t

positive numeric denoting time

model

a PCM model object

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 k matrix specifying the upper triangular factor of the measurement error variance-covariance matrix. The product SE Default: SE = matrix(0.0, PCMNumTraits(model), PCMNumTraits(model)).

regime

an integer or a character denoting the regime in model for which to do the calculation; Defaults to PCMRegimes(model)[1L], meaning the first regime in the model.

verbose

a logical indicating if (debug) messages should be written on the console (Defaults to FALSE).

Value

A numeric k x k matrix

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)

# PCMVarAtTime(1, modelBM)

# note that the variance at time 0 is not the 0 matrix because the model has a non-zero
# environmental deviation
PCMVarAtTime(0, modelBM)
# }

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