plotmgpCovFun: Plot auto- or cross-covariance function of a multivariate...

In GPFDA: Gaussian Process for Functional Data Analysis

Plot auto- or cross-covariance function of a multivariate Gaussian process

Description

Plot auto- or cross-covariance function of a multivariate Gaussian process

Usage

plotmgpCovFun(
  type = "Cov",
  output,
  outputp,
  Data,
  hp,
  idx,
  ylim = NULL,
  xlim = NULL,
  mar = c(4.5, 5.1, 2.2, 0.8),
  oma = c(0, 0, 0, 0),
  cex.lab = 1.5,
  cex.axis = 1,
  cex.main = 1.5
)

Arguments

type	Logical. It can be either 'Cov' (for covariance function) or 'Cor' (for corresponding correlation function).
output	Integer identifying one element of the multivariate process.
outputp	Integer identifying one element of the multivariate process. If 'output' and 'outputp' are the same, the auto-covariance function will be plotted. Otherwise, the cross-covariance function between 'output' and 'outputp' will be plotted.
Data	List of two elements: 'input' and 'response'. The element 'input' is a list of N vectors, where each vector represents the input covariate values for a particular output. The element 'response' is the corresponding list of N matrices (if there are multiple realisations) or vectors (for a single realisation) representing the response variables.
hp	Vector of hyperparameters
idx	Index vector identifying to which output the elements of concatenated vectors correspond to.
ylim	Graphical parameter
xlim	Graphical parameter
mar	Graphical parameter passed to par().
oma	Graphical parameter passed to par().
cex.lab	Graphical parameter passed to par().
cex.axis	Graphical parameter passed to par().
cex.main	Graphical parameter passed to par().

Value

A plot

Examples

## See examples in vignette:
# vignette("mgpr", package = "GPFDA")

GPFDA documentation built on May 7, 2022, 5:06 p.m.