View source: R/mgp.functions.R
plotmgpCovFun | R Documentation |
Plot auto- or cross-covariance function of a multivariate Gaussian process
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 )
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(). |
A plot
## See examples in vignette: # vignette("mgpr", package = "GPFDA")
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