plot.gpfr: Plot GPFR model for either training or prediction

In GPFDA: Gaussian Process for Functional Data Analysis

Plot GPFR model for either training or prediction

Description

Plot GPFR model for either training or prediction

Usage

## S3 method for class 'gpfr'
plot(
  x,
  type = c("raw", "meanFunction", "fitted", "prediction"),
  ylab = "y",
  xlab = "t",
  ylim = NULL,
  realisations = NULL,
  alpha = 0.05,
  colourTrain = 2,
  colourNew = 4,
  mar = c(4.5, 5.1, 2.2, 0.8),
  oma = c(0, 0, 1, 0),
  cex.lab = 1.5,
  cex.axis = 1,
  cex.main = 1.5,
  ...
)

Arguments

x	Plot GPFR for training or prediction from a given object of 'gpfr' class.
type	Required type of plots. Options are: 'raw', 'meanFunction', 'fitted' and 'prediction'.
ylab	Title for the y axis.
xlab	Title for the x axis.
ylim	Graphical parameter. If NULL (default), it is chosen automatically.
realisations	Index vector identifying which training realisations should be plotted. If NULL (default), all training realisations are plotted. For predictions, 'realisations' should be '0' if no training realisation is to be plotted.
alpha	Significance level used for 'fitted' or 'prediction'. Default is 0.05.
colourTrain	Colour for training realisations when 'type' is set to 'prediction' and 'realisations' is positive.
colourNew	Colour for predictive mean for the new curve when 'type' is set to 'prediction'.
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().
...	Other graphical parameters passed to plot().

Value

A plot.

Examples

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

GPFDA documentation built on Sept. 11, 2023, 1:08 a.m.