plot.varKernel: Plotting varKernel objects

In DistributionalDDA: Distributional Data Analysis in Bayes Spaces

Plotting varKernel objects

Description

Plots a varKernel object as an image or contour plot.

Usage

## S3 method for class 'varKernel'
plot(
  x,
  ...,
  support = getSupport(x),
  SD = supportDiscretization(support, nEval),
  C = x(SD$all, SD$all),
  col = par("col"),
  pch = par("pch"),
  lty = par("lty"),
  lwd = par("lwd"),
  cex = par("lwd"),
  type = c("both", "image", "contour"),
  nEval = 513,
  add = FALSE,
  xlim = getSupport1DPlottingLimits(support),
  ylim = rev(xlim),
  zlim = range(breaks),
  breaks = pretty(c(C), ncol),
  ncol = 10,
  cols = grDevices::hcl.colors(length(breaks) - 1, "YlOrRd", rev = TRUE),
  dWidth = (xlim[2] - xlim[1])/200,
  xlab = "x",
  ylab = xlab,
  zlab = "c",
  notColour = "#FFFFFF"
)

## S3 method for class 'variationKernel'
plot(
  x,
  ...,
  support = getSupport(x),
  SD = supportDiscretization(support, nEval),
  V = x(SD$all),
  col = par("col"),
  pch = par("pch"),
  lty = par("lty"),
  lwd = par("lwd"),
  cex = par("lwd"),
  type = c("both", "image", "contour"),
  nEval = 513,
  add = FALSE,
  xlim = getSupport1DPlottingLimits(support),
  ylim = rev(xlim),
  zlim = range(breaks),
  breaks = pretty(V, ncol),
  ncol = 10,
  cols = grDevices::hcl.colors(length(breaks) - 1, "YlOrRd", rev = TRUE),
  dWidth = (xlim[2] - xlim[1])/200,
  xlab = "x",
  ylab = "x",
  zlab = "c",
  notColour = "#FFFFFF"
)

Arguments

x	an function of two arguments to be plotted. The function is expected to be in an outer vectorized way, i.e. that it returns a matrix, if both arguments are vectors.
...	further arguments for plot
support	the support of the first argument of the function
SD	a support discretization object used to discretized the function
C	the variance kernel evaluated at SD$all
col	a (vector of) plotting colors recycled over functions
pch	a (vector of) plotting chars recycled over functions used for the discrete support
lty	a (vector of) line types recycled over the functions used for the lines of the continuous support
lwd	line width for the countour plot
cex	see par: size of plotting symbols
type	either "both", "image", "contour" defining whether it should be represented by an image or a contour or both.
nEval	the number of discretization points for the continuous support
add	a boolean, if TRUE functions are added to an existing plot, if FALSE a new plot is generated.
xlim	the xlim of a new plot
ylim	the ylim of a new plot, if NULL it will be computed from the range of the functions.
zlim	the limits for plotting the covariance values
breaks	the values for which to change color and where to draw the countour lines.
ncol	the number of colours, it is one less then the number of breaks
cols	the colours to represent the values between the breaks
dWidth	discrete Width, the width to draw the discrete covariance values, which would theoretically take only an infinitely think line.
xlab	the label of the x-axis
ylab	the label of the y-axis y-axis
zlab	the label of the covariance values, not used yet
notColour	the colour to plot outside the range of values
V	the variation kernel evaluated at SD$all

Value

'NULL' invisibly, called for the side effect of producing an image and/or contour plot of the kernel.

Functions

• plot(variationKernel): method for variationKernel

Examples

C = varKernel(function(x,y) exp(-outer(x,y,"-")^2),basis=B2N01Basis5)
plot(C)
V = variationKernel(function(x) 1-exp(-outer(x,x,"-")^2),basis=B2N01Basis5)
plot(V)

DistributionalDDA documentation built on Oct. 20, 2026, 5:06 p.m.