kepdf-class {pdfCluster}

Class "kepdf"

Description

This class encapsulates results of the application of function kepdf.

Objects from the Class

Objects can be created by calls of the form new("kepdf", ...) or as a result of a call to kepdf.

Slots

call:

Object of class "call", corresponding to the matched call.

x:

Object of class "matrix" representing the data points used to estimate the probability density function.

eval.points:

Object of class "matrix" representing the data points at which the density is evaluated.

estimate

The values of the density estimate at the evaluation points.

kernel:

Object of class "character" giving the selected kernel.

bwtype:

Object of class "character" giving the selected type of estimator.

par:

Object of class "list" providing the parameters used to estimate the density. Its elements are h, hx, and possibly alpha.

See kepdf for further details.

Methods

plot

signature(x = "kepdf", y = "ANY")

Plots objects of kepdf-class. plot-methods are available for density estimates of:

• one-dimensional data;

• two-dimensional data: contour, image or perspective plots are available;

• multi-dimensional data: matrix of plots of all the pairs of two-dimensional marginal kernel density estimates.

See plot,kepdf-method for further details.

show

signature(object = "kepdf")

Prints the following elements:

• the class of the object;

• the selected kernel;

• the selected type of estimator;

• either the fixed smoothing parameters or the smoothing parameters of each observation;

• the density estimates at the evaluation points.

summary

signature(object = "kepdf")

Provides a summary of kepdf-class object by printing the highest density data point and the row or index position of a percentage top density data points, possibly given as optional argument prop.

See Also

h.norm, kepdf, plot,kepdf-method, plot-methods, show-methods, summary-methods.

Examples

#
showClass("kepdf")

#
data(wine)
#select only "Barolo"-type wines
x <- wine[1:59,3] 
pdf <- kepdf(x)
pdf
summary(pdf)
summary(pdf, props = 10*seq(1, 9, by = 1))