kde {ks}R Documentation

Kernel density estimate for multivariate data

Description

Kernel density estimate for 2- to 6-dimensional data

Usage

 kde(x, H, gridsize, supp=3.7, eval.points)

Arguments

x matrix of data values
H bandwidth matrix
gridsize vector of number of grid points
supp effective support for standard normal is [-supp, supp]
eval.points points that density estimate is evaluated at (required for dimensions > 3)

Details

The kernel density estimate is computed exactly i.e. binning is not used.

If gridsize is not set to a specific value, then it defaults to 50 grid points in each co-ordinate direction i.e. rep(50, d). Not required to be set if specifying eval.points.

If eval.points is not specified, then the density estimate is automatically computed over a grid whose resolution is controlled by gridsize (a grid is required for plotting).

Value

Kernel density estimate is an object of class kde which is a list with 4 fields

x data points - same as input
eval.points points that density estimate is evaluated at
estimate density estimate at eval.points
H bandwidth matrix

References

Wand, M.P. & Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall. London.

See Also

plot.kde

Examples

### bivariate example
data(unicef)
H.pi <- Hpi(unicef, nstage=1)
fhat <- kde(unicef, H.pi)

### 4-variate example
library(MASS)
data(iris)
ir <- iris[,1:4][iris[,5]=="setosa",]
H.scv <- Hscv(ir)
fhat <- kde(ir, H.scv, eval.points=ir)  

[Package ks version 1.3.4 Index]