ks (version 1.4.2)

kde: 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)

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

Details

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

If eval.points=NULL (default) then the density estimate is automatically computed over a grid whose resolution is controlled by gridsize (default is 100 in each co-ordinate direction).

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)