ks (version 1.4.1)

kde: Kernel density estimate for multivariate data

Description

Kernel density estimate for 1- to 6-dimensional data.

Usage

kde(x, H, h, gridsize, binned=FALSE, bgridsize, supp=3.7, eval.points)

Arguments

x

matrix of data values

H

bandwidth matrix

h

scalar bandwidth

gridsize

vector of number of grid points - used for display and binning

binned

flag for binned estimation (default is FALSE)

bgridsize

vector of binning grid sizes - only required if binned=TRUE

supp

effective support for standard normal is [-supp, supp]

eval.points

points at which density estimate is evaluated

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 at which the density estimate is evaluated
• estimate
  density estimate at eval.points
• H
  bandwidth matrix (>1-d only) or
• h
  scalar bandwidth (1-d only)

Details

For d = 1, 2, 3, 4, and if eval.points is not specified, then the density estimate is computed over a grid defined by gridsize (if binned=FALSE) or by bgridsize (if binned=TRUE).

For d = 1, 2, 3, 4, and if eval.points is specified, then the density estimate is computed exactly at eval.points. For d > 4, the kernel density estimate is computed exactly and eval.points must be specified.

References

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

See Also

plot.kde

Examples

### See examples in ? plot.kde