kde {ks}R Documentation

Kernel density estimate for multivariate data

Description

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

Usage

kde(x, H, h, gridsize, gridtype, xmin, xmax, supp=3.7, eval.points,
    binned=FALSE, bgridsize, positive=FALSE, adj.positive)

Arguments

x matrix of data values
H bandwidth matrix
h scalar bandwidth
gridsize vector of number of grid points
gridtype not yet implemented
xmin vector of minimum values for grid
xmax vector of maximum values for grid
supp effective support for standard normal is [-supp, supp]
eval.points points at which density estimate is evaluated
binned flag for binned estimation (default is FALSE)
bgridsize vector of binning grid sizes - required if binned=TRUE
positive flag if 1-d data are positive (default is FALSE)
adj.positive adjustment added to data i.e. when positive=TRUE KDE is carried out on log(x + adj.positive). Default is the minimum of x.

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.

The default xmin is min(x) - Hmax*supp and xmax is max(x) + Hmax*supp where Hmax is the maximim of the diagonal elements of H.

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
h scalar bandwidth (1-d only)

References

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

See Also

plot.kde

Examples

### See examples in ? plot.kde  

[Package ks version 1.6.2 Index]