kda.kde {ks}R Documentation

Kernel density estimate for kernel discriminant analysis for multivariate data

Description

Kernel density estimate for kernel discriminant analysis for 1- to 6-dimensional data

Usage

kda.kde(x, x.group, Hs, hs, prior.prob=NULL, gridsize, xmin, xmax,
        supp=3.7, eval.points=NULL, binned=FALSE, bgridsize)

Arguments

x matrix of training data values
x.group vector of group labels for training data
Hs (stacked) matrix of bandwidth matrices
hs vector of scalar bandwidths
prior.prob vector of prior probabilities
gridsize vector of number of grid points
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 kernel estimation
bgridsize vector of binning grid sizes - only required if binned=TRUE

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 is computed exactly at eval.points.

For d > 4, the kernel density estimate is computed exactly and eval.points must be specified.

If you have prior probabilities then set prior.prob to these. Otherwise prior.prob=NULL is the default i.e. use the sample proportions as estimates of the prior probabilities.

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

The kernel density estimate for kernel discriminant analysis is based on kde, one density estimate for each group.
The result from kda.kde is a density estimate for discriminant analysis is an object of class kda.kde which is a list with 6 fields

x data points - same as input
x.group group labels - same as input
eval.points points that density estimate is evaluated at
estimate density estimate at eval.points
prior.prob prior probabilities
H bandwidth matrices (>1-d only) or
h bandwidths (1-d only)

References

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

See Also

plot.kda.kde

Examples

### See examples in ? plot.kda.kde

[Package ks version 1.6.2 Index]