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, supp=3.7,
        eval.points=NULL)

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
supp effective support for standard normal is [-supp, supp]
eval.points points that density estimate is evaluated at

Details

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.

For d > 1, the kernel density estimate is computed exactly i.e. binning is not used. For d = 1, the binned estimator from the KernSmooth library is used.

For d = 1, 2, 3, if eval.points is not specified, then the density estimate is automatically computed over a grid whose resolution is controlled by gridsize (default is 101, 51 x 51 and 51 x 51 x 51 respectively).

For d > 3, eval.points must be specified.

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 sample proportions of each group
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

### bivariate example - restricted iris dataset  
library(MASS)
data(iris)
ir <- iris[,1:2]
ir.gr <- iris[,5]

H <- Hkda(ir, ir.gr, bw="plugin", pre="scale")
kda.fhat <- kda.kde(ir, ir.gr, H=H)

[Package ks version 1.4.3 Index]