ks (version 1.5.5)

kda.kde: 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, 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

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

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)

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.

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