ks (version 1.4.3)

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)

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)

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.

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)