ks (version 1.5.10)

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, xmin, xmax,
        supp=3.7, eval.points=NULL, binned=FALSE, bgridsize)

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.

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.

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