ks (version 1.8.11)

kde: Kernel density estimate for multivariate data

Description

Kernel density estimate for 1- to 6-dimensional data.

Usage

kde(x, H, h, gridsize, gridtype, xmin, xmax, supp=3.7, eval.points, 
    binned=FALSE, bgridsize, positive=FALSE, adj.positive, w,
    compute.cont=FALSE, approx.cont=TRUE)

Arguments

x

matrix of data values

H

bandwidth matrix(ces)

h

scalar bandwidth(s)

gridsize

vector of number of grid points

gridtype

not yet implemented

xmin

vector of minimum values for grid

xmax

vector of maximum values for grid

supp

effective support for standard normal.

eval.points

points at which density estimate is evaluated

binned

flag for binned estimation. Default is FALSE.

bgridsize

vector of binning grid sizes

positive

flag if 1-d data are positive. Default is FALSE.

adj.positive

adjustment applied to data x <- log(x + adj.positive) when positive=TRUE. Default is the minimum of x.

w

vector of weights (non-negative and sum is equal to sample size). Default is a vector of all ones.

compute.cont

flag for computing probability contour levels from 1% to 99%. Default is FALSE.

approx.cont

flag for computing approximate probability contour levels. Default is TRUE.

Value

• A kernel density estimate is an object of class kde:
• x
  data points - same as input
• eval.points
  points at which the density estimate is evaluated
• estimate
  density estimate at eval.points
• H
  bandwidth matrix
• h
  scalar bandwidth (1-d only)
• w
  weights
• cont
  probability contour levels

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

supp is the effective support for a normal kernel, i.e. all values outside [-supp,supp]^d are set to zero. The default xmin is min(x)-Hmax*supp and xmax is max(x)+Hmax*supp where Hmax is the maximum of the diagonal elements of H.The default weights w is a vector of all ones.

See Also

plot.kde

Examples

## trivariate example
library(MASS)
x <- iris[,1:3]
H.pi <- Hpi(x, pilot="dscalar")
fhat <- kde(x, H=H.pi, compute.cont=TRUE)  
plot(fhat, drawpoints=TRUE)

## See other examples in ? plot.kde