dkde, pkde, qkde, rkde {ks}R Documentation

Functions for 1-dimensional kernel density estimates

Description

Functions for 1-dimensional kernel density estimates.

Usage

 pkde(q, fhat)
 qkde(p, fhat)
 dkde(x, fhat)
 rkde(n, fhat, positive=FALSE)
 

Arguments

x,q vector of quantiles
p vector of probabilities
n number of observations
positive flag to compute KDE on the positive real line. Default is FALSE.
fhat kernel density estimate, object of class "kde"

Details

pkde uses the Simpson's rule is used for the numerical integration. rkde uses Silverman (1986)'s method to generate a random sample from a KDE.

Value

For the kernel density estimate fhat, pkde computes the cumulative probability for the quantile q, qkde computes the quantile corresponding to the probability p, dkde computes the density value at x and rkde computes a random sample of size n.

References

Silverman, B. (1986) Density Estimation for Statistics and Data Analysis. Chapman & Hall/CRC. London.

Examples

x <- rnorm.mixt(n=10000, mus=0, sigmas=1, props=1)
fhat <- kde(x=x, h=hpi(x))
p1 <- pkde(fhat=fhat, q=c(-1, 0, 0.5))
qkde(fhat=fhat, p=p1)     ## should be close to c(-1, 0, 0.5)

x1 <- rkde(fhat, n=100)
plot(fhat)
fhat1 <- kde(x=x1, h=hpi(x1))
plot(fhat1, add=TRUE, col=2)
fhat2 <- dkde(x=x1, fhat=fhat1)
points(x1, fhat2, col=3)

[Package ks version 1.6.2 Index]