ks (version 1.6.13)

dkde, pkde, qkde, rkde: 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"

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.

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.

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)