ks (version 1.10.7)

kde.1d: Functions for univariate kernel density estimates

Description

Functions for 1-dimensional kernel density estimates.

Usage

dkde(x, fhat)
 pkde(q, fhat)
 qkde(p, 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 Simpson's rule 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

# NOT RUN {
x <- rnorm.mixt(n=10000, mus=0, sigmas=1, props=1)
fhat <- kde(x=x, binned=TRUE)
p1 <- pkde(fhat=fhat, q=c(-1, 0, 0.5))
qkde(fhat=fhat, p=p1)    
y <- rkde(fhat=fhat, n=100)
# }