rkde: Derived quantities from kernel density estimates

In ks: Kernel Smoothing

Derived quantities from kernel density estimates

Description

Derived quantities from 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

Details

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

Value

For the 1-d kernel density estimate fhat, pkde computes the cumulative probability for the quantile q, qkde computes the quantile corresponding to the probability p.

For any kernel density estimate, dkde computes the density value at x (it is an alias for predict.kde), rkde computes a random sample of size n.

References

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

Examples

set.seed(8192)
x <- rnorm.mixt(n=10000, mus=0, sigmas=1, props=1)
fhat <- kde(x=x)
p1 <- pkde(fhat=fhat, q=c(-1, 0, 0.5))
qkde(fhat=fhat, p=p1)    
y <- rkde(fhat=fhat, n=100)

x <- rmvnorm.mixt(n=10000, mus=c(0,0), Sigmas=invvech(c(1,0.8,1)))
fhat <- kde(x=x)
y <- rkde(fhat=fhat, n=1000)
fhaty <- kde(x=y)
plot(fhat, col=1)
plot(fhaty, add=TRUE, col=2)

ks documentation built on Aug. 11, 2023, 1:10 a.m.