kde.1d: Functions for univariate kernel density estimates

Description Usage Arguments Details Value References Examples

Description

Functions for 1-dimensional kernel density estimates.

Usage

1
2
3
4
 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 Simpson's rule 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

1
2
3
4
5
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)


Search within the ks package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.