kde.1d: Functions for univariate kernel density estimates In ks: Kernel Smoothing

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) ```

ks documentation built on July 4, 2017, 9:45 a.m.