dkde, pkde, qkde, rkde {ks} | R Documentation |
Functions for 1-dimensional kernel density estimates.
pkde(q, fhat) qkde(p, fhat) dkde(x, fhat) rkde(n, fhat, positive=FALSE)
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" |
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.
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
.
Silverman, B. (1986) Density Estimation for Statistics and Data Analysis. Chapman & Hall/CRC. London.
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)