Derived quantities from kernel density estimates
Derived quantities from kernel density estimates.
dkde(x, fhat) pkde(q, fhat) qkde(p, 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 |
pkde uses the trapezoidal rule for the numerical
integration. rkde uses
Silverman (1986)'s method to generate a random sample from a KDE.
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.
Silverman, B. (1986) Density Estimation for Statistics and Data Analysis. Chapman & Hall/CRC. London.
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) plot(fhaty, add=TRUE, col=2)
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