kde1d (version 1.1.1)

dkde1d: Working with a kde1d object

Description

Density, distribution function, quantile function and random generation for a 'kde1d' kernel density estimate.

Usage

dkde1d(x, obj)
pkde1d(q, obj)
qkde1d(p, obj)
rkde1d(n, obj, quasi = FALSE)

Value

The density, distribution function or quantile functions estimates evaluated respectively at x, q, or p, or a sample of n random deviates from the estimated kernel density.

Arguments

x

vector of density evaluation points.

obj

a kde1d object.

q

vector of quantiles.

p

vector of probabilities.

n

integer; number of observations.

quasi

logical; the default (FALSE) returns pseudo-random numbers, use TRUE for quasi-random numbers (generalized Halton, see randtoolbox::sobol()).

Details

dkde1d() gives the density, pkde1d() gives the distribution function, qkde1d() gives the quantile function, and rkde1d() generates random deviates.

The length of the result is determined by n for rkde1d(), and is the length of the numerical argument for the other functions.

See Also

kde1d()

Examples

set.seed(0) # for reproducibility
x <- rnorm(100) # simulate some data
fit <- kde1d(x) # estimate density
dkde1d(0, fit) # evaluate density estimate (close to dnorm(0))
pkde1d(0, fit) # evaluate corresponding cdf (close to pnorm(0))
qkde1d(0.5, fit) # quantile function (close to qnorm(0))
hist(rkde1d(100, fit)) # simulate