Working with a kde1d object

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)

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()).

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.

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))
#> [1] 0.4495823
pkde1d(0, fit) # evaluate corresponding cdf (close to pnorm(0))
#> [1] 0.5008513
qkde1d(0.5, fit) # quantile function (close to qnorm(0))
#> [1] -0.001893213
hist(rkde1d(100, fit)) # simulate