distributions_bernstein_densities: Density, distribution function, quantile function, random...

In JonasMoss/polygrams: Density Estimation with Mixtures of Disjoint Bernstein Polynomials

Description

Density, distribution function, quantile function, random generation, and pth derivative for a Bernstein density with parameter vector lambda.

Usage

dxbernstein(x, lambda, p = 1, support = c(0, 1), log = FALSE)

dbernstein(x, lambda, p = 1, support = c(0, 1), log = FALSE)

pbernstein(x, lambda, p = 1, support = c(0, 1), log.p = FALSE)

Arguments

x, q	vector of quantiles.
lambda	A vector of positive weights summing to 1.
p	vector of probabilities.
support	The support of the
log, log.p	logical; if TRUE, probabilities p are given as log(p).
n	number of observations. If codelength(n) > 1, the length is taken to be the number required.
lower.tail	logical; if TRUE (default), probabilities are P[x≤X] otherwise, P[X<x].

Value

dbernstein gives the density, pbernstein the distribution function, qbernstein the quantile function, rbeta generates random deviates, and dxbernstein gives the pth derivative of the density function.

Functions

• dxbernstein: Calculates pth derivative of a Bernstein density.

• dbernstein: Calculates a Bernstein density.

• pbernstein: Calculates the distribution function of a Bernstein density.

Examples

## Do a parametric bootstrap.
set.seed(1337)
data = rbeta(200, 2, 7)
polygram_object = polygram(data, s = 3, m = 4)
current_median = qpolygram(0.5, polygram_object)
medians = replicate(100, {
  new_data = rpolygram(200, polygram_object)
  new_median = qpolygram(0.5, polygram(new_data, s = 3, m = 4))
  new_median
})
plot(polygram(sqrt(200)*(current_median - medians), s = 4, m = 4,
              support = c(-1,1)))

JonasMoss/polygrams documentation built on Nov. 8, 2019, 5:19 p.m.