Kumaraswamy: Kumaraswamy distribution

In AndrMenezes/unitModalReg: Unit Modal Parametric Regression Models

Description

Density function, distribution function, quantile function, random number generation function for the Kumaraswamy distribution re-parametrized in terms of the mode.

Usage

dkum(x, mu, phi, log = FALSE)

pkum(q, mu, phi, lower.tail = TRUE, log.p = FALSE)

qkum(p, mu, phi, lower.tail = TRUE, log.p = FALSE)

rkum(n, mu, phi)

Arguments

x, q	vector of positive quantiles.
mu	location parameter indicating the mode.
phi	shape parameter.
log, log.p	logical; If TRUE, probabilities p are given as log(p).
lower.tail	logical; If TRUE, (default), P(X ≤q x) are returned, otherwise P(X > x).
p	vector of probabilities.
n	number of observations. If length(n) > 1, the length is taken to be the number required.

Value

dkum gives the density, pkum gives the distribution function, qkum gives the quantile function and rkum generates random deviates.

Invalid arguments will return an error message.

Author(s)

André Felipe B. Menezes [e-mail]

Examples

set.seed(6969)
x <- rkum(n = 1e4, mu = 0.5, phi = 3.0)
R <- range(x)
S <- seq(from = R[1], to = R[2], by = 0.01)
hist(x, prob = TRUE, main = 'unit-gamma distribution')
lines(S, dkum(x = S, mu = 0.5, phi = 3.0), col = 2)
plot(ecdf(x))
lines(S, pkum(q = S, mu = 0.5, phi = 3.0), col = 2)
plot(quantile(x, probs = S), type = "l")
lines(qkum(p = S, mu = 0.5, phi = 3.0), col = 2)

AndrMenezes/unitModalReg documentation built on March 12, 2021, 5:24 a.m.