BP: Reparameterized Beta Prime (BP) distribution for fitting a...

In santosneto/BPmodel: Beta-Prime Regression Model

Description

The function BP() defines the Reparameterized BP distribution for a gamlss.family object to be used in gamlss. The functions dBP, pBP, qBP and rBP define the density, distribution function, quantile function and random generation of the BP distribution.

Usage

BP(mu.link = "log", sigma.link = "log")

dBP(x, mu = 1, sigma = 1, log = FALSE)

pBP(q, mu = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE)

rBP(n = 1, mu = 1, sigma = 1)

qBP(p, mu = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE)

Arguments

mu.link	Defines the mu.link, with "log" link as the default for the mu parameter.
sigma.link	Defines the sigma.link, with "log" link as the default for the sigma parameter.
x, q	vector of quantiles.
mu	vector of scale parameter values.
sigma	vector of shape parameter values.
log	logical; if TRUE, quantiles are given as log.
lower.tail	logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].
log.p	log.p logical; if TRUE, probabilities p are given as log(p).
n	number of observations. If length(n) > 1, the length is taken to be the number required.
p	vector of probabilities.

Value

returns a gamlss.family object which can be used to fit a BP distribution in the gamlss function.

Note

For the function BP(), mu is the mean and sigma is the precision parameter of the BP distribution.

Author(s)

Manoel Santos-Neto [e-mail]

References

Rigby, R.A., Stasinopoulos, D.M., Heller, G.Z., and De Bastiani, F. Distributions for modeling location, scale, and shape: Using GAMLSS in R, London: Chapman and Hall/CRC, 2019.

Stasinopoulos D.M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F. Flexible Regression and Smoothing: Using GAMLSS in R, London: Chapman and Hall/CRC, 2017

Bourguignon, M., Santos-Neto, M. and Castro, M. A new regression model for positive random variables with skewed and long tail. METRON, v. 79, p. 33–55, 2021. doi: 10.1007/s40300-021-00203-y

Examples

y <- rBP(n = 100)
hist(y)
plot(function(x) dBP(x), 0.0001, 8)
gamlss::gamlss(y ~ 1, family = BP)

santosneto/BPmodel documentation built on Jan. 18, 2022, 4:53 p.m.