ZABP: Zero-Adjusted Beta-Prime (ZABP) distribution for fitting a...

In santosneto/BPmodel: Beta-Prime Regression Model

Description

The functions dZABP, pZABP, qZABP and rZABP define the density, distribution function, quantile function and random generation for the ZABP.

Usage

dZABP(x, mu = 1.0, sigma = 1.0, nu = 0.1, log = FALSE)

pZABP(q, mu = 1, sigma = 1, nu = 0.1, lower.tail = TRUE, log.p = FALSE)

qZABP(p, mu = 1, sigma = 1, nu = 0.1, lower.tail = TRUE, log.p = FALSE)

rZABP(n, mu = 1, sigma = 1, nu = 0.1)

Arguments

x, q	vector of quantiles.
mu	vector of scale parameter values.
sigma	vector of shape parameter values.
nu	vector of mixture parameter values.
log, log.p	logical; if TRUE, probabilities p are given as log(p).
lower.tail	logical; if TRUE (default), probabilities are P[X <= x], 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.

Author(s)

Manoel Santos-Neto [e-mail], F.J.A. Cysneiros [e-mail], Victor Leiva [e-mail] and Michelli Barros [e-mail]

References

Leiva, V., Santos-Neto, M., Cysneiros, F.J.A., Barros, M. (2016) A methodology for stochastic inventory models based on a zero-adjusted Birnbaum-Saunders distribution. Applied Stochastic Models in Business and Industry., 32(1), 74–89. doi:10.1002/asmb.2124.

Santos-Neto, M., Cysneiros, F.J.A., Leiva, V., Barros, M. (2016) Reparameterized Birnbaum-Saunders regression models with varying precision. Electronic Journal of Statistics, 10, 2825–2855. doi: 10.1214/16-EJS1187.

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