Hurdle: Hurdle Distributions

In brms: Bayesian Regression Models using 'Stan'

Hurdle Distributions

Description

Density and distribution functions for hurdle distributions.

Usage

dhurdle_poisson(x, lambda, hu, log = FALSE)

phurdle_poisson(q, lambda, hu, lower.tail = TRUE, log.p = FALSE)

dhurdle_negbinomial(x, mu, shape, hu, log = FALSE)

phurdle_negbinomial(q, mu, shape, hu, lower.tail = TRUE, log.p = FALSE)

dhurdle_gamma(x, shape, scale, hu, log = FALSE)

phurdle_gamma(q, shape, scale, hu, lower.tail = TRUE, log.p = FALSE)

dhurdle_lognormal(x, mu, sigma, hu, log = FALSE)

phurdle_lognormal(q, mu, sigma, hu, lower.tail = TRUE, log.p = FALSE)

Arguments

x	Vector of quantiles.
hu	hurdle probability
log	Logical; If TRUE, values are returned on the log scale.
q	Vector of quantiles.
lower.tail	Logical; If TRUE (default), return P(X <= x). Else, return P(X > x) .
log.p	Logical; If TRUE, values are returned on the log scale.
mu, lambda	location parameter
shape	shape parameter
sigma, scale	scale parameter

Details

The density of a hurdle distribution can be specified as follows. If x = 0 set f(x) = \theta. Else set f(x) = (1 - \theta) * g(x) / (1 - G(0)) where g(x) and G(x) are the density and distribution function of the non-hurdle part, respectively.

brms documentation built on Sept. 23, 2024, 5:08 p.m.