# Hurdle: Hurdle Distributions In brms: Bayesian Regression Models using 'Stan'

## Description

Density and distribution functions for hurdle distributions.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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) = θ. Else set f(x) = (1 - θ) * 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 Aug. 23, 2021, 5:08 p.m.