dev_beta_binom: Beta-Binomial Deviances

In extras: Helper Functions for Bayesian Analyses

Beta-Binomial Deviances

Description

This parameterization of the beta-binomial distribution uses an expected probability parameter, prob, and a dispersion parameter, theta. The parameters of the underlying beta mixture are alpha = (2 * prob) / theta and beta = (2 * (1 - prob)) / theta. This parameterization of theta is unconventional, but has useful properties when modelling. When theta = 0, the beta-binomial reverts to the binomial distribution. When theta = 1 and prob = 0.5, the parameters of the beta distribution become alpha = 1 and beta = 1, which correspond to a uniform distribution for the beta-binomial probability parameter.

Usage

dev_beta_binom(x, size = 1, prob = 0.5, theta = 0, res = FALSE)

Arguments

x	A non-negative whole numeric vector of values.
size	A non-negative whole numeric vector of the number of trials.
prob	A numeric vector of values between 0 and 1 of the probability of success.
theta	A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial).
res	A flag specifying whether to return the deviance residual as opposed to the deviance.

Value

An numeric vector of the corresponding deviances or deviance residuals.

See Also

Other dev_dist: dev_bern(), dev_binom(), dev_gamma(), dev_gamma_pois(), dev_lnorm(), dev_neg_binom(), dev_norm(), dev_pois(), dev_pois_zi(), dev_skewnorm(), dev_student()

Examples

dev_beta_binom(c(0, 1, 2), 10, 0.5, 0.1)

extras documentation built on April 4, 2025, 2:40 a.m.