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.
log_lik_beta_binom(x, size = 1, prob = 0.5, theta = 0)A non-negative whole numeric vector of values.
A non-negative whole numeric vector of the number of trials.
A numeric vector of values between 0 and 1 of the probability of success.
A non-negative numeric vector of the dispersion for the mixture models (student, gamma-Poisson and beta-binomial).
An numeric vector of the corresponding log-likelihoods.
Other log_lik_dist:
log_lik_bern(),
log_lik_binom(),
log_lik_gamma_pois_zi(),
log_lik_gamma_pois(),
log_lik_gamma(),
log_lik_lnorm(),
log_lik_neg_binom(),
log_lik_norm(),
log_lik_pois_zi(),
log_lik_pois(),
log_lik_skewnorm(),
log_lik_student()
log_lik_beta_binom(c(0, 1, 2), 1, 0.5, 0)
#> [1] -0.6931472 -0.6931472 -Inf