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.
ran_beta_binom(n = 1, size = 1, prob = 0.5, theta = 0)A non-negative whole number of the number of random samples to generate.
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).
A numeric vector of the random samples.
Other ran_dist:
ran_bern(),
ran_binom(),
ran_gamma_pois_zi(),
ran_gamma_pois(),
ran_gamma(),
ran_lnorm(),
ran_neg_binom(),
ran_norm(),
ran_pois_zi(),
ran_pois(),
ran_skewnorm(),
ran_student()
ran_beta_binom(10, 1, 0.5, 0)
#> [1] 1 1 1 0 0 0 0 0 1 0