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 numeric vector of the random samples.
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).
Other ran_dist:
ran_bern()
,
ran_binom()
,
ran_gamma()
,
ran_gamma_pois()
,
ran_gamma_pois_zi()
,
ran_lnorm()
,
ran_neg_binom()
,
ran_norm()
,
ran_pois()
,
ran_pois_zi()
,
ran_skewnorm()
,
ran_student()