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.
dev_beta_binom(x, size = 1, prob = 0.5, theta = 0, res = FALSE)
An numeric vector of the corresponding deviances or deviance residuals.
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).
A flag specifying whether to return the deviance residual as opposed to the deviance.
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()
dev_beta_binom(c(0, 1, 2), 10, 0.5, 0.1)
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