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.
res_beta_binom(
x,
size = 1,
prob = 0.5,
theta = 0,
type = "dev",
simulate = FALSE
)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 string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data.
A flag specifying whether to simulate residuals.
An numeric vector of the corresponding residuals.
Other res_dist:
res_bern(),
res_binom(),
res_gamma_pois_zi(),
res_gamma_pois(),
res_gamma(),
res_lnorm(),
res_neg_binom(),
res_norm(),
res_pois_zi(),
res_pois(),
res_skewnorm(),
res_student()
res_beta_binom(c(0, 1, 2), 4, 0.5, 0.1)
#> [1] -2.2434148 -0.9346019 0.0000000