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
)
An numeric vector of the corresponding 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 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.
Other res_dist:
res_bern()
,
res_binom()
,
res_gamma()
,
res_gamma_pois()
,
res_gamma_pois_zi()
,
res_lnorm()
,
res_neg_binom()
,
res_norm()
,
res_pois()
,
res_pois_zi()
,
res_skewnorm()
,
res_student()
res_beta_binom(c(0, 1, 2), 4, 0.5, 0.1)
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