dFBB: Probability mass function of the flexible beta-binomial...

In FlexReg: Regression Models for Bounded Continuous and Discrete Responses

Probability mass function of the flexible beta-binomial distribution

Description

The function computes the probability mass function of the flexible beta-binomial distribution.

Usage

dFBB(x, size, mu, theta = NULL, phi = NULL, p, w)

Arguments

x	a vector of quantiles.
size	the total number of trials.
mu	the mean parameter. It must lie in (0, 1).
theta	the overdispersion parameter. It must lie in (0, 1).
phi	the precision parameter, an alternative way to specify the overdispersion parameter theta. It must be a real positive value.
p	the mixing weight. It must lie in (0, 1).
w	the normalized distance among component means. It must lie in (0, 1).

Details

The FBB distribution is a special mixture of two beta-binomial distributions with probability mass function

f_{FBB}(x;\mu,\phi,p,w) = p BB(x;\lambda_1,\phi)+(1-p)BB(x;\lambda_2,\phi),

for x \in \lbrace 0, 1, \dots, n \rbrace, where BB(x;\cdot,\cdot) is the beta-binomial distribution with a mean-precision parameterization. Moreover, \phi=(1-\theta)/\theta>0 is a precision parameter, 0<p<1 is the mixing weight, 0<\mu=p\lambda_1+(1-p)\lambda_2<1 is the overall mean, 0<w<1 is the normalized distance between component means, and \lambda_1=\mu+(1-p)w and \lambda_2=\mu-pw are the scaled means of the first and second component of the mixture, respectively.

Value

A vector with the same length as x.

References

Ascari, R., Migliorati, S. (2021). A new regression model for overdispersed binomial data accounting for outliers and an excess of zeros. Statistics in Medicine, 40(17), 3895–3914. doi:10.1002/sim.9005

Examples

dFBB(x = c(5,7,8), size=10, mu = .3, phi = 20, p = .5, w = .5)

FlexReg documentation built on Sept. 29, 2023, 9:06 a.m.