n: the number of values to generate. If length(n) > 1, the length is taken to be the number required.
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 clusters. It must lie in (0, 1).
Returns
A vector of length n.
Examples
rFBB(n =100, size =40, mu =.5, theta =.4, p =.3, w =.6)rFBB(n =100, size =40, mu =.5, phi =1.5, p =.3, w =.6)
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