zinbOptimizeDispersion: (ZINB) model. The ZINB distribution is parametrized by three...

In drisso/learn2count: Structure Learning for Count Data

(ZINB) model. The ZINB distribution is parametrized by three parameters: the mean value and the dispersion of the negative binomial distribution, and the probability of the zero component.

Description

(ZINB) model. The ZINB distribution is parametrized by three parameters: the mean value and the dispersion of the negative binomial distribution, and the probability of the zero component.

Usage

zinbOptimizeDispersion(mu, logitPi, Y, n)

Arguments

mu	the vector mean of the negative binomial
logitPi	the vector of logit of the probabilities of the zero component
Y	the vector of counts
n	length of the returned vector

Examples

n <- 10
mu <- seq(10,50,length.out=n)
logitPi <- rnorm(1)
zeta <- rnorm(1)
Y <- rnbinom(n=n, size=exp(zeta), mu=mu)
learn2count:::zinbOptimizeDispersion ( mu, logitPi,Y,n)

drisso/learn2count documentation built on March 25, 2023, 4:21 p.m.