fit.zinb: Internal function

In semisup: Semi-Supervised Mixture Model

Description

This function fits the semi-supervised zero-inflated negative binomial mixture model. It is called by fit.wrap.

Usage

fit.zinb(y, z, phi, pi, gamma, it.em, epsilon)

Arguments

y	observations: numeric vector of length n
z	class labels: integer vector of length n, with entries 0, 1 and NA
phi	dispersion parameters: numeric vector of length q, or NULL
pi	zero-inflation parameter(s): numeric vector of length q, or NULL
gamma	offset: numeric vector of length n, or NULL
it.em	(maximum) number of iterations in the EM algorithm: positive integer (defaults to 100)
epsilon	convergence criterion for the EM algorithm: non-negative numeric (defaults to 1e-04)

Value

This function returns the parameter estimates, the posterior probabilities, and the likelihood.

See Also

This is an internal function. The user functions are mixtura and scrutor.

Examples

# data simulation
n <- 100
z <- rep(0:1,each=n/2)
gamma <- runif(n=n,min=0,max=2)
y <- rnbinom(n=n,mu=gamma*(5+2*z),size=1/0.05)
y[sample(1:n,size=0.2*n)] <- 0
z[(n/4):n] <- NA

# model fitting
fit.zinb(y,z,phi=0.05,pi=0.2,gamma=gamma,
it.em=100,epsilon=1e-04)

semisup documentation built on Nov. 8, 2020, 6:32 p.m.