COUNT (version 1.3.4)

ml.nbc: NBC: maximum likelihood linear negative binomial regression

Description

ml.nbc is a maximum likelihood function for estimating canonical linear negative binomial (NB-C) data.

Usage

ml.nbc(formula, data, start=NULL, verbose=FALSE)

Arguments

formula

an object of class '"formula"': a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.

data

a mandatory data frame containing the variables in the model.

start

an optional vector of starting values for the parameters.

verbose

a logical flag to indicate whether the fit information should be printed.

Value

The function returns a dataframe with the following components:

Details

ml.nbc is used like glm.nb, but without saving ancillary statistics.

References

Hilbe, J.M. (2011), Negative Binomial Regression, second edition, Cambridge University Press.

See Also

glm.nb, ml.nb1, ml.nb2

Examples

# Table 10.12, Hilbe. J.M. (2011), Negative Binomial Regression, 
#   2nd ed. Cambridge University Press (adapted)

## Not run: 
# data(medpar)
# nobs <- 50000
# x2 <- runif(nobs)
# x1 <- runif(nobs)
# xb <- 1.25*x1 + .1*x2 - 1.5
# mu <- 1/(exp(-xb)-1)
# p <- 1/(1+mu)
# r <- 1
# gcy <- rnbinom(nobs, size=r, prob = p)
# test <- data.frame(gcy, x1, x2)
# nbc <- ml.nbc(gcy ~ x1 + x2, data=test)
# nbc
# ## End(Not run)