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)

Value

The function returns a dataframe with the following components:

Estimate

ML estimate of the parameter

SE

Asymptotic estimate of the standard error of the estimate of the parameter

Z

The Z statistic of the asymptotic hypothesis test that the population value for the parameter is 0.

LCL

Lower 95% confidence interval for the parameter estimate.

UCL

Upper 95% confidence interval for the parameter estimate.

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.

Author

Andrew Robinson, Universty of Melbourne, Australia, and Joseph M. Hilbe, Arizona State University, and Jet Propulsion Laboratory, California Institute of Technology

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)

if (FALSE) {
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
}