COUNT (version 1.3.4)

ml.nb1: NB1: maximum likelihood linear negative binomial regression

Description

ml.nb1 is a maximum likelihood function for estimating linear negative binomial (NB1) data. Output consists of a table of parameter estimates, standard errors, z-value, and confidence intervals.

Usage

ml.nb1(formula, data, offset=0, 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.

offset

this can be used to specify an _a priori_ known component to be included in the linear predictor during fitting. The offset should be provided on the log scale.

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.nb1 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.nbc, ml.nb2

Examples

# Table 10.8, Hilbe. J.M. (2011), Negative Binomial Regression, 
#   2nd ed. Cambridge University Press (adapted)
data(medpar)
medpar$type <- factor(medpar$type)
med.nb1 <- ml.nb1(los ~ hmo + white + type, data = medpar)
med.nb1