ml.nb2 {COUNT} R Documentation

## NB2: maximum likelihood linear negative binomial regression

### Description

ml.nb2 is a maximum likelihood function for estimating linear negative binomial (NB2) data. Output consists of a table of parameter estimates, standard errors, z-value, and confidence intervals.

### Usage

ml.nb2(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.

### Details

ml.nb2 is used like glm.nb, but without saving ancillary statistics.

### 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.

### Author(s)

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

### References

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

glm.nb, ml.nbc, ml.nb1

### Examples

# Table 8.7, Hilbe. J.M. (2011), Negative Binomial Regression,
#   2nd ed. Cambridge University Press (adapted)
data(medpar)
medpar$type <- factor(medpar$type)
med.nb2 <- ml.nb2(los ~ hmo + white + type, data = medpar)
med.nb2


[Package COUNT version 1.3.4 Index]