ml.nbc {COUNT} R Documentation

## 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.

### Details

ml.nbc 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.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)


[Package COUNT version 1.3.4 Index]