ml.nb2: NB2: maximum likelihood linear negative binomial regression

In COUNT: Functions, Data and Code for Count Data

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.

See Also

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

Example output

Loading required package: msme
Loading required package: MASS
Loading required package: lattice
Loading required package: sandwich
               Estimate         SE         Z        LCL         UCL
(Intercept)  2.31214519 0.06794358 34.030372  2.1789758 2.445314604
hmo         -0.06809686 0.05323976 -1.279060 -0.1724468 0.036253069
white       -0.13052184 0.06853619 -1.904422 -0.2648528 0.003809104
type2        0.22049993 0.05056730  4.360524  0.1213880 0.319611832
type3        0.70437929 0.07606068  9.260754  0.5553003 0.853458232
alpha        0.44522693 0.01978011 22.508817  0.4064579 0.483995950

COUNT documentation built on May 2, 2019, 2:37 a.m.