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

In COUNT: Functions, Data and Code for Count Data

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.

Details

ml.nb1 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.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

Example output

Loading required package: msme
Loading required package: MASS
Loading required package: lattice
Loading required package: sandwich
               Estimate         SE          Z         LCL         UCL
(Intercept)  2.34918407 0.06023641 38.9994023  2.23112070  2.46724744
hmo         -0.04533566 0.05004714 -0.9058592 -0.14342805  0.05275673
white       -0.12951295 0.06071130 -2.1332593 -0.24850710 -0.01051880
type2        0.16175471 0.04585569  3.5274735  0.07187757  0.25163186
type3        0.41879257 0.06553258  6.3906006  0.29034871  0.54723643
alpha        4.57898241 0.22015969 20.7984599  4.14746943  5.01049540

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