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R: NB2: maximum likelihood linear negative binomial regression

ml.nb2

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.

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

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(COUNT)
Loading required package: msme
Loading required package: MASS
Loading required package: lattice
Loading required package: sandwich
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/COUNT/ml.nb2.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ml.nb2
> ### Title: NB2: maximum likelihood linear negative binomial regression
> ### Aliases: ml.nb2
> ### Keywords: models
> 
> ### ** 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
               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
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>