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Last data update: 2014.03.03

R: NB2: maximum likelihood Poisson regression

ml.pois

R Documentation

NB2: maximum likelihood Poisson regression

Description

ml.pois is a maximum likelihood function for estimating Poisson data. Output consists of a table of parameter estimates, standard errors, z-value, and confidence intervals. An offset may be declared as an option.

Usage

ml.pois(formula, data, offset=0, start=NULL, verbose=FALSE)

Arguments

`formula`	an object of class '"formula"': a symbolic description of the model to be fitted.
`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.pois is used like glm, but does not provide ancillary statistics.

Value

The function returns a dataframe with the following components:

`Estimate`	ML estimate of the parameters
`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 estimates.
`UCL`	Upper 95% confidence interval for the parameter estimates.

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.pois <- ml.pois(los ~ hmo + white + type, data = medpar)
med.pois

data(rwm5yr)
lyear <- log(rwm5yr$year)
rwm.poi <- ml.pois(docvis ~ outwork + age + female, offset=lyear, data =
rwm5yr)
rwm.poi
exp(rwm.poi$Estimate)
exp(rwm.poi$LCL)
exp(rwm.poi$UCL)

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.pois.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ml.pois
> ### Title: NB2: maximum likelihood Poisson regression
> ### Aliases: ml.pois
> ### 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.pois <- ml.pois(los ~ hmo + white + type, data = medpar)
> med.pois
               Estimate         SE         Z        LCL         UCL
hmo         -0.07245215 0.02395388 -3.024652 -0.1194017 -0.02550255
white       -0.15390014 0.02741409 -5.613907 -0.2076318 -0.10016853
type2        0.22212541 0.02105063 10.551959  0.1808662  0.26338465
type3        0.70913650 0.02614185 27.126483  0.6578985  0.76037453
(Intercept)  2.33282363 0.02720954 85.735518  2.2794929  2.38615432
> 
> data(rwm5yr)
> lyear <- log(rwm5yr$year)
> rwm.poi <- ml.pois(docvis ~ outwork + age + female, offset=lyear, data =
+ rwm5yr)
> rwm.poi
               Estimate           SE          Z         LCL         UCL
outwork      0.22021745 0.0092667367   23.76429  0.20205464  0.23838025
age          0.02003592 0.0003709838   54.00754  0.01930879  0.02076305
female       0.22329872 0.0089781374   24.87139  0.20570157  0.24089587
(Intercept) -7.54745586 0.0176325415 -428.04129 -7.58201564 -7.51289608
> exp(rwm.poi$Estimate)
[1] 1.2463477149 1.0202379864 1.2501939764 0.0005274503
> exp(rwm.poi$LCL)
[1] 1.2239148841 1.0194964123 1.2283865633 0.0005095332
> exp(rwm.poi$UCL)
[1] 1.2691917115 1.0209801000 1.2723885341 0.0005459975
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>