ml.pois {COUNT}

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.

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.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)