summary.hurdle: Summarzing Hurdle Regression Models for Count Data

Description

summary methods for class "hurdle"

Usage

## S3 method for class 'hurdle':
summary(object,...)
## S3 method for class 'summary.hurdle':
print(x, digits = max(3,getOption("digits")-3), ...)

Arguments

object

object inheriting from class "hurdle"

an object of class "summary.hurdle", usually a result of a call to summary.hurdle

digits

the number of significant digits to use when printing

...

further arguments passed to or from other methods

Value

The function summary.hurdle computes and returns a list of summary statistics from the zero-inflated regression model, including
coefficentsa matrix, with columns for the MLEs, their standard errors, $z$-statistic, and corresponding (two-sided) $p$-value.
vcThe estimated variance-covariance matrix of the MLEs
betaThe MLEs from the count component of the model
gammaThe MLEs from the hurdle component of the model
thetaIf a negative binomial count model is fit, the MLE of the over-dispersion parameter
llhThe value of the log-likelihood function at the MLEs

Details

print.summary.hurdle tries to be smart about formatting the display of the MLEs, standard errors, etc, essentially using the same code as appears in link{print.summary.lm}

Examples

Run this code

data(bioChemists)
hp <- hurdle(count=art ~ .,
              x = ~ fem + mar + kid5 + phd + ment,
              z = ~ fem + mar + kid5 + phd + ment,
              data=bioChemists,trace=TRUE)
summary(hp)

hnb <- hurdle(count=art ~ .,
              x = ~ fem + mar + kid5 + phd + ment,
              z = ~ fem + mar + kid5 + phd + ment,
              dist="negbin",
              data=bioChemists,trace=TRUE)
summary(hnb)