summary.betareg: Methods for betareg Objects

Description

Methods for extracting information from fitted beta regression model objects of class "betareg".

Usage

# S3 method for betareg
summary(object, phi = NULL, type = "quantile", ...)
# S3 method for betareg
coef(object, model = c("full", "mean", "precision"), phi = NULL, ...)
# S3 method for betareg
vcov(object, model = c("full", "mean", "precision"), phi = NULL, ...)
# S3 method for betareg
bread(x, phi = NULL, ...)
# S3 method for betareg
estfun(x, phi = NULL, ...)

Arguments

object, x: fitted model object of class "betareg".
phi: logical indicating whether the parameters in the precision model (for phi) should be reported as full model parameters (TRUE) or nuisance parameters (FALSE). The default is taken from object$phi.
type: character specifying type of residuals to be included in the summary output, see residuals.betareg.
model: character specifying for which component of the model coefficients/covariance should be extracted. (Only used if phi is NULL.)
...: currently not used.

Details

A set of standard extractor functions for fitted model objects is available for objects of class "betareg", including methods to the generic functions print and summary which print the estimated coefficients along with some further information. The summary in particular supplies partial Wald tests based on the coefficients and the covariance matrix. As usual, the summary method returns an object of class "summary.betareg" containing the relevant summary statistics which can subsequently be printed using the associated print method.

A logLik method is provided, hence AIC can be called to compute information criteria.

References

Cribari-Neto F, Zeileis A (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1--24. tools:::Rd_expr_doi("10.18637/jss.v034.i02")

Ferrari SLP, Cribari-Neto F (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799--815.

Simas AB, Barreto-Souza W, Rocha AV (2010). Improved Estimators for a General Class of Beta Regression Models. Computational Statistics & Data Analysis, 54(2), 348--366.

Examples

Run this code

options(digits = 4)

data("GasolineYield", package = "betareg")

gy2 <- betareg(yield ~ batch + temp | temp, data = GasolineYield)

summary(gy2)
coef(gy2)
vcov(gy2)
logLik(gy2)
AIC(gy2)

coef(gy2, model = "mean")
coef(gy2, model = "precision")
summary(gy2, phi = FALSE)