Methods for betareg Objects

Description

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

Usage

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## S3 method for class 'betareg'
summary(object, phi = NULL, type = "sweighted2", ...)

## S3 method for class 'betareg'
coef(object, model = c("full", "mean", "precision"), phi = NULL, ...)
## S3 method for class 'betareg'
vcov(object, model = c("full", "mean", "precision"), phi = NULL, ...)
## S3 method for class 'betareg'
bread(x, phi = NULL, ...)
## S3 method for class '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. Note that the default residuals "sweighted2" might be burdensome to compute in large samples and hence might need modification in such applications.

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

References

Cribari-Neto, F., and Zeileis, A. (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1–24. http://www.jstatsoft.org/v34/i02/.

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

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

See Also

betareg

Examples

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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)