Methods for betareg Objects
Methods for extracting information from fitted beta
regression model objects of class "betareg".
## 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, ...)object, x |
fitted model object of class |
phi |
logical indicating whether the parameters in the precision model
(for phi) should be reported as full model parameters ( |
type |
character specifying type of residuals to be included in the
summary output, see |
model |
character specifying for which component of the model coefficients/covariance
should be extracted. (Only used if |
... |
currently not used. |
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.
Cribari-Neto, F., and Zeileis, A. (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1–24. doi: 10.18637/jss.v034.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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.