# summary.betareg

##### Methods for betareg Objects

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

.

- Keywords
- regression

##### Usage

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

##### Examples

```
# NOT RUN {
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)
# }
```

*Documentation reproduced from package betareg, version 3.1-3, License: GPL-2 | GPL-3*