# Methods for betareg Objects

### Description

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

.

### Usage

1 2 3 4 5 6 7 8 9 10 11 | ```
## 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 |

`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. |

### 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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |