summary.gcmr: Methods for gcmr Objects

In gcmr: Gaussian Copula Marginal Regression

Methods for gcmr Objects

Description

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

Usage

## S3 method for class 'gcmr'
summary(object, ...)

## S3 method for class 'gcmr'
coef(object, ...)
## S3 method for class 'gcmr'
vcov(object, ...)
## S3 method for class 'gcmr'
bread(x, ...)
## S3 method for class 'gcmr'
estfun(x, ...)

Arguments

object, x	a fitted marginal regression model of class gcmr.
...	additional arguments, but currently not used.

Value

The function summary.gcmr returns an object of class "summary.glm", a list with some components of the gcmr object, plus

coefficients	a list with components marginal and copula containing the maximum likelihood estimates of the marginal and Gaussian copula parameters, respectively.
aic	Akaike Information Criterion.

Function coef returns the estimated coefficients and vcov their variance-covariance matrix. Functions bread and estfun extract the components of the robust sandwich variance matrix that can be computed with the sandwich package (Zeileis, 2004; 2006).

Author(s)

Guido Masarotto and Cristiano Varin.

References

Zeileis, A. (2004). Econometric computing with HC and HAC covariance matrix estimators. Journal of Statistical Software 11, issue 10.

Zeileis, A. (2006). Object-oriented computation of sandwich estimators. Journal of Statistical Software 16, issue 9.

See Also

bread, estfun, gcmr, sandwich.

Examples

data(epilepsy)
fit <- gcmr(counts ~ offset(log(time)) + visit + trt + visit:trt, data = epilepsy, 
subset = (id != 49), marginal = negbin.marg, cormat = cluster.cormat(id, "ar1"), 
options=gcmr.options(seed=123, nrep=c(25,100) ))
summary(fit)

gcmr documentation built on July 18, 2022, 9:06 a.m.