gee_criteria {multgee}

Variable and Covariance Selection Criteria

Description

Reports commonly used criteria for variable selection and for selecting the "working" association structure for one or several fitted models from the multgee package.

Usage

gee_criteria(object, ...)

Arguments

object

an object of the class LORgee.

...

optionally more objects of the class LORgee.

Details

The Quasi Information Criterion (QIC), the Correlation Information Criterion (CIC) and the Rotnitzky and Jewell Criterion (RJC) are used for selecting the best association structure. The QICu criterion is used for selecting the best subset of covariates. When choosing among GEE models with different association structures but with the same subset of covariates, the model with the smallest value of QIC, CIC or RJC should be preffered. When choosing between GEE models with different number of covariates, the model with the smallest QICu value should be preferred.

Value

A vector or matrix with the QIC, QICu, CIC, RJC and the number of regression parameters (including intercepts).

Author(s)

Anestis Touloumis

References

Hin, L.Y. and Wang, Y.G. (2009) Working correlation structure identification in generalized estimating equations. Statistics in Medicine 28, 642–658.

Pan, W. (2001) Akaike's information criterion in generalized estimating equations. Biometrics 57, 120–125.

Rotnitzky, A. and Jewell, N.P. (1990) Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data. Biometrika 77, 485–497.

See Also

nomLORgee and ordLORgee.

Examples

data(arthritis)
fitmod <- ordLORgee(formula = y ~ factor(time) + factor(trt) + factor(baseline),
data = arthritis, id = id, repeated = time, LORstr = "uniform")
fitmod1 <- update(fitmod, formula = .~. + age + factor(sex))
gee_criteria(fitmod, fitmod1)