mmmgee: Covariance Matrix Estimation for Multiple Marginal GEE Models
In mmmgee: Simultaneous Inference for Multiple Linear Contrasts in GEE Models

Description Usage Arguments Value Author(s) References See Also Examples

Calculate the covariance matrix for a stacked vector of regression coefficients from multiple marginal GEE models fitted with geem2.

1	mmmgee(x, biascorr = FALSE)

`x`	a list of `geem` objects fitted with `geem2`. The `geem` objects must be different models calculated with data from the same subjects. In particular, the parameter `id` in the call to `geem2` must refer to the same subjects in each model.
`biascorr`	logical, if `TRUE`, a bias corrected covariance matrix is calculate by extending the method due to Mancl and DeRouen to multiple models. See references.

A list with class mmmgee containing the following components:

beta: The stacked vector of regression coefficient estimates from the models in x.
V: The estimated covariance matrix of the regression coefficient estimates.
A: The outer component of V=ABA.
B: The inner component of V=ABA.
biascorr: The value of the input argument biascorr (logical).
n: A vector with the number of clusters in each model in x.
p: A vector with number of regression coefficients in each model in x.

Robin Ristl, robin.ristl@meduniwien.ac.at

Lloyd A. Mancl, Timothy A. DeRouen. A covariance estimator for GEE with improved small sample properties. Biometrics, 2001, 57(1):126-134.

geem2, mmmgee.test

data(keratosis)
m1<-geem2(clearance~trt,id=id,data=keratosis,family=binomial,corstr="independence")
m2<-geem2(pain~trt,id=id,data=keratosis[keratosis$lesion==1,],family=gaussian,corstr="independence")
mmmgee(x=list(m1,m2),biascorr=TRUE)