# vcov.glmgee: Estimate of the variance-covariance matrix in GEEs In glmtoolbox: Set of Tools to Data Analysis using Generalized Linear Models

## Description

Computes the type-`type` estimate of the variance-covariance matrix from an object of the class glmgee.

## Usage

 ```1 2``` ```## S3 method for class 'glmgee' vcov(object, ..., type = c("robust", "jackknife")) ```

## Arguments

 `object` An object of the class glmgee which is obtained from the fit of a generalized estimating equation. `...` further arguments passed to or from other methods. For example, `k`, that is, the magnitude of the penalty in the QIC (or the QICu), which by default is set to be 2. `type` an (optional) character string indicating the type of estimator which should be used. The available options are: robust estimator ("robust"), jackknife estimator computed from the one-step approximations of the “leave-one-out” estimates of the parameter vector ("jackknife"). By default, `type` is set to be "robust".

## Value

A `matrix` with the type-`type` estimate of the variance-covariance matrix.

## References

Lipsitz S.R., Laird N.M. and Harrington D.P. (1990) Using the jackknife to estimate the variance of regression estimators from repeated measures studies. Communications in Statistics - Theory and Methods 19, 821–845.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```## Example 1 mod <- size ~ poly(days,4) + treat fit1 <- glmgee(mod, id=tree, family=Gamma("log"), data=spruce, corstr="Exchangeable") vcov(fit1) vcov(fit1,type="jackknife") ## Example 2 mod <- dep ~ visit + group fit2 <- glmgee(mod, id=subj, family=gaussian, corstr="AR-1", data=depression) vcov(fit2) vcov(fit2,type="jackknife") ## Example 3 mod <- depressd ~ visit + group fit3 <- glmgee(mod, id=subj, family=binomial, corstr="Stationary-M-dependent(3)", data=depression) vcov(fit3) vcov(fit3,type="jackknife") ```

glmtoolbox documentation built on June 9, 2021, 9:07 a.m.