vcov.glmgee: Estimate of the variance-covariance matrix in GEEs

In glmtoolbox: Set of Tools to Data Analysis using Generalized Linear Models

Estimate of the variance-covariance matrix in GEEs

Description

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

Usage

## S3 method for class 'glmgee'
vcov(
  object,
  ...,
  type = c("robust", "df-adjusted", "model", "bias-corrected", "jackknife")
)

Arguments

object	An object of the class glmgee.
...	further arguments passed to or from other methods.
type	an (optional) character string indicating the type of estimator which should be used. The available options are: robust sandwich-type estimator ("robust"), degrees-of-freedom-adjusted estimator ("df-adjusted"), bias-corrected estimator ("bias-corrected"), and the model-based or naive estimator ("model"). As default, type is set to "robust".

Value

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

References

Mancl L.A., DeRouen T.A. (2001) A Covariance Estimator for GEE with Improved Small-Sample Properties. Biometrics 57:126-134.

Vanegas L.H., Rondon L.M., Paula G.A. (2023) Generalized Estimating Equations using the new R package glmtoolbox. The R Journal 15:105-133.

Examples

###### Example 1: Effect of ozone-enriched atmosphere on growth of sitka spruces
data(spruces)
mod <- size ~ poly(days,4) + treat
fit1 <- glmgee(mod, id=tree, family=Gamma(log), data=spruces, corstr="Exchangeable")
vcov(fit1)
vcov(fit1,type="bias-corrected")

###### Example 2: Treatment for severe postnatal depression
data(depression)
mod <- depressd ~ visit + group
fit3 <- glmgee(mod, id=subj, family=binomial(logit), corstr="AR-M-dependent", data=depression)
vcov(fit3)
vcov(fit3,type="bias-corrected")

###### Example 3: Treatment for severe postnatal depression (2)
mod <- dep ~ visit*group
fit2 <- glmgee(mod, id=subj, family=gaussian(identity), corstr="AR-M-dependent", data=depression)
vcov(fit2)
vcov(fit2,type="bias-corrected")

glmtoolbox documentation built on Sept. 11, 2024, 7:32 p.m.