R: Cluster-robust variance-covariance matrix for a geeglm...

vcovCR.geeglm {clubSandwich}

R Documentation

Cluster-robust variance-covariance matrix for a geeglm object.

Description

vcovCR returns a sandwich estimate of the variance-covariance matrix of a set of regression coefficient estimates from an geeglm object.

Usage

## S3 method for class 'geeglm'
vcovCR(
  obj,
  cluster,
  type,
  target = NULL,
  inverse_var = NULL,
  form = "sandwich",
  ...
)

Arguments

`obj`	Fitted model for which to calculate the variance-covariance matrix
`cluster`	Expression or vector indicating which observations belong to the same cluster. Required for `geeglm` objects.
`type`	Character string specifying which small-sample adjustment should be used, with available options `"CR0"`, `"CR1"`, `"CR1p"`, `"CR1S"`, `"CR2"`, or `"CR3"`. See "Details" section of `vcovCR` for further information.
`target`	Optional matrix or vector describing the working variance-covariance model used to calculate the `CR2` and `CR4` adjustment matrices. If a vector, the target matrix is assumed to be diagonal. If not specified, the target is taken to be the estimated variance function.
`inverse_var`	Optional logical indicating whether the weights used in fitting the model are inverse-variance. If not specified, `vcovCR` will attempt to infer a value.
`form`	Controls the form of the returned matrix. The default `"sandwich"` will return the sandwich variance-covariance matrix. Alternately, setting `form = "meat"` will return only the meat of the sandwich and setting `form = B`, where `B` is a matrix of appropriate dimension, will return the sandwich variance-covariance matrix calculated using `B` as the bread. `form = "estfun"` will return the (appropriately scaled) estimating function, the transposed crossproduct of which is equal to the sandwich variance-covariance matrix.
`...`	Additional arguments available for some classes of objects.

Value

An object of class c("vcovCR","clubSandwich"), which consists of a matrix of the estimated variance of and covariances between the regression coefficient estimates.

Examples


if (requireNamespace("geepack", quietly = TRUE)) {

  library(geepack)
  data(dietox, package = "geepack")
  dietox$Cu <- as.factor(dietox$Cu)
  mf <- formula(Weight ~ Cu * (Time + I(Time^2) + I(Time^3)))
  gee1 <- geeglm(mf, data=dietox, id=Pig, family=poisson("identity"), corstr="ar1")
  V_CR <- vcovCR(gee1, cluster = dietox$Pig, type = "CR2")
  coef_test(gee1, vcov = V_CR, test = "Satterthwaite")
  
}