clubSandwich (version 0.2.2)

vcovCR.rma.uni: Cluster-robust variance-covariance matrix for a rma.uni object.

Description

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

Usage

# S3 method for rma.uni
vcovCR(obj, cluster, type, target, inverse_var,
  form = "sandwich", ...)

Arguments

obj

Fitted model for which to calcualte the variance-covariance matrix

cluster

Expression or vector indicating which observations belong to the same cluster. Required for rma.uni objects.

type

Character string specifying which small-sample adjustment should be used.

target

Optional matrix or vector describing the working variance-covariance model used to calculate the CR2 and CR4 adjustment matrices. If not specified, the target is taken to be diagonal with entries equal to the estimated marginal variance of the effect sizes.

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.

...

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.

See Also

vcovCR

Examples

# NOT RUN {
library(metafor)
data(corrdat, package = "robumeta")

mfor_fit <- rma.uni(effectsize ~ males + college + binge,
                     vi = var, data = corrdat, method = "FE")
mfor_fit
mfor_CR2 <- vcovCR(mfor_fit, type = "CR2", cluster = corrdat$studyid)
mfor_CR2
coef_test(mfor_fit, vcov = mfor_CR2, test = c("Satterthwaite", "saddlepoint"))
Wald_test(mfor_fit, constraints = 2:4, vcov = mfor_CR2)

# }