clubSandwich (version 0.2.2)

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

Description

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

Usage

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

Arguments

obj

Fitted model for which to calcualte the variance-covariance matrix

cluster

Optional expression or vector indicating which observations belong to the same cluster. If not specified, will be set to the factor in the random-effects structure with the fewest distinct levels. Caveat emptor: the function does not check that the random effects are nested.

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 the estimated variance-covariance structure of the rma.mv object.

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(hierdat, package = "robumeta")

mfor_fit <- rma.mv(effectsize ~ binge + followup + sreport + age, 
                 V = var, random = list(~ 1 | esid, ~ 1 | studyid),
                 data = hierdat)
mfor_fit

mfor_CR2 <- vcovCR(mfor_fit, type = "CR2")
mfor_CR2
coef_test(mfor_fit, vcov = mfor_CR2, test = c("Satterthwaite", "saddlepoint"))

Wald_test(mfor_fit, constraints = c(2,4), vcov = mfor_CR2)
Wald_test(mfor_fit, constraints = 2:5, vcov = mfor_CR2)
# }