vcovCR.rma.mv {clubSandwich} | R Documentation |
vcovCR
returns a sandwich estimate of the variance-covariance matrix
of a set of regression coefficient estimates from a
rma.mv
object.
## S3 method for class 'rma.mv'
vcovCR(obj, cluster, type, target, inverse_var, form = "sandwich", ...)
obj |
Fitted model for which to calculate 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, with available options |
target |
Optional matrix or vector describing the working
variance-covariance model used to calculate the |
inverse_var |
Optional logical indicating whether the weights used in
fitting the model are inverse-variance. If not specified, |
form |
Controls the form of the returned matrix. The default
|
... |
Additional arguments available for some classes of objects. |
An object of class c("vcovCR","clubSandwich")
, which consists
of a matrix of the estimated variance of and covariances between the
regression coefficient estimates.
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 = constrain_zero(c(2,4)), vcov = mfor_CR2)
Wald_test(mfor_fit, constraints = constrain_zero(2:5), vcov = mfor_CR2)