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vcovCR
returns a sandwich estimate of the variance-covariance matrix
of a set of regression coefficient estimates from a
rma.uni
object.
# S3 method for rma.uni
vcovCR(obj, cluster, type, target, inverse_var, form = "sandwich", ...)
An object of class c("vcovCR","clubSandwich")
, which consists
of a matrix of the estimated variance of and covariances between the
regression coefficient estimates.
Fitted model for which to calculate the variance-covariance matrix
Expression or vector indicating which observations
belong to the same cluster. Required for rma.uni
objects.
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.
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.
Optional logical indicating whether the weights used in
fitting the model are inverse-variance. If not specified, vcovCR
will attempt to infer a value.
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.
vcovCR
pkgs_available <-
requireNamespace("metafor", quietly = TRUE) &
requireNamespace("metadat", quietly = TRUE)
if (pkgs_available) withAutoprint({
library(metafor)
data(dat.assink2016, package = "metadat")
mfor_fit <- rma.uni(yi ~ year + deltype, vi = vi,
data = dat.assink2016)
mfor_fit
mfor_CR2 <- vcovCR(mfor_fit, type = "CR2", cluster = dat.assink2016$study)
mfor_CR2
coef_test(mfor_fit, vcov = mfor_CR2, test = c("Satterthwaite", "saddlepoint"))
Wald_test(mfor_fit, constraints = constrain_zero(2:4), vcov = mfor_CR2)
})
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