Cluster-robust variance-covariance matrix for an lmerMod object.

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

Usage

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

Arguments

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 getGroups(obj).
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 not specified, the target is taken to be the estimated variance-covariance structure of the lmerMod 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. 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("lme4", quietly = TRUE)) {

library(lme4)
sleep_fit <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
vcovCR(sleep_fit, type = "CR2")

}
#> 
#> Attaching package: ‘lme4’
#> The following object is masked from ‘package:nlme’:
#> 
#>     lmList
#>             (Intercept)      Days
#> (Intercept)   46.574572 -1.451096
#> Days          -1.451096  2.389463

pkgs_available <- 
  requireNamespace("lme4", quietly = TRUE) & 
  requireNamespace("mlmRev", quietly = TRUE)

if (pkgs_available) {

data(egsingle, package = "mlmRev")
subset_ids <- levels(egsingle$schoolid)[1:10]
math_model <- lmer(math ~ year * size + female + black + hispanic 
                   + (1 | schoolid) + (1 | childid), 
                   data = egsingle, subset = schoolid %in% subset_ids)
vcovCR(math_model, type = "CR2")
}
#> Warning: Some predictor variables are on very different scales: consider rescaling
#>               (Intercept)          year          size    femaleMale
#> (Intercept)  3.783508e-02  4.761442e-03 -4.339287e-05 -7.507242e-03
#> year         4.761442e-03  1.055863e-02 -2.769454e-06 -3.659318e-03
#> size        -4.339287e-05 -2.769454e-06  8.544310e-08  6.516505e-06
#> femaleMale  -7.507242e-03 -3.659318e-03  6.516505e-06  9.417935e-03
#> black1      -7.037052e-03  3.942216e-04 -1.389629e-06 -4.086171e-03
#> hispanic1    1.603053e-02  6.423362e-03 -1.644442e-05 -4.676828e-03
#> year:size   -6.313817e-06 -1.201793e-05  8.181346e-09  3.644258e-06
#>                    black1     hispanic1     year:size
#> (Intercept) -7.037052e-03  1.603053e-02 -6.313817e-06
#> year         3.942216e-04  6.423362e-03 -1.201793e-05
#> size        -1.389629e-06 -1.644442e-05  8.181346e-09
#> femaleMale  -4.086171e-03 -4.676828e-03  3.644258e-06
#> black1       2.086581e-02  3.779801e-03  9.423158e-07
#> hispanic1    3.779801e-03  1.375137e-02 -6.919894e-06
#> year:size    9.423158e-07 -6.919894e-06  1.553385e-08

Cluster-robust variance-covariance matrix for an lmerMod object.

Usage

Arguments

Value

See also

Examples