rlmerMod object.Computes the robust score sandwich \(\hat{V}_{IF} = \hat{A}^{-1}
\hat{B} \hat{A}^{-T}\), where \(\hat{A}\) is the Schur-complement
(marginal) Jacobian of the profiled \(\beta\)-score and \(\hat{B}
= \sum_j s_j s_j^T\) sums the per-cluster \(\beta\)-score
contributions \(s_j = \sum_{i \in j} x_i \psi_e(\hat{r}_i)\). Equal
to the user-facing vcov(object, type = "sandwich").
vcov_sandwich(fit, cluster = NULL, correction = c("G1", "none"))A \(p \times p\) covariance matrix for \(\hat{\beta}\), with dimnames from the fixed-effect coefficient names and attribute
"n.clusters".
rlmerMod object.
Cluster specification; see resolveCluster.
One of "G1" (default, applies \(J/(J-1)\))
or "none".
Exact for a single (nested) grouping factor; approximate for crossed
factors (a warning is issued via resolveCluster). With
few clusters, set correction = "G1" (default) for the
\(J/(J-1)\) small-sample scaling.
Small-J caveat. The G1 correction is necessary but not
sufficient at very small \(J\): in a simulation study CI
coverage drops to ~0.89 at \(J = 8\) (vs. nominal 0.95), and
Wald-style hypothesis tests using the sandwich are anti-conservative
(Type-I ~3-4x nominal). The function emits a warning for \(J <
20\). For inference at small \(J\) prefer vcov_type =
"default" or pair the sandwich CI with a bootstrap calibration
(e.g. confintROB); the sandwich is most useful at \(J
\gtrsim 50\).
\(\hat{\sigma}, \hat{\theta}\) are held fixed (partial sandwich); the returned variance is the leading-order fixed-effects covariance.
vcov, caseweightIF