Estimating the variance of the estimating functions of a regression model by cross products of the empirical estimating functions.
meat(x, adjust = FALSE, ...)
a fitted model object.
logical. Should a finite sample adjustment be made? This amounts to multiplication with \(n/(n-k)\) where \(n\) is the number of observations and \(k\) the number of estimated parameters.
arguments passed to the
For some theoretical background along with implementation details see Zeileis (2006).
A \(k \times k\) matrix corresponding containing the scaled cross products of the empirical estimating functions.
Zeileis A (2006). “Object-Oriented Computation of Sandwich Estimators.” Journal of Statistical Software, 16(9), 1--16. doi: 10.18637/jss.v016.i09
Zeileis A, Köll S, Graham N (2020). “Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R.” Journal of Statistical Software, 95(1), 1--36. doi: 10.18637/jss.v095.i01