Making Sandwiches with Bread and Meat
Constructing sandwich covariance matrix estimators by multiplying bread and meat matrices.
sandwich(x, bread. = bread, meat. = meat, ...)
x |
a fitted model object. |
bread. |
either a bread matrix or a function for computing
this via |
meat. |
either a bread matrix or a function for computing
this via |
... |
arguments passed to the |
sandwich is a simple convenience function that
takes a bread matrix (i.e., estimator of the expectation of the negative
derivative of the estimating functions) and a meat matrix (i.e.,
estimator of the variance of the estimating functions) and multiplies
them to a sandwich with meat between two slices of bread. By default
bread and meat are called.
Some theoretical background along with implementation details is introduced in Zeileis (2006) and also used in Zeileis et al. (2020).
A matrix containing the sandwich covariance matrix estimate. Typically, this should be an k x k matrix corresponding to k parameters.
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
x <- sin(1:10) y <- rnorm(10) fm <- lm(y ~ x) sandwich(fm) vcovHC(fm, type = "HC")
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