meat: A Simple Meat Matrix Estimator

In sandwich: Robust Covariance Matrix Estimators

A Simple Meat Matrix Estimator

Description

Estimating the variance of the estimating functions of a regression model by cross products of the empirical estimating functions.

Usage

meat(x, adjust = FALSE, ...)

Arguments

x	a fitted model object.
adjust	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 estfun function.

Details

For some theoretical background along with implementation details see Zeileis (2006).

Value

A k \times k matrix corresponding containing the scaled cross products of the empirical estimating functions.

References

Zeileis A (2006). “Object-Oriented Computation of Sandwich Estimators.” Journal of Statistical Software, 16(9), 1–16. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v095.i01")}

See Also

sandwich, bread, estfun

Examples

x <- sin(1:10)
y <- rnorm(10)
fm <- lm(y ~ x)

meat(fm)
meatHC(fm, type = "HC")
meatHAC(fm)

sandwich documentation built on Sept. 16, 2024, 3:08 a.m.