A Simple Meat Matrix Estimator

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.

See also

References

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

Examples

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

meat(fm)
#>             (Intercept)         x
#> (Intercept)   0.5500547 0.3489064
#> x             0.3489064 0.3073370
meatHC(fm, type = "HC")
#>             (Intercept)         x
#> (Intercept)   0.5500547 0.3489064
#> x             0.3489064 0.3073370
meatHAC(fm)
#>             (Intercept)         x
#> (Intercept)   0.5870363 0.4398522
#> x             0.4398522 0.4492865