meat.Rd
Estimating the variance of the estimating functions of a regression model by cross products of the empirical estimating functions.
meat(x, adjust = FALSE, ...)
x | a fitted model object. |
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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 |
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
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