Generic function for extracting the empirical estimating functions of a fitted model.

estfun(x, ...)

Arguments

x

a fitted model object.

...

arguments passed to methods.

Value

A matrix containing the empirical estimating functions. Typically, this should be an $$n \times k$$ matrix corresponding to $$n$$ observations and $$k$$ parameters. The columns should be named as in coef or terms, respectively.

The estimating function (or score function) for a model is the derivative of the objective function with respect to the parameter vector. The empirical estimating functions is the evaluation of the estimating function at the observed data ($$n$$ observations) and the estimated parameters (of dimension $$k$$).

lm, glm

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

## linear regression
x <- 1:9
y <- sin(1:9/5)
m <- lm(y ~ x)

## estimating function: (y - x'beta) * x
estfun(m)
#>   (Intercept)           x
#> 1 -0.13577304 -0.13577304
#> 2 -0.04481531 -0.08963062
#> 3  0.03061755  0.09185265
#> 4  0.08353989  0.33415956
#> 5  0.10786351  0.53931755
#> 6  0.09864034  0.59184202
#> 7  0.05225971  0.36581794
#> 8 -0.03340770 -0.26726157
#> 9 -0.15892494 -1.43032449
residuals(m) * cbind(1, x)
#>                             x
#>  [1,] -0.13577304 -0.13577304
#>  [2,] -0.04481531 -0.08963062
#>  [3,]  0.03061755  0.09185265
#>  [4,]  0.08353989  0.33415956
#>  [5,]  0.10786351  0.53931755
#>  [6,]  0.09864034  0.59184202
#>  [7,]  0.05225971  0.36581794
#>  [8,] -0.03340770 -0.26726157
#>  [9,] -0.15892494 -1.43032449