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

## estimating function: (y - x'beta) * x
estfun(fm)
#>    (Intercept)          x
#> 1   -0.7015578 -0.5903406
#> 2    0.4494980  0.4087274
#> 3    2.2437203  0.3166338
#> 4   -1.0336182  0.7822448
#> 5    1.2040418 -1.1545849
#> 6   -1.4882323  0.4158352
#> 7   -0.5838511 -0.3835824
#> 8   -0.2694160 -0.2665489
#> 9    0.5953328  0.2453477
#> 10  -0.4159175  0.2262679
residuals(fm) * cbind(1, x)
#>                           x
#>  [1,] -0.7015578 -0.5903406
#>  [2,]  0.4494980  0.4087274
#>  [3,]  2.2437203  0.3166338
#>  [4,] -1.0336182  0.7822448
#>  [5,]  1.2040418 -1.1545849
#>  [6,] -1.4882323  0.4158352
#>  [7,] -0.5838511 -0.3835824
#>  [8,] -0.2694160 -0.2665489
#>  [9,]  0.5953328  0.2453477
#> [10,] -0.4159175  0.2262679