scorefun: Extract Empirical Estimating Functions

In yeagle/RWiener: Wiener Process Distribution Functions

Description

!EXPERIMENTAL FUNCTION!

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

!EXPERIMENTAL FUNCTION!

Usage

scorefun(x, ...)
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 x 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 - scorefun - 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).

The estfun function is basically the score function, but with the additional functionality to sum up the scores by the given covariable id in the dataset.

References

Zeileis A (2006), Object-Oriented Computation of Sandwich Estimators. Journal of Statistical Software, 16(9), 1–16. URL http://www.jstatsoft.org/v16/i09/.

Examples

## generate random data
dat <- rwiener(100,1,.2,.5,0.5)

## fit wdm
wdm1 <- wdm(dat)

## estimating function
scores <- scorefun(wdm1)

## print
head(scores)

## plot
par(mfrow=c(2,2))
plot(scores[,1]);plot(scores[,2]);plot(scores[,3]);plot(scores[,4])

yeagle/RWiener documentation built on May 5, 2020, 3:47 a.m.