cooks.distance.estex: Compute the Cook's distance measure of influential data on mixed effects models

Description

Cook's Distance is a measure indicating to what extent model parameters are influenced by (a set of) influential data on which the model is based. This function computes the Cook's distance based on the information returned by the influence() function.

Usage

# S3 method for estex
cooks.distance(model, parameters=0, sort=FALSE, …)

Arguments

model

An object as returned by the influence() function, containing the altered estimates of a mixed effects regression model

parameters

Used to define a selection of parameters. If parameters=0 (default), Cook's Distance is calculated based on all parameters in the model

sort

If sort=TRUE the values of Cook's Distance are ordered based on magnitude. If sort=FALSE (default) no sorting takes place.

…

Currently not used

Value

A one-column matrix is returned containing values for the Cook's Distance based on the selected (fixed) parameters of the model. Each row shows the Cook's Distance associated with each evaluated set of influential data (data nested within each evaluated level of the grouping factor).

References

Nieuwenhuis, R., Te Grotenhuis, M., & Pelzer, B. (2012). Influence.ME: tools for detecting influential data in mixed effects models. R Journal, 4(2), 38???47.

Belsley, D.A., Kuh, E. & Welsch, R.E. (1980). Regression Diagnostics. Identifying Influential Data and Source of Collinearity. Wiley.

Snijders, T.A. & Bosker, R.J. (1999). Multilevel Analysis, an introduction to basic and advanced multilevel modeling. Sage.

Van der Meer, T., Te Grotenhuis, M., & Pelzer, B. (2010). Influential Cases in Multilevel Modeling: A Methodological Comment. American Sociological Review, 75(1), 173-178.

Examples

Run this code


data(school23)
model <- lmer(math ~ structure + SES  + (1 | school.ID), data=school23)

alt.est <- influence(model, group="school.ID")
cooks.distance(alt.est)