influence.ME (version 0.8.1)

ME.cook: 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 estex() function.

Usage

ME.cook(estex, parameters = 0, plot=FALSE, sort=FALSE, ...)

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

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.

See Also

estex, ME.dfbetas, dp.ME.cook

Examples

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

 alt.est <- estex(model, "school.ID")
 ME.cook(alt.est)
 ME.cook(alt.est, plot=TRUE, cutoff=.17)