influence.ME (version 0.9-6)

pchange: Compute the percentage change, as measure of influential data

Description

Computes the percentile change, as a measure of influential data. This unstandardized measure can serve to help interpret the magnitude of the influence single or combined grouping levels exert on mixed effects models. The percentage change in parameter estimates between a (mixed effects) regression model based on a full set of data, and a model from which a (potentially influential) subset of data is removed. A value of percentage change is calculated for each parameter in the model separately, based on the information returned by the influence() function.

Usage

pchange(estex, parameters = 0, sort=FALSE, to.sort=NA, abs=FALSE)

Arguments

estex

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), percentage change are calculated for all parameters in the model

sort

If sort=TRUE the values of percentage change are ordered based on magnitude. If sort=FALSE (default) no sorting takes place.

to.sort

Specify on which variable the percentage changes must be sorted. If only one variable present (either in the model, or due to the selection specified in parameters), this parameter can be omitted. If percentage changes are calculated for multiple variables, and sort=TRUE, specification of to.sort is required, or an error is returned.

abs

If abs=TRUE, the absolute values of percentage change are returned, while if abs=FALSE (default), both positive and negative values are possible. If both abs=TRUE and sort=TRUE, the abs parameters precedes the sort parameter, and thus the absolute values of percentage change are sorted.

Value

A matrix is returned, containing values of percentage change for each (selected) fixed parameter estimate of the model, and separately for each evaluated set of influential data.

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

influence, cooks.distance.estex, dfbetas.estex

Examples

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

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