ME.dfbetas {influence.ME}R Documentation

Compute the DFBETAS measure of influential data

Description

DFBETAS (standardized difference of the beta) is a measure that standardizes the absolute difference 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 for DFBETAS is calculated for each parameter in the model separately. This function computes the DFBETAS based on the information returned by the estex() function.

Usage

ME.dfbetas(estex, parameters = 0, plot=FALSE, sort=FALSE, to.sort=NA, abs=FALSE, ...)

Arguments

estex An object as returned by the estex() function, containing the altered estimates of a mixed effects regression model
parameters Used to define a selection of parameters. If parameters=0 (default), all DFBETAS is calculated for parameters in the model
plot If plot=TRUE, the results from the ME.dfbetas() function are forwarded to the dp.ME.dfbetas() function, which creates a visual representation of the values for DFBETAS
sort If sort=TRUE the values of DFBETAS are ordered based on magnitude. If sort=FALSE (default) no sorting takes place.
to.sort Specify on which variable the DFBETAS 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 DFBETAS is 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 DFBETAS 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 DFBETAS are sorted.
... Further arguments passed on to the dp.ME.dfbetas() function.

Value

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

Author(s)

Rense Nieuwenhuis, Ben Pelzer, Manfred te Grotenhuis

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. Influential cases in multi-level modeling. A methodological comment on 'National context, religiosity, and volunteering' by Ruiter and De Graaf. Current status: conditionally accepted by the American Sociological Review.

See Also

estex, ME.cook

Examples

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

 alt.est <- estex(model, "school.ID")
 ME.dfbetas(alt.est)
 ME.dfbetas(alt.est, plot=TRUE, layout=c(1,3))

[Package influence.ME version 0.6.1 Index]