dfbetas.estex: Compute the DFBETAS measure of influential data

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/dfbetas.estex.r


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 influence() function.


## S3 method for class 'estex'
dfbetas(model, parameters = 0, sort=FALSE, to.sort=NA, abs=FALSE, ...)



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


Used to define a selection of parameters. If parameters=0 (default), DFBETAS is calculated for all parameters in the model


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


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.


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.


Currently not used


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


Rense Nieuwenhuis, Ben Pelzer, Manfred te Grotenhuis


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.

See Also

influence.mer, cooks.distance.estex


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

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

influence.ME documentation built on May 19, 2017, 5:36 p.m.
Search within the influence.ME package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs in the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.