ME.dfbetas {influence.ME} | R Documentation |
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.
ME.dfbetas(estex, parameters = 0, plot=FALSE, sort=FALSE, to.sort=NA, abs=FALSE, ...)
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), DFBETAS is calculated for all 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. |
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
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: Accepted for publication in the American Sociological Review.
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))