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

In influence.ME: Tools for Detecting Influential Data in Mixed Effects Models

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.

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. (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

## Not run: 
 data(school23)
 model <- lmer(math ~ structure + SES  + (1 | school.ID), data=school23)

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

## End(Not run)

Example output

Loading required package: lme4
Loading required package: Matrix

Attaching package: ‘influence.ME’

The following object is masked from ‘package:stats’:

    influence

      (Intercept)     structure        SES
6053  -0.99099688  -11.39199887  5.6842054
6327   0.11920164    3.74828257 -4.1967144
6467  -0.11950980    1.90704406 -0.7827495
7194   0.03230393    0.41933377  0.2954229
7472   9.66083849   51.64448426 -3.0092482
7474   2.73257914   25.86912901 -0.7588419
7801  -0.25451710   -2.74005850  3.0477259
7829  -2.42413020  -41.49813953  3.1835829
7930   0.06935626    2.26531994 -3.0541613
24371  0.29784593    2.46201370 -4.5558686
24725  0.54075540    1.98406783 -4.9555447
25456  0.04107689    0.09331978 -0.7389065
25642 -0.30074630   -3.70476262  4.2882003
26537  0.59012732    5.00099330  0.7850121
46417 -0.27555933    2.22548177  0.5078338
47583  0.77570724    8.76242517 -4.2292816
54344 -5.72277086 -208.12269251  7.6103732
62821 -5.13024613  -83.85633615  5.2402802
68448  1.44319403   12.14066669 -2.1855012
68493  0.22337872    0.00616739 -1.5986123
72080 -0.27259899   -1.86864068  2.4680606
72292  0.22817217    1.60841202 -3.4224829
72991 -0.01970189    2.64635543 -2.5192315

influence.ME documentation built on May 2, 2019, 9:33 a.m.