influence.ME-package: Influence.ME: Tools for detecting influential data in mixed...

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

Description

influence.ME calculates measures of influence for mixed effects models estimated with lme4. The basic rationale behind measuring influential cases is that when iteratively single units are omitted from the data, models based on these data should not produce substantially different estimates. To standardize the assessment of how influential a (single group of) observation(s) is, several measures of influence are common practice. First, DFBETAS is a standardized measure of the absolute difference between the estimate with a particular case included and the estimate without that particular case. Second, Cook's distance provides an overall measurement of the change in all parameter estimates, or a selection thereof.

Details

Package:	influence.ME
Type:	Package
Version:	0.9.2
Date:	2013-01-15
License:	GPL-3
LazyLoad:	yes

Calculating measures of influential data on a mixed effects regression model entails the re-estimation of this model for each set of potentially influential data separately. The influence() function does this, and returns the altered estimates resulting from each re-estimation. These altered estimates can subsequently be entered to the cooks.distance and dfbetas methods, to calculate Cook's Distance and the DFBETAS (standardized difference of the beta) measures.

Author(s)

Rense Nieuwenhuis, Ben Pelzer, Manfred te Grotenhuis

Maintainer: Rense Nieuwenhuis <[e-mail]>

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, pchange, sigtest

Examples

## Not run: 
data(school23)

model.a <- lmer(math ~ structure + SES  + (1 | school.ID), data=school23)
alt.est.a <- influence(model.a, "school.ID")
 
model.b <- exclude.influence(model.a, "school.ID", "7472")
alt.est.b <- influence(model.b, "school.ID")

cooks.distance(alt.est.b)

model.c <- exclude.influence(model.b, "school.ID", "54344")
alt.est.c <- influence(model.c, "school.ID")

cooks.distance(alt.est.c)

## 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

             [,1]
6053  0.077204623
6327  0.034220027
6467  0.013300828
7194  0.006187283
7474  0.180078285
7801  0.017974482
7829  0.059029189
7930  0.016529879
24371 0.037188268
24725 0.080976030
25456 0.001669426
25642 0.028506983
26537 0.033476444
46417 0.041831451
47583 0.052031303
54344 0.457534894
62821 0.212424219
68448 0.086382815
68493 0.018075408
72080 0.012646315
72292 0.022623982
72991 0.019026904
              [,1]
6053  0.0819079704
6327  0.0374105644
6467  0.0118708228
7194  0.0007747943
7474  0.1648510306
7801  0.0174353664
7829  0.1590318391
7930  0.0169573953
24371 0.0398678040
24725 0.0956976006
25456 0.0033809836
25642 0.0331188012
26537 0.0387483150
46417 0.0626395586
47583 0.0345636327
62821 0.4605839518
68448 0.0762718308
68493 0.0246282078
72080 0.0131880978
72292 0.0323756523
72991 0.0217163433

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