influence.ME (version 0.8.1)

exclude.influence: Exclude the influence of a grouped set of observations in mixed effects models.

Description

Using mixed effects regression models, exclude.influence excludes the influence of a group of cases grouped within a single grouping factor, or a set of grouping factors. The function returns a model in which the influence a grouped set of observations has on both the variance and point-estimate of the (random) intercept.

Usage

exclude.influence(model, grouping, level, gf="single", delete=TRUE)

Value

Mixed effects regression model of class 'mer', with a modified random intercept and dummy variables indicating the estimates of the neutralized influence of selected grouping levels.

Details

To apply the basic logic of influential cases to mixed effects models one has to measure the influence of a particular higher level unit on the estimates of a higher level predictor. This means that the mixed effects model has to be adjusted to neutralize the unit's influence on that estimate, while at the same time allowing the unit's lower-level cases to help estimate the effects of the lower-level predictors in the model. This procedure is based on a modification of the intercept and the addition of a dummy variable for the cases that might be influential.

The model that is returned by exclude.influence thus contains a modified intercept, and one or more additional dummy variables. To help identify this model as modified (which is required when in a later stage the influence of additional grouping levels is excluded), the intercept is renamed to 'intercept.alt'. The additional dummy variables, indicating the observations associated with the grouping factor levels of which the influence was neutralized, are labeled starting with 'estex.', combined with the label of the neutralized grouping level.

References

Belsley, D.A., Kuh, E. & Welsch, R.E. (1980). Regression Diagnostics. Identifying Influential Data and Source of Collinearity. Wiley.

Langford, I. H. and Lewis, T. (1998). Outliers in multilevel data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 161:121-160.

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

estex

Examples

data(school23)
 model.a <- lmer(math ~ structure + SES  + (1 | school.ID), data=school23)
 summary(model.a)
 model.b <- exclude.influence(model.a, "school.ID", "7472")
 summary(model.b)
 model.c <- exclude.influence(model.a, "school.ID", c("7472", "62821"))
 summary(model.c)
 
 data(Penicillin)
 model.d <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin)
 summary(model.d)
 model.e <- exclude.influence(model.d, "sample", "A", gf="all")
 summary(model.e)