influence.ME (version 0.8.1)

estex: Estex returns mixed model estimates, iteratively excluding the influence of data nested within single grouping factors.

Description

estex() is the workhorse function of the influence.ME package. Based on a priorly estimated mixed effects regression model (estimated using lme4), the estex() function iteratively modifies the mixed effects model to neutralize the effect a grouped set of data has on the parameters, and which returns returns the fixed parameters of these iteratively modified models. These are used to compute measures of influential data.

Usage

estex(model, group, select = 0, gf="single", count = FALSE, delete=TRUE, ...)

Value

The object returned by estex() of class "alt.est" contains the 'altered estimates' required by several other functions to calculate measures of influential data. A list containing six elements is returned:or.fixedFixed estimates of the original model (based on the full data)or.seStandard Error of the estimates of the original modelor.vcovVariance / Covariance matrix of the original modelalt.fixedMatrix of the fixed parameters estimate, after iteratively subsets of data are removed. Altered estimates associated with the deletion of data nested within each grouping factor are provided.alt.seMatrix of the standard errors of the fixed parameter estimates, after iteratively subsets of data are removed. Altered estimates associated with the deletion of data nested within each grouping factor are provided.alt.vcovVariance / Covariance matrix of the altered models, after iteratively subsets of data are removed. Altered estimates associated with the deletion of data nested within each grouping factor are provided.

Details

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 apply this logic 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. estex() is the workhorse function of this likewise called package. Based on a priorly estimated mixed effects regression model (of the 'mer' class), the estex() function iteratively modifies the mixed effects model by neutralizing the effect a grouped set of data has on the parameters, and which returns returns the fixed parameters of these iteratively modified models. The returned object (see 'value') contains information which is required for functions computing various measures of influential data.

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

ME.dfbetas, ME.cook

Examples

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