modelCaseAnalysis: Provides graphs and/or tests for problematic cases for a...

In lmSupport: Support for Linear Models

Description

Provides diagnositic graphs and visual cut points for identification of points that are univaraite outliers, high leverage, regression outliers, and/or influential

Usage

modelCaseAnalysis(Model, Type = "RESIDUALS", Term = NULL, ID = row.names(Model$model))

Arguments

Model	a linear model produced by lm.
Type	Type = c('RESIDUALS', 'UNIVARIATE', 'HATVALUES', 'COOKSD', 'DFBETAS', 'INFLUENCEPLOT' 'COVRATIO') RESIDUALS (default) = regression outliers, UNIVARIATE = univariate outliers, HATVALUES = leverage, COOKSD = model influence, DFBETAS= individual parameter influence, INFLUENCEPLOT= leverage X influence, COVRATIO = inflation of SEs.
Term	Term from model to display. Used only by DFBETAS. DEFAULT is NULL with all terms displayed
ID	Use to identify points. Default = row.names(Model$model). NULL = no identification

Value

Side effect of plot is main goal for function. Also returns a list with Rownames and CaseAnalysis Values for cases identified. No list returned if DFBETAS without single term identified.

Author(s)

John J. Curtin [e-mail]

References

Fox, J. (1991). Regression diagnostics. SAGE Series (79) Quantitative Applictions in the Social Science.

Examples

##NOT RUN
##m = lm(FPS~BAC+TA, data=BAC)
##Cases = modelCaseAnalysis(m,'RESIDUALS')
##BAC[Cases$Rownames,]

##modelCaseAnalysis(m,'DFBETAS')
##modelCaseAnalysis(m,'DFBETAS', 'assets')

lmSupport documentation built on May 2, 2019, 2:14 p.m.