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infIndexPlot

Influence Index Plot

Description

Provides index plots of influence and related diagnostics for a regression model.

Usage

infIndexPlot(model, ...)

influenceIndexPlot(model, ...)

## S3 method for class 'lm'
infIndexPlot(model, vars=c("Cook", "Studentized", "Bonf", "hat"),
    id=TRUE, grid=TRUE, main="Diagnostic Plots", ...)

## S3 method for class 'influence.merMod'
infIndexPlot(model,
    vars = c("dfbeta", "dfbetas", "var.cov.comps",
    "cookd"), id = TRUE, grid = TRUE, main = "Diagnostic Plots", ...)
## S3 method for class 'influence.lme'
infIndexPlot(model,
    vars = c("dfbeta", "dfbetas", "var.cov.comps",
    "cookd"), id = TRUE, grid = TRUE, main = "Diagnostic Plots", ...)

Arguments

`model`	A regression object of class `lm`, `glm`, or `lmerMod`, or an influence object for a `lmer`, `glmer`, or `lme` object (see `influence.mixed.models`). The `"lmerMod"` method calls the `"lm"` method and can take the same arguments.
`vars`	All the quantities listed in this argument are plotted. Use `"Cook"` for Cook's distances, `"Studentized"` for Studentized residuals, `"Bonf"` for Bonferroni p-values for an outlier test, and and `"hat"` for hat-values (or leverages) for a linear or generalized linear model, or `"dfbeta"`, `"dfbetas"`, `"var.cov.comps"`, and `"cookd"` for an influence object derived from a mixed model. Capitalization is optional. All but `"dfbeta"` and `"dfbetas"` may be abbreviated by the first one or more letters.
`main`	main title for graph
`id`	a list of named values controlling point labelling. The default, `TRUE`, is equivalent to `id=list(method="y", n=2, cex=1, col=carPalette()[1], location="lr")`; `FALSE` suppresses point labelling. See `showLabels` for details.
`grid`	If TRUE, the default, a light-gray background grid is put on the graph.
`...`	Arguments passed to `plot`

Value

Used for its side effect of producing a graph. Produces index plots of diagnostic quantities.

Author(s)

Sanford Weisberg sandy@umn.edu and John Fox

References

Cook, R. D. and Weisberg, S. (1999) Applied Regression, Including Computing and Graphics. Wiley.

Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage. Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.

Weisberg, S. (2014) Applied Linear Regression, Fourth Edition, Wiley.

Examples

influenceIndexPlot(lm(prestige ~ income + education + type, Duncan))

car

Companion to Applied Regression

v3.0-10

GPL (>= 2)

Authors

John Fox [aut, cre], Sanford Weisberg [aut], Brad Price [aut], Daniel Adler [ctb], Douglas Bates [ctb], Gabriel Baud-Bovy [ctb], Ben Bolker [ctb], Steve Ellison [ctb], David Firth [ctb], Michael Friendly [ctb], Gregor Gorjanc [ctb], Spencer Graves [ctb], Richard Heiberger [ctb], Pavel Krivitsky [ctb], Rafael Laboissiere [ctb], Martin Maechler [ctb], Georges Monette [ctb], Duncan Murdoch [ctb], Henric Nilsson [ctb], Derek Ogle [ctb], Brian Ripley [ctb], William Venables [ctb], Steve Walker [ctb], David Winsemius [ctb], Achim Zeileis [ctb], R-Core [ctb]

Initial release

2020-09-23