Influence Index Plots for Multivariate Linear Models

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Description

Provides index plots of some diagnostic measures for a multivariate linear model: Cook's distance, a generalized (squared) studentized residual, hat-values (leverages), and Mahalanobis squared distances of the residuals.

Usage

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## S3 method for class 'mlm'
infIndexPlot(model, 
	infl = mlm.influence(model, do.coef = FALSE), FUN = det, 
	vars = c("Cook", "Studentized", "hat", "DSQ"), 
	main = paste("Diagnostic Plots for", deparse(substitute(model))), 
	pch = 19, 
	labels, 
	id.method = "y", id.n = if (id.method[1] == "identify") Inf else 0, 
	id.cex = 1, id.col = palette()[1], id.location = "lr", 
	grid = TRUE, ...)

Arguments

model

A multivariate linear model object of class mlm .

infl

influence measure structure as returned by mlm.influence

FUN

For m>1, the function to be applied to the H and Q matrices returning a scalar value. FUN=det and FUN=tr are possible choices, returning the |H| and tr(H) respectively.

vars

All the quantities listed in this argument are plotted. Use "Cook" for generalized Cook's distances, "Studentized" for generalized Studentized residuals, "hat" for hat-values (or leverages), and DSQ for the squared Mahalanobis distances of the model residuals. Capitalization is optional. All may be abbreviated by the first one or more letters.

main

main title for graph

pch

Plotting character for points

id.method,labels,id.n,id.cex,id.col,id.location

Arguments for the labelling of points. The default is id.n=0 for labeling no points. See showLabels for details of these arguments.

grid

If TRUE, the default, a light-gray background grid is put on the graph

...

Arguments passed to plot

Details

This function produces index plots of the various influence measures calculated by influence.mlm, and in addition, the measure based on the Mahalanobis squared distances of the residuals from the origin.

Value

None. Used for its side effect of producing a graph.

Author(s)

Michael Friendly; borrows code from car::infIndexPlot

References

Barrett, B. E. and Ling, R. F. (1992). General Classes of Influence Measures for Multivariate Regression. Journal of the American Statistical Association, 87(417), 184-191.

Barrett, B. E. (2003). Understanding Influence in Multivariate Regression Communications in Statistics - Theory and Methods, 32, 667-680.

See Also

influencePlot, Mahalanobis, infIndexPlot,

Examples

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# iris data
data(iris)
iris.mod <- lm(as.matrix(iris[,1:4]) ~ Species, data=iris)
infIndexPlot(iris.mod, col=iris$Species, id.n=3)

# Sake data
data(Sake, package="heplots")
Sake.mod <- lm(cbind(taste,smell) ~ ., data=Sake)
infIndexPlot(Sake.mod, id.n=3)

# Rohwer data
data(Rohwer, package="heplots")
Rohwer2 <- subset(Rohwer, subset=group==2)
rownames(Rohwer2)<- 1:nrow(Rohwer2)
rohwer.mlm <- lm(cbind(SAT, PPVT, Raven) ~ n + s + ns + na + ss, data=Rohwer2)
infIndexPlot(rohwer.mlm, id.n=3)