Provides index plots of some diagnostic measures for a multivariate linear model: Cook's distance, a generalized (squared) studentized residual, hatvalues (leverages), and Mahalanobis squared distances of the residuals.
1 2 3 4 5 6 7 8 9 10  ## 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, ...)

model 
A multivariate linear model object of class 
infl 
influence measure structure as returned by 
FUN 
For 
vars 
All the quantities listed in this argument are plotted. Use 
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 
grid 
If TRUE, the default, a lightgray background grid is put on the graph 
... 
Arguments passed to 
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.
None. Used for its side effect of producing a graph.
Michael Friendly; borrows code from car::infIndexPlot
Barrett, B. E. and Ling, R. F. (1992). General Classes of Influence Measures for Multivariate Regression. Journal of the American Statistical Association, 87(417), 184191.
Barrett, B. E. (2003). Understanding Influence in Multivariate Regression Communications in Statistics  Theory and Methods, 32, 667680.
influencePlot
,
Mahalanobis
,
infIndexPlot
,
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  # 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)

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