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.

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 light-gray 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), 184-191.

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

`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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