# infIndexPlot.mlm: Influence Index Plots for Multivariate Linear Models In mvinfluence: Influence Measures and Diagnostic Plots for Multivariate Linear Models

## 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

 ``` 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, ...) ```

## 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.

`influencePlot.mlm`, `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) ```