robustbase: covPlot – R documentation

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covPlot

Robust Distance Plots

Description

Shows the Mahalanobis distances based on robust and classical estimates of the location and the covariance matrix in different plots. The following plots are available:

index plot of the robust and mahalanobis distances
distance-distance plot
Chisquare QQ-plot of the robust and mahalanobis distances
plot of the tolerance ellipses (robust and classic)
Scree plot - Eigenvalues comparison plot

Usage

## S3 method for class 'mcd'
plot(x,
     which = c("all", "dd", "distance", "qqchi2",
               "tolEllipsePlot", "screeplot"),
     classic = FALSE, ask = (which[1] == "all" && dev.interactive()),
     cutoff, id.n, labels.id = rownames(x$X), cex.id = 0.75,
     label.pos = c(4,2), tol = 1e-7, ...)

covPlot(x,
     which = c("all", "dd", "distance", "qqchi2",
               "tolEllipsePlot", "screeplot"),
     classic = FALSE, ask = (which[1] == "all" && dev.interactive()),
     m.cov = covMcd(x),
     cutoff = NULL, id.n, labels.id = rownames(x), cex.id = 0.75,
     label.pos = c(4,2), tol = 1e-07, ...)

Arguments

`x`	For the `plot()` method, a `mcd` object, typically result of `covMcd`. For `covPlot()`, the numeric data matrix such as the `X` component as returned from `covMcd`.
`which`	string indicating which plot to show. See the Details section for a description of the options. Defaults to `"all"`.

classic

whether to plot the classical distances too. Defaults to FALSE.

`ask`	logical indicating if the user should be asked before each plot, see `par(ask=.)`. Defaults to `which == "all" && dev.interactive()`.
`cutoff`	the cutoff value for the distances.
`id.n`	number of observations to be identified by a label. If not supplied, the number of observations with distance larger than `cutoff` is used.
`labels.id`	vector of labels, from which the labels for extreme points will be chosen. `NULL` uses observation numbers.
`cex.id`	magnification of point labels.
`label.pos`	positioning of labels, for the left half and right half of the graph respectively (used as `text(.., pos=*)`).
`tol`	tolerance to be used for computing the inverse, see `solve`. Defaults to `tol = 1e-7`.
`m.cov`	an object similar to those of class `"mcd"`; however only its components `center` and `cov` will be used. If missing, the MCD will be computed (via `covMcd()`).
`...`	other parameters to be passed through to plotting functions.

Details

These functions produce several plots based on the robust and classical location and covariance matrix. Which of them to select is specified by the attribute which. The plot method for "mcd" objects is calling covPlot() directly, whereas covPlot() should also be useful for plotting other (robust) covariance estimates. The possible options are:

distance: index plot of the robust distances
dd: distance-distance plot
qqchi2: a qq-plot of the robust distances versus the quantiles of the chi-squared distribution
tolEllipsePlot: a tolerance ellipse plot, via tolEllipsePlot()
screeplot: an eigenvalues comparison plot - screeplot

The Distance-Distance Plot, introduced by Rousseeuw and van Zomeren (1990), displays the robust distances versus the classical Mahalanobis distances. The dashed line is the set of points where the robust distance is equal to the classical distance. The horizontal and vertical lines are drawn at values equal to the cutoff which defaults to square root of the 97.5% quantile of a chi-squared distribution with p degrees of freedom. Points beyond these lines can be considered outliers.

References

P. J. Rousseeuw and van Zomeren, B. C. (1990). Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association 85, 633–639.

P. J. Rousseeuw and K. van Driessen (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212–223.

Examples

data(Animals, package ="MASS")
brain <- Animals[c(1:24, 26:25, 27:28),]
mcd <- covMcd(log(brain))

plot(mcd, which = "distance", classic = TRUE)# 2 plots
plot(mcd, which = "dd")
plot(mcd, which = "tolEllipsePlot", classic = TRUE)
op <- par(mfrow = c(2,3))
plot(mcd) ## -> which = "all" (5 plots)
par(op)

## same plots for another robust Cov estimate:
data(hbk)
hbk.x <- data.matrix(hbk[, 1:3])
cOGK <- covOGK(hbk.x, n.iter = 2, sigmamu = scaleTau2,
               weight.fn = hard.rejection)
covPlot(hbk.x, m.cov = cOGK, classic = TRUE)

robustbase

Basic Robust Statistics

v0.93-7

GPL (>= 2)

Authors

Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>), Peter Rousseeuw [ctb] (Qn and Sn), Christophe Croux [ctb] (Qn and Sn), Valentin Todorov [aut] (most robust Cov), Andreas Ruckstuhl [aut] (nlrob, anova, glmrob), Matias Salibian-Barrera [aut] (lmrob orig.), Tobias Verbeke [ctb, fnd] (mc, adjbox), Manuel Koller [aut] (mc, lmrob, psi-func.), Eduardo L. T. Conceicao [aut] (MM-, tau-, CM-, and MTL- nlrob), Maria Anna di Palma [ctb] (initial version of Comedian)

Initial release

2021-01-04