plot.mcd  R Documentation 
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
distancedistance plot
Chisquare QQplot of the robust and mahalanobis distances
plot of the tolerance ellipses (robust and classic)
Scree plot  Eigenvalues comparison plot
## 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 = 1e7, ...) 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 = 1e07, ...)
x 
For the 
which 
string indicating which plot to show. See the
Details section for a description of the options. Defaults to 
.
classic 
whether to plot the classical distances too.
Defaults to 
.
ask 
logical indicating if the user should be asked
before each plot, see 
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

labels.id 
vector of labels, from which the labels for extreme
points will be chosen. 
cex.id 
magnification of point labels. 
label.pos 
positioning of labels, for the left half and right
half of the graph respectively (used as 
tol 
tolerance to be used for computing the inverse, see

m.cov 
an object similar to those of class 
... 
other parameters to be passed through to plotting functions. 
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
distancedistance plot
qqchi2
a qqplot of the robust distances versus the quantiles of the chisquared distribution
tolEllipsePlot
a tolerance ellipse plot, via
tolEllipsePlot()
screeplot
an eigenvalues comparison plot  screeplot
The DistanceDistance 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 chisquared distribution with p degrees of freedom. Points beyond these lines can be considered outliers.
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.
tolEllipsePlot
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)
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