plot.mcd {robustbase} | 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:
## S3 method for class 'mcd': plot(x, which = c("all", "dd", "distance", "qqchi2", "tolEllipsePlot", "screeplot"), classic = FALSE, ask = (which=="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 == "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, ...)
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. |
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
dd
qqchi2
tolEllipsePlot
tolEllipsePlot()
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.
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.
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)