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, tol = 1e-7, ...)
x |
a mcd object, typically result of 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. Default is 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. |
tol |
tolerance to be used for computing the inverse, see
solve . Defaults to tol = 1e-7 . |
... |
other parameters to be passed through to plotting functions. |
This function produces several plots based on the robust and classical
location and covariance matrix. Which of them to select is specified
by the attribute which
.
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
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.
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)