| r6pack | R Documentation |
Compute six initial robust estimators of multivariate location and
“scatter” (scale); then, for each, compute the distances
d_{ij} and take the h (h > n/2) observations
with smallest distances. Then compute the statistical distances based
on these h observations.
Return the indices of the observations sorted in increasing order.
r6pack(x, h, full.h, scaled = TRUE, scalefn = rrcov.control()$scalefn)
x |
n x p data matrix |
h |
integer, typically around (and slightly larger than) |
full.h |
logical specifying if the full (length n) observation
ordering should be returned; otherwise only the first |
scaled |
logical indicating if the data |
scalefn |
a |
The six initial estimators are
Hyperbolic tangent of standardized data
Spearmann correlation matrix
Tukey normal scores
Spatial sign covariance matrix
BACON
Raw OGK estimate for scatter
a h' \times 6 matrix of observation
indices, i.e., with values from 1,\dots,n. If
full.h is true, h' = n, otherwise h' = h.
Valentin Todorov, based on the original Matlab code by
Tim Verdonck and Mia Hubert. Martin Maechler for tweaks
(performance etc), and full.h.
Hubert, M., Rousseeuw, P. J. and Verdonck, T. (2012) A deterministic algorithm for robust location and scatter. Journal of Computational and Graphical Statistics 21, 618–637.
covMcd(*, nsamp = "deterministic");
CovSest(*, nsamp = "sdet") from package rrcov.
data(pulpfiber)
dim(m.pulp <- data.matrix(pulpfiber)) # 62 x 8
dim(fr6 <- r6pack(m.pulp, h = 40, full.h= FALSE)) # h x 6 = 40 x 6
dim(fr6F <- r6pack(m.pulp, h = 40, full.h= TRUE )) # n x 6 = 62 x 6
stopifnot(identical(fr6, fr6F[1:40,]))
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