covNNC()
estimates robust covariance/dispersion matrices by the
nearest neighbor variance estimation (NNVE) or (rather)
“Nearest Neighbor Cleaning” (NNC) method of Wang and Raftery
(2002, JASA).
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X |
matrix in which each row represents an observation or point and each column represents a variable. |
k |
desired number of nearest neighbors (default is 12) |
pnoise |
percent of added noise |
emconv |
convergence tolerance for EM |
bound |
value used to identify surges in variance caused by
outliers wrongly included as signal points ( |
extension |
whether or not to continue after reaching the last
chi-square distance. The default is to continue,
which is indicated by setting |
devsm |
when |
A list with components
cov |
covariance matrix |
mu |
mean vector |
postprob |
posterior probability |
classification |
classification (0=noise otherwise 1) obtained
by rounding |
innc |
list of initial nearest neighbor cleaning results (components are the covariance, mean, posterior probability and classification) |
Terms of use: GPL version 2 or newer.
MM: Even though covNNC()
is backed by a serious scientific
publication, I cannot recommend its use at all.
Naisyin Wang nwang@stat.tamu.edu and Adrian Raftery raftery@stat.washington.edu with contributions from Chris Fraley fraley@stat.washington.edu.
covNNC()
, then named cov.nnve()
, used to be (the only
function) in CRAN package covRobust (2003), which was archived
in 2012.
Martin Maechler allowed ncol(X) == 1
,
sped up the original code, by reducing the amount of scaling;
further, the accuracy was increased (using internal q.dDk()
).
Wang, N. and Raftery, A. (2002) Nearest neighbor variance estimation (NNVE): Robust covariance estimation via nearest neighbor cleaning (with discussion). Journal of the American Statistical Association 97, 994–1019.
see also University of Washington Statistics Technical Report 368 (2000) http://www.stat.washington.edu/www/research/reports
cov.mcd
from package MASS;
covMcd
, and covOGK
from package robustbase.
The whole package rrcov.
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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.