This computes a matrix formalising 'local shape', i.e., aggregated
standardised variance/covariance in a Mahalanobis neighbourhood of the data
points. This can be used for finding clusters when used as one of the
covariance matrices in
Invariant Coordinate Selection (function ics
in package
ICS
), see Hennig's
discussion and rejoinder of Tyler et al. (2009).
localshape(xdata,proportion=0.1,mscatter="mcd",mcdalpha=0.8,
covstandard="det")
The local shape matrix.
objects times variables data matrix.
proportion of points to be considered as neighbourhood.
"mcd" or "cov"; specified minimum covariance determinant or classical covariance matrix to be used for Mahalanobis distance computation.
if mscatter="mcd"
, this is the alpha parameter
to be used by the MCD covariance matrix, i.e. one minus the
asymptotic breakdown point, see covMcd
.
one of "trace", "det" or "none", determining by what constant the pointwise neighbourhood covariance matrices are standardised. "det" makes the affine equivariant, as noted in the discussion rejoinder of Tyler et al. (2009).
Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en
Tyler, D. E., Critchley, F., Duembgen, L., Oja, H. (2009) Invariant coordinate selection (with discussion). Journal of the Royal Statistical Society, Series B, 549-592.
options(digits=3)
data(iris)
localshape(iris[,-5],mscatter="cov")
Run the code above in your browser using DataLab