fpc: awcoord – R documentation

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awcoord

Asymmetric weighted discriminant coordinates

Description

Asymmetric weighted discriminant coordinates as defined in Hennig (2003). Asymmetric discriminant projection means that there are two classes, one of which is treated as the homogeneous class (i.e., it should appear homogeneous and separated in the resulting projection) while the other may be heterogeneous. The principle is to maximize the ratio between the projection of a between classes separation matrix and the projection of the covariance matrix within the homogeneous class. Points are weighted according to their (robust) Mahalanobis distance to the homogeneous class.

Usage

awcoord(xd, clvecd, clnum=1, mahal="square", method="classical",
                     clweight=switch(method,classical=FALSE,TRUE),
                     alpha=0.99, subsample=0, countmode=1000, ...)

Arguments

`xd`	the data matrix; a numerical object which can be coerced to a matrix.
`clvecd`	integer vector of class numbers; length must equal `nrow(xd)`.
`clnum`	integer. Number of the homogeneous class.
`mahal`	"md" or "square". If "md", the points are weighted by the square root of the `alpha`-quantile of the corresponding chi squared distribution over the roots of their Mahalanobis distance to the homogeneous class, unless this is smaller than 1. If "square" (which is recommended), the (originally squared) Mahalanobis distance and the unrooted quantile is used.
`method`	one of "mve", "mcd" or "classical". Covariance matrix used within the homogeneous class and for the computation of the Mahalanobis distances. "mcd" and "mve" are robust covariance matrices as implemented in `cov.rob`. "classical" refers to the classical covariance matrix.
`clweight`	logical. If `FALSE`, only the points of the heterogeneous class are weighted. This, together with `method="classical"`, computes AWC as defined in Hennig (2003). If `TRUE`, all points are weighted. This, together with `method="mcd"`, computes ARC as defined in Hennig (2003).
`alpha`	numeric between 0 and 1. The corresponding quantile of the chi squared distribution is used for the downweighting of points. Points with a smaller Mahalanobis distance to the homogeneous class get full weight.
`subsample`	integer. If 0, all points are used. Else, only a subsample of `subsample` of the points is used.
`countmode`	optional positive integer. Every `countmode` algorithm runs `awcoord` shows a message.
`...`	no effect

Details

The square root of the homogeneous classes covariance matrix is inverted by use of tdecomp, which can be expected to give reasonable results for singular within-class covariance matrices.

Value

List with the following components

`ev`	eigenvalues in descending order.
`units`	columns are coordinates of projection basis vectors. New points `x` can be projected onto the projection basis vectors by `x %*% units`
`proj`	projections of `xd` onto `units`.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/

References

Hennig, C. (2004) Asymmetric linear dimension reduction for classification. Journal of Computational and Graphical Statistics 13, 930-945 .

Hennig, C. (2005) A method for visual cluster validation. In: Weihs, C. and Gaul, W. (eds.): Classification - The Ubiquitous Challenge. Springer, Heidelberg 2005, 153-160.

Examples

set.seed(4634)
  face <- rFace(600,dMoNo=2,dNoEy=0)
  grface <- as.integer(attr(face,"grouping"))
  awcf <- awcoord(face,grface==1)
  # awcf2 <- ancoord(face,grface==1, method="mcd")
  plot(awcf$proj,col=1+(grface==1))
  # plot(awcf2$proj,col=1+(grface==1))
  # ...done in one step by function plotcluster.

fpc

Flexible Procedures for Clustering

v2.2-9

GPL

Authors

Christian Hennig <christian.hennig@unibo.it>

Initial release

2020-12-06