fpc (version 1.2-2)

discrproj: Linear dimension reduction for classification

Description

An interface for ten methods of linear dimension reduction in order to separate the groups optimally in the projected data. Includes classical discriminant coordinates, methods to project differences in mean and covariance structure, asymmetric methods (separation of a homogeneous class from a heterogeneous one), local neighborhood-based methods and methods based on robust covariance matrices.

Usage

discrproj(x, clvecd, method="awc", clnum=1, ...)

Arguments

x

the data matrix; a numerical object which can be coerced to a matrix.

clvecd

vector of class numbers which can be coerced into integers; length must equal nrow(xd).

method

one of [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object] Note that "bc", "vbc", "adc", "awc", "arc" and "anc" assume that there are o

clnum

integer. Number of the class which is attempted to plot homogeneously by "asymmetric methods", which are the methods assuming that there are only two classes, as indicated above.

...

additional parameters passed to the projection methods.

Value

• discrproj returns the output of the chosen projection method, which is a list with at least the components ev, units, proj. For detailed informations see the help pages of the projection methods.
• ev
  eigenvalues in descending order, usually indicating portion of information in the corresponding direction.
• 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.

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. Seber, G. A. F. (1984). Multivariate Observations. New York: Wiley.

Fukunaga (1990). Introduction to Statistical Pattern Recognition (2nd ed.). Boston: Academic Press.

See Also

discrcoord, batcoord, mvdcoord, adcoord, awcoord, ncoord, ancoord.

rFace for generation of the example data used below.

Examples

set.seed(4634)
face <- rFace(300,dMoNo=2,dNoEy=0,p=3)
grface <- as.integer(attr(face,"grouping"))
discrproj(face,grface, method="nc")$units
discrproj(face,grface, method="wnc")$units
discrproj(face,grface==1, method="arc")$units