discrproj | R Documentation |
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.
discrproj(x, clvecd, method="dc", clnum=NULL, ignorepoints=FALSE,
ignorenum=0, ...)
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
|
method |
one of
Note that "bc", "vbc", "adc", "awc", "arc" and "anc" assume that there are only two classes. |
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. |
ignorepoints |
logical. If |
ignorenum |
one of the potential values of the components of
|
... |
additional parameters passed to the projection methods. |
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 |
proj |
projections of |
Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/
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.
discrcoord
, batcoord
,
mvdcoord
, adcoord
,
awcoord
, ncoord
,
ancoord
.
rFace
for generation of the example data used below.
set.seed(4634)
face <- rFace(300,dMoNo=2,dNoEy=0,p=3)
grface <- as.integer(attr(face,"grouping"))
# The abs in the following is there to unify the output,
# because eigenvectors are defined only up to their sign.
# Statistically it doesn't make sense to compute absolute values.
round(abs(discrproj(face,grface, method="nc")$units),digits=2)
round(abs(discrproj(face,grface, method="wnc")$units),digits=2)
round(abs(discrproj(face,grface, clnum=1, method="arc")$units),digits=2)
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