PCA.centers.SDA: principal component analysis for symbolic objects described...

View source: R/PCA.SDA.r

PCA.centers.SDAR Documentation

principal component analysis for symbolic objects described by symbolic interavl variables. Centers algorithm

Description

principal component analysis for symbolic objects described by symbolic interavl variables. Centers algorithm

Usage

PCA.centers.SDA(t,pc.number=2)

Arguments

t

symbolic interval data: a 3-dimensional table, first dimension represents object number, second dimension - variable number, and third dimension contains lower- and upper-bounds of intervals (Simple form of symbolic data table)

pc.number

number of principal components

Details

See file ../doc/PCA_SDA.pdf for further details

Value

Data in reduced space (symbolic interval data: a 3-dimensional table)

Author(s)

Andrzej Dudek andrzej.dudek@ue.wroc.pl

Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/symbolicDA/

References

Billard L., Diday E. (eds.) (2006), Symbolic Data Analysis, Conceptual Statistics and Data Mining, John Wiley & Sons, Chichester.

Bock H.H., Diday E. (eds.) (2000), Analysis of symbolic data. Explanatory methods for extracting statistical information from complex data, Springer-Verlag, Berlin.

Diday E., Noirhomme-Fraiture M. (eds.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester.

See Also

PCA.mrpca.SDA, PCA.spaghetti.SDA, PCA.spca.SDA, PCA.vertices.SDA

Examples

# Example will be available in next version of package, thank You for your patience :-)

symbolicDA documentation built on May 28, 2022, 1:08 a.m.