| PCA.vertices.SDA {symbolicDA} | R Documentation |
principal component analysis for symbolic objects described by symbolic interavl variables. Vertices algorithm
Description
principal component analysis for symbolic objects described by symbolic interavl variables. Vertices algorithm
Usage
PCA.vertices.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.centers.SDA,
PCA.mrpca.SDA,
PCA.spaghetti.SDA,
PCA.spca.SDA
Examples
# Example will be available in next version of package, thank You for your patience :-)