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