symbolicDA (version 0.7-1)

PCA.vertices.SDA: 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)

Value

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

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

Author

Andrzej Dudek

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

Details

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

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 :-)