RSDA (version 2.0.8)

sym.histogram.pca: Histogram Principal Components Analysis

Description

This functions allows us to execute a histogram principal components analysis from a symbolic data table with continuos, interval or histogram variables that can be mixed.

Usage

sym.histogram.pca(sym.data, method = c('histogram', 'classic'))

Arguments

sym.data

Symbolic data table.

method

The method to be used.

Value

Return a symbolic data table.

References

Diday, E., Rodriguez O. and Winberg S. (2000). Generalization of the Principal Components Analysis to Histogram Data, 4th European Conference on Principles and Practice of Knowledge Discovery in Data Bases, September 12-16, 2000, Lyon, France.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

See Also

sym.interval.pca

Examples

# NOT RUN {
data(example7)
res<-sym.histogram.pca(example7)
class(res) <- c('sym.data.table')
sym.scatterplot(res[,1],res[,2], labels=TRUE,col='red',main='Histogram PCA')
sym.scatterplot3d(res[,1],res[,2],res[,3],color='blue',
                  main='Histogram PCA')
# }