| interscal.SDA {symbolicDA} | R Documentation |
Multidimensional scaling for symbolic interval data - InterScal algorithm
Description
Multidimensional scaling for symbolic interval data - InterScal algorithm
Usage
interscal.SDA(x,d=2,calculateDist=FALSE)
Arguments
x |
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) |
d |
Dimensionality of reduced space |
calculateDist |
if TRUE x are treated as raw data and min-max dist matrix is calulated. See details |
Details
Interscal is the adaptation of well-known classical multidimensional scaling for symbolic data. The input for Interscal is the interval-valued dissmilirarity matrix. Such dissmilarity matrix can be obtained from symbolic data matrix (that contains only interval-valued variables), judgements obtained from experts, respondents. See Lechevallier Y. (2001) for details on calculating interval-valued distance. See file ../doc/Symbolic_MDS.pdf for further details
Value
xprim |
coordinates of rectangles |
stress.sym |
final STRESSSym value |
Author(s)
Andrzej Dudek andrzej.dudek@ue.wroc.pl Marcin Pełka marcin.pelka@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.
Lechevallier Y. (ed.), Scientific report for unsupervised classification, validation and cluster analysis, Analysis System of Symbolic Official Data - Project Number IST-2000-25161, project report.
See Also
Examples
# LONG RUNNING - UNCOMMENT TO RUN
#sda<-parse.SO("samochody")
#data<-sda$indivIC
#mds<-interscal.SDA(data, d=2, calculateDist=TRUE)