symbolicDA (version 0.7-1)

iscal.SDA: Multidimensional scaling for symbolic interval data - IScal algorithm

Description

Multidimensional scaling for symbolic interval data - IScal algorithm

Usage

iscal.SDA(x,d=2,calculateDist=FALSE)

Value

xprim

coordinates of rectangles

STRESSSym

final STRESSSym value

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

Author

Andrzej Dudek

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

Details

IScal, which was proposed by Groenen et. al. (2006), is an adaptation of well-known nonmetric multidimensional scaling for symbolic data. It is an iterative algorithm that uses I-STRESS objective function. This function is normalized within the range [0; 1] and can be interpreted like classical STRESS values. IScal, like Interscal and SymScal, requires interval-valued dissimilarity 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

References

Billard L., Diday E. (red.) (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. (red.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester.

Groenen P.J.F, Winsberg S., Rodriguez O., Diday E. (2006), I-Scal: multidimensional scaling of interval dissimilarities, Computational Statistics and Data Analysis, 51, pp. 360-378. Available at: tools:::Rd_expr_doi("10.1016/j.csda.2006.04.003").

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

interscal.SDA,symscal.SDA

Examples

# Example will be available in next version of package, thank You for your patience :-)