Weighted metric multidimensional scaling
AbstractThis paper establishes a general framework for metric scaling of any distance measure between individuals based on a rectangular individuals-by-variables data matrix. The method allows visualization of both individuals and variables as well as preserving all the good properties of principal axis methods such as principal components and correspondence analysis, based on the singular-value decomposition, including the decomposition of variance into components along principal axes which provide the numerical diagnostics known as contributions. The idea is inspired from the chi-square distance in correspondence analysis which weights each coordinate by an amount calculated from the margins of the data table. In weighted metric multidimensional scaling (WMDS) we allow these weights to be unknown parameters which are estimated from the data to maximize the fit to the original distances. Once this extra weight-estimation step is accomplished, the procedure follows the classical path in decomposing a matrix and displaying its rows and columns in biplots.
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Bibliographic InfoPaper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 777.
Date of creation: Sep 2004
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Web page: http://www.econ.upf.edu/
Biplot; correspondence analysis; distance; multidimensional scaling; singular-value decomposition;
Find related papers by JEL classification:
- C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
- C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
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- J. Gower & P. Legendre, 1986. "Metric and Euclidean properties of dissimilarity coefficients," Journal of Classification, Springer, vol. 3(1), pages 5-48, March.
- Michael Greenacre & Rafael Pardo, 2005. "Multiple correspondence analysis of a subset of response categories," Economics Working Papers 881, Department of Economics and Business, Universitat Pompeu Fabra.
- Michael Greenacre & Rafael Pardo, 2004. "Subset correspondence analysis: Visualizing relationships among a selected set of response categories from a questionnaire survey," Economics Working Papers 791, Department of Economics and Business, Universitat Pompeu Fabra.
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