Hello all together, today we can share our experiences on MDS with you. Our data is like this: matrix of distances with more than 500 rows and columns, distances between 0 and 1, small distances are more important than bigger ones. We got the best results with level=ordinal, which fits an ordinal MDS To estimate the smaller distances with higher accuracy we used the following weights: weight=(observed distances)**(-5) Especially increasing the number of levels of the MDS (up to 15 or 20) gave better results. Better results were measured as a lower Stress value, better fit plot with randomly scattered points around the diagonal, better Shepard-Plot (observes versus estimated distances). Our Shepard-Plot showed less variance for smaller distances than for bigger ones due to our weights. For further information see P. Groenen, I. Borg (2005), "Modern Multidimensional Scaling", Springer Badikidiki
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