Example 1

The "beauty" of MDS is that we can analyze any kind of distance or similarity matrix. These similarities can represent people's ratings of similarities between objects, the percent agreement between judges, the number of times a subjects fails to discriminate between stimuli, etc. For example, MDS methods used to be very popular in psychological research on person perception where similarities between trait descriptors were analyzed to uncover the underlying dimensionality of people's perceptions of traits (see, for example Rosenberg, 1977). In this example 6x6 similarities (extracted directly from questionare correlation matrix) is used to perform classical MD scaling.

    Uses StatTools, Statistics, MtxExpr;
    procedure Example(mds: TMtxMDScaling);
    begin
      // similarities matrix (symmetric with 1.0 on diagonal)ž
      mds.Data.SetIt(5,5,false,
          [ 1.00, 0.3, 0.2, 0.25, 0.33,
            0.30, 1.0, 0.11, 0.21, 0.8,
            0.20, 0.11, 1.0, 0.40, 0.5,
            0.25, 0.21, 1.0, 0.10, 0.05,
            0.33, 0.80, 0.5, 0.05, 1.00]);
      mds.DataFormat := mdFormatSimilarities;
      // use "standard" Euclidian metric
      mds.DistanceMethod := pwdistEuclidian;
      // define number of desired dimensions (1)
      mds.Dimensions := 1;
      // Do the math
      mds.Recalc;
      // check Stress, DHat, EigeValues to evaluate GOF if (1) dimension is used
    end;

    #include "Math387.hpp"
    #include "StatTools.hpp"
    #include "Statistics.hpp"
    void __fastcall Example(TMtxMDScaling* mds)
    {
      // similarities matrix (symmetric with 1.0 on diagonal)ž
      mds->Data->SetIt(5,5,false, OPENARRAY(TSample,
          ( 1.00, 0.3, 0.2, 0.25, 0.33,
            0.30, 1.0, 0.11, 0.21, 0.8,
            0.20, 0.11, 1.0, 0.40, 0.5,
            0.25, 0.21, 1.0, 0.10, 0.05,
            0.33, 0.80, 0.5, 0.05, 1.00)));
      mds->DataFormat = mdFormatSimilarities;
      // use "standard" Euclidian metric
      mds->DistanceMethod = pwdistEuclidian;
      // define number of desired dimensions (1)
      mds->Dimensions = 1;
      // Do the math
      mds->Recalc();
      // check Stress, DHat, EigeValues to evaluate GOF if (1) dimension is used
    }

    using Dew.Stats;
    using Dew.Stats.Units;
    using Dew.Math;
    namespace Dew.Examples
    {
      private void Example(StatTools.TMtxMDScaling mds)
      {
        // similarities matrix (symmetric with 1.0 on diagonal)ž
        mds.Data.SetIt(5,5,false, new double[] {
             1.00, 0.3, 0.2, 0.25, 0.33,
            0.30, 1.0, 0.11, 0.21, 0.8,
            0.20, 0.11, 1.0, 0.40, 0.5,
            0.25, 0.21, 1.0, 0.10, 0.05,
            0.33, 0.80, 0.5, 0.05, 1.00});
        mds.DataFormat = StatTools.mdFormatSimilarities;
        // use "standard" Euclidian metric
        mds.DistanceMethod = Statistics.pwdistEuclidian;
        // define number of desired dimensions (1)
        mds.Dimensions = 1;
        // Do the math
        mds.Recalc();
        // check Stress, DHat, EigeValues to evaluate GOF if (1) dimension is used
      }
    }

Properties