Classical multidimensional scaling.
Parameters |
Description |
D |
Distance matrix. |
Y |
Returns the coordinates of object in reduced space.> |
EigenValues |
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NumDim |
Defines number of dimensions/variables to use in classical MD scaling algorithm. |
Uses MtxExpr,Math387,Statistics; procedure Example; var D,Y,X,DHat: Matrix; Eigen: Vector; stress: double; begin X.SetIt(5,2,false,[1,2,3,4,3,11,7,8,9,4]); // Use all dimensions i.e. 2 PairwiseDistance(X,D,2); // Reduce to just one variable (1d subspace) MDScaleMetric(D,Y,Eigen,1); // Calculate estimated distance matrix PairwiseDistance(Y,DHat,1); // Calculate stress - measure of GOF stress := MDScaleStress(D,DHat); // if stress > 0.2, the GOF is poor. end;
#include "MtxExpr.hpp" #include "Math387.hpp" #include "Statistics.hpp" void __fastcall Example() { sMatrix X,Y,D,DHat; sVector eigen; X.SetIt(5,2,false,OPENARRAY(double,(1,2,3,4,3,11,7,8,9,4))); // Use all dimensions i.e. 2 PairwiseDistance(X,D,2); // Reduce to just one variable (1d subspace) MDScaleMetric(D,Y,eigen,1); // Calculate estimated distance matrix PairwiseDistance(Y,DHat,1); // Calculate stress - measure of GOF double stress = MDScaleStress(D,DHat); // if stress > 0.2, the GOF is poor. }
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