Statistics.PCA Function

Perform a PCA on Data matrix, where Data columns are variables and rows are the observables.

Pascal

procedure PCA(const Data: TMtx; const PC: TMtx; const ZScores: TMtx; const EigenVec: TVec; const VarPct: TVec = nil; const PCAMode: TPCAMode = PCACorrMat); overload;

File

Statistics

Description

The (optional) PCAMode parameter defines whether the analysis should be run on correlation or covariance matrix. PCA procedure returns the principal components in matrix PC, the Z-scores (data, transformed in the PC space) in ZScores, the eigenvalues of the covariance matrix (variances) in the EigenVec vector and (optional) the percentage of total variance in VarPct vector. The PC, ZScores, EigenVec and VarPct dimensions are adjusted automatically.

See Also

PCAResiduals, BartlettTest

Example

In this example we derive the covariance matrix from original data and get the same results as in first example.

Uses Statistics, MtxExpr;
procecure Example;
var Data, PC: Matrix;
    Variances,VarPercent : Vector;
begin
    Data.SetIt(2,4,false,[1,3,5,2
                        2,5,7,9]);

  PCA(data,PC,Variances,VarPercent,PCCovMat);  //works on raw data
  // ... Variances  = [29,0,0,0]
  // VarPercent = [100,0,0,0]
end;

#include "Statistics.hpp"
#include "MtxExpr.hpp"
void __fastcall Example()
{
  sMatrix data, PC;
  sVector variances, varPercent;
    data.SetIt(2,4,false,OPENARRAY(double,
                        (1,3,5,2,
                        2,5,7,9)));

  PCA(data,PC,variances,varPercent,TPCAMode::PCACovMat); //works on raw data
  // ... variances  = [29,0,0,0]
  // varPercent = [100,0,0,0]
}

Statistics, Functions, Example, See Also

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