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Statistics.PCA Method ([In] TMtx, [In] TMtx, [In] TMtx, [In] TVec, [In] TVec, [In] TPCAMode)
Statistics Class  Statistics Members  Dew.Stats.Units Namespace  PCA Method  Example  See Also
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Perform a PCA on Data matrix, where Data columns are variables and rows are the observables.

Syntax
C#
Visual Basic
public static void PCA([In] TMtx Data, [In] TMtx PC, [In] TMtx ZScores, [In] TVec EigenVec, [In] TVec VarPct, [In] TPCAMode PCAMode);
Remarks

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
Example

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

using Dew.Math; using Dew.Stat.Units; namespace Dew.Examples { private void Example() { Matrix data = new Matrix(0, 0); Matrix PC = new Matrix(0, 0); Matrix Z = new Matrix(0, 0); Vector variances = new Vector(0); Vector varPercent = new Vector(0); data.SetIt(2, 4, false, new double[] {1,3,5,2, 2,5,7,9}); Statistics.PCA(data, PC, Z, variances, varPercent, TPCAMode.PCACovMat); //works on raw data // ... variances = [29,0,0,0] // varPercent = [100,0,0,0] } }
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