Overload List
| # | Signature | Description |
|---|---|---|
| 1 | void PCA(TMtx *CovMat, TMtx *PC, TVec *EigenVec, TVec *VarPct = null); | Performs a principal component analysis (PCA). |
| 2 | void PCA(TMtx *Data, TMtx *PC, TMtx *ZScores, TVec *EigenVec, TVec *VarPct = null, const TPCAMode PCAMode = TPCAMode::PCACorrMat); | Perform a PCA on Data matrix, where Data columns are variables and rows are the observables. |
Overload 1: void PCA(TMtx *CovMat, TMtx *PC, TVec *EigenVec, TVec *VarPct = null);
Performs a principal component analysis (PCA).
Perform a PCA by using the original data covariance matrix CovMat. Return the principal components in PC matrix, eigenvalues of the covariance matrix (variances) in vector EigenVec and (optional) the percentage of total variance in vector VarPct. The PC, EigenVec and VarPct dimensions are adjusted automatically.
Overload 2: void PCA(TMtx *Data, TMtx *PC, TMtx *ZScores, TVec *EigenVec, TVec *VarPct = null, const TPCAMode PCAMode = TPCAMode::PCACorrMat);
Perform a PCA on Data matrix, where Data columns are variables and rows are the observables.
| # | Name | Type | Description |
|---|---|---|---|
| 1 | Data | TMtx * | |
| 2 | PC | TMtx * | |
| 3 | ZScores | TMtx * | |
| 4 | EigenVec | TVec * | |
| 5 | VarPct = null | TVec * | |
| 6 | PCAMode = TPCAMode::PCACorrMat | const TPCAMode |
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.