NMath Reference Guide

## Sparse |

Class SparsePls performs a Partial Least Squares (PLS) calculation
for the model X ~ Y with variable selection.
The LASSO penalization is used on the pairs of loading
vectors.
SparsePls allows matrices with missing values in them by using
the NIPALS algorithm to estimate them. Missing values are represented
as Double.NaN.

Inheritance Hierarchy

Syntax

The SparsePls type exposes the following members.

Constructors

Name | Description | |
---|---|---|

SparsePls(SparsePLSMode, Int32, Int32, Int32, Double) | Constructs a SparsePls object from the given parameters. | |

SparsePls(DoubleMatrix, DoubleMatrix, Int32, Int32, Int32, SparsePLSMode, Int32, Double) | Constructs a SparsePls object from the given parameters and performs the sparse PLS calculation on the given data. The data is first centered and scaled by standard deviation. |

Properties

Name | Description | |
---|---|---|

CenteredScaledX | Matrix of scaled, centered X values. | |

CenteredScaledY | Matrix of scaled centered Y values. | |

CMatrix | Matrix of coefficients used internally for prediction. | |

Coefficients |
Coefficient matrix that may be used for prediction.
(Overrides IPLS2CalcCoefficients) | |

IsGood |
Indicates whether the most recent calculation was successful. For
SparsePls a return value of false most likely indicates that the
iterative algorithm did not converge before reaching the maximum
number of iterations. Check the Message property for
further information in this case.
(Overrides IPLS2CalcIsGood) | |

Iterations | Number of iterations of the algorthm for each component | |

KeepX | Get and sets the number of X variables kept in the model for each component. | |

KeepY | Get and sets the number of Y variables kept in the model for each component. | |

MaxIterations | Gets the max iterations performed by the iterative algorithm. | |

Message |
Gets any message that may have been generated by the algorithm. For example,
if the calculation is unsuccessful, the message should indicate the
reason.
(Overrides IPLS2CalcMessage) | |

Mode | Gets and sets the PLS mode. | |

NumComponents | Gets the number components. | |

PredictorLoadings |
Gets the predictor variable loadings. This is an alias for
XLoadings.
(Overrides IPLS2CalcPredictorLoadings) | |

PredictorScores |
Gets the predictor variable scores. This is an alias for
XVariates.
(Overrides IPLS2CalcPredictorScores) | |

Tolerance | Gets the tolerance used for convergence determination of the iterative algorithm. | |

XLoadings | Gets the matrix of X loadings. | |

XVariates | Gets the matrix of X variates or scores. | |

YLoadings | Gets the matrix of Y loadings. | |

YVariates | Gets the matrix of Y variates or scores. |

Methods

Name | Description | |
---|---|---|

Calculate |
Calculates the sparse PLS fit for the given data and number of components.
X and Y values are first centered and scaled by their standard deviations.
(Overrides IPLS2CalcCalculate(DoubleMatrix, DoubleMatrix, Int32)) | |

Clone |
Creates a deep copy of self.
(Overrides IPLS2CalcClone) | |

HotellingsT2 |
Calculaties Hotelling's T2 statistic for each sample. T2 can be viewed as the
squared distance from a samples projection into the subspace to the centroid
of the subspace, or, more simply, the variation of the sample point within
the model.
(Inherited from IPLS2Calc) | |

Predict(DoubleMatrix) |
Uses the calculated model to predict the observed values from
a matrix of predictor values.
(Overrides IPLS2CalcPredict(DoubleMatrix)) | |

Predict(DoubleVector) |
Used the calculated model to predict the observed value from the given
predictor vector.
(Overrides IPLS2CalcPredict(DoubleVector)) | |

QResiduals |
Calculates the Q residuals for in sample in the model. The Q residual
for a given sample is the distance between the sample and its projection
in the subspace of the model.
(Inherited from IPLS2Calc) |

See Also