PartialLeastSquaresModel Class

Represents a Partial Least Squares (PLS) model.

Definition

Namespace: Extreme.Statistics.Multivariate
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
C#
public class PartialLeastSquaresModel : Model
Inheritance
Object  →  Model  →  PartialLeastSquaresModel

Remarks

Use the PartialLeastSquaresModel to perform a partial least squares calculation.

Partial least squares is a technique that fits combinations of independent variables called factors to one or more dependent variables. The factors are chosen to maximize the covariance between the factors and the dependent variables.

Partial least squares is useful when the number of independent variables is large compared to the number of observations, or when variables are highly correlated.

Fitting the model is done with one of two standard algorithms: NIPALS (Nonlinear Iterative PArtial Least Squares) or SIMPLS (Statistically Inspired Modification of Partial Least Squares). The two algorithms give identical results when there is only one dependent variable.

Constructors

PartialLeastSquaresModel(IDataFrame, String, Int32) Constructs a new PartialLeastSquaresModel.
PartialLeastSquaresModel(Matrix<Double>, Matrix<Double>, Int32) Constructs a new PartialLeastSquaresModel.
PartialLeastSquaresModel(Vector<Double>, Matrix<Double>, Int32) Constructs a new PartialLeastSquaresModel.
PartialLeastSquaresModel(IDataFrame, String[], String[], Int32) Constructs a new PartialLeastSquaresModel.

Properties

BaseFeatureIndex Gets an index containing the keys of the columns that are required inputs to the model.
(Inherited from Model)
Coefficients Gets a matrix containing the (unstandardized) coefficients of the Partial Least Squares regression.
Computed Gets whether the model has been computed.
(Inherited from Model)
Obsolete.
Data Gets an object that contains all the data used as input to the model.
(Inherited from Model)
DependentVariables Gets a matrix that contains the dependent variables that are to be fitted.
Fitted Gets whether the model has been computed.
(Inherited from Model)
IndependentVariables Gets a matrix whose columns contain the independent variables in the model.
InputSchema Gets the schema for the features used for fitting the model.
(Inherited from Model)
Intercepts Gets a matrix containing the intercept term of the Partial Least Squares regression.
MaxDegreeOfParallelism Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model)
Method Gets or sets the method used to compute the PLS model.
ModelSchema Gets the collection of variables used in the model.
(Inherited from Model)
NumberOfComponents Gets or sets the number of components to calculate.
NumberOfObservations Gets the number of observations the model is based on.
(Inherited from Model)
ParallelOptions Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model)
PredictedValues Gets the residuals of the dependent variables.
StandardizedCoefficients Gets a matrix containing the standardized coefficients of the Partial Least Squares regression.
Status Gets the status of the model, which determines which information is available.
(Inherited from Model)
SupportsWeights Indicates whether the model supports case weights.
(Inherited from Model)
VariableImportanceInProjection Gets a matrix that contains the Variable Importance in Projection (VIP) value for each variable and each number of components.
WeightMatrix Gets the weights of the independent variables.
Weights Gets or sets the actual weights.
(Inherited from Model)
XCumulativeVarianceExplained Gets a vector containing the cumulative proportion of variance in the independent variables explained by each the component
XDistanceToModel Gets a vector containing the distance to the model of the independent variables.
XLoadings Gets the scores matrix of the independent variables.
XResiduals Gets the residuals of the independent variables.
XScalingMethod Gets or sets how independent variables are scaled.
XScores Gets the loadings matrix of the independent variables.
XVarianceExplained Gets a vector containing the proportion of variance in the independent variables explained by each the component
YCumulativeVarianceExplained Gets a vector containing the cumulative proportion of variance in the dependent variables explained by each the component
YDistanceToModel Gets a vector containing the distance to the model of the dependent variables.
YLoadings Gets the scores matrix of the dependent variables.
YResiduals Gets the residuals of the dependent variables.
YScalingMethod Gets or sets how dependent variables are scaled.
YScores Gets the loadings matrix of the dependent variables.
YVarianceExplained Gets a vector containing the proportion of variance in the dependent variables explained by each the component

Methods

Compute() Computes the model.
(Inherited from Model)
Obsolete.
Compute(ParallelOptions) Computes the model.
(Inherited from Model)
Obsolete.
EqualsDetermines whether the specified object is equal to the current object.
(Inherited from Object)
FinalizeAllows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object)
Fit() Fits the model to the data.
(Inherited from Model)
Fit(ParallelOptions) Fits the model to the data.
(Inherited from Model)
FitCore Computes the model.
(Overrides Model.FitCore(ModelInput, ParallelOptions))
GetHashCodeServes as the default hash function.
(Inherited from Object)
GetTypeGets the Type of the current instance.
(Inherited from Object)
MemberwiseCloneCreates a shallow copy of the current Object.
(Inherited from Object)
Predict(IDataFrame, ModelInputFormat) Predicts the value of the dependent variables corresponding to the specified features.
Predict(Matrix<Double>, ModelInputFormat) Predicts the value of the dependent variables corresponding to the specified features.
Predict(Vector<Double>, ModelInputFormat) Predicts the value of the dependent variables corresponding to the specified features.
PredictCore(Matrix<Double>) Predicts the value of the dependent variable based on the specified values of the features.
PredictCore(Vector<Double>) Predicts the value of the dependent variable based on the specified values of the features.
Press Returns the Predicted REsidual Sum of Squares (PRESS) value for the specified test features and targets.
ResetComputation Clears all fitted model parameters.
(Inherited from Model)
Obsolete.
ResetFit Clears all fitted model parameters.
(Inherited from Model)
RootMeanPress Returns the square root of the mean of the Predicted REsidual Sum of Squares (PRESS) value for the specified test features and targets.
SetDataSource Uses the specified data frame as the source for all input variables.
(Inherited from Model)
Summarize() Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model)
Summarize(SummaryOptions) Returns a string containing a human-readable summary of the object using the specified options.
(Inherited from Model)
ToStringReturns a string that represents the current object.
(Inherited from Model)

See Also