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  • Extreme.Statistics.Multivariate
    • DendrogramNode Class
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    • LinearDiscriminantFunction Class
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    • PartialLeastSquaresMethod Enumeration
    • PartialLeastSquaresModel Class
    • PrincipalComponent Class
    • PrincipalComponentAnalysis Class
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  • PartialLeastSquaresModel Class
    • PartialLeastSquaresModel Constructors
    • Properties
    • Methods

PartialLeastSquaresModel Class

Extreme Optimization Numerical Libraries for .NET Professional
Represents a Partial Least Squares (PLS) model.
Inheritance Hierarchy

SystemObject
  Extreme.DataAnalysis.ModelsModel
    Extreme.Statistics.MultivariatePartialLeastSquaresModel

Namespace:  Extreme.Statistics.Multivariate
Assembly:  Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1
Syntax

C#
VB
C++
F#
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public class PartialLeastSquaresModel : Model
Public Class PartialLeastSquaresModel
	Inherits Model
public ref class PartialLeastSquaresModel : public Model
type PartialLeastSquaresModel =  
    class
        inherit Model
    end

The PartialLeastSquaresModel type exposes the following members.

Constructors

  NameDescription
Public methodPartialLeastSquaresModel(IDataFrame, String, Int32)
Constructs a new PartialLeastSquaresModel.
Public methodPartialLeastSquaresModel(MatrixDouble, MatrixDouble, Int32)
Constructs a new PartialLeastSquaresModel.
Public methodPartialLeastSquaresModel(VectorDouble, MatrixDouble, Int32)
Constructs a new PartialLeastSquaresModel.
Public methodPartialLeastSquaresModel(IDataFrame, String, String, Int32)
Constructs a new PartialLeastSquaresModel.
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Properties

  NameDescription
Public propertyBaseFeatureIndex
Gets an index containing the keys of the columns that are required inputs to the model.
(Inherited from Model.)
Public propertyCoefficients
Gets a matrix containing the (unstandardized) coefficients of the Partial Least Squares regression.
Public propertyComputed Obsolete.
Gets whether the model has been computed.
(Inherited from Model.)
Public propertyData
Gets an object that contains all the data used as input to the model.
(Inherited from Model.)
Public propertyDependentVariables
Gets a matrix that contains the dependent variables that are to be fitted.
Public propertyFitted
Gets whether the model has been computed.
(Inherited from Model.)
Public propertyIndependentVariables
Gets a matrix whose columns contain the independent variables in the model.
Public propertyInputSchema
Gets the schema for the features used for fitting the model.
(Inherited from Model.)
Public propertyIntercepts
Gets a matrix containing the intercept term of the Partial Least Squares regression.
Public propertyMaxDegreeOfParallelism
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model.)
Public propertyMethod
Gets or sets the method used to compute the PLS model.
Public propertyModelSchema
Gets the collection of variables used in the model.
(Inherited from Model.)
Public propertyNumberOfComponents
Gets or sets the number of components to calculate.
Public propertyNumberOfObservations
Gets the number of observations the model is based on.
(Inherited from Model.)
Protected propertyParallelOptions
Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model.)
Public propertyPredictedValues
Gets the residuals of the dependent variables.
Public propertyStandardizedCoefficients
Gets a matrix containing the standardized coefficients of the Partial Least Squares regression.
Public propertyStatus
Gets the status of the model, which determines which information is available.
(Inherited from Model.)
Public propertySupportsWeights
Indicates whether the model supports case weights.
(Inherited from Model.)
Public propertyVariableImportanceInProjection
Gets a matrix that contains the Variable Importance in Projection (VIP) value for each variable and each number of components.
Public propertyWeightMatrix
Gets the weights of the independent variables.
Public propertyWeights
Gets or sets the actual weights.
(Inherited from Model.)
Public propertyXCumulativeVarianceExplained
Gets a vector containing the cumulative proportion of variance in the independent variables explained by each the component
Public propertyXDistanceToModel
Gets a vector containing the distance to the model of the independent variables.
Public propertyXLoadings
Gets the scores matrix of the independent variables.
Public propertyXResiduals
Gets the residuals of the independent variables.
Public propertyXScalingMethod
Gets or sets how independent variables are scaled.
Public propertyXScores
Gets the loadings matrix of the independent variables.
Public propertyXVarianceExplained
Gets a vector containing the proportion of variance in the independent variables explained by each the component
Public propertyYCumulativeVarianceExplained
Gets a vector containing the cumulative proportion of variance in the dependent variables explained by each the component
Public propertyYDistanceToModel
Gets a vector containing the distance to the model of the dependent variables.
Public propertyYLoadings
Gets the scores matrix of the dependent variables.
Public propertyYResiduals
Gets the residuals of the dependent variables.
Public propertyYScalingMethod
Gets or sets how dependent variables are scaled.
Public propertyYScores
Gets the loadings matrix of the dependent variables.
Public propertyYVarianceExplained
Gets a vector containing the proportion of variance in the dependent variables explained by each the component
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Methods

  NameDescription
Public methodCompute Obsolete.
Computes the model.
(Inherited from Model.)
Public methodCompute(ParallelOptions) Obsolete.
Computes the model.
(Inherited from Model.)
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodFit
Fits the model to the data.
(Inherited from Model.)
Public methodFit(ParallelOptions)
Fits the model to the data.
(Inherited from Model.)
Protected methodFitCore
Computes the model.
(Overrides ModelFitCore(ModelInput, ParallelOptions).)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodPredict(IDataFrame, ModelInputFormat)
Predicts the value of the dependent variables corresponding to the specified features.
Public methodPredict(MatrixDouble, ModelInputFormat)
Predicts the value of the dependent variables corresponding to the specified features.
Public methodPredict(VectorDouble, ModelInputFormat)
Predicts the value of the dependent variables corresponding to the specified features.
Protected methodPredictCore(MatrixDouble)
Predicts the value of the dependent variable based on the specified values of the features.
Public methodPredictCore(VectorDouble)
Predicts the value of the dependent variable based on the specified values of the features.
Public methodPress
Returns the Predicted REsidual Sum of Squares (PRESS) value for the specified test features and targets.
Public methodResetComputation Obsolete.
Clears all fitted model parameters.
(Inherited from Model.)
Public methodResetFit
Clears all fitted model parameters.
(Inherited from Model.)
Public methodRootMeanPress
Returns the square root of the mean of the Predicted REsidual Sum of Squares (PRESS) value for the specified test features and targets.
Public methodSetDataSource
Uses the specified data frame as the source for all input variables.
(Inherited from Model.)
Public methodSummarize
Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model.)
Public methodSummarize(SummaryOptions)
Returns a string containing a human-readable summary of the object using the specified options.
(Inherited from Model.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Model.)
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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.

See Also

Reference

Extreme.Statistics.Multivariate Namespace

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