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RegressionModelT Class

Extreme Optimization Numerical Libraries for .NET Professional

Represents a statistical model.

Inheritance Hierarchy

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SystemObject
  Extreme.DataAnalysis.ModelsModel
    Extreme.DataAnalysis.ModelsRegressionModelT
      Extreme.StatisticsGeneralizedLinearModel
      Extreme.StatisticsLinearRegressionModel
      Extreme.StatisticsNonlinearRegressionModel
      Extreme.StatisticsRegularizedRegressionModel
      Extreme.StatisticsSimpleRegressionModel

Namespace:  Extreme.DataAnalysis.Models
Assembly:  Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1

Syntax

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C#

VB

C++

F#

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public abstract class RegressionModel<T> : Model

Public MustInherit Class RegressionModel(Of T)
	Inherits Model

generic<typename T>
public ref class RegressionModel abstract : public Model

[<AbstractClassAttribute>]
type RegressionModel<'T> =  
    class
        inherit Model
    end

Type Parameters

T

The RegressionModelT type exposes the following members.

Constructors

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	Name	Description
	RegressionModelT	Constructs a new univariate model based on a model specification.

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Properties

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	Name	Description
	AdjustedRSquared	Gets the adjusted R Squared value for the regression.
	AnovaTable	Gets the AnovaTable that summarizes the results of this model.
	BaseFeatureIndex	Gets an index containing the keys of the columns that are required inputs to the model. (Inherited from Model.)
	Computed	Obsolete. Gets whether the model has been computed. (Inherited from Model.)
	CovarianceMatrix	Gets the covariance matrix of the model parameters.
	Data	Gets an object that contains all the data used as input to the model. (Inherited from Model.)
	DegreesOfFreedom	Gets the total degrees of freedom of the data.
	DependentVariable	Gets a vector that contains the dependent variable that is to be fitted.
	Fitted	Gets whether the model has been computed. (Inherited from Model.)
	FStatistic	Gets the F statistic for the regression.
	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.)
	LogLikelihood	Gets the log-likelihood that the model generated the data.
	MaxDegreeOfParallelism	Gets or sets the maximum degree of parallelism enabled by this instance. (Inherited from Model.)
	ModelSchema	Gets the collection of variables used in the model. (Inherited from Model.)
	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.)
	Parameters	Gets the collection of parameters associated with this model.
	ParameterValues	Gets the values of the parameters associated with this model.
	Predictions	Gets a vector containing the model's predicted values for the dependent variable.
	PValue	Gets the probability corresponding to the F statistic for the regression.
	Residuals	Gets a vector containing the residuals of the model.
	ResidualSumOfSquares	Gets the sum of squares of the residuals of the model.
	RSquared	Gets the R Squared value for the regression.
	StandardError	Gets the standard error of the 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.)
	Weights	Gets or sets the actual weights. (Inherited from Model.)

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Methods

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	Name	Description
	Compute	Obsolete. Computes the model. (Inherited from Model.)
	Compute(ParallelOptions)	Obsolete. Computes the model. (Inherited from Model.)
	Contains	Returns whether another RegressionModelT is nested within this instance.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows 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 to the specified input using the specified parallelization options. (Inherited from Model.)
	GetAkaikeInformationCriterion	Returns the Akaike information criterion (AIC) value for the model.
	GetBayesianInformationCriterion	Returns the Bayesian information criterion (BIC) value for the model.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Predict(IDataFrame, ModelInputFormat)	Predicts the value of the output corresponding to the specified features.
	Predict(MatrixT, ModelInputFormat)	Predicts the value of the output corresponding to the specified features.
	Predict(VectorT, ModelInputFormat)	Predicts the value of the output corresponding to the specified features.
	PredictCore(MatrixT, Boolean)	Predicts the value of the dependent variable based on the specified values of the features.
	PredictCore(VectorT, Boolean)	Predicts the value of the dependent variable based on the specified values of the features.
	ResetComputation	Obsolete. Clears all fitted model parameters. (Inherited from Model.)
	ResetFit	Clears all fitted model parameters. (Inherited from Model.)
	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. (Overrides ModelSummarize(SummaryOptions).)
	ToString	Returns a string that represents the current object. (Inherited from Model.)

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Remarks

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This is an abstract base class and cannot be instantiated directly. Instead, use one of the inherited types, as listed in the table below:

Class	Description
SimpleRegressionModel	A simple linear regression model with one independent variable.
LinearRegressionModel	A regression model with multiple independent variables.
PolynomialRegressionModel	A linear regression model that uses a polynomial in one variable.
GeneralizedLinearModel	A multiple linear regression model with multiple independent variables.
NonlinearRegressionModel	A multiple linear regression model with multiple independent variables.

Note to inheritors: When you inherit from RegressionModelT,you must override FitCore(ModelInput, ParallelOptions).

See Also

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Reference

Extreme.DataAnalysis.Models Namespace

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