LinearRegression Class

Class LinearRegression computes a multiple linear regression from an input matrix of independent variable values and vector of dependent variable values.

Inheritance Hierarchy

SystemObject
CenterSpace.NMath.CoreRegressionBase
CenterSpace.NMath.CoreLinearRegression

Namespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.4

Syntax

C++

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[SerializableAttribute]
public class LinearRegression : RegressionBase, 
	ICloneable

<SerializableAttribute>
Public Class LinearRegression
	Inherits RegressionBase
	Implements ICloneable

[SerializableAttribute]
public ref class LinearRegression : public RegressionBase, 
	ICloneable

[<SerializableAttribute>]
type LinearRegression =  
    class
        inherit RegressionBase
        interface ICloneable
    end

The LinearRegression type exposes the following members.

Constructors

	Name	Description
	LinearRegression	Default constructor. Constructs a LinearRegression instance with all sizes equal to zero.
	LinearRegression(DataFrame, IDFColumn)	Constructs a LinearRegression instance with the specifed regresssion data and observation column. By default, the model parameter values are computed using a QR factorization.
	LinearRegression(DataFrame, Int32)	Constructs a LinearRegression instance with the specifed regresssion data and an index to the observation column. By default, the model parameter values are computed using a QR factorization.
	LinearRegression(DoubleMatrix, DoubleVector)	Constructs a LinearRegression instance with the specifed regresssion matrix and observation vector. By default, the model parameter values are computed using a QR factorization.
	LinearRegression(DataFrame, IDFColumn, IRegressionCalculation)	Constructs a LinearRegression instance with the specifed regresssion data and observation column. Model parameter values are computed using the specified regression calculator.
	LinearRegression(DataFrame, IDFColumn, Boolean)	Constructs a LinearRegression instance with the specifed regresssion data and observation column, optionally adding an intercept parameter. By default, the model parameter values are computed using a QR factorization.
	LinearRegression(DataFrame, Int32, IRegressionCalculation)	Constructs a LinearRegression instance with the specifed regresssion data and an index to the observation column. Model parameter values are computed using the specified regression calculator.
	LinearRegression(DataFrame, Int32, Boolean)	Constructs a LinearRegression instance with the specifed regresssion data and an index to the observation column, optionally adding an intercept parameter. By default, the model parameter values are computed using a QR factorization.
	LinearRegression(DoubleMatrix, DoubleVector, IRegressionCalculation)	Constructs a LinearRegression instance with the specifed regresssion matrix and observation vector. Model parameter values are computed using the specified regression calculator.
	LinearRegression(DoubleMatrix, DoubleVector, Boolean)	Constructs a LinearRegression instance with the specifed regresssion matrix and observation vector, optionally adding an intercept parameter. By default, the model parameter values are computed using a QR factorization.
	LinearRegression(DataFrame, IDFColumn, Boolean, IRegressionCalculation)	Constructs a LinearRegression instance with the specifed regresssion data and observation column, optionally adding an intercept parameter. The model parameter values are computed using the specified regression calculator.
	LinearRegression(DataFrame, Int32, Boolean, IRegressionCalculation)	Constructs a LinearRegression instance with the specifed regresssion data and an index to the observation column, optionally adding an intercept parameter. The model parameter values are computed using the specified regression calculator.
	LinearRegression(DoubleMatrix, DoubleVector, Boolean, IRegressionCalculation)	Constructs a LinearRegression instance with the specifed regresssion matrix and observation vector, optionally adding an intercept parameter. The model parameter values are computed using the specified regression calculator.

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Properties

	Name	Description
	CheckData	Gets and sets a boolean indicating whether or not to check input data for non-numeric values. If input data is large checking all values for NaN's and infinities can be a performance consideration.
	ColumnResizeIncrement	Gets and sets the amount by which the regression matrix is resized if columns are added. (Inherited from RegressionBase.)
	CovarianceMatrix	Gets the covariance matrix.
	HasInterceptParameter	Returns true if the model has an intercept parameter; otherwise, false. (Inherited from RegressionBase.)
	Intercept	Gets the intercept. (Inherited from RegressionBase.)
	IsGood	Returns true if the model parameters were successfuly computed; otherwise, false. (Inherited from RegressionBase.)
	NumberOfObservations	Gets the number of observations. (Inherited from RegressionBase.)
	NumberOfParameters	Gets the number of parameters in the model. (Inherited from RegressionBase.)
	NumberOfPredictors	Gets the number of predictors. (Inherited from RegressionBase.)
	Observations	Gets the vector of observations. (Inherited from RegressionBase.)
	ParameterCalculationErrorMessage	Gets the error message associated with a failed parameter calculation. (Inherited from RegressionBase.)
	ParameterEstimates	Gets an array of parameter objects which may be used to perform hypothesis tests on individual parameters in the model.
	Parameters	Gets the computed model parameters. (Inherited from RegressionBase.)
	PredictorMatrix	Gets the predictor matrix. (Inherited from RegressionBase.)
	RegressionCalculator	Gets and sets the regression calculation object used for computing the model parameters.
	RegressionMatrix	Gets the regression matrix. (Inherited from RegressionBase.)
	Residuals	Get the vector of residuals.
	RowResizeIncrement	Gets and sets the amount by which the regression matrix is resized if rows are added. (Inherited from RegressionBase.)
	Variance	Gets an estimate of the variance.

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Methods

	Name	Description
	AddInterceptParameter	Adds an intercept parameter to the model and recalculates the model parameters. (Inherited from RegressionBase.)
	AddObservation	Adds the given observation to the model, and recalculates the model parameters. (Inherited from RegressionBase.)
	AddObservations	Adds the given observations to the model, and recalculates the model parameters. (Inherited from RegressionBase.)
	AddPredictor	Adds a predictor to the model, and recalculates the model parameters. (Inherited from RegressionBase.)
	AddPredictors	Adds predictors to the model, and recalculates the model parameters. (Inherited from RegressionBase.)
	Clone	Creates a deep copy of this LinearRegression.
	GetStandardizedResiduals	Returns the standardized residuals (also known as the internally studentized residuals).
	GetStudentizedResiduals	Returns the (externally) studentized residuals.
	PredictedObservation	Returns the value of the dependent variable predicted by the model for the given set of predictor values.
	PredictedObservations	Returns the values of the dependent variable predicted by the model for the given sets of predictor values.
	PredictionInterval	Returns a confidence interval for the value of the dependent variable predicted by the model for the given set of predictor values.
	RecalculateParameters	Recalculates the model parameters. (Overrides RegressionBaseRecalculateParameters.)
	RemoveInterceptParameter	Removes the intercept parameter from the model, and recalculates the model parameters. (Inherited from RegressionBase.)
	RemoveObservation	Removes the row at the indicated index from the predictor matrix and the corresponding element from the observation vector, and recalculates the model parameters. (Inherited from RegressionBase.)
	RemoveObservations	Removes the specified rows from the predictor matrix, and recalculates the model parameters. (Inherited from RegressionBase.)
	RemovePredictor	Removes the specified predictor from the model, and recalculates the model parameters. (Inherited from RegressionBase.)
	RemovePredictors	Removes the specified predictors from the model, and recalculates the model parameters. (Inherited from RegressionBase.)
	SetRegressionData(DataFrame, IDFColumn, Boolean)	Sets the regression matrix, observation vector, and intercept option to the specified values, and recalculates the model parameters.
	SetRegressionData(DoubleMatrix, DoubleVector, Boolean)	Sets the regression matrix, observation vector, and intercept option to the specified values, and recalculates the model parameters.
	VarianceInflationFactor	An index which measures how much the variance of a coefficient (square of the standard deviation) is increased because of collinearity.
	VarianceInflationFactors	An index which measures how much the variance of a coefficient (square of the standard deviation) is increased because of collinearity.

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Fields

	Name	Description
	colResizeIncrement_	Number of columns to add when adding variables (if needed). (Inherited from RegressionBase.)
	DEFAULT_REGRESSION_CALCULATION	Default regression calculation is QRRegressionCalculation.
	errorMessage_	Explains errors, if any. (Inherited from RegressionBase.)
	hasIntercept_	Does the model have an intercept parameter? (Inherited from RegressionBase.)
	isGood_	Is the regression good? (Inherited from RegressionBase.)
	observationData_	Full set of observations. (Inherited from RegressionBase.)
	observations_	Subvector of the observation data used in the current regression model. observations_ = observationData_[regMatRowSlice_]. (Inherited from RegressionBase.)
	parameters_	Model parameters. (Inherited from RegressionBase.)
	regMatColSlice_	regressionMatrx_ = regressionData_[regMatRowSlice_, regMatColSlice_] (Inherited from RegressionBase.)
	regMatRowSlice_	regressionMatrx_ = regressionData_[regMatRowSlice_, regMatColSlice_] (Inherited from RegressionBase.)
	regressionData_	The full set of regression data. (Inherited from RegressionBase.)
	regressionMatrix_	A submatrix of the regression used in this regression model. regressionMatrx_ = regressionData_[regMatRowSlice_, regMatColSlice_] (Inherited from RegressionBase.)
	rowResizeIncrement_	Number of rows to add when adding observations (if needed). (Inherited from RegressionBase.)

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Reference

CenterSpace.NMath.Core Namespace