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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
[SerializableAttribute]
public class LinearRegression : RegressionBase, 
	ICloneable

The LinearRegression type exposes the following members.

Constructors
 NameDescription
Public methodLinearRegression Default constructor. Constructs a LinearRegression instance with all sizes equal to zero.
Public methodLinearRegression(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.
Public methodLinearRegression(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.
Public methodLinearRegression(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.
Public methodLinearRegression(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.
Public methodLinearRegression(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.
Public methodLinearRegression(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.
Public methodLinearRegression(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.
Public methodLinearRegression(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.
Public methodLinearRegression(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.
Public methodLinearRegression(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.
Public methodLinearRegression(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.
Public methodLinearRegression(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
 NameDescription
Public propertyCheckData 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.
Public propertyColumnResizeIncrement Gets and sets the amount by which the regression matrix is resized if columns are added.
(Inherited from RegressionBase)
Public propertyCovarianceMatrix Gets the covariance matrix.
Public propertyHasInterceptParameter Returns true if the model has an intercept parameter; otherwise, false.
(Inherited from RegressionBase)
Public propertyIntercept Gets the intercept.
(Inherited from RegressionBase)
Public propertyIsGood Returns true if the model parameters were successfuly computed; otherwise, false.
(Inherited from RegressionBase)
Public propertyNumberOfObservations Gets the number of observations.
(Inherited from RegressionBase)
Public propertyNumberOfParameters Gets the number of parameters in the model.
(Inherited from RegressionBase)
Public propertyNumberOfPredictors Gets the number of predictors.
(Inherited from RegressionBase)
Public propertyObservations Gets the vector of observations.
(Inherited from RegressionBase)
Public propertyParameterCalculationErrorMessage Gets the error message associated with a failed parameter calculation.
(Inherited from RegressionBase)
Public propertyParameterEstimates Gets an array of parameter objects which may be used to perform hypothesis tests on individual parameters in the model.
Public propertyParameters Gets the computed model parameters.
(Inherited from RegressionBase)
Public propertyPredictorMatrix Gets the predictor matrix.
(Inherited from RegressionBase)
Public propertyRegressionCalculator Gets and sets the regression calculation object used for computing the model parameters.
Public propertyRegressionMatrix Gets the regression matrix.
(Inherited from RegressionBase)
Public propertyResiduals Get the vector of residuals.
Public propertyRowResizeIncrement Gets and sets the amount by which the regression matrix is resized if rows are added.
(Inherited from RegressionBase)
Public propertyVariance Gets an estimate of the variance.
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Methods
 NameDescription
Public methodAddInterceptParameter Adds an intercept parameter to the model and recalculates the model parameters.
(Inherited from RegressionBase)
Public methodAddObservation Adds the given observation to the model, and recalculates the model parameters.
(Inherited from RegressionBase)
Public methodAddObservations Adds the given observations to the model, and recalculates the model parameters.
(Inherited from RegressionBase)
Public methodAddPredictor Adds a predictor to the model, and recalculates the model parameters.
(Inherited from RegressionBase)
Public methodAddPredictors Adds predictors to the model, and recalculates the model parameters.
(Inherited from RegressionBase)
Public methodClone Creates a deep copy of this LinearRegression.
Public methodGetStandardizedResiduals Returns the standardized residuals (also known as the internally studentized residuals).
Public methodGetStudentizedResiduals Returns the (externally) studentized residuals.
Public methodPredictedObservation Returns the value of the dependent variable predicted by the model for the given set of predictor values.
Public methodPredictedObservations Returns the values of the dependent variable predicted by the model for the given sets of predictor values.
Public methodPredictionInterval Returns a confidence interval for the value of the dependent variable predicted by the model for the given set of predictor values.
Public methodRecalculateParameters Recalculates the model parameters.
(Overrides RegressionBaseRecalculateParameters)
Public methodRemoveInterceptParameter Removes the intercept parameter from the model, and recalculates the model parameters.
(Inherited from RegressionBase)
Public methodRemoveObservation 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)
Public methodRemoveObservations Removes the specified rows from the predictor matrix, and recalculates the model parameters.
(Inherited from RegressionBase)
Public methodRemovePredictor Removes the specified predictor from the model, and recalculates the model parameters.
(Inherited from RegressionBase)
Public methodCode exampleRemovePredictors Removes the specified predictors from the model, and recalculates the model parameters.
(Inherited from RegressionBase)
Public methodSetRegressionData(DataFrame, IDFColumn, Boolean) Sets the regression matrix, observation vector, and intercept option to the specified values, and recalculates the model parameters.
Public methodSetRegressionData(DoubleMatrix, DoubleVector, Boolean) Sets the regression matrix, observation vector, and intercept option to the specified values, and recalculates the model parameters.
Public methodVarianceInflationFactor An index which measures how much the variance of a coefficient (square of the standard deviation) is increased because of collinearity.
Public methodVarianceInflationFactors 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
 NameDescription
Protected fieldcolResizeIncrement_ Number of columns to add when adding variables (if needed).
(Inherited from RegressionBase)
Public fieldStatic memberDEFAULT_REGRESSION_CALCULATION Default regression calculation is QRRegressionCalculation.
Protected fielderrorMessage_ Explains errors, if any.
(Inherited from RegressionBase)
Protected fieldhasIntercept_ Does the model have an intercept parameter?
(Inherited from RegressionBase)
Protected fieldisGood_ Is the regression good?
(Inherited from RegressionBase)
Protected fieldobservationData_ Full set of observations.
(Inherited from RegressionBase)
Protected fieldobservations_ Subvector of the observation data used in the current regression model. observations_ = observationData_[regMatRowSlice_].
(Inherited from RegressionBase)
Protected fieldparameters_ Model parameters.
(Inherited from RegressionBase)
Protected fieldregMatColSlice_ regressionMatrx_ = regressionData_[regMatRowSlice_, regMatColSlice_]
(Inherited from RegressionBase)
Protected fieldregMatRowSlice_ regressionMatrx_ = regressionData_[regMatRowSlice_, regMatColSlice_]
(Inherited from RegressionBase)
Protected fieldregressionData_ The full set of regression data.
(Inherited from RegressionBase)
Protected fieldregressionMatrix_ A submatrix of the regression used in this regression model. regressionMatrx_ = regressionData_[regMatRowSlice_, regMatColSlice_]
(Inherited from RegressionBase)
Protected fieldrowResizeIncrement_ Number of rows to add when adding observations (if needed).
(Inherited from RegressionBase)
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See Also