Linear
            
            
            
            Definition
Namespace: Extreme.Statistics
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
Overload List
| Linear | Constructs a new LinearRegressionModel. | 
| Linear | Constructs a new LinearRegressionModel. | 
| Linear | Constructs a new LinearRegressionModel. | 
| Linear | Constructs a new LinearRegressionModel. | 
| Linear | Constructs a new LinearRegressionModel. | 
| Linear | Constructs a fitted linear regression model. | 
LinearRegressionModel(Vector<Double>, Vector<Double>[])
            Constructs a new LinearRegressionModel.
            
public LinearRegressionModel(
	Vector<double> dependentVariable,
	params Vector<double>[] independentVariables
)Parameters
Exceptions
| ArgumentNullException | dependentVariable is null. | 
LinearRegressionModel(IDataFrame, String, String)
            Constructs a new LinearRegressionModel.
            
public LinearRegressionModel(
	IDataFrame dataFrame,
	string formula,
	string weightVariable = null
)Parameters
- dataFrame IDataFrame
- A IDataFrame containing the data for the regression.
- formula String
- A formula that specifies the variables in the model.
- weightVariable String (Optional)
- (Optional.) The name of the variable that contains the case weights. May be null.
Exceptions
| ArgumentNullException | dataFrame is null. -or- formula is null. | 
| FormatException | The formula is not in the correct format. | 
LinearRegressionModel(IDataFrame, String, String[])
            Constructs a new LinearRegressionModel.
            
public LinearRegressionModel(
	IDataFrame dataFrame,
	string dependentVariable,
	params string[] independentVariables
)Parameters
- dataFrame IDataFrame
- A IDataFrame containing the data for the regression.
- dependentVariable String
- The name of the dependent variable.
- independentVariables String[]
- An array containing the names of the independent variables.
LinearRegressionModel(Vector<Double>, Matrix<Double>, Boolean, Vector<Double>)
            Constructs a new LinearRegressionModel.
            
public LinearRegressionModel(
	Vector<double> dependentData,
	Matrix<double> independentData,
	bool noIntercept = false,
	Vector<double> weightData = null
)Parameters
- dependentData Vector<Double>
- A vector that contains the data for the dependent variable.
- independentData Matrix<Double>
- A matrix that contains the data for the independent variables.
- noIntercept Boolean (Optional)
- Specifies whether the intercept (constant) term should be omitted from the model.
- weightData Vector<Double> (Optional)
- (Optional.) A vector that contains the weight for each observation.
Remarks
Each column of independentData corresponds to an independent variable.
Exceptions
| ArgumentNullException | dependentData is null. -or- independentData is null. | 
LinearRegressionModel(IDataFrame, String, String[], Boolean, String)
            Constructs a new LinearRegressionModel.
            
public LinearRegressionModel(
	IDataFrame dataFrame,
	string dependentVariable,
	string[] independentVariables,
	bool noIntercept = false,
	string weightVariable = null
)Parameters
- dataFrame IDataFrame
- A IDataFrame containing the data for the regression.
- dependentVariable String
- The name of the dependent variable.
- independentVariables String[]
- An array containing the names of the independent variables.
- noIntercept Boolean (Optional)
- Indicates whether a constant term should be excluded from the model. The default is false.
- weightVariable String (Optional)
- The name of the variable that contains the case weights. May be null.
Exceptions
| ArgumentNullException | dataFrame is null. -or- dependentVariable is null. -or- independentVariables is null. | 
LinearRegressionModel(ModelInput, Vector<Double>, SymmetricMatrix<Double>, Int32, Double)
            Constructs a fitted linear regression model.
            
public LinearRegressionModel(
	ModelInput specification,
	Vector<double> parameters,
	SymmetricMatrix<double> covarianceMatrix,
	int degreesOfFreedom,
	double standardError
)Parameters
- specification ModelInput
- parameters Vector<Double>
- covarianceMatrix SymmetricMatrix<Double>
- degreesOfFreedom Int32
- standardError Double
Remarks
Models created with this constructor do not carry any information about the source data or the fit results.