LogisticRegressionModel Constructor

Definition

Namespace: Numerics.NET.Statistics
Assembly: Numerics.NET (in Numerics.NET.dll) Version: 10.9.0

Overload List

LogisticRegressionModel(ICategoricalVector, Vector<Double>[])	Constructs a new LogisticRegressionModel.
LogisticRegressionModel(IDataFrame, String)	Constructs a new LogisticRegressionModel.
LogisticRegressionModel(IDataFrame, String, String[])	Constructs a new LogisticRegressionModel.
LogisticRegressionModel(Vector<Double>, Matrix<Double>, Vector<Double>)	Constructs a new SimpleRegressionModel.
LogisticRegressionModel(ICategoricalVector, Vector<Double>[], Vector<Double>, LogisticRegressionMethod)	Constructs a new LogisticRegressionModel.
LogisticRegressionModel(ModelInput, IIndex, Vector<Double>, SymmetricMatrix<Double>, Int32)	Constructs a fitted logistic regression model.

LogisticRegressionModel(ICategoricalVector, Vector<Double>[])

Constructs a new LogisticRegressionModel.

public LogisticRegressionModel(
	ICategoricalVector dependentVariable,
	params Vector<double>[] independentVariables
)

Visual Basic

Public Sub New ( 
	dependentVariable As ICategoricalVector,
	ParamArray independentVariables As Vector(Of Double)()
)

C++/CLI

public:
LogisticRegressionModel(
	ICategoricalVector^ dependentVariable, 
	... array<Vector<double>^>^ independentVariables
)

new : 
        dependentVariable : ICategoricalVector * 
        independentVariables : Vector<float>[] -> LogisticRegressionModel

Parameters

dependentVariable ICategoricalVector: A categorical vector that specifies the dependent variable.
independentVariables Vector<Double>[]: An array of vectors that contain the independent variables.

Exceptions

ArgumentNullException

dependentVariable is null.

-or-

independentVariables is null.

LogisticRegressionModel(IDataFrame, String)

Constructs a new LogisticRegressionModel.

public LogisticRegressionModel(
	IDataFrame dataFrame,
	string formula
)

Visual Basic

Public Sub New ( 
	dataFrame As IDataFrame,
	formula As String
)

C++/CLI

public:
LogisticRegressionModel(
	IDataFrame^ dataFrame, 
	String^ formula
)

new : 
        dataFrame : IDataFrame * 
        formula : string -> LogisticRegressionModel

Parameters

dataFrame IDataFrame: A data frame containing the data for the regression.
formula String: A formula that specifies the variables in the model.

Exceptions

FormatException

The formula is not in the correct format.

LogisticRegressionModel(IDataFrame, String, String[])

Constructs a new LogisticRegressionModel.

public LogisticRegressionModel(
	IDataFrame dataFrame,
	string dependentVariable,
	params string[] independentVariables
)

Visual Basic

Public Sub New ( 
	dataFrame As IDataFrame,
	dependentVariable As String,
	ParamArray independentVariables As String()
)

C++/CLI

public:
LogisticRegressionModel(
	IDataFrame^ dataFrame, 
	String^ dependentVariable, 
	... array<String^>^ independentVariables
)

new : 
        dataFrame : IDataFrame * 
        dependentVariable : string * 
        independentVariables : string[] -> LogisticRegressionModel

Parameters

dataFrame IDataFrame: A data frame containing the data for the regression.
dependentVariable String: The name of the dependent variable.
independentVariables String[]: An array containing the names of the independent variable.

LogisticRegressionModel(Vector<Double>, Matrix<Double>, Vector<Double>)

Constructs a new SimpleRegressionModel.

public LogisticRegressionModel(
	Vector<double> dependentData,
	Matrix<double> independentData,
	Vector<double>? weights = null
)

Visual Basic

Public Sub New ( 
	dependentData As Vector(Of Double),
	independentData As Matrix(Of Double),
	Optional weights As Vector(Of Double) = Nothing
)

C++/CLI

public:
LogisticRegressionModel(
	Vector<double>^ dependentData, 
	Matrix<double>^ independentData, 
	Vector<double>^ weights = nullptr
)

new : 
        dependentData : Vector<float> * 
        independentData : Matrix<float> * 
        ?weights : Vector<float> 
(* Defaults:
        let _weights = defaultArg weights null
*)
-> LogisticRegressionModel

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 variable.
weights Vector<Double> (Optional): Optional case weights, validated when fitting. Weights must be finite and nonnegative, match the observation count, and contain at least one positive value.

Remarks

Columns of the matrix specify independent variables. An intercept is included in the model.

Exceptions

ArgumentNullException

dependentData is null.

-or-

independentData is null.

LogisticRegressionModel(ICategoricalVector, Vector<Double>[], Vector<Double>, LogisticRegressionMethod)

Constructs a new LogisticRegressionModel.

public LogisticRegressionModel(
	ICategoricalVector dependentVariable,
	Vector<double>[] independentVariables,
	Vector<double>? weights = null,
	LogisticRegressionMethod method = LogisticRegressionMethod.Automatic
)

Visual Basic

Public Sub New ( 
	dependentVariable As ICategoricalVector,
	independentVariables As Vector(Of Double)(),
	Optional weights As Vector(Of Double) = Nothing,
	Optional method As LogisticRegressionMethod = LogisticRegressionMethod.Automatic
)

C++/CLI

public:
LogisticRegressionModel(
	ICategoricalVector^ dependentVariable, 
	array<Vector<double>^>^ independentVariables, 
	Vector<double>^ weights = nullptr, 
	LogisticRegressionMethod method = LogisticRegressionMethod::Automatic
)

new : 
        dependentVariable : ICategoricalVector * 
        independentVariables : Vector<float>[] * 
        ?weights : Vector<float> * 
        ?method : LogisticRegressionMethod 
(* Defaults:
        let _weights = defaultArg weights null
        let _method = defaultArg method LogisticRegressionMethod.Automatic
*)
-> LogisticRegressionModel

Parameters

dependentVariable ICategoricalVector: A vector that specifies the dependent variable.
independentVariables Vector<Double>[]: An array of vectors that contain the independent variables.
weights Vector<Double> (Optional): Optional case weights, validated when fitting. Weights must be finite and nonnegative, match the observation count, and contain at least one positive value.
method LogisticRegressionMethod (Optional): Optional. A LogisticRegressionMethod value that specifies the kind of logistic regression to perform. The default infers binary or nominal regression from the outcome categories.

Exceptions

ArgumentNullException

dependentVariable is null.

-or-

independentVariables is null.

LogisticRegressionModel(ModelInput, IIndex, Vector<Double>, SymmetricMatrix<Double>, Int32)

Constructs a fitted logistic regression model.

public LogisticRegressionModel(
	ModelInput specification,
	IIndex categoryIndex,
	Vector<double> parameters,
	SymmetricMatrix<double> covarianceMatrix,
	int degreesOfFreedom
)

Visual Basic

Public Sub New ( 
	specification As ModelInput,
	categoryIndex As IIndex,
	parameters As Vector(Of Double),
	covarianceMatrix As SymmetricMatrix(Of Double),
	degreesOfFreedom As Integer
)

C++/CLI

public:
LogisticRegressionModel(
	ModelInput^ specification, 
	IIndex^ categoryIndex, 
	Vector<double>^ parameters, 
	SymmetricMatrix<double>^ covarianceMatrix, 
	int degreesOfFreedom
)

new : 
        specification : ModelInput * 
        categoryIndex : IIndex * 
        parameters : Vector<float> * 
        covarianceMatrix : SymmetricMatrix<float> * 
        degreesOfFreedom : int -> LogisticRegressionModel

Parameters

specification ModelInput: The input to the model.
categoryIndex IIndex: The index that specifies the categories or classes of the model.
parameters Vector<Double>: The values of the model parameters.
covarianceMatrix SymmetricMatrix<Double>: The covariance matrix of the model parameters.
degreesOfFreedom Int32: The degrees of freedom of the model.

Remarks

Models created with this constructor do not carry fit diagnostics. Coefficients and covariance are copied. Parameter names are preserved when supplied, otherwise derived from the expanded feature schema and outcome categories. The category count selects binary or nominal prediction.

Exceptions

ArgumentNullException	One of the reference arguments is null.
ArgumentException	There are fewer than two categories or the specification has no feature schema.
DimensionMismatchException	The coefficient count or covariance order does not match the feature schema and nonreference categories.
ArgumentOutOfRangeException	Degrees of freedom is not positive.

LogisticRegressionModel Constructor

Definition

Overload List

LogisticRegressionModel(ICategoricalVector, Vector<Double>[])

Parameters

Exceptions

LogisticRegressionModel(IDataFrame, String)

Parameters

Exceptions

LogisticRegressionModel(IDataFrame, String, String[])

Parameters

LogisticRegressionModel(Vector<Double>, Matrix<Double>, Vector<Double>)

Parameters

Remarks

Exceptions

LogisticRegressionModel(ICategoricalVector, Vector<Double>[], Vector<Double>, LogisticRegressionMethod)

Parameters

Exceptions

LogisticRegressionModel(ModelInput, IIndex, Vector<Double>, SymmetricMatrix<Double>, Int32)

Parameters

Remarks

Exceptions

See Also

Reference