NewtonRaphsonParameterCalc Class

Parameter calculation for a logistic regression model. The parameters are computed to maximize the log likelihood function for the model, using the Newton Raphson algorithm to compute the zeros of the first order partial derivaties of the log likelihood function.

Inheritance Hierarchy

SystemObject
CenterSpace.NMath.CoreNewtonRaphsonParameterCalc

Namespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.3

Syntax

C++

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[SerializableAttribute]
public class NewtonRaphsonParameterCalc : ILogisticRegressionCalc, 
	ICloneable

<SerializableAttribute>
Public Class NewtonRaphsonParameterCalc
	Implements ILogisticRegressionCalc, ICloneable

[SerializableAttribute]
public ref class NewtonRaphsonParameterCalc : ILogisticRegressionCalc, 
	ICloneable

[<SerializableAttribute>]
type NewtonRaphsonParameterCalc =  
    class
        interface ILogisticRegressionCalc
        interface ICloneable
    end

The NewtonRaphsonParameterCalc type exposes the following members.

Constructors

	Name	Description
	NewtonRaphsonParameterCalc	Constructs a NewtonRaphsonParameterCalc object with a default tolerance of 1e-8, and a default value of 1000 for the maximum number of iterations.
	NewtonRaphsonParameterCalc(Double)	Constructs a NewtonRaphsonParameterCalc object with the given tolerance. A default value of 1000 is used for the maximum number of iterations.
	NewtonRaphsonParameterCalc(Int32)	Constructs a NewtonRaphsonParameterCalc object with the given maximum number of iterations. A default value of 1e-8 is used for the tolerance.
	NewtonRaphsonParameterCalc(Int32, Double)	Constructs a NewtonRaphsonParameterCalc object with the given maximum number of iterations and tolerance.
	NewtonRaphsonParameterCalc(Int32, Double, Boolean)	Constructs a NewtonRaphsonParameterCalc object with the given maximum number of iterations and tolerance.

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Properties

	Name	Description
	CalculationMessage	This string will contain information about algorithm failure, or if the regression matrix is rank deficient and pseudo inverses were used. Otherwise it will be empty.
	Converged	Gets a boolean value indicating whether or not the algorithm converged.
	FailIfNotFullRank	Gets and sets a boolean value indicating whether or not parameter calculation should fail if the input matrix of observed values, the regression matrix does not have full rank. The default value is false.
	InformationMatrix	The information matrix is the matrix of negated second partial derivations of the log likelihood function. It is the inverse of the variance/covariance matrix for the parameter estimates.
	IsGood	Gets a boolean indicating success of failure of the calculation.
	Iterations	Gets the number of iterations performed by the algorithm.
	MaxIterations	Gets the sets the maximum number of iterations performed by the algorithm. Default is 1000.
	SolutionChange	Gets the change in the solution value on the last iteration of the algorithm.
	Tolerance	Gets and sets the tolerance used to determine convergence of the algorithm. The algorithm is judged converged when the norm of the solution change is less than Tolerance * max(B), where max(B) is the magnitude of the largest solution component. Default value is 1e-8.

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Methods

	Name	Description
	CalculateParameters	Calculate the parameters for the logistic model.
	Clone	Creates a deep copy of self.

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Remarks

This algorithm is equivalent to, and sometimes refered to, as an iteratively reweighted least squares. Each iteration involves solving a linear system of the form X'WX = b, where X is the regression matrix, X' is its transpose and W is a diagonal matrix of weights. The matrix X'WX will be singular if the matrix X does not have full rank. The NewtonRaphsonParameterCalc has a property called FailIfNotFullRank which if true will fail in this case. if the property FailIfNotFullRank is false the linear system will be solved using a pseudo-inverse and the calculation will not fail.

Reference

CenterSpace.NMath.Core Namespace