MultinomialRegression Class

Multinomial regression for nominal model. McCullagh, P., and J.A. Nelder (1990) Generalized Linear Models, 2nd edition, Chapman and Hall;CRC Press.

Inheritance Hierarchy

SystemObject
FinMath.LeastSquaresMultinomialRegression

Namespace: FinMath.LeastSquares
Assembly: FinMath (in FinMath.dll) Version: 2.4.7-0a995bd0ea1854c2c868ec3f8dae606c5777e170

Syntax

[SerializableAttribute]
public class MultinomialRegression

The MultinomialRegression type exposes the following members.

Constructors

	Name	Description
	MultinomialRegression	Initializes Multinomial regression.

Properties

	Name	Description
	Iterations	Amount of iterations to perform.
	IterationsDone	Completed iterations.
	Status	Status of the regression. Can be on the following: FMStatus.MaximumIterations, FMStatus.MethodSucceeded;

Methods

	Name	Description
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Estimate(Matrix, Matrix)	Computes predicted probabilities for the multinomial logistic regression model with predictors X.
	Estimate(Vector, Vector)	Computes predicted probabilities for the multinomial logistic regression model with predictor X.
	EstimateResidual	Computes deviance residuals.
	EstimateResiduals	Computes deviance residuals.
	GetFittedIntercepts	(K-1) vector of intercepts estimate. Each value corresponds to the estimated intercept term.
	GetFittedParameters	The result B is a (P+1)-by-(K-1) matrix of estimate. Each column corresponds to the estimated intercept term and predictor coefficients, one for each of the first K-1 multinomial categories. The estimates for the K-th category are taken to be zero.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)