LogisticRegressionParameterCalc Class

SystemObject
  CenterSpace.NMath.CoreRegressionBase
    CenterSpace.NMath.CoreLogisticRegressionParameterCalc

[SerializableAttribute]
public class LogisticRegression<ParameterCalc> : RegressionBase, 
	ICloneable
where ParameterCalc : new(), ILogisticRegressionCalc

<SerializableAttribute>
Public Class LogisticRegression(Of ParameterCalc As {New, ILogisticRegressionCalc})
	Inherits RegressionBase
	Implements ICloneable

[SerializableAttribute]
generic<typename ParameterCalc>
where ParameterCalc : gcnew(), ILogisticRegressionCalc
public ref class LogisticRegression : public RegressionBase, 
	ICloneable

[<SerializableAttribute>]
type LogisticRegression<'ParameterCalc when 'ParameterCalc : new() and ILogisticRegressionCalc> = 
    class
        inherit RegressionBase
        interface ICloneable
    end

Logistic regression is used to model the relationship between a binary response variable and one or more predictor variables, which may be either discrete or continuous. Binary outcome data is common in medical applications. For example, the binary response variable might be whether or not a patient is alive five years after treatment for cancer or whether the patient has an adverse reaction to a new drug. As in multiple regression, we are interested in finding an appropriate combination of predictor variables to help explain the binary outcome. Let Y be a dichotomous random variable denoting the outcome of some experiment, and let X = (x1, x2, ... , xp – 1) be a collection of predictor variables. Denote the conditional probability that the outcome is present by P(Y = 1|x) = π(x), where π(x) has the form: exp(B0 + B1*x1 + B2*x2 + ... + Bp-1*xp-1) / (1 + exp(B0 + B1*x1 + B2*x2 + ... + Bp-1*xp-1)) If the xj are varied and the n values Y1,Y2, ... , Yn of Y are observed, we write: πi = exp(B0 + B1*xi1 + B2*xi2 + ... + Bp-1*xip-1) / (1 + exp(B0 + B1*xi1 + B2*xi2 + ... + Bp-1*xip-1)) The logistic regression problem is then to obtain an estimate of the vector: B = (B0, B1,..., Bp-1)