Class LogisticRegression
- java.lang.Object
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- dev.nm.stat.regression.linear.logistic.LogisticRegression
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- All Implemented Interfaces:
LinearModel
public class LogisticRegression extends Object implements LinearModel
A logistic regression (sometimes called the logistic model or logit model) is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve. It is a generalized linear model used for binomial regression. This particular implementation works with binary data (y).- See Also:
- Wikipedia: Logistic regression
- P. J. MacCullagh and J. A. Nelder, "pp.114, Section 4.4, Likelihood functions for binary data," in Generalized Linear Models, 2nd ed."
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Constructor Summary
Constructors Constructor Description LogisticRegression(LMProblem problem)
Constructs a Logistic instance.LogisticRegression(LogisticProblem problem)
Constructs a Logistic instance.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description double
AIC()
Gets the AIC.LogisticBeta
beta()
Gets \(\hat{\beta}\) and statistics.double
Ey(Vector x)
Calculates the probability of occurrence (y = 1).static RealScalarFunction
logLikelihood(LogisticProblem problem)
Constructs the log-likelihood function for a logistic regression problem.double
ML()
Gets the maximum log-likelihood.LogisticResiduals
residuals()
Gets the residual analysis of an OLS regression.
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Constructor Detail
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LogisticRegression
public LogisticRegression(LogisticProblem problem)
Constructs a Logistic instance.- Parameters:
problem
- the logistic regression problem to be solved
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LogisticRegression
public LogisticRegression(LMProblem problem)
Constructs a Logistic instance.- Parameters:
problem
- the logistic regression problem to be solved
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Method Detail
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logLikelihood
public static RealScalarFunction logLikelihood(LogisticProblem problem)
Constructs the log-likelihood function for a logistic regression problem.- Returns:
- the log-likelihood function
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Ey
public double Ey(Vector x)
Calculates the probability of occurrence (y = 1).- Specified by:
Ey
in interfaceLinearModel
- Parameters:
x
- the independent variables- Returns:
- the probability of occurrence
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beta
public LogisticBeta beta()
Description copied from interface:LinearModel
Gets \(\hat{\beta}\) and statistics.- Specified by:
beta
in interfaceLinearModel
- Returns:
- \(\hat{\beta}\) and statistics
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residuals
public LogisticResiduals residuals()
Description copied from interface:LinearModel
Gets the residual analysis of an OLS regression.- Specified by:
residuals
in interfaceLinearModel
- Returns:
- the residual analysis
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ML
public double ML()
Gets the maximum log-likelihood.- Returns:
- the maximum log-likelihood
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AIC
public double AIC()
Gets the AIC.- Returns:
- the AIC
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