public class GeneralizedLinearModel extends Object implements LinearModel
E(Y) = μ = g-1(Xβ)where E(Y) is the expected value of Y; Xβ is the linear predictor, a linear combination of unknown parameters, β; g is the link function. The R equivalent function is
glm.| Constructor and Description |
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GeneralizedLinearModel(GLMProblem problem)
Solves a generalized linear problem using the Iterative Re-weighted Least Squares algorithm.
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GeneralizedLinearModel(GLMProblem problem,
GLMFitting fitting)
Constructs a
GeneralizedLinearModel instance. |
| Modifier and Type | Method and Description |
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double |
AIC()
Gets the Akaike information criterion (AIC).
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GLMBeta |
beta()
Gets the GLM coefficients estimator, β^.
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double |
Ey(Vector x)
Computes the expectation \(E(y(x))\) given an input.
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GLMResiduals |
residuals()
Gets the residual analysis of this GLM regression.
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public GeneralizedLinearModel(GLMProblem problem, GLMFitting fitting)
GeneralizedLinearModel instance.problem - the generalized linear regression problem to be solvedfitting - the fitting method, c.f., GLMFittingpublic GeneralizedLinearModel(GLMProblem problem)
problem - the generalized linear regression problem to be solvedIWLSpublic double Ey(Vector x)
LinearModelEy in interface LinearModelx - an inputpublic GLMBeta beta()
beta in interface LinearModelpublic GLMResiduals residuals()
residuals in interface LinearModelpublic double AIC()
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