Interface GLMFitting
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- All Known Implementing Classes:
IWLS,QuasiGLMNewtonRaphson
public interface GLMFittingThis interface represents a fitting method for estimating β in a Generalized Linear Model (GLM). John Nelder and Robert Wedderburn proposed an iteratively re-weighted least squares method for maximum likelihood estimation of the model parameters, β. Maximum-likelihood estimation remains popular and is the default method on many statistical computing packages. Other approaches, including Bayesian approaches and least squares fits to variance stabilized responses, have been developed.- See Also:
- Wikipedia:
Generalized linear model,
IWLS
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description ImmutableVectorbetaHat()Gets the estimates of β, β^, as in E(Y) = μ = g-1(Xβ)voidfit(GLMProblem problem, Vector beta0Initial)Fits a Generalized Linear Model.doublelogLikelihood()ImmutableVectormu()Gets μ as in E(Y) = μ = g-1(Xβ)ImmutableVectorweights()Gets the weights assigned to the observations.
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Method Detail
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fit
void fit(GLMProblem problem, Vector beta0Initial)
Fits a Generalized Linear Model. This method must be called before the three get methods.- Parameters:
problem- the generalized linear regression problem to be solvedbeta0Initial- initial guess for β^
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mu
ImmutableVector mu()
Gets μ as inE(Y) = μ = g-1(Xβ)
- Returns:
- μ
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betaHat
ImmutableVector betaHat()
Gets the estimates of β, β^, as inE(Y) = μ = g-1(Xβ)
- Returns:
- β^
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weights
ImmutableVector weights()
Gets the weights assigned to the observations.- Returns:
- the weights
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logLikelihood
double logLikelihood()
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