Interface GLMFitting
 All Known Implementing Classes:
IWLS
,QuasiGLMNewtonRaphson
public interface GLMFitting
This interface represents a fitting method for estimating β in a
Generalized Linear Model (GLM).
John Nelder and Robert Wedderburn proposed an iteratively reweighted least
squares method for maximum likelihood estimation of the model parameters,
β. Maximumlikelihood 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:

Method Summary

Method Details

fit
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^

mu
ImmutableVector mu()Gets μ as inE(Y) = μ = g^{1}(Xβ)
 Returns:
 μ

betaHat
ImmutableVector betaHat()Gets the estimates of β, β^, as inE(Y) = μ = g^{1}(Xβ)
 Returns:
 β^

weights
ImmutableVector weights()Gets the weights assigned to the observations. Returns:
 the weights

logLikelihood
double logLikelihood()
