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 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:
  • Method Details

    • 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 solved
      beta0Initial - initial guess for β^
    • mu

      Gets μ as in
      E(Y) = μ = g-1(Xβ)
      Returns:
      μ
    • betaHat

      ImmutableVector betaHat()
      Gets the estimates of β, β^, as in
      E(Y) = μ = g-1(Xβ)
      Returns:
      β^
    • weights

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

      double logLikelihood()