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:
    Wikipedia: Generalized linear model, IWLS
    • Method Detail

      • fit

        void fit​(GLMProblem problem,
                 Vector beta0Initial)
        Fits a Generalized Linear Model.

        This method must be called before the three get methods.

        problem - the generalized linear regression problem to be solved
        beta0Initial - initial guess for β^
      • betaHat

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

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

        double logLikelihood()