## 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.
Wikipedia: Generalized linear model, IWLS
• ### Method Summary

All Methods
Modifier and Type Method Description
ImmutableVector betaHat()
Gets the estimates of β, β^, as in E(Y) = μ = g-1(Xβ)
void fit​(GLMProblem problem, Vector beta0Initial)
Fits a Generalized Linear Model.
double logLikelihood()
ImmutableVector mu()
Gets μ as in E(Y) = μ = g-1(Xβ)
ImmutableVector weights()
Gets the weights assigned to the observations.
• ### Method Detail

• #### 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

ImmutableVector 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()