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:
 Wikipedia:
Generalized linear model,
IWLS


Method Summary
All Methods Instance Methods Abstract 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 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()

