GLMFitting (NM DEV API (v1.2.4) by NM)

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 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 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()
```