public class GeneralizedLinearModel
extends java.lang.Object
implements java.io.Serializable
In a GLM, each outcome of the dependent variables, Y, is assumed to be generated from a particular distribution in the exponential family, a large range of probability distributions that includes the normal, binomial and Poisson distributions, among others. The mean, μ, of the distribution depends on the independent variables, X, through
E(Y) = μ = g-1(Xβ)
where E(Y) is the expected value of Y;
Xβ is the linear predictor, a linear combination of unknown parameters, β;
g is the link function.
The R equivalent function is glm.
| Modifier and Type | Field and Description |
|---|---|
double |
AIC |
Beta |
beta
the GLM coefficients β^ statistics
|
GLMProblem |
problem
the generalized linear regression problem to be solved
|
Residuals |
residuals
the residual analysis of this GLM regression
|
| Constructor and Description |
|---|
GeneralizedLinearModel(GLMProblem problem)
Solve a generalized linear problem using the Iterative Re-weighted Least Squares algorithm.
|
GeneralizedLinearModel(GLMProblem problem,
Fitting fitting)
Construct a GeneralizedLinearModel instance.
|
public final GLMProblem problem
public final Beta beta
public final Residuals residuals
public final double AIC
public GeneralizedLinearModel(GLMProblem problem, Fitting fitting)
problem - the generalized linear regression problem to be solvedfitting - the fitting method, c.f., Fittingpublic GeneralizedLinearModel(GLMProblem problem)
problem - the generalized linear regression problem to be solved