Package | Description |
---|---|
dev.nm.stat.regression.linear.glm | |
dev.nm.stat.regression.linear.glm.modelselection | |
dev.nm.stat.regression.linear.glm.quasi |
Modifier and Type | Method and Description |
---|---|
void |
IWLS.fit(GLMProblem probelm,
Vector beta0Initial) |
void |
GLMFitting.fit(GLMProblem problem,
Vector beta0Initial)
Fits a Generalized Linear Model.
|
Constructor and Description |
---|
GeneralizedLinearModel(GLMProblem problem)
Solves a generalized linear problem using the Iterative Re-weighted Least Squares algorithm.
|
GeneralizedLinearModel(GLMProblem problem,
GLMFitting fitting)
Constructs a
GeneralizedLinearModel instance. |
GLMResiduals(GLMProblem problem,
Vector fitted)
Performs residual analysis for a GLM regression.
|
Modifier and Type | Method and Description |
---|---|
GLMProblem |
GLMModelSelection.problem()
Returns the original GLM problem.
|
Modifier and Type | Method and Description |
---|---|
int |
BackwardElimination.Step.eliminate(GLMProblem problem,
Matrix subA) |
int |
EliminationByZValue.eliminate(GLMProblem problem,
Matrix subA) |
int |
EliminationByAIC.eliminate(GLMProblem problem,
Matrix subA) |
int |
SelectionByZValue.select(GLMProblem problem,
Matrix subA,
int[] factorChoices) |
int |
ForwardSelection.Step.select(GLMProblem problem,
Matrix subA,
int[] factorChoices) |
int |
SelectionByAIC.select(GLMProblem problem,
Matrix subA,
int[] factorChoices) |
Constructor and Description |
---|
BackwardElimination(GLMProblem problem)
Constructs a GLM model using the backward elimination method, with
EliminationByAIC as the default algorithm.
|
BackwardElimination(GLMProblem problem,
BackwardElimination.Step step)
Constructs a GLM model using the backward elimination method.
|
ForwardSelection(GLMProblem problem)
Constructs a GLM model using the forward selection method, with SelectionByAIC
as the default algorithm.
|
ForwardSelection(GLMProblem problem,
ForwardSelection.Step step)
Constructs a GLM model using the forward selection method.
|
GLMModelSelection(GLMProblem problem)
Constructs automatically a GLM model to explain the observations.
|
Modifier and Type | Class and Description |
---|---|
class |
QuasiGLMProblem
This class represents a quasi generalized linear regression problem.
|
Modifier and Type | Method and Description |
---|---|
void |
QuasiGLMNewtonRaphson.fit(GLMProblem problem,
Vector beta0Initial) |
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