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#Com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection Classes and Interfaces - 10 results found.
NameDescriptionTypePackageFramework
BackwardEliminationConstructs a GLM model for a set of observations using the backward elimination method.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShu
BackwardElimination .StepInterfacecom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShu
EliminationByAICIn each step, a factor is dropped if the resulting model has the least AIC, until no factor removal can result in a model with AIC lower than the current AIC.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShu
EliminationByZValueIn each step, the factor with the least z-value is dropped, until all z-values are greater than the critical value (given by the significance level).Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShu
ForwardSelectionConstructs a GLM model for a set of observations using the forward selection method.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShu
ForwardSelection .StepInterfacecom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShu
GLMModelSelectionGiven a set of observations {y, X}, we would like to construct a GLM to explain the data.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShu
GLMModelSelection .ModelNotFoundThrow a ModelNotFound exception when fail to construct a model toSee Also:Serialized FormClasscom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShu
SelectionByAICIn each step, a factor is added if the resulting model has the highest AIC, until no factor addition can result in a model with AIC higher than the current AIC.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShu
SelectionByZValueIn each step, the most significant factor is added, until all remaining factors are insignificant.Classcom.numericalmethod.suanshu.stats.regression.linear.glm.modelselectionSuanShu