BackwardElimination |
Constructs a GLM model for a set of observations using the backward elimination method.
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EliminationByAIC |
In 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.
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EliminationByZValue |
In 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).
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ForwardSelection |
Constructs a GLM model for a set of observations using the forward selection method.
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GLMModelSelection |
Given a set of observations {y, X}, we would like to construct a GLM to explain the data.
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SelectionByAIC |
In 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.
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SelectionByZValue |
In each step, the most significant factor is added, until all remaining factors are
insignificant.
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