Backward Elimination P Value. Stop when all p-values are less than acrit. Begingroup Momentarily putting aside problems with stepwise model selection Im interested in generalizing the smaller AIC.
Stop when all p-values are less than acrit. SL 005 Fit the model with all possible predictors. Kittler 1978 surveys the feature-selection algorithms that have been developed for pattern recognition.
StepFullModel direction both test F This can display both the AIC values as well as the F and P values.
Backward Elimination consists of the following steps. 32 - Backward Unlike forward stepwise selection it begins with the full least squares model containing all p predictors and then iteratively removes the least useful predictor one-at-a-time. StepFullModel direction backward test F and for stepwise selection simply. The Likelihood Ratio p-value you describe is fine but in routines like Rs lm estimatestderr is being compared to a t-distribution.
