![]() Lastly, we pick a single best model from among M 0…M p using AIC.Next, pick the best among these k models and call it M k-1. Next, for k = p, p-1, … 1, we fit all k models that contain all but one of the predictors in M k, for a total of k-1 predictor variables.First, we fit a model using all p predictors.The following code shows how to perform backward stepwise selection: #define intercept-only modelīackward <- step(all, direction=' backward', scope= formula(all), trace=0) It turned out that none of these models produced a significant reduction in AIC, thus we stopped the procedure. Next, we fit every possible four-predictor model.The model that produced the lowest AIC and also had a statistically significant reduction in AIC compared to the two-predictor model added the predictor hp. Next, we fit every possible three-predictor model.The model that produced the lowest AIC and also had a statistically significant reduction in AIC compared to the single-predictor model added the predictor cyl. Next, we fit every possible two-predictor model.The model that produced the lowest AIC and also had a statistically significant reduction in AIC compared to the intercept-only model used the predictor wt. Next, we fit every possible one-predictor model.First, we fit the intercept-only model.This can take up quite a bit of space if there are a large number of predictor variables. Note: The argument trace=0 tells R not to display the full results of the stepwise selection. ![]() #view results of forward stepwise regression Intercept_only <- lm(mpg ~ 1, data=mtcars)įorward <- step(intercept_only, direction=' forward', scope= formula(all), trace=0) The following code shows how to perform forward stepwise selection: #define intercept-only model
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