You need to compare the performance of the different models for choosing the best one. regsubsets(), which has the tuning parameter nvmax specifying the maximal number of predictors to incorporate in the model (See Chapter It returns multiple models with different size up to nvmax.Step.model <- stepAIC(full.model, direction = "both", It return the best final model.įull.model <- lm(Fertility ~., data = swiss) It has an option named direction, which can take the following values: i) “both” (for stepwise regression, both forward and backward selection) “backward” (for backward selection) and “forward” (for forward selection). stepAIC(), which choose the best model by AIC.There are many functions and R packages for computing stepwise regression. After adding each new variable, remove any variables that no longer provide an improvement in the model fit (like backward selection). You start with no predictors, then sequentially add the most contributive predictors (like forward selection). Stepwise selection (or sequential replacement), which is a combination of forward and backward selections.Backward selection (or backward elimination), which starts with all predictors in the model (full model), iteratively removes the least contributive predictors, and stops when you have a model where all predictors are statistically significant.
There are three strategies of stepwise regression (James et al. The stepwise regression (or stepwise selection) consists of iteratively adding and removing predictors, in the predictive model, in order to find the subset of variables in the data set resulting in the best performing model, that is a model that lowers prediction error.
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