Stepwise regression is a procedure we can use to build a regression model from a set of predictor variables by entering and removing predictors in a stepwise manner into the model until there is no statistically valid reason to enter or remove any more.

The goal of stepwise regression is to build a regression model that includes all of the predictor variables that are statistically significantly related to the response variable.

This tutorial explains how to perform the following stepwise regression procedures in R:

- Forward Stepwise Selection
- Backward Stepwise Selection
- Both-Direction Stepwise Selection

For each example we’ll use the built-in **mtcars** dataset:

#view first six rows ofmtcarshead(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1

We will fit a multiple linear regression model using *mpg *(miles per gallon) as our response variable and all of the other 10 variables in the dataset as potential predictors variables.

For each example will use the built-in step() function from the stats package to perform stepwise selection, which uses the following syntax:

**step(intercept-only model, direction, scope)**

where:

**intercept-only model**: the formula for the intercept-only model**direction:**the mode of stepwise search, can be either “both”, “backward”, or “forward”**scope:**a formula that specifies which predictors we’d like to attempt to enter into the model

**Example 1: Forward Stepwise Selection**

The following code shows how to perform forward stepwise selection:

#define intercept-only model intercept_only <- lm(mpg ~ 1, data=mtcars) #define model with all predictors all <- lm(mpg ~ ., data=mtcars) #perform forward stepwise regression forward <- step(intercept_only, direction='forward', scope=formula(all), trace=0) #view results of forward stepwise regression forward$anova Step Df Deviance Resid. Df Resid. Dev AIC 1 NA NA 31 1126.0472 115.94345 2 + wt -1 847.72525 30 278.3219 73.21736 3 + cyl -1 87.14997 29 191.1720 63.19800 4 + hp -1 14.55145 28 176.6205 62.66456 #view final model forward$coefficients (Intercept) wt cyl hp 38.7517874 -3.1669731 -0.9416168 -0.0180381

**Note:** The argument trace=0 tells R not to display the full results of the stepwise selection. This can take up quite a bit of space if there are a large number of predictor variables.

Here is how to interpret the results:

- First, we fit the intercept-only model. This model had an AIC of
**115.94345**. - Next, we fit every possible one-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*. This model had an AIC of**73.21736**. - 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 single-predictor model added the predictor
*cyl*. This model had an AIC of**63.19800**. - 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 two-predictor model added the predictor
*hp*. This model had an AIC of**62.66456**. - Next, we fit every possible four-predictor model. It turned out that none of these models produced a significant reduction in AIC, thus we stopped the procedure.

The final model turns out to be:

**mpg ~ 38.75 – 3.17*wt – 0.94*cyl – 0.02*hyp**

**Example 2: Backward Stepwise Selection**

The following code shows how to perform backward stepwise selection:

#define intercept-only model intercept_only <- lm(mpg ~ 1, data=mtcars) #define model with all predictors all <- lm(mpg ~ ., data=mtcars) #perform backward stepwise regression backward <- step(all, direction='backward', scope=formula(all), trace=0) #view results of backward stepwise regression backward$anova Step Df Deviance Resid. Df Resid. Dev AIC 1 NA NA 21 147.4944 70.89774 2 - cyl 1 0.07987121 22 147.5743 68.91507 3 - vs 1 0.26852280 23 147.8428 66.97324 4 - carb 1 0.68546077 24 148.5283 65.12126 5 - gear 1 1.56497053 25 150.0933 63.45667 6 - drat 1 3.34455117 26 153.4378 62.16190 7 - disp 1 6.62865369 27 160.0665 61.51530 8 - hp 1 9.21946935 28 169.2859 61.30730 #view final model backward$coefficients (Intercept) wt qsec am 9.617781 -3.916504 1.225886 2.935837

Here is how to interpret the results:

- First, we fit a model using all
*p*predictors. Define this as M_{p}. - 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. Next, pick the best among these k models and call it M_{k-1}. - Lastly, we pick a single best model from among M
_{0}…M_{p}using AIC.

The final model turns out to be:

**mpg ~ 9.62 – 3.92*wt + 1.23*qsec + 2.94*am**

**Example 3: Both-Direction Stepwise Selection**

The following code shows how to perform both-direction stepwise selection:

#define intercept-only model intercept_only <- lm(mpg ~ 1, data=mtcars) #define model with all predictors all <- lm(mpg ~ ., data=mtcars) #perform backward stepwise regression both <- step(intercept_only, direction='both', scope=formula(all), trace=0) #view results of backward stepwise regression both$anova Step Df Deviance Resid. Df Resid. Dev AIC 1 NA NA 31 1126.0472 115.94345 2 + wt -1 847.72525 30 278.3219 73.21736 3 + cyl -1 87.14997 29 191.1720 63.19800 4 + hp -1 14.55145 28 176.6205 62.66456 #view final model both$coefficients (Intercept) wt cyl hp 38.7517874 -3.1669731 -0.9416168 -0.0180381

Here is how to interpret the results:

- First, we fit the intercept-only model.
- Next, we added predictors to the model sequentially just like we did in forward-stepwise selection. However, after adding each predictor we also removed any predictors that no longer provided an improvement in model fit.
- We repeated this process until we reached a final model.

The final model turns out to be:

**mpg ~ 9.62 – 3.92*wt + 1.23*qsec + 2.94*am**

Note that forward stepwise selection and both-direction stepwise selection produced the same final model while backward stepwise selection produced a different model.

**Additional Resources**

How to Test the Significance of a Regression Slope

How to Read and Interpret a Regression Table

A Guide to Multicollinearity in Regression