Running a regression model with too many variables – especially irrelevant ones – will lead to a needlessly complex model. Stepwise can help to choose the best variables to add.

Packages you need:

`library(MASS)`

First, choose a model and throw every variable you think has an impact on your dependent variable!

I hear the voice of my undergrad professor in my ear: try not to go for the “throw spaghetti at the wall and just see what fits” approach.

We choose variables because we have some theoretical rationale for any potential relationship. Or else we could end up stumbling on spurious relationships. Like the one between Nick Cage movies and incidence of pool drowning.

However …

… beyond just using our sound theoretical understanding of the complex phenomena we study in order to choose our model variables …

… one additional way to supplement and gauge which variables add to – or more importantly omit from – the model is to choose the one with the smallest amount of error. We can operationalise this as the model with the *lowest *Akaike information criterion (AIC).

AIC is an estimator of in-sample prediction error and is similar to the *adjusted* R-squared measures we see in our regression output summaries, which effectively penalise us for adding more variables to the model.

Lower scores can indicate a more parsimonious model, relative to a model fit with a higher AIC. It can therefore give an indication of the **relative **quality of statistical models for a given set of data.

As a caveat, we can only compare AIC scores with models that are fit to explain variance of the *same *dependent / response variable.

`data(mtcars)`

summary(car_model <- lm(mpg ~., data = mtcars))

With our model, we can now feed it into the stepwise function. For the `direction`

argument, you can choose between backward and forward stepwise selection,

- Forward steps: start the model with
**no**predictors, just one intercept and search through all the single-variable models, adding variables, until we find the the best one (the one that results in the lowest residual sum of squares)

- Backward steps: we start stepwise with
**all**the predictors and removes variable with the least statistically significant (the largest p-value) one by one until we find the lost AIC.

Backward stepwise is generally better because starting with the full model has the advantage of considering the effects of all variables simultaneously.

Unlike backward elimination, forward stepwise selection is more suitable in settings where the *number of variables is bigger than the sample size*.

So tldr: unless the number of candidate variables is greater than the sample size (such as dealing with genes), using a backward stepwise approach is default choice.

You can also choose `direction = "both"`

:

`step_car <- stepAIC(car_model, trace = TRUE, direction= "both")`

If you add the `trace = TRUE`

, R prints out all the steps.

I’ll show the last step to show you the output.

The goal is to have the combination of variables that has the lowest AIC or lowest residual sum of squares (RSS).

The last line is the final model that we assign to `step_car `

object.

`stargazer(car_model, step_car, type = "text")`

We can see that the stepwise model has only three variables compared to the ten variables in my original model.

And even with far fewer variables, the R2 has decreased by an insignificant amount. In fact the Adjusted R2 increased because we are not being penalised for throwing so many unnecessary variables.

So we can quickly find a model that loses no explanatory power by is far more parsimonious.

Plus in the original model, only one variable is significant but in the stepwise variable all three of the variables are significant.

From the `olsrr `

package

`step_plot <- ols_step_both_aic(car_model)`

plot(step_plot)