Build regression model from a set of candidate predictor variables by entering and removing predictors based on akaike information criteria, in a stepwise manner until there is no variable left to enter or remove any more.
ols_step_both_aic(model, progress = FALSE, details = FALSE) # S3 method for ols_step_both_aic plot(x, print_plot = TRUE, ...)
| model | An object of class |
|---|---|
| progress | Logical; if |
| details | Logical; if |
| x | An object of class |
| print_plot | logical; if |
| ... | Other arguments. |
ols_step_both_aic returns an object of class "ols_step_both_aic".
An object of class "ols_step_both_aic" is a list containing the
following components:
model with the least AIC; an object of class lm
variables added/removed from the model
addition/deletion
akaike information criteria
error sum of squares
regression sum of squares
rsquare
adjusted rsquare
total number of steps
ols_stepaic_both() has been deprecated. Instead use ols_step_both_aic().
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
Other variable selection procedures: ols_step_all_possible,
ols_step_backward_aic,
ols_step_backward_p,
ols_step_best_subset,
ols_step_forward_aic,
ols_step_forward_p