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