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_stepaic_both(model, details = FALSE) # S3 method for ols_stepaic_both plot(x, ...)
model | An object of class |
---|---|
details | Logical; if |
x | An object of class |
... | Other arguments. |
ols_stepaic_both
returns an object of class "ols_stepaic_both"
.
An object of class "ols_stepaic_both"
is a list containing the
following components:
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
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
,
ols_step_best_subset
,
ols_step_forward
,
ols_stepaic_backward
,
ols_stepaic_forward
# NOT RUN { # stepwise regression model <- lm(y ~ ., data = stepdata) ols_stepaic_both(model) # }# NOT RUN { # stepwise regression plot model <- lm(y ~ ., data = stepdata) k <- ols_stepaic_both(model) plot(k) # }