olsrr (version 0.5.3)

ols_step_both_aic: Stepwise AIC regression

Description

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.

Usage

ols_step_both_aic(model, progress = FALSE, details = FALSE)
# S3 method for ols_step_both_aic
plot(x, print_plot = TRUE, ...)

Value

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

model with the least AIC; an object of class lm

predictors

variables added/removed from the model

method

addition/deletion

aics

akaike information criteria

ess

error sum of squares

rss

regression sum of squares

rsq

rsquare

arsq

adjusted rsquare

steps

total number of steps

Arguments

model

An object of class lm.

progress

Logical; if TRUE, will display variable selection progress.

details

Logical; if TRUE, details of variable selection will be printed on screen.

x

An object of class ols_step_both_aic.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

...

Other arguments.

Deprecated Function

ols_stepaic_both() has been deprecated. Instead use ols_step_both_aic().

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

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

Examples

if (FALSE) {
# stepwise regression
model <- lm(y ~ ., data = stepdata)
ols_step_both_aic(model)

# stepwise regression plot
model <- lm(y ~ ., data = stepdata)
k <- ols_step_both_aic(model)
plot(k)

# final model
k$model

}