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, ...)

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.

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

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

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 }