olsrr (version 0.5.0)

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, details = FALSE)
# S3 method for ols_step_both_aic
plot(x, ...)

Arguments

model

An object of class lm.

details

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

x

An object of class ols_step_both_aic.

...

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:

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

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

# NOT RUN {
# stepwise regression
model <- lm(y ~ ., data = stepdata)
ols_step_both_aic(model)
# }
# NOT RUN {
# }
# NOT RUN {
# stepwise regression plot
model <- lm(y ~ ., data = stepdata)
k <- ols_step_both_aic(model)
plot(k)
# }
# NOT RUN {
# }