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, 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

# stepwise regression model <- lm(y ~ ., data = stepdata) ols_step_both_aic(model)
#> Stepwise Selection Method #> ------------------------- #> #> Candidate Terms: #> #> 1 . x1 #> 2 . x2 #> 3 . x3 #> 4 . x4 #> 5 . x5 #> 6 . x6 #> #> #> Variables Entered/Removed: #> #> <U+2714> x6 #> <U+2714> x1 #> <U+2714> x3 #> <U+2714> x2 #> <U+2716> x6 #> <U+2714> x4 #> #> No more variables to be added or removed.
#> #> #> Stepwise Summary #> ---------------------------------------------------------------------------------- #> Variable Method AIC RSS Sum Sq R-Sq Adj. R-Sq #> ---------------------------------------------------------------------------------- #> x6 addition 33473.297 6241.497 13986.736 0.69145 0.69143 #> x1 addition 32931.758 6074.156 14154.076 0.69972 0.69969 #> x3 addition 31912.722 5771.842 14456.391 0.71466 0.71462 #> x2 addition 29304.296 5065.587 15162.646 0.74958 0.74953 #> x6 removal 29302.317 5065.592 15162.641 0.74958 0.74954 #> x4 addition 29300.814 5064.705 15163.528 0.74962 0.74957 #> ---------------------------------------------------------------------------------- #>
# stepwise regression plot model <- lm(y ~ ., data = stepdata) k <- ols_step_both_aic(model)
#> Stepwise Selection Method #> ------------------------- #> #> Candidate Terms: #> #> 1 . x1 #> 2 . x2 #> 3 . x3 #> 4 . x4 #> 5 . x5 #> 6 . x6 #> #> #> Variables Entered/Removed: #> #> <U+2714> x6 #> <U+2714> x1 #> <U+2714> x3 #> <U+2714> x2 #> <U+2716> x6 #> <U+2714> x4 #> #> No more variables to be added or removed.
plot(k)