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, ...)

model | An object of class |
---|---|

details | Logical; if |

x | An object of class |

... | Other arguments. |

`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 with the least AIC; an object of class `lm`

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

`ols_stepaic_both()`

has been deprecated. Instead use `ols_step_both_aic()`

.

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_aic`

,
`ols_step_backward_p`

,
`ols_step_best_subset`

,
`ols_step_forward_aic`

,
`ols_step_forward_p`

# NOT RUN { # 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 # }