Build regression model from a set of candidate predictor variables by entering and removing predictors based on akaike information criterion, in a stepwise manner until there is no variable left to enter or remove any more.

blr_step_aic_both(model, details = FALSE, ...) # S3 method for blr_step_aic_both plot(x, text_size = 3, ...)

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

details | Logical; if |

... | Other arguments. |

x | An object of class |

text_size | size of the text in the plot. |

`blr_step_aic_both`

returns an object of class `"blr_step_aic_both"`

.
An object of class `"blr_step_aic_both"`

is a list containing the
following components:

model with the least AIC; an object of class `glm`

candidate predictor variables

variables added/removed from the model

addition/deletion

akaike information criteria

bayesian information criteria

deviances

total number of steps

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

Other variable selection procedures:
`blr_step_aic_backward()`

,
`blr_step_aic_forward()`

,
`blr_step_p_backward()`

,
`blr_step_p_forward()`

if (FALSE) { model <- glm(y ~ ., data = stepwise) # selection summary blr_step_aic_both(model) # print details at each step blr_step_aic_both(model, details = TRUE) # plot plot(blr_step_aic_both(model)) # final model k <- blr_step_aic_both(model) k$model }