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

blr_step_aic_backward(model, ...) # S3 method for default blr_step_aic_backward(model, progress = FALSE, details = FALSE, ...) # S3 method for blr_step_aic_backward plot(x, text_size = 3, print_plot = TRUE, ...)

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

... | Other arguments. |

progress | Logical; if |

details | Logical; if |

x | An object of class |

text_size | size of the text in the plot. |

print_plot | logical; if |

`blr_step_aic_backward`

returns an object of class
`"blr_step_aic_backward"`

. An object of class
`"blr_step_aic_backward"`

is a list containing the following components:

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

candidate predictor variables

total number of steps

variables removed from the model

akaike information criteria

bayesian information criteria

deviances

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

Other variable selection procedures:
`blr_step_aic_both()`

,
`blr_step_aic_forward()`

,
`blr_step_p_backward()`

,
`blr_step_p_forward()`

if (FALSE) { model <- glm(honcomp ~ female + read + science + math + prog + socst, data = hsb2, family = binomial(link = 'logit')) # elimination summary blr_step_aic_backward(model) # print details of each step blr_step_aic_backward(model, details = TRUE) # plot plot(blr_step_aic_backward(model)) # final model k <- blr_step_aic_backward(model) k$model }