Build regression model from a set of candidate predictor variables by entering predictors based on chi square statistic, in a stepwise manner until there is no variable left to enter any more.

blr_step_aic_forward(model, ...) # S3 method for default blr_step_aic_forward(model, progress = FALSE, details = FALSE, ...) # S3 method for blr_step_aic_forward 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_forward`

returns an object of class
`"blr_step_aic_forward"`

. An object of class
`"blr_step_aic_forward"`

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 entered into 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_backward()`

,
`blr_step_aic_both()`

,
`blr_step_p_backward()`

,
`blr_step_p_forward()`

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