blr_step_aic_both {blorr} | R Documentation |

## Stepwise AIC selection

### Description

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.

### Usage

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

### Arguments

`model` |
An object of class |

`details` |
Logical; if |

`...` |
Other arguments. |

`x` |
An object of class |

`text_size` |
size of the text in the plot. |

### Value

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

`candidates` |
candidate predictor variables |

`predictors` |
variables added/removed from the model |

`method` |
addition/deletion |

`aics` |
akaike information criteria |

`bics` |
bayesian information criteria |

`devs` |
deviances |

`steps` |
total number of steps |

### References

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

### See Also

Other variable selection procedures:
`blr_step_aic_backward()`

,
`blr_step_aic_forward()`

,
`blr_step_p_backward()`

,
`blr_step_p_forward()`

### Examples

```
## Not run:
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
## End(Not run)
```

*blorr*version 0.3.0 Index]