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, ...)

## Arguments

model |
An object of class `lm` . |

details |
Logical; if `TRUE` , details of variable selection will be
printed on screen. |

... |
Other arguments. |

x |
An object of class `blr_step_aic_both` . |

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:

candidatescandidate predictor variables

predictorsvariables added/removed from the model

methodaddition/deletion

aicsakaike information criteria

bicsbayesian information criteria

devsdeviances

stepstotal 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))
# }