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, ...)

## Arguments

model |
An object of class `glm` . |

... |
Other arguments. |

progress |
Logical; if `TRUE` , will display variable selection progress. |

details |
Logical; if `TRUE` , will print the regression result at
each step. |

x |
An object of class `blr_step_aic_forward` . |

text_size |
size of the text in the plot. |

print_plot |
logical; if `TRUE` , prints the plot else returns a plot object. |

## Value

`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:

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

candidatescandidate predictor variables

stepstotal number of steps

predictorsvariables entered into the model

aicsakaike information criteria

bicsbayesian information criteria

devsdeviances

## References

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

## See also

## Examples

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
}