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, ...)

## Arguments

model |
An object of class `glm` ; the model should include all
candidate predictor variables. |

... |
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_backward` . |

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_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:

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

candidatescandidate predictor variables

stepstotal number of steps

predictorsvariables removed from 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 + 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
}