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, details = FALSE, ...)
# S3 method for default
blr_step_aic_backward(model, details = FALSE, ...)
# S3 method for blr_step_aic_backward
plot(x, text_size = 3, ...)

## Arguments

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

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

... |
Other arguments. |

x |
An object of class `blr_step_aic_backward` . |

text_size |
size of the text in the plot. |

## 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:

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

Other variable selection procedures: `blr_step_aic_both`

,
`blr_step_aic_forward`

,
`blr_step_p_backward`

,
`blr_step_p_forward`

## Examples

# NOT RUN {
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))
# }