blr_step_aic_both function

Stepwise AIC selection

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 class '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.

Returns

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 glm

• 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

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)

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()