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.

Usage

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.

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:

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

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

if (FALSE) { # \dontrun{
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

} # }