blr_step_aic_forward function

Stepwise AIC forward selection

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, ...)

## Default S3 method:
blr_step_aic_forward(model, progress = FALSE, details = FALSE, ...)

## S3 method for class '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.

Returns

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:

• model: model with the least AIC; an object of class glm

• candidates: candidate predictor variables

• steps: total number of steps

• predictors: variables entered into the model

• aics: akaike information criteria

• bics: bayesian information criteria

• devs: deviances

Examples

## Not run:

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
## 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_both(), blr_step_p_backward(), blr_step_p_forward()