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, ...)

# S3 method for default
blr_step_aic_forward(model, progress = FALSE, details = FALSE, ...)

# S3 method for 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.

Value

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

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()

Examples

if (FALSE) { 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 }