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, ...)
model | An object of class |
---|---|
... | Other arguments. |
progress | Logical; if |
details | Logical; if |
x | An object of class |
text_size | size of the text in the plot. |
print_plot | logical; if |
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 with the least AIC; an object of class glm
candidate predictor variables
total number of steps
variables entered into the model
akaike information criteria
bayesian information criteria
deviances
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
Other variable selection procedures:
blr_step_aic_backward()
,
blr_step_aic_both()
,
blr_step_p_backward()
,
blr_step_p_forward()
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 }