blr_step_aic_forward {blorr} | R Documentation |
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, ...)
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 |
model with the least AIC; an object of class |
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 |
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()
## 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)