blr_step_aic_both {blorr} | R Documentation |
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.
blr_step_aic_both(model, details = FALSE, ...)
## S3 method for class 'blr_step_aic_both'
plot(x, text_size = 3, ...)
model |
An object of class |
details |
Logical; if |
... |
Other arguments. |
x |
An object of class |
text_size |
size of the text in the plot. |
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 |
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 |
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_forward()
,
blr_step_p_backward()
,
blr_step_p_forward()
## Not run:
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
## End(Not run)