Build regression model from a set of candidate predictor variables by removing predictors based on akaike information criterion, in a stepwise manner until there is no variable left to remove any more.
Usage
blr_step_aic_backward(model, ...)
# Default S3 method
blr_step_aic_backward(model, progress = FALSE, details = FALSE, ...)
# S3 method for class 'blr_step_aic_backward'
plot(x, text_size = 3, print_plot = TRUE, ...)Arguments
- model
An object of class
glm; the model should include all candidate predictor variables.- ...
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_backward.- 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_backward returns an object of class
"blr_step_aic_backward". An object of class
"blr_step_aic_backward" 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 removed from 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_both(),
blr_step_aic_forward(),
blr_step_p_backward(),
blr_step_p_forward()
Examples
if (FALSE) { # \dontrun{
model <- glm(honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = 'logit'))
# elimination summary
blr_step_aic_backward(model)
# print details of each step
blr_step_aic_backward(model, details = TRUE)
# plot
plot(blr_step_aic_backward(model))
# final model
k <- blr_step_aic_backward(model)
k$model
} # }