ols_step_backward_aic: Stepwise AIC backward regression

In olsrr: Tools for Building OLS Regression Models

Description

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

ols_step_backward_aic(model, ...)

## Default S3 method:
ols_step_backward_aic(model, progress = FALSE,
  details = FALSE, ...)

## S3 method for class 'ols_step_backward_aic'
plot(x, print_plot = TRUE, ...)

Arguments

model	An object of class lm; 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 ols_step_backward_aic.
print_plot	logical; if TRUE, prints the plot else returns a plot object.

Value

ols_step_backward_aic returns an object of class "ols_step_backward_aic". An object of class "ols_step_backward_aic" is a list containing the following components:

model	model with the least AIC; an object of class lm
steps	total number of steps
predictors	variables removed from the model
aics	akaike information criteria
ess	error sum of squares
rss	regression sum of squares
rsq	rsquare
arsq	adjusted rsquare

Deprecated Function

ols_stepaic_backward() has been deprecated. Instead use ols_step_backward_aic().

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

Other variable selection procedures: ols_step_all_possible, ols_step_backward_p, ols_step_best_subset, ols_step_both_aic, ols_step_forward_aic, ols_step_forward_p

Examples

# stepwise backward regression
model <- lm(y ~ ., data = surgical)
ols_step_backward_aic(model)

# stepwise backward regression plot
model <- lm(y ~ ., data = surgical)
k <- ols_step_backward_aic(model)
plot(k)

# final model
k$model

olsrr documentation built on Feb. 10, 2020, 5:07 p.m.