ols_step_both_aic: Stepwise AIC regression

In cmlopera/olsrr: Tools for Building OLS Regression Models

Description

Build regression model from a set of candidate predictor variables by entering and removing predictors based on akaike information criteria, in a stepwise manner until there is no variable left to enter or remove any more.

Usage

ols_step_both_aic(model, details = FALSE)

## S3 method for class 'ols_step_both_aic'
plot(x, ...)

Arguments

model	An object of class lm.
details	Logical; if TRUE, details of variable selection will be printed on screen.
x	An object of class ols_step_both_aic.
...	Other arguments.

Value

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

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

Deprecated Function

ols_stepaic_both() has been deprecated. Instead use ols_step_both_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_aic, ols_step_backward_p, ols_step_best_subset, ols_step_forward_aic, ols_step_forward_p

Examples

## Not run: 
# stepwise regression
model <- lm(y ~ ., data = stepdata)
ols_step_both_aic(model)

# stepwise regression plot
model <- lm(y ~ ., data = stepdata)
k <- ols_step_both_aic(model)
plot(k)

# final model
k$model


## End(Not run)

cmlopera/olsrr documentation built on May 26, 2019, 10:34 a.m.