ols_step_both_aic: Stepwise AIC regression

In 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, progress = FALSE, details = FALSE)

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

Arguments

model	An object of class lm.
progress	Logical; if TRUE, will display variable selection progress.
details	Logical; if TRUE, details of variable selection will be printed on screen.
x	An object of class ols_step_both_aic.
print_plot	logical; if TRUE, prints the plot else returns a plot object.
...	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)

Example output

Attaching package: 'olsrr'

The following object is masked from 'package:datasets':

    rivers

Stepwise Selection Method 
-------------------------

Candidate Terms: 

1 . x1 
2 . x2 
3 . x3 
4 . x4 
5 . x5 
6 . x6 


Variables Entered/Removed: 

- x6 added 
- x1 added 
- x3 added 
- x2 added 
- x6 removed 
- x4 added 

No more variables to be added or removed.

                                  Stepwise Summary                                  
----------------------------------------------------------------------------------
Variable     Method        AIC         RSS        Sum Sq       R-Sq      Adj. R-Sq 
----------------------------------------------------------------------------------
x6          addition    33473.297    6241.497    13986.736    0.69145      0.69143 
x1          addition    32931.758    6074.156    14154.076    0.69972      0.69969 
x3          addition    31912.722    5771.842    14456.391    0.71466      0.71462 
x2          addition    29304.296    5065.587    15162.646    0.74958      0.74953 
x6          removal     29302.317    5065.592    15162.641    0.74958      0.74954 
x4          addition    29300.814    5064.705    15163.528    0.74962      0.74957 
----------------------------------------------------------------------------------

Stepwise Selection Method 
-------------------------

Candidate Terms: 

1 . x1 
2 . x2 
3 . x3 
4 . x4 
5 . x5 
6 . x6 


Variables Entered/Removed: 

- x6 added 
- x1 added 
- x3 added 
- x2 added 
- x6 removed 
- x4 added 

No more variables to be added or removed.NULL

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