ols_step_all_possible: All possible regression

In cmlopera/olsrr: Tools for Building OLS Regression Models

Description

Fits all regressions involving one regressor, two regressors, three regressors, and so on. It tests all possible subsets of the set of potential independent variables.

Usage

ols_step_all_possible(model, ...)

## S3 method for class 'ols_step_all_possible'
plot(x, model = NA, ...)

Arguments

model	An object of class lm.
...	Other arguments.
x	An object of class ols_best_subset.

Value

ols_step_all_possible returns an object of class "ols_step_all_possible". An object of class "ols_step_all_possible" is a data frame containing the following components:

n	model number
predictors	predictors in the model
rsquare	rsquare of the model
adjr	adjusted rsquare of the model
predrsq	predicted rsquare of the model
cp	mallow's Cp
aic	akaike information criteria
sbic	sawa bayesian information criteria
sbc	schwarz bayes information criteria
gmsep	estimated MSE of prediction, assuming multivariate normality
jp	final prediction error
pc	amemiya prediction criteria
sp	hocking's Sp

Deprecated Function

ols_all_subset() has been deprecated. Instead use ols_step_all_possible().

References

Mendenhall William and Sinsich Terry, 2012, A Second Course in Statistics Regression Analysis (7th edition). Prentice Hall

See Also

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

Examples

model <- lm(mpg ~ disp + hp, data = mtcars)
k <- ols_step_all_possible(model)
k

# plot
plot(k)

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