ols_step_best_subset: Best subsets regression

In olsrr: Tools for Building OLS Regression Models

Description

Select the subset of predictors that do the best at meeting some well-defined objective criterion, such as having the largest R2 value or the smallest MSE, Mallow's Cp or AIC.

Usage

ols_step_best_subset(model, ...)

## S3 method for class 'ols_step_best_subset'
plot(x, model = NA, print_plot = TRUE,
  ...)

Arguments

model	An object of class lm.
...	Other inputs.
x	An object of class ols_step_best_subset.
print_plot	logical; if TRUE, prints the plot else returns a plot object.

Value

ols_step_best_subset returns an object of class "ols_step_best_subset". An object of class "ols_step_best_subset" 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_best_subset() has been deprecated. Instead use ols_step_best_subset().

References

Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.

See Also

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

Examples

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_step_best_subset(model)

# plot
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
k <- ols_step_best_subset(model)
plot(k)

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