olsrr: ols_step_best_subset – R documentation

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ols_step_best_subset

Best subsets regression

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.

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

Tools for Building OLS Regression Models

v0.5.3

MIT + file LICENSE

Authors

Aravind Hebbali [aut, cre]

Initial release