olsrr: ols_step_both_aic – R documentation

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ols_step_both_aic

Stepwise AIC regression

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.

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)

olsrr

Tools for Building OLS Regression Models

v0.5.3

MIT + file LICENSE

Authors

Aravind Hebbali [aut, cre]

Initial release