blorr: blr_step_aic_both – R documentation

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blr_step_aic_both

Stepwise AIC selection

Description

Build regression model from a set of candidate predictor variables by entering and removing predictors based on akaike information criterion, in a stepwise manner until there is no variable left to enter or remove any more.

Usage

blr_step_aic_both(model, details = FALSE, ...)

## S3 method for class 'blr_step_aic_both'
plot(x, text_size = 3, ...)

Arguments

`model`	An object of class `lm`.
`details`	Logical; if `TRUE`, details of variable selection will be printed on screen.
`...`	Other arguments.
`x`	An object of class `blr_step_aic_both`.
`text_size`	size of the text in the plot.

Value

blr_step_aic_both returns an object of class "blr_step_aic_both". An object of class "blr_step_aic_both" is a list containing the following components:

`model`	model with the least AIC; an object of class `glm`
`candidates`	candidate predictor variables
`predictors`	variables added/removed from the model
`method`	addition/deletion
`aics`	akaike information criteria
`bics`	bayesian information criteria
`devs`	deviances
`steps`	total number of steps

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

## Not run: 
model <- glm(y ~ ., data = stepwise)

# selection summary
blr_step_aic_both(model)

# print details at each step
blr_step_aic_both(model, details = TRUE)

# plot
plot(blr_step_aic_both(model))

# final model
k <- blr_step_aic_both(model)
k$model


## End(Not run)

blorr

Tools for Developing Binary Logistic Regression Models

v0.3.0

MIT + file LICENSE

Authors

Aravind Hebbali [aut, cre] (<https://orcid.org/0000-0001-9220-9669>)

Initial release