olsrr (version 0.5.0)

ols_step_forward_aic: Stepwise AIC forward regression

Description

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

Usage

ols_step_forward_aic(model, ...)
# S3 method for default
ols_step_forward_aic(model, details = FALSE, ...)
# S3 method for ols_step_forward_aic
plot(x, ...)

Arguments

model

An object of class lm.

...

Other arguments.

details

Logical; if TRUE, will print the regression result at each step.

x

An object of class ols_step_forward_aic.

Value

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

steps

total number of steps

predictors

variables added to the model

aics

akaike information criteria

ess

error sum of squares

rss

regression sum of squares

rsq

rsquare

arsq

adjusted rsquare

Deprecated Function

ols_stepaic_forward() has been deprecated. Instead use ols_step_forward_aic().

References

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

See Also

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

Examples

# NOT RUN {
# stepwise forward regression
model <- lm(y ~ ., data = surgical)
ols_step_forward_aic(model)
# }
# NOT RUN {
# }
# NOT RUN {
# stepwise forward regression plot
model <- lm(y ~ ., data = surgical)
k <- ols_step_forward_aic(model)
plot(k)
# }
# NOT RUN {
# }