olsrr (version 0.4.0)

ols_stepaic_forward: Stepwise AIC Forward Regression

Description

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

Usage

ols_stepaic_forward(model, ...)
# S3 method for default
ols_stepaic_forward(model, details = FALSE, ...)
# S3 method for ols_stepaic_forward
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_stepaic_forward

Value

ols_stepaic_forward returns an object of class "ols_stepaic_forward". An object of class "ols_stepaic_forward" 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

References

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

Examples

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