olsrr (version 0.1.0)

ols_stepaic_backward: Stepwise AIC Backward Regression

Description

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

Usage

ols_stepaic_backward(model, ...)
# S3 method for ols_stepaic_backward
plot(x, ...)

Arguments

model

an object of class lm; the model should include all candidate predictor variables

...

other arguments

x

an object of class ols_stepaic_backward

Value

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

steps

f statistic

predictors

p value of score

aics

degrees of freedom

ess

fitted values of the regression model

rss

name of explanatory variables of fitted regression model

rsq

response variable

arsq

predictors

References

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

Examples

# stepwise backward regression
model <- lm(y ~ ., data = surgical)
ols_stepaic_backward(model)

# stepwise backward regression plot
model <- lm(y ~ ., data = surgical)
k <- ols_stepaic_backward(model)
plot(k)