Build regression model from a set of candidate predictor variables by removing predictors based on akaike information criterion, in a stepwise manner until there is no variable left to remove any more.
ols_stepaic_backward(model, ...) # S3 method for default ols_stepaic_backward(model, details = FALSE, ...) # S3 method for ols_stepaic_backward plot(x, ...)
model | An object of class |
---|---|
... | Other arguments. |
details | Logical; if |
x | An object of class |
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:
total number of steps
variables removed from the model
akaike information criteria
error sum of squares
regression sum of squares
rsquare
adjusted rsquare
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
Other variable selection procedures: ols_step_all_possible
,
ols_step_backward
,
ols_step_best_subset
,
ols_step_forward
,
ols_stepaic_both
,
ols_stepaic_forward
# NOT RUN { # stepwise backward regression model <- lm(y ~ ., data = surgical) ols_stepaic_backward(model) # }# NOT RUN { # stepwise backward regression plot model <- lm(y ~ ., data = surgical) k <- ols_stepaic_backward(model) plot(k) # }