Description Usage Arguments Value Deprecated Function References See Also Examples
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.
1 2 3 4 5 6 7 | ols_step_backward_aic(model, ...)
## Default S3 method:
ols_step_backward_aic(model, details = FALSE, ...)
## S3 method for class 'ols_step_backward_aic'
plot(x, ...)
|
model |
An object of class |
... |
Other arguments. |
details |
Logical; if |
x |
An object of class |
ols_step_backward_aic
returns an object of class "ols_step_backward_aic"
.
An object of class "ols_step_backward_aic"
is a list containing the
following components:
model |
model with the least AIC; an object of class |
steps |
total number of steps |
predictors |
variables removed from the model |
aics |
akaike information criteria |
ess |
error sum of squares |
rss |
regression sum of squares |
rsq |
rsquare |
arsq |
adjusted rsquare |
ols_stepaic_backward()
has been deprecated. Instead use ols_step_backward_aic()
.
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_p
,
ols_step_best_subset
,
ols_step_both_aic
,
ols_step_forward_aic
,
ols_step_forward_p
1 2 3 4 5 6 7 8 9 10 11 | # stepwise backward regression
model <- lm(y ~ ., data = surgical)
ols_step_backward_aic(model)
# stepwise backward regression plot
model <- lm(y ~ ., data = surgical)
k <- ols_step_backward_aic(model)
plot(k)
# final model
k$model
|
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