Description Usage Arguments Value Deprecated Function References See Also Examples
Build regression model from a set of candidate predictor variables by entering and removing predictors based on akaike information criteria, in a stepwise manner until there is no variable left to enter or remove any more.
1 2 3 4 | ols_step_both_aic(model, details = FALSE)
## S3 method for class 'ols_step_both_aic'
plot(x, ...)
|
model |
An object of class |
details |
Logical; if |
x |
An object of class |
... |
Other arguments. |
ols_step_both_aic
returns an object of class "ols_step_both_aic"
.
An object of class "ols_step_both_aic"
is a list containing the
following components:
model |
model with the least AIC; an object of class |
predictors |
variables added/removed from the model |
method |
addition/deletion |
aics |
akaike information criteria |
ess |
error sum of squares |
rss |
regression sum of squares |
rsq |
rsquare |
arsq |
adjusted rsquare |
steps |
total number of steps |
ols_stepaic_both()
has been deprecated. Instead use ols_step_both_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_aic
,
ols_step_backward_p
,
ols_step_best_subset
,
ols_step_forward_aic
,
ols_step_forward_p
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## Not run:
# stepwise regression
model <- lm(y ~ ., data = stepdata)
ols_step_both_aic(model)
# stepwise regression plot
model <- lm(y ~ ., data = stepdata)
k <- ols_step_both_aic(model)
plot(k)
# final model
k$model
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.