Fits all regressions involving one regressor, two regressors, three
regressors, and so on. It tests all possible subsets of the set of potential
independent variables.
Usage
ols_step_all_possible(model, ...)
# S3 method for ols_step_all_possible
plot(x, model = NA, print_plot = TRUE,
...)
Value
ols_step_all_possible returns an object of class "ols_step_all_possible".
An object of class "ols_step_all_possible" is a data frame containing the
following components:
n
model number
predictors
predictors in the model
rsquare
rsquare of the model
adjr
adjusted rsquare of the model
predrsq
predicted rsquare of the model
cp
mallow's Cp
aic
akaike information criteria
sbic
sawa bayesian information criteria
sbc
schwarz bayes information criteria
gmsep
estimated MSE of prediction, assuming multivariate normality
jp
final prediction error
pc
amemiya prediction criteria
sp
hocking's Sp
Arguments
model
An object of class lm.
...
Other arguments.
x
An object of class ols_best_subset.
print_plot
logical; if TRUE, prints the plot else returns a plot object.
Deprecated Function
ols_all_subset() has been deprecated. Instead use ols_step_all_possible().
References
Mendenhall William and Sinsich Terry, 2012, A Second Course in Statistics Regression Analysis (7th edition).
Prentice Hall
See Also
Other variable selection procedures: ols_step_backward_aic,
ols_step_backward_p,
ols_step_best_subset,
ols_step_both_aic,
ols_step_forward_aic,
ols_step_forward_p