Build regression model from a set of candidate predictor variables by entering predictors based on akaike information criterion, in a stepwise manner until there is no variable left to enter any more.
ols_step_forward_aic(model, ...) # S3 method for default ols_step_forward_aic(model, progress = FALSE, details = FALSE, ...) # S3 method for ols_step_forward_aic plot(x, print_plot = TRUE, ...)
model | An object of class |
---|---|
... | Other arguments. |
progress | Logical; if |
details | Logical; if |
x | An object of class |
print_plot | logical; if |
ols_step_forward_aic
returns an object of class "ols_step_forward_aic"
.
An object of class "ols_step_forward_aic"
is a list containing the
following components:
model with the least AIC; an object of class lm
total number of steps
variables added to the model
akaike information criteria
error sum of squares
regression sum of squares
rsquare
adjusted rsquare
ols_stepaic_forward()
has been deprecated. Instead use ols_step_forward_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_both_aic
,
ols_step_forward_p
#> #> Selection Summary #> ---------------------------------------------------------------------------- #> Variable AIC Sum Sq RSS R-Sq Adj. R-Sq #> ---------------------------------------------------------------------------- #> liver_test 771.875 3804272.477 4565248.060 0.45454 0.44405 #> alc_heavy 761.439 4743349.776 3626170.761 0.56674 0.54975 #> enzyme_test 750.509 5515514.136 2854006.401 0.65900 0.63854 #> pindex 735.715 6278360.060 2091160.477 0.75015 0.72975 #> bcs 730.620 6535804.090 1833716.447 0.78091 0.75808 #> ----------------------------------------------------------------------------# stepwise forward regression plot model <- lm(y ~ ., data = surgical) k <- ols_step_forward_aic(model) plot(k)# final model k$model#> #> Call: #> lm(formula = paste(response, "~", paste(preds, collapse = " + ")), #> data = l) #> #> Coefficients: #> (Intercept) liver_test alc_heavy enzyme_test pindex bcs #> -1178.330 58.064 317.848 9.748 8.924 59.864 #>