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, details = FALSE, ...)

# S3 method for ols_step_forward_aic
plot(x, ...)

Arguments

model

An object of class lm.

...

Other arguments.

details

Logical; if TRUE, will print the regression result at each step.

x

An object of class ols_step_forward_aic.

Value

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

model with the least AIC; an object of class lm

steps

total number of steps

predictors

variables added to the model

aics

akaike information criteria

ess

error sum of squares

rss

regression sum of squares

rsq

rsquare

arsq

adjusted rsquare

Deprecated Function

ols_stepaic_forward() has been deprecated. Instead use ols_step_forward_aic().

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See also

Examples

# stepwise forward regression model <- lm(y ~ ., data = surgical) ols_step_forward_aic(model)
#> Forward Selection Method #> ------------------------ #> #> Candidate Terms: #> #> 1 . bcs #> 2 . pindex #> 3 . enzyme_test #> 4 . liver_test #> 5 . age #> 6 . gender #> 7 . alc_mod #> 8 . alc_heavy #> #> #> Variables Entered: #> #> <U+2714> liver_test #> <U+2714> alc_heavy #> <U+2714> enzyme_test #> <U+2714> pindex #> <U+2714> bcs #> #> No more variables to be added.
#> #> 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)
#> Forward Selection Method #> ------------------------ #> #> Candidate Terms: #> #> 1 . bcs #> 2 . pindex #> 3 . enzyme_test #> 4 . liver_test #> 5 . age #> 6 . gender #> 7 . alc_mod #> 8 . alc_heavy #> #> #> Variables Entered: #> #> <U+2714> liver_test #> <U+2714> alc_heavy #> <U+2714> enzyme_test #> <U+2714> pindex #> <U+2714> bcs #> #> No more variables to be added.
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 #>