Is step_lincomb() a real multicollinearity-filter, involving outcomes and predictors? I mean in the sense to reliably avoid the Dummy Variable Trap...
'What is the Dummy Variable Trap? The Dummy Variable Trap occurs when two or more dummy variables created by one-hot encoding are highly correlated (multi-collinear) . This means that one variable can be predicted from the others, making it difficult to interpret predicted coefficient variables in regression models.'
...Further more, I mean the case, if a predictor x, is too highly correlated to the outcome y. But this would mean, I would have to include the predictor in step_lincomb() to analize the correlation between outcome and predictors and maybe loose it or?
In other words... is step_lincomb() the convenient successor of info <- car::vif(myModel)?