The same statement is true for the "plotmo" function. It also does not
handle the situations right if the training functions contains
interactions. You can try this out using this code:
library(caret)
data(trees)
m = train(Volume~(Girth+Height)^2, data=trees, method="lm")
plotmo(m$finalModel)
Max, thanks for your answer!
> predict.train() will handle the formulas. If you want to compare the
> models in terms of their predictive performance, set the seeds prior
> to running the model. This will ensure that the same resampling
> indices are used in train(). If you do this, the resamples()
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