There is no "magic bullet" (package) for your problem. You must either learn enough statistics to understand how to analyze your data, or consult with someone who does.
FWIW collinearity is not in general amenable to automatic removal. However, you can identify which inputs are collinear with each other, and omit the redundant ones next iteration of your analysis, using (for example) the approach suggested by Uwe. Deciding WHICH of the redundant inputs is most appropriate to keep is the part computers are not so good at... that is where you must be smarter or more creative than the computer.
Also, it would help you get responses if you included the context (earlier discussion) in your replies.. most people do not use Nabble here. Reading and following the requests in the footer of every message will also help.
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Roberto <rmoscetti at unitus.it> wrote:
I do not know, because I tried to use rfe function (Backwards Feature
Selection, Caret Package) to select wavelengths useful for a prediction
model. Otherwise, rfe function give me back a lot of warning messages
about
collinearity between variables.
So, I do not know if your script can be useful.
I tried to use VIF-Regression to select variables, but rfe function
advise
me with the same warning messages again.
What do you think about that?
Thank you very much for your help.
Best,
Roberto
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