Hi Everybody,
Lets say you have a bunch of climate variables (20+) that you want to test
against Procrustes-aligned coordinates. You reduce this down to, say, six
variables using a stepwise procedure to exclude highly correlated variables
using the 'vifstep' function ("usdm" package).
You would then like to test if these six climate variables can be further
reduced in number via a Stepwise Regression (i.e., Akaike's Information
Criteria, AIC) on a 'procD.lm' full model, in order to determine if there
is a reduced model that describes the most amount of shape variation with
the least number of climate predictors. But the 'step' ("stats") and
'stepAIC' ("MASS" package) functions don't appear to work on this kind of
data/model, for arrays or matrices.
How would you go about this?
I found a very similar question posted on ResearchGate back in 2019, which,
as of now, has zero answers. So I thought I'd try here. Any ideas are
greatly appreciated.
Best regards,
Rex
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