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If you do some kind of PCA, that should help work out the correlations between the coefficients, though you are right these can be hard to interpret (although s
by projectramo 8y ago
If you do some kind of PCA, that should help work out the correlations between the coefficients, though you are right these can be hard to interpret (although sometimes they have a natural interpretation).
And typically, you would demean, and divide by the standard deviation.
I guess the coefficients are harder to interpret in this second case than if you did not transform them, but they're still interpretable.
- a-dub 8y agoOne trick I like to do in PCA is to sweep each of the variables, one at a time, in the low-d subspace and then project back up. Looking at these sweeps in the original space gives some hand-wavey "intuition" for what each low-d variable is capturing.