Timeline for Find variables most responsible for variance between groups
Current License: CC BY-SA 3.0
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| Feb 27, 2017 at 19:21 | comment | added | ekatko1 | Yes, that is what I was thinking as well, except, what if the different groups have different principal components? Maybe it would be best to calculate the principal components separately for each class. From there, you can compare principal components between classes by taking the variance of their means. If a principal component is found in one class, and no the other, you could give it a mean value of zero to the class where it is not found. Then, the largest variance would suggest that the effect of this PC differs strongly between groups. | |
| Feb 27, 2017 at 10:04 | comment | added | Hypercube | Thank you for the answer! This R package looks very useful. I don't understand how you're suggesting to use PCA though. One idea I've had is to run PCA on the entire dataset, finding some principal components (PCs) that explain most of the variance, and then to calculate the mean value of the PCs for each class. By finding the PC axes along which the class means are furthest apart, I will have some idea of which of the original $x_i$ are most responsible for the variance between classes. I'm not sure whether this is sensible. | |
| Feb 27, 2017 at 5:07 | history | answered | ekatko1 | CC BY-SA 3.0 |