В Wed, 12 Oct 2022 17:18:26 +0530 Ashim Kapoor <ashimkap...@gmail.com> пишет:
> My problem is that I am building an index based on Principal > Components Analysis. > When the index is high it should indicate stress in the market. Have you considered using supervised methods, like PLS, to predict stress in the market? Imagine what happens when you take the points where there's stress in the market and feed only those to PCA. The first principal direction is now gone (if there is a variation along this axis, it's much smaller than it was), so now some other direction occupies its place. Even if the first direction is preserved, after centering, there are now points with low values of PC1, despite all points should correspond to stress in the market. Apologies if the paragraph above is complete nonsense, a reasonable researched would always conduct the analysis on a representative sample of the points, and the whole point of the proposed index is that high stress is indicated by points on the positive end of the multivariate sausage that PCA considers the data to be. If that's the case, post-processing the signs as described by Chris Evans could be the right solution. -- Best regards, Ivan ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.