- How is the performence of Zeegzag?
- Sigwit address creatation
- Is possible specify who can send bitcoins to me?
- Called of God or called of man?
- How would a Reformed theologian distinguish his or her view of atonement?
- Evidence that Peter alone was made a Bishop by Christ?
- How do you use the future of the verb devoir when using another verb aswell?
- clarify diffence between present, perfect, imperfect and pluperfect in French
- AR filter length
- (Faster) RCNN dataset physical markers
- how to create a DFT with scalloping/spectal leakage
- correctnes of multi prime RSA algorithm
- Can PRF F with generator P be secure?
- What to expect about the mobile phone usage during a flight to China and based on the CAAC regulations
- At what time of day does US visa expire?
- Travel Issues to Turkey
- Can a US green card holder transiting through China leave the airport? Recommendation for sight-seeing in Shanghai?
- Amtrak Seating NY to DC
- The soul as the form of the body – considering massive changes of the body
- Why can't uniformity of nature (in principle) be proven deductively?
Paradox with the loadings of a Principal Component
I have a dataset with 500+ variables and have done PCA.
There is a particular Principal Component that is correlated more than the others with an Event of Interest -- a binary variable -- but still is one of the least important Principal Components ranking at the 425 position.
I scaled the loadings of these Principal Component to be able to discern more clearly which Variables contribute significantly more --either in a positive or negative way-- to the formation of this Principal Component.
There is a Variable measuring the number of Mobile Transactions and there is a variable that is the Square of the Former (say X and X^2).
Variable X has a scaled loading value of 2.33 (2.33 standard deviations from the mean which is zero) and the Variable X^2 has a scaled loading value of -1.4.
Since the loadings have been scaled and these numbers represent std. deviations, the information they convey is by how much the loadings of X and X^2 respectively are larger or smaller fro