I am approaching PCA analysis for the first time, and have difficulties on interpreting the results. This is my biplot (produced by Matlab's functions
biplot, red dots are PC scores, blue lines correspond to eigenvectors; data were not standardized; first two PCs account for the ~98% of the total variance of my original dataset):
My personal interpretation is that (if I get it right) all variables (b1 to b7) have positive values in the PC1 axis, while b5 and b7 are positive in PC2's and b1 to b4 are negative.
Since all the variables are positive in PC1, those which constrain the system the most are b5 and (then) b7 and b4 (in PC1 axis).
In PC2 axis, the two set of variables have opposite effects on the system (up to me guessing what's the intrinsic meaning of it).
Talking about the scores, they are well clustered, thus I think they all respond to the system quite uniformly, except some outsiders.
- Is there a connection between the blue vectors direction and the position of the scores? Meaning, do variable vectors which end close to some scores have something to do with those same scores?
- If data are well clustered, how do I interpret the results of the PCA in terms of what variable has a major (or minor) influence on the system?