I'm hoping there's an easy jmp solution to a PCA problem I'm having. I have my jmp database which constitutes landmark locations from a suite of specimens. I've run a PCA on the data, and I can easily view the eigenvectors to see which landmarks collectively have the greatest degree of variation within the dataset. But what I'm looking to do is compare the PC scores from different landmarks between specimens. Just as an example, let's say landmarks 1-5 encompass the greatests points of variation, but now I want to see and compare the individual PC scores from each point between specimens. There was a previous post (https://community.jmp.com/t5/Discussions/PCA-how-to-find-component-scores-X-Y-coordinates-for-each-p...) that seemed like a similar question, but the link in the one response is no longer valid. Also, I would like to compare PC scores and a measurement variable from my specimens to test for allometry. Is there a way to find the singular PC score for each specimen in the different PC axes? Thanks for all of the help!
I am sorry that the link broke. Here is the new URL: Principal Components Analysis. You can save the scores from the PCA or first rotate a subset of the eigenvectors and then save the resulting factors.