I came thinking that many users of PCA are looking to get new (a smaller set) variables Y from a set of variables X. But the example provided with this package does not appear to do that. Moreover, the methods do not offer to do it for you.
In fact, to the clueless, it's not obvious how to even get these. And, moreover, the example that comes with this package does not tell you what to do if you have a set of non-commensurable variables X, ie which should be normalized according to standard practice, before running PCA.
I am not statistically sophisticated, and maybe there are reasons to make the above uses hard for people who would abuse PCA? Or maybe I have overlooked something. But, I believe all that needs to be added is:
- normalize the input X columns to make X~
- multiply the pca.components_.dot( X~)
- and make this available, eg as pca.scores_ ?
Could/should this be added to either the code or docs?
I came thinking that many users of PCA are looking to get new (a smaller set) variables Y from a set of variables X. But the example provided with this package does not appear to do that. Moreover, the methods do not offer to do it for you.
In fact, to the clueless, it's not obvious how to even get these. And, moreover, the example that comes with this package does not tell you what to do if you have a set of non-commensurable variables X, ie which should be normalized according to standard practice, before running PCA.
I am not statistically sophisticated, and maybe there are reasons to make the above uses hard for people who would abuse PCA? Or maybe I have overlooked something. But, I believe all that needs to be added is:
Could/should this be added to either the code or docs?