Re: PCA
On Jun 2, 7:03 am, Andersen <andersen_...@xxxxxxxxxxx> wrote:
Hello,
I have a few q about PCA.
Why would one NOT want to rotate when using PCA?
It depends on what you want to do with your PCA. Some reasons for not
rotating: your primary reason for doing PCA is to find a lower
dimensional representation for your data so that plots can be drawn;
you are using PCA to find multivariate outliers via T-squared or QRes
(or possibly others) statistics; you want to know which combination of
variables causes most of the variability in the system.
Also what happens if the variables are multivariate normal? How will the
components be distributed?
Jackson, J.E. (1991) "A User's Guide to Principal Components", John
Wiley and Sons. See chapter 4 for inferential procedures.
--
Paige Miller
paige\dot\miller \at\ kodak\dot\com
.
Relevant Pages
- Re: basic description of PCA terms
... > description which is PCA used usually for data reduction and PCA as ... Principal Components is ... > analysis is because the original poster mentioned "latent variables". ... >>> Sorry, Data Matter. ...
(sci.stat.math) - Re: Difference between Principal Components Analysis and Factor Analysis?
... What is the difference between PCA and Factor Analysis? ... original axes of X into orthogonal axes of the PCs. ... That is Principal Components Factor Analysis. ... We have dozens and dozens of "analytic rotation methods" ...
(sci.stat.math) - Re: Cant perform PCA
... Dave Krebs wrote: ... functions such as "processpca" to compute principal components due to memory limitations. ... Does anyone have an m-file that performs PCA interatively, returning one component at a time, or any algorithm that allows PCA to be performed in some way without computing the entire covariance matrix? ... The PRINCOMP function computes a PCA directly from the data. ...
(comp.soft-sys.matlab) - Re: Principle Component Analysis
... I read a book about clearing multicollinearity of the independent variables by PCA. ... The result could be in the form of latent roots or latent vector but the problem is how do i use this PCA in regression? ... HOWEVER -- there is a serious problem here -- some of the principal components may not be predictive of the Y variables. ...
(sci.stat.math) - Re: basic description of PCA terms
... description which is PCA used usually for data reduction and PCA as ... I have not heard principal components referred to as "latent variables" ...
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