Factor Analysis by Principal Component Method

I am trying to replicate a paper that implements Factor Analysis by Principal Component Method. I have been reading a lot in Matlab but all the examples I see use MLE as an estimation method. Hence, my question is how can I change the estimation method to Principal Component Method in Factor Analysis? I just need unrotated factors at the moment to become familiar with Matlab.

Let's take this example:

load stocks

[Loadings,specificVar,T,stats] = factoran(stocks,3,'rotate','none'); %estimated by MLE

Is it possible to change the estimation strategy in that function?

I also found this that seems to be the only solution (at least to my limited knowledge)

function anfactpcwod(X)

Any advice?

[Jeff Miller]

Maybe you can get what you want by using the pca function instead of factoran.

I could be wrong, but I don't think MLE and PCA are alternatives in the sense that your question implies. MLE is an estimation criterion, like least squares. PCA and factor analysis differ with respect to the nature of the structure being estimated (by whatever criterion).

[Armando MAROZZI]

PC is also an estimation method for Factor Analysis. How Principal Component differs from FA estimated with PC method, if any difference exists, I have no idea. I'll try "pca" and see whether the results differ. Thanks a lot!

Answer 1

[Dinesh Yadav]

PCA estimation in MATLAB is presently supported in two methods:

First one is in estimation the illumination of the scene in RGB image.

https://in.mathworks.com/help/images/ref/illumpca.html

Second one is estimation of missing data using ALS(alternate least squares) algorithm

https://in.mathworks.com/help/stats/pca.html