I am an author of the paper behind the fad package. I suspect that the call is not correct. Actually, fad does not quite account for time series or other structured data and you have to enter it, as in all general EFA packages as a n x p matrix, with n the number of observations and p the number of coordinates. So, if you can provide a reproducible example, I can look into it, or you can also file an issue on the github site. One thing to note that EFA requires all variances in the dispersion matrix to be positive, and it is possible that your images have some background where there is no activity and hence the sd for those pixel/voxels are zero.
Of course, ideally, your EFA should account for the image structure, but that is a different topic and not part of fad or any similar package. Ranjan PS: I monitor this e-mail address only through this list. Sent: Saturday, May 08, 2021 at 5:05 AM From: "Hyun Soo Park" <hy...@snu.ac.kr> To: "r-help@r-project.org" <r-help@r-project.org> Subject: [R] factor analysis of dynamic structure (FADS) for a huge time-series data Dear R users, I want to find the latent factors from a kind of time-series data describing temporal changes of concentration using a factor analysis technique called 'factor analysis of dynamic structure (FADS).' I learned how to form the data for the analysis using a proper package embedding FADS, such as 'fad' package. The analysis with 'fad' worked and gave me results, but the problem was raised when the time-series data is vast. The time-series data extracted from the 3-dimensional matrix (i.e., 3D image volume of 50 x 50 x 163) repeatedly acquired at 54-time points is consisted of 50 x 50 x 163 x 54 = 22,005,000 observations. The desired number of the latent factor (k) is 4. What I got from fad(MATRIX, k) is following: Error in fun(A, k, nu, nv, opts, mattype = "matrix") : TridiagEigen: eigen decomposition failed When I resize the matrix smaller into 5 x 5 x 15, it gives me what I wanted properly. I found that some resampling methods such as random sampling, data stratification, etc., could resolve this kind of problem, but I have no ideas which one could be appropriate. Please teach me with any ideas and comments. Thanks in advance, Park -- *연구중점교수, 분당서울대학교병원* *전화번호:* (사무실) +82-31-787-2936 (휴대전화) +82-10-8833-2806 *팩스:* +82-31-787-4018 *이메일:* hy...@snu.ac.kr *Hyun Soo Park, PhD* *--* *Research professor* Department of Nuclear Medicine Seoul National University Bundang Hospital, Seongnam, Korea *Telephone:* (Office) +82-31-787-2936 (Mobile) +82-10-8833-2806 *Fax:* +82-31-787-4018 *email:* hy...@snu.ac.kr [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html[http://www.R-project.org/posting-guide.html] and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
