[R] factor analysis of dynamic structure (FADS) for a huge time-series data

Jeff Newmiller jdnewm|| @end|ng |rom dcn@d@v|@@c@@u@
Sat May 8 23:00:42 CEST 2021

This not being a question about R, but rather about statistics, or possibly about a contributed package, means (per the Posting Guide) that you should be asking in a statistics forum like stats.stackexchange.com or corresponding with the author of the package in question. If you are lucky someone here will have something to offer, but it is not very likely.

On May 8, 2021 3:05:12 AM PDT, Hyun Soo Park <hyuns using snu.ac.kr> wrote:
>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
>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
>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
>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,

Sent from my phone. Please excuse my brevity.

More information about the R-help mailing list