[R] factor analysis of dynamic structure (FADS) for a huge time-series data
jdnewm|| @end|ng |rom dcn@d@v|@@c@@u@
Sat May 8 23:00:42 CEST 2021
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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.
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