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

[Hyun Soo Park]

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

-- 
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*Hyun Soo Park, PhD*
*--*
*Research professor*
Department of Nuclear Medicine
Seoul National University Bundang Hospital, Seongnam, Korea
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