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
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