analysis opmatrix → tableimport fullseye as fs; fs.ledger.mv_pca(X=None, standardize=True, n_components=None, threshold=0.9, n_parallel=200, parallel_stat='mean', seed=0, memory_budget=None) -> 'dict' (実装を直接呼ぶなら import multivar; multivar.mv_pca(X=None, standardize=True, n_components=None, threshold=0.9, n_parallel=200, parallel_stat='mean', seed=0, memory_budget=None) -> 'dict'、台帳から引くなら opsmultivar.get("mv_pca"))Principal components of a table (samples x variables) and how many of them the data support.
PCA is the SVD of the centred (by default also unit-variance) matrix
Xc = U S V^T: scores U S, loadings V, eigenvalues S^2/(n-1) (the
covariance or correlation eigenvalues — the tests check this identity).
The number of components is reported three ways, because each fails differently:
cumulative — the smallest count reaching threshold of the variance (a
convention, not a test: with many noisy columns it keeps noise);parallel — Horn’s parallel analysis (Horn 1965): keep component i while its
eigenvalue exceeds the parallel_stat ("mean" as in Horn, or "p95") of
the i-th eigenvalue of n_parallel normal random tables of the same shape
treated the same way;mdl — the MDL criterion of Wax & Kailath 1985 (mathspectral.n_sources_mdl,
reused) on the centred data; it assumes white noise of equal variance in every
column, which standardising breaks, so it is computed only with
standardize=False (homogeneous columns: pixels, features in one unit) and is
None otherwise.n_components fixes how many scores/loadings to return (default: the parallel
count, at least 1). Accepts a matrix or a feat_image_vector table.
py -3.11 examples/multivar_inspection_tour.pypy -3.11 examples/poc_simcal_thin_film.pytable を入力に取れる)—
analysis)mv_pls · rsm_fit · doe_main_effects
Provenance: multivar.py — MULTIVAR operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.