analysis opmatrix × signal → tableimport fullseye as fs; fs.ledger.mv_pls(X=None, Y=None, n_components=None, max_components=10, cv=7, method='nipals', standardize=True) -> 'dict' (実装を直接呼ぶなら import multivar; multivar.mv_pls(X=None, Y=None, n_components=None, max_components=10, cv=7, method='nipals', standardize=True) -> 'dict'、台帳から引くなら opsmultivar.get("mv_pls"))Partial least squares regression of Y on X with cross-validated Q^2, the Q^2 rule and VIP.
PLS finds directions in X that both vary and predict Y — the tool for many
correlated predictors and few runs (image features -> process settings, or the
reverse). method="nipals" (default, Wold, Sjöström & Eriksson 2001) or
"simpls" (de Jong 1993; identical to NIPALS for a single response — a test pins
it). X and Y are centred and, by default, scaled to unit variance.
Q^2 is computed by cv-fold cross-validation with interleaved folds (run i in fold
i mod cv, as in SIMCA’s default of 7 groups): Q^2_a = 1 - PRESS_a / SS_{a-1} per
component (SS_0 = total sum of squares of Y, SS_{a-1} the residual sum of
squares with a-1 components), cumulative Q^2 = 1 - PRESS_A / SS_0.
n_components=None keeps adding components while Q^2_a >= 0.0975
(= 1 - 0.95^2; Wold et al. 2001) up to max_components; if not even the first
passes, one component is fitted and significant is False — the data then do not
support a predictive model, and the coefficients should not be used.
VIP (variable importance in projection) ranks X columns; the conventional threshold
is VIP > 1. coef / intercept predict Y in its original units from X in its
original units.
py -3.11 examples/multivar_inspection_tour.pytable を入力に取れる)—
analysis)mv_pca · 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.