analysis op• Datenarten: matrix × signal → table
• Aufruf: import fullseye as fs; fs.ledger.mv_pls(X=None, Y=None, n_components=None, max_components=10, cv=7, method='nipals', standardize=True) -> 'dict' (die Implementierung direkt: import multivar; multivar.mv_pls(X=None, Y=None, n_components=None, max_components=10, cv=7, method='nipals', standardize=True) -> 'dict'; aus dem Register: opsmultivar.get("mv_pls"))
Partial-Least-Squares-Regression von Y auf X mit kreuzvalidiertem Q^2, der Q^2-Regel und VIP.
> Die ausführliche Beschreibung unten ist der Originaltext — Zusammenfassung und Überschriften sind übersetzt.
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.
• Leitfaden zur Familie design_of_experiments
• Katalog der Beispieldaten (Download-URLs / Lizenzen) — 2-D nutzt skimage.data (BSD/Public Domain) plus synthetische Bilder, 3-D nennt Download-URLs echter Datenquellen (Stanford, PDS, …).
• Herkunft und Literatur der Operatoren — die Quellen der Forschung/Verfahren, auf denen diese Operatorfamilie beruht.
• Der kanonische Algorithmus (Autor, Jahr) und seine Anwendungen stehen im Familienleitfaden oben.
• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py
table als Eingabe)—
analysis)mv_pca · rsm_fit · doe_main_effects
*Provenance: multivar.py — MULTIVAR Operator-Registry. Diese Notiz wird von tools/opdocs.py md erzeugt (nicht von Hand bearbeiten).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.