analysis op• 데이터 종류: matrix × signal → table
• 호출: 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'(구현을 직접 호출하려면 import multivar; multivar.mv_pls(X=None, Y=None, n_components=None, max_components=10, cv=7, method='nipals', standardize=True) -> 'dict', 원장에서 가져오려면 opsmultivar.get("mv_pls"))
Y를 X에 회귀하는 부분최소제곱(교차검증 Q^2, Q^2 성분 수 규칙, VIP 포함).
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
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.
• design_of_experiments 패밀리 가이드
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py
table 를 입력으로 받는 것)—
analysis)mv_pca · rsm_fit · doe_main_effects
*Provenance: multivar.py — MULTIVAR 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.