analysis op• 資料種類:matrix × signal → table
• 呼叫: 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'(要直接呼叫實作,import multivar; multivar.mv_pls(X=None, Y=None, n_components=None, max_components=10, cv=7, method='nipals', standardize=True) -> 'dict';從台帳取用則 opsmultivar.get("mv_pls"))
Y 對 X 的偏最小平方迴歸，附交叉驗證 Q^2、Q^2 成分數規則與 VIP。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
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.
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者、年份)與用途見上面的族使用指南。
• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py
table 作為輸入)—
analysis)mv_pca · rsm_fit · doe_main_effects
*Provenance: multivar.py — MULTIVAR 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.