analysis op• Data kinds: matrix × signal → table
• Call: 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' (implementation को सीधे call करना हो तो import multivar; multivar.mv_pls(X=None, Y=None, n_components=None, max_components=10, cv=7, method='nipals', standardize=True) -> 'dict', registry से लेना हो तो opsmultivar.get("mv_pls"))
X पर Y का partial least squares regression, cross-validated Q^2, Q^2 नियम और VIP सहित।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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 family guide
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py
table को input के रूप में लेते हैं)—
analysis)mv_pca · rsm_fit · doe_main_effects
*Provenance: multivar.py — MULTIVAR operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.