analysis op• Datenarten: design × signal → table
• Aufruf: import fullseye as fs; fs.ledger.rsm_fit(design=None, y=None, model='quadratic') -> 'dict' (die Implementierung direkt: import multivar; multivar.rsm_fit(design=None, y=None, model='quadratic') -> 'dict'; aus dem Register: opsmultivar.get("rsm_fit"))
Antwortfläche an ein design (kodierte Einheiten) anpassen: ANOVA, Lack-of-Fit, stationärer Punkt.
> Die ausführliche Beschreibung unten ist der Originaltext — Zusammenfassung und Überschriften sind übersetzt.
model: "linear" (b0 + sum b_i x_i), "interaction" (+ b_ij x_i x_j) or "quadratic" (+ b_ii x_i^2) — fitted by least squares on the coded matrix of the design (so coefficients are comparable across factors).
Refuses a model the plan cannot support: if the model matrix is rank-deficient (a two-level plan cannot see x^2 — every x_i^2 equals the intercept; a resolution III fraction cannot separate a main effect from a two-factor interaction) the op raises ValueError naming the aliased terms, instead of returning one of infinitely many least-squares solutions. It also needs more runs than terms (a residual to judge with).
Returns coefficients with standard errors and t, the ANOVA (model / residual and, when the plan has replicated points, lack of fit vs pure error with its F test), R^2, adjusted R^2, predicted R^2 (from PRESS). For the quadratic model, the stationary point x_s = -B^{-1} b / 2 (B symmetric with b_ii on the diagonal and b_ij/2 off it) in coded and natural units, the predicted response there, the eigenvalues of B (canonical analysis: all negative = maximum, all positive = minimum, mixed = saddle, any near zero = ridge) and whether x_s lies inside the explored region.
• 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 · mv_pls · 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.