doe_main_effects — MULTIVAR analysis op

• Data kinds: design × signal → table

• Call: import fullseye as fs; fs.ledger.doe_main_effects(design=None, y=None, goal='larger') -> 'dict' (implementation को सीधे call करना हो तो import multivar; multivar.doe_main_effects(design=None, y=None, goal='larger') -> 'dict', registry से लेना हो तो opsmultivar.get("doe_main_effects"))

उपयोग

हर factor के level means — designed experiment की response table — dispersion और SN के साथ।

> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।

y is (n,) or (n, r) with r repeated measurements (or noise-array conditions) per run. For each factor and level: the mean of y (location), the mean of log s over the runs at that level (dispersion, needs r >= 2), the main-effect range (max - min of the level means) and its share of the total sum of squares.

SN ratios are computed per run (Taguchi): larger-the-better -10 log10(mean(1/y^2)) (the same formula as spc.spc_mt_sn_ratio, reused), smaller-the-better -10 log10(mean(y^2)), nominal-the-best 10 log10(ybar^2/s^2), and their level means are reported in sn. They are not the basis of the recommended levels: an SN ratio mixes location and dispersion in one number, and a level can raise it by lowering the mean (Box 1988; Nair 1992). best_levels chooses from the location table by goal ("larger", "smaller", or "nominal" — which picks the levels of smallest dispersion and leaves the mean to an adjustment factor), and sn_agrees says where the SN table would have chosen differently.

उपयोग की विस्तृत guide

• design_of_experiments family guide

संदर्भ (sample data, साहित्य)

• 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।

चलाए जा सकने वाले उदाहरण (verified samples जो सच में यह op call करते हैं)

• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py

वे अगले ops जिनसे type जुड़ता है (table को input के रूप में लेते हैं)

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इसी category के (analysis)

mv_pca · mv_pls · rsm_fit

*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.