doe_main_effects — MULTIVAR `analysis` op

• データ種: design × signal → table

• 呼び出し: import fullseye as fs; fs.ledger.doe_main_effects(design=None, y=None, goal='larger') -> 'dict' (実装を直接呼ぶなら import multivar; multivar.doe_main_effects(design=None, y=None, goal='larger') -> 'dict'、台帳から引くなら opsmultivar.get("doe_main_effects"))

使い方

因子ごとの水準平均(計画実験の応答表)と、散らばり・SN 比。

> 以下の詳細説明は原文のままです —— 要約と見出しは訳出済み。

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.

詳しい使い方ガイド

• design_of_experiments ファミリガイド

参考(サンプルデータ・文献)

• サンプルデータカタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。

• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。

• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。

実行できる例(この op を実際に呼ぶ検証済みサンプル)

• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py

型が繋がる次の op(`table` を入力に取れる)

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同カテゴリ(`analysis`)

mv_pca · mv_pls · rsm_fit

*Provenance: multivar.py — MULTIVAR operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。*

doe_main_effects — MULTIVAR analysis op

使い方