doe_main_effects — MULTIVAR `analysis` op

• 資料種類:design × signal → table

• 呼叫: import fullseye as fs; fs.ledger.doe_main_effects(design=None, y=None, goal='larger') -> 'dict'(要直接呼叫實作,import multivar; multivar.doe_main_effects(design=None, y=None, goal='larger') -> 'dict';從台帳取用則 opsmultivar.get("doe_main_effects"))

用法

各因子的水準平均（設計實驗的反應表），附離散度與 SN 比。

> 以下的詳細說明為原文 —— 摘要與標題已翻譯。

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.

詳細使用指南

• design_of_experiments 族使用指南

參考(範例資料、文獻)

• 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。

可執行的範例(實際呼叫該運算子並已驗證的樣例)

• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py

型別可銜接的下一個運算子(可接受 `table` 作為輸入)

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同類別(`analysis`)

mv_pca · mv_pls · rsm_fit

*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)。*

doe_main_effects — MULTIVAR analysis op

用法