doe_main_effects — MULTIVAR `analysis` op

• 数据种类:design × signal → table

• 调用: import fullseye as fs; fs.ledger.doe_main_effects(design=None, y=None, goal='larger') -> 'dict'(要直接调用实现,import multivar; multivar.doe_main_effects(design=None, y=None, goal='larger') -> 'dict';从台账取用则 opsmultivar.get("doe_main_effects"))

用法

各因子的水平均值（设计实验的响应表），附离散度与 SN 比。

> 以下的详细说明为原文 —— 摘要与标题已翻译。

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.

详细使用指南

• design_of_experiments 族使用指南

参考(示例数据、文献)

• 示例数据目录(下载 URL / 许可证) —— 2-D 用 skimage.data(BSD/公有领域)加合成图,3-D 给出真实数据源(Stanford/PDS 等)的下载 URL。

• 算子来历与参考文献 —— 该算子族所依据的研究/方法出处。

• 算法的正典(作者、年份)与用途见上面的族使用指南。

可运行的示例(实际调用该算子并已验证的样例)

• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py

类型可衔接的下一个算子(可接受 `table` 作为输入)

—

同类别(`analysis`)

mv_pca · mv_pls · rsm_fit

*Provenance: multivar.py — MULTIVAR 算子登记表。本条目由 tools/opdocs.py md 自动生成(请勿手工编辑)。*

doe_main_effects — MULTIVAR analysis op

用法