msa_attribute_agreement — SPC msa op

• Data kinds: table → table

• Call: import fullseye as fs; fs.ledger.msa_attribute_agreement(table, appraiser='appraiser', part='part', rating='rating') (to call the implementation directly, import spc; spc.msa_attribute_agreement(table, appraiser='appraiser', part='part', rating='rating'); from the registry, opsspc.get("msa_attribute_agreement"))

Usage

Agreement between appraisers on attribute (pass/fail) inspection (`table`).

> The detailed description below is the original text — the summary and the headings are translated.

同じ部品を複数の検査員が判定した表から、検査員の対ごとの Cohen のカッパと

全体の Fleiss のカッパ、および素の一致率を返す。

★カッパは一致率そのものではない。`kappa = (p_o - p_e)/(1 - p_e)` で、

偶然でも起きる一致 p_e を割り引いた残りを測る —— 合格率 95 % の工程では

でたらめに判を押しても素の一致率は 90 % を超えるので、一致率だけ見ると

「よく合っている」と読めてしまう。

★門にできる厳密な値: 全員が同じ判定 → `kappa = 1`(厳密)。2 人 x 2 カテゴリの

Cohen のカッパは `2(ad-bc)/((a+b)(b+d)+(a+c)(c+d))` という閉形式と一致する。

独立でたらめな判定では期待値 0(こちらは標本ごとに揺れるので区間で見る)。

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_measurement_system_analysis — py -3.11 examples/poc_measurement_system_analysis.py

Ops the type connects to (they accept table as input)

msa_anova_table · msa_gauge_rr · msa_bias_linearity · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo · gum_validate

Same category (msa)

msa_anova_table · msa_gauge_rr · msa_bias_linearity

*Provenance: spc.py — SPC operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.