# Vera Data Nominal Generating

> Server-side extension that completes the full analysis pipeline for nominal (unordered multi-class) outcome variables after vera-data-nominal-reviewing has run. Adds remaining association tests (Chi-square/Fisher's for categorical, ANOVA/Kruskal-Wallis for continuous predictors, pairwise class comparisons), subgroup analysis with interaction tests, full modeling (multinomial logistic regression with RRR, LDA, CART, Random Forest, LightGBM multi-class), confusion matrices, unified variable importance (0-100), and cross-method insight synthesis. Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation and code style variation for natural, non-repetitive output. Triggered after vera-data-nominal-reviewing completes and its PART 0–2 artifacts are present (see ../../../../CROSS-SKILL- INTERFACE.md).

- Skill: `verasuperhub/vera-data-nominal-generating` (Agent Skill, multi-file: 27 files)
- Install (CLI): `npx skillmds@latest add verasuperhub/vera-data-nominal-generating`
- Raw SKILL.md: https://api.skillmd.com/api/skills/verasuperhub/vera-data-nominal-generating/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: VeraSuperHub (https://skillmd.com/u/verasuperhub)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/verasuperhub/vera-data-nominal-generating

---


# Nominal Outcome — Full Analysis & Manuscript Generation

## Table of Contents

- [Workflow](#workflow)
- [Additional Inputs](#additional-inputs)
- [Output Structure](#output-structure)
- [Key References (read before generation)](#key-references-read-before-generation)
- [Reporting Standards](#reporting-standards)
- [Multinomial-Logit Assumption: IIA](#multinomial-logit-assumption-iia)
- [Cross-Skill Interface](#cross-skill-interface)


Open-source skill. Read `reference/specs/output-variation-protocol.md`
before every generation — apply all variation layers.

## Workflow

Continues from where vera-data-nominal-reviewing stopped (PART 0-2 done).

| Step | Responsibility | Executor | Document | Input | Output |
|---|---|---|---|---|---|
| Additional tests | Run Additional Tests | Main Agent | `workflow/step04-run-additional-tests.md` | Prior step output | PART 3 code + prose |
| Subgroup | Analyze Subgroups | Main Agent | `workflow/step05-analyze-subgroups.md` | Prior step output | PART 4 code + prose |
| Modeling | Fit Models | Main Agent | `workflow/step06-fit-models.md` | Prior step output | PART 5 code + prose |
| Comparison | Compare Models | Main Agent | `workflow/step07-compare-models.md` | Prior step output | PART 6 code + prose |
| Manuscript | Generate Manuscript | Main Agent | `workflow/step08-generate-manuscript.md` | Prior step output | methods.md + results.md |

## Additional Inputs

Collect if not already provided:
- Target discipline (for reporting conventions)
- Target journal or style (APA 7th, STROBE, etc.)
- Research question / hypothesis
- Subgroup variable (if subgroup analysis desired)
- Reference category for multinomial logistic regression

## Output Structure

```
output/
├── methods.md
├── results.md
├── tables/             ← Markdown + CSV per table
├── figures/            ← PNGs, 300 DPI
├── references.bib
├── code.R              ← Style-varied
└── code.py             ← Style-varied
```

## Key References (read before generation)

| File | Purpose |
|---|---|
| `reference/specs/output-variation-protocol.md` | Output quality variation layers |
| `reference/specs/code-style-variation.md` | Seven-dimension code style diversity |
| `reference/patterns/sentence-bank.md` | 4-6 phrasings per result type |
| `reference/rules/reporting-standards.md` | Hard rules for statistical reporting |

## Reporting Standards

Same as vera-data-nominal-reviewing, plus:
- RRR: "RRR = X.XX, 95% CI [X.XX, X.XX]" — relative to stated reference category
- Always state which category is the reference for multinomial logistic
- Confusion matrix: report overall accuracy + per-class precision/recall (in-sample caveat if N small)
- LDA: report discriminant function loadings, Wilks' lambda, canonical correlations
- Chi-square: report observed vs expected, Cramer's V with interpretation
- Coefficients: class-specific log-odds with SE; convert to RRR for interpretation
- Tree-based with small N: frame as "exploratory"; never claim predictive validity

## Multinomial-Logit Assumption: IIA

Multinomial logistic regression assumes **Independence of Irrelevant Alternatives (IIA)** — the relative odds of choosing category A vs B should not change when category C is added to or removed from the choice set. IIA is often violated when alternatives are close substitutes (e.g., "bus" vs "metro" in a transport-mode study).

- **Test IIA** with the Hausman–McFadden test (`mlogit::hmftest` in R; manual implementation in Python). Report χ², df, p.
- If IIA is violated (p < .05) or theoretically implausible, report the violation and consider: nested logit (ordered substitutability), multinomial probit (no IIA), or a generalized estimating equations approach. Do NOT silently proceed.
- Document explicitly in Methods: "We assessed IIA using the Hausman–McFadden test. [Result.]"
- The tree-based / LDA analyses in Path A do NOT make the IIA assumption and serve as a robustness check when IIA is suspect.

## Cross-Skill Interface

```
Method Unit Contract:
├── code_r           → .R script (style-varied)
├── code_python      → .py script (style-varied)
├── methods_md       → methods.md (varied structure)
├── results_md       → results.md (varied phrasing)
├── tables/          → Markdown + CSV
├── figures/         → PNGs 300 DPI (varied layout)
├── references_bib   → .bib with cited references
└── comparison       → cross-method narrative (in results.md)
```

Invoked directly after `vera-data-nominal-reviewing` or orchestrated by `vera-data-application-pipelining`.