# Vera Data Ordinal Generating

> Server-side extension that completes the full analysis pipeline for ordinal outcome variables after vera-data-ordinal-reviewing has run. Adds nonparametric tests, subgroup analysis, and dual-path modeling: Path A ignores ordering (multinomial logistic, CART, RF, LightGBM) while Path B respects ordering (proportional odds with Brant test, adjacent-category logit, continuation-ratio logit, stereotype model, ordinal-aware trees and LightGBM). Cross-path synthesis compares variable importance rankings across both paths. Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation, code and code style variation for natural, non-repetitive output. Triggered after vera-data-ordinal-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md).

- Skill: `verasuperhub/vera-data-ordinal-generating` (Agent Skill, multi-file: 19 files)
- Install (CLI): `npx skillmds@latest add verasuperhub/vera-data-ordinal-generating`
- Raw SKILL.md: https://api.skillmd.com/api/skills/verasuperhub/vera-data-ordinal-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-ordinal-generating

---


# Ordinal Outcome — Full Analysis & Manuscript Generation

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

## Workflow

Continues from where vera-data-ordinal-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)

## 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-ordinal-reviewing, plus:
- Cumulative odds ratios: always with 95% CI, "cumulative OR = X.XX, 95% CI [X.XX, X.XX]"
- Proportional odds assumption: Brant test chi-squared(df) = X.XX, p = .XXX; if violated, state which predictors
- Coefficients: log-odds with SE always; report cumulative OR for interpretation
- If proportional odds violated: report adjacent-category, continuation-ratio, and stereotype models
- Multinomial logistic (Path A): class-specific OR with 95% CI per class contrast
- Adjacent-category logit: adjacent-category OR with 95% CI
- Continuation-ratio logit: continuation-ratio OR with 95% CI
- Stereotype model: scaling parameters with coefficients, 95% CI
- LightGBM importance: gain-based, normalized to 0-100
- Tree-based with small N: frame as "exploratory"; never claim predictive validity
- Spearman rho: report with 95% CI when available

## 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-ordinal-reviewing` or orchestrated by `vera-data-application-pipelining`.