---
name: vera-stat-doe-analyzing
description: >-
  Server-side extension that completes the full analysis pipeline for
  designed experiments (DOE) after vera-stat-doe-testing has run. Adds
  simple effects analysis, contrast analysis, effect magnitude ranking,
  response surface methodology (RSM) with contour plots and canonical
  analysis, fractional factorial alias structure, split-plot error terms,
  residual diagnostics, optimal factor settings via desirability function,
  and tree-based variable importance (RF + LightGBM). Generates
  manuscript-ready methods.md and results.md with formatted tables,
  publication-quality figures, and references.bib. Applies output variation,
  code style variation for natural, non-repetitive output. Triggered after vera-stat-doe-testing
  completes, or direct request with an experimental design
  dataset.
user-invocable: true
allowed-tools: Read, Bash, Write, Edit, Grep, Glob
---

# Design of Experiments -- 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-stat-doe-testing stopped (PART 0-2 done).

| Step | File | Executor | Output |
|---|---|---|---|
| Additional tests | `workflow/04-run-additional-tests.md` | Main Agent | PART 3 code + prose |
| Subgroup | `workflow/05-analyze-subgroups.md` | Main Agent | PART 4 code + prose |
| Modeling | `workflow/06-fit-models.md` | Main Agent | PART 5 code + prose |
| Comparison | `workflow/07-compare-models.md` | Main Agent | PART 6 code + prose |
| Manuscript | `workflow/08-generate-manuscript.md` | Main Agent | methods.md + results.md |

## Additional Inputs

Collect if not already provided:
- Target discipline (for reporting conventions)
- Target journal or style (APA 7th, CONSORT for experiments, etc.)
- Research question / hypothesis
- Whether optimization of response is desired (maximize, minimize, target)

## 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-stat-doe-testing, plus:
- SS Type III always for unbalanced designs
- F(df1, df2) = X.XX, p, partial eta-squared for every effect
- Effect estimates with SE for all contrasts
- Design resolution for fractional factorial
- R-squared for RSM models (these ARE experiments, so "explained" is appropriate)
- Contour plot interpretation: saddle point, maximum, minimum, or ridge
- Canonical analysis: eigenvalues and eigenvectors for second-order RSM
- Tree-based with small N: frame as "exploratory"; never claim predictive validity

## 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-stat-doe-testing` or orchestrated by `vera-stat-application-pipeline`.