vera-data-multivariate-generating

name	vera-data-multivariate-generating
description	Server-side extension that completes the full analysis pipeline for multiple continuous outcome variables after vera-data-multivariate-reviewing has run. Adds follow-up univariate ANOVAs with Bonferroni-corrected pairwise comparisons, full discriminant function analysis with classification accuracy, MANCOVA and two-way MANOVA, profile analysis (parallelism, equal levels, flatness), canonical correlation analysis, PCA for dimension reduction, multivariate multiple regression, and tree-based importance comparison across DVs. Synthesizes insights across methods and 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-multivariate- reviewing completes and its PART 0–2 artifacts are present (see ../../../..
allowed-tools	Read, Bash, Write, Edit, Grep, Glob

Multivariate 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-multivariate-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)
• Whether DVs are on same scale (for profile analysis)

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-multivariate-reviewing, plus:

• Canonical correlations: report with Wilks' lambda significance test per dimension
• Discriminant loadings: report structure coefficients (correlations with function)
• Classification accuracy: overall and per-group hit rates
• Profile analysis: report parallelism F, equal levels F, flatness F with df and p
• PCA: report eigenvalues, proportion of variance, cumulative proportion
• Component loadings: report only loadings >= |.40| for interpretation
• Multivariate regression: Pillai's Trace for overall test, individual R-squared per DV
• Tree-based: frame as exploratory; compare importance rankings across DVs

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-multivariate-reviewing or orchestrated by vera-data-application-pipelining.