OpenClaw R Stats

When to Use

User asks for any statistical analysis, hypothesis testing, group comparison, prediction, association, survival analysis, meta-analysis, causal inference, power/sample size, or mentions R statistical packages.

What This Skill Does NOT Do

• Claim causality from observational data (use "associated with")
• Run large exploratory fishing without clear user intent
• Silently ignore assumption violations
• Report only p-values (always include effect sizes and CIs)

Pre-Flight (Mandatory)

1. Confirm dataset exists and is readable
2. Run schema inspection: bash {baseDir}/scripts/run-rstats.sh schema --data <path>
3. Report: rows, columns, types, missing values
4. If missing > 5%, warn. If n < 30, warn small sample.

Environment Setup

First time or errors: bash {baseDir}/scripts/run-rstats.sh doctor

Install by profile (only when needed):

Profile	Script	Methods
Core	install-core.R	t-test, regression, ANOVA, chi-sq
Survival	install-survival.R	KM, Cox, competing risks, RMST
Missing	install-missing.R	MICE, MCAR test
Mixed	install-mixed.R	LMM, GLMM, GEE, ICC
Bayes	install-bayes.R	brms, Bayes factors
Causal	install-causal.R	PSM, IPTW, IV, DiD, RDD, TMLE
Meta	install-meta.R	meta-analysis, NMA
SEM	install-sem.R	SEM, CFA, lavaan
Diagnostic	install-diagnostic.R	ROC, kappa, alpha
Advanced	install-advanced.R	GAM, quantile, zero-inflated
Power	install-power.R	power/sample size

Workflow

1. Determine analysis type (see references/METHOD_TABLE.md)
2. Inspect dataset schema
3. Build JSON spec:

{
  "dataset_path": "<path>",
  "analysis_type": "<type>",
  "outcome": "<column>",
  "predictors": ["<col1>"],
  "hypothesis": "<plain language>",
  "alpha": 0.05,
  "seed": 42,
  "output_dir": "<path>"
}

4. Save as .json, run: bash {baseDir}/scripts/run-rstats.sh analyze --spec <path>
5. Read summary.json + report.md
6. Present: Summary → Statistics → Interpretation → Plots → Assumptions → Caveats

Analysis Selection

For the complete 82-method table with user intent mapping, see references/METHOD_TABLE.md.

Quick lookup — most common:

Intent	analysis_type
Compare 2 groups	ttest or wilcoxon
Compare 3+ groups	anova or kruskal
Categorical association	chisq or fisher
Predict continuous	linear_regression
Predict binary	logistic_regression
Survival curves	kaplan_meier
Survival regression	cox_regression
Meta-analysis	meta_analysis
Causal effect	propensity_match or did
Power/sample size	power_analysis

Automatic Method Switching

• Non-normal + n < 30 → wilcoxon over ttest
• Unequal variance → Welch t-test (equal_var: false)
• Expected cells < 5 → fisher over chisq
• Overdispersion in Poisson → suggest negative binomial
• Heteroscedastic residuals → robust SE warning

Reporting Rules (Non-Negotiable)

Every analysis MUST include:

• Sample size (n) and missing data handling
• Method name and rationale
• Point estimates with confidence intervals
• Effect sizes (Cohen's d, η², R², OR, HR, etc.)
• Assumption check results
• Limitations

Language: "associated with" / "evidence suggests" — NEVER "proves" / "causes"

Spec Field Reference

See references/SPEC_REFERENCE.md for required/optional fields per analysis_type.