Generate a downstream analysis plan from a MOAT audit

plan_analysis() converts a moat_audit object into a structured, readable downstream analysis plan. It does not rerun audit statistics; it summarizes the existing audit diagnostics into recommended formulas, validation schemes, batch strategy, and sensitivity analyses.

Usage

plan_analysis(audit, verbose = FALSE)

Arguments

audit

A moat_audit object.

verbose

A single logical value. When TRUE, include module-level risk reasons in the printed plan. Defaults to FALSE.

Value

A moat_analysis_plan object.

Examples

data("toy_moat")
audit <- moat(toy_moat, outcome = "outcome", batch = "batch", n_perm = 99)
plan_analysis(audit)
#> 
#> ── MOAT analysis plan ──────────────────────────────────────────────────────────
#> ℹ Overall risk: HIGH
#> 
#> ── Recommended formulas ──
#> 
#> • Differential abundance: `~ outcome + batch`
#> • PERMANOVA: `distance ~ outcome + batch`
#> 
#> ── Validation ──
#> 
#> • standard_cv: Standard cross-validation is acceptable for the supplied leakage
#> variables.
#> 
#> ── Batch strategy ──
#> 
#> • sensitivity_required: Batch explains substantial microbiome variation; report
#> analyses with explicit batch sensitivity checks.
#> 
#> ── Sensitivity analyses ──
#> 
#> • Repeat microbiome association analyses with and without batch terms where
#> identifiable.
#> • Report distance-specific PERMANOVA results and batch R2 alongside outcome R2.
#> 
#> ── Warnings ──
#> 
#> ! Batch audit for aitchison distance has high risk (batch R2 = 0.945; PERMANOVA = high, dispersion = low, PCoA = high).
#> ! Batch audit for bray distance has high risk (batch R2 = 0.948; PERMANOVA = high, dispersion = low, PCoA = high).
#> ! Feature-level batch diagnostic is high (50 feature-batch associations with adjusted p <= 0.05 and batch R2 >= 0.1; max feature batch R2 = 0.973).
#> ! Batch-dominated microbiome signal requires explicit sensitivity analysis.