Reproducibility workflow

This vignette shows how to make power analyses reproducible: capture a manifest (scenario fingerprint, seed, session info), bundle results with metadata and labels, export to CSV/JSON, and use the manifest to regenerate the same outputs.

library(mixpower)

Run analysis and capture manifest

Run your analysis as usual and create a manifest from the same scenario and seed you used. The manifest records a scenario digest (so you can verify the same design/assumptions later), seed strategy, R and mixpower versions, optional session info, and git SHA when in a repo.

d <- mp_design(clusters = list(subject = 30), trials_per_cell = 4)
a <- mp_assumptions(
  fixed_effects = list(`(Intercept)` = 0, condition = 0.3),
  residual_sd = 1,
  icc = list(subject = 0.1)
)
scn <- mp_scenario_lme4(
  y ~ condition + (1 | subject),
  design = d,
  assumptions = a,
  test_method = "wald"
)

seed <- 123
res <- mp_power(scn, nsim = 20, seed = seed)
manifest <- mp_manifest(scn, seed = seed, session = FALSE)
manifest
#> <mp_manifest>
#>   scenario_digest: 81c8db483e995593 ...
#>   seed:123 (fixed)
#>   timestamp: 2026-02-10 01:29:06 EST 
#>   r_version: 4.5.2 
#>   mixpower_version: 0.1.0 
#>   git_sha: 317d17b

Bundle results and export

Combine the result, manifest, and optional labels into a single bundle. Then write a publication-ready table (and, for JSON, manifest and labels) to disk.

bundle <- mp_bundle_results(
  res,
  manifest,
  study_id = "power_2024_01",
  analyst = "analyst",
  notes = "Initial power run for condition effect"
)
bundle
#> <mp_bundle>
#>   result: mp_power 
#>   study_id: power_2024_01 
#>   analyst: analyst 
#> <mp_manifest>
#>   scenario_digest: 81c8db483e995593 ...
#>   seed:123 (fixed)
#>   timestamp: 2026-02-10 01:29:06 EST 
#>   r_version: 4.5.2 
#>   mixpower_version: 0.1.0 
#>   git_sha: 317d17b
tab <- mp_report_table(bundle)
tab
#>   power_estimate ci_low ci_high failure_rate singular_rate n_effective nsim
#> 1              0      0       0            0             0          20   20
mp_write_results(bundle, "power_results.csv", format = "csv", row.names = FALSE)
mp_write_results(bundle, "power_results.json", format = "json")

(Export is skipped in the vignette to avoid writing to the user’s working directory.)

Regenerating from manifest and seed

To reproduce the same run later:

  1. Restore the same scenario (design, assumptions, formula, test). The manifest’s scenario_digest is a fingerprint of formula, design, assumptions, and test; you can recompute it with mp_manifest(scn, session = FALSE)$scenario_digest and compare to the stored value.
  2. Use the same seed from the manifest: mp_power(scn, nsim = 20, seed = manifest$seed).
  3. Use the same nsim, alpha, and failure_policy as in the original run (store these in your notes or in the bundle labels if needed).

Example: re-run with the stored seed and confirm the power estimate matches.

res2 <- mp_power(scn, nsim = 20, seed = manifest$seed)
all.equal(res$power, res2$power)
#> [1] TRUE

One-row manifest for saving

You can flatten the manifest to a one-row data frame (e.g. to append to a log) by building it from the list, omitting the long session_info if desired:

m <- mp_manifest(scn, seed = 123, session = FALSE)
df_row <- data.frame(
  scenario_digest = m$scenario_digest,
  seed = m$seed,
  seed_strategy = m$seed_strategy,
  timestamp = m$timestamp,
  r_version = m$r_version,
  mixpower_version = m$mixpower_version,
  git_sha = m$git_sha,
  stringsAsFactors = FALSE
)
df_row
#>                                                    scenario_digest seed
#> 1 81c8db483e99559349c12c06e4cb32a095e1364c68af1bb6a8fe13f7863d6cf4  123
#>   seed_strategy               timestamp r_version mixpower_version
#> 1         fixed 2026-02-10 01:29:06 EST     4.5.2            0.1.0
#>                                    git_sha
#> 1 317d17b82dcbfd920dc6ce9edf45ab597e03dd4f