Prepare Simulated Outcome Data for Analysis — prep_sim

Convert an simulate_data() result with a gen_outcome() generator into an analysis-ready data set and inferred brms formula. The helper recomputes observed between- and within-person predictors from the raw simulated columns, creates within-person centered lag columns for ar1() terms, and creates a complr() object when the outcome is compositional.

Usage

prep_sim_analysis(sim, outcome = NULL, drop_lag_na = FALSE)

Arguments

sim: An mlsim_data object returned by simulate_data().
outcome: Optional character scalar naming the outcome generator. When NULL, the helper uses the only generator with distribution = "outcome".
drop_lag_na: Logical scalar. When FALSE, the default, first rows in each series are retained and lag-derived columns are left as NA. When TRUE, rows with missing lag-derived predictors are removed.

Value

An mlsim_analysis object, a list with:

data: Analysis data with derived between/within and lag columns.
formula: An inferred brms formula.
complr: A complr object for compositional outcomes, otherwise NULL.
metadata: Preparation metadata, including derived column names and formula mappings.

Details

The analysis formula is inferred from the stored gen_outcome() formula. Simulation terms between(x) and within(x) become observed-data columns named x_between and x_within, computed from x by the simulation group identifier. These columns are recomputed even when columns with the same names already exist in sim$data.

For dynamic formulas, ar1() is translated to within-person centered lag predictors. For compositional outcomes, the helper rebuilds the ILR coordinates through complr() using the simulator's parts and SBP metadata, then lags the generated z coordinates used by brmcoda().

Examples

params <- list(
  location = list(beta = matrix(0, nrow = 1, dimnames = list("(Intercept)", "y"))),
  scale = list(beta = matrix(log(0.2), nrow = 1, dimnames = list("(Intercept)", "y")))
)
sim <- simulate_data(
  n = 5,
  seed = 1,
  generators = list(
    outcome = gen_outcome(
      y ~ 1,
      scale = sigma ~ 1,
      params = params,
      burnin = 0
    )
  )
)
analysis <- prep_sim_analysis(sim)
analysis$formula
#> y ~ 1 
#> sigma ~ 1