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LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d0_13   [metric: success RATE]
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model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
observed groups: stim n=3, control n=3   nrep=120, alpha=0.05

--- fitted on real data ---
stim x log(day) interaction: F(1,66)=12.243  p(resid)=0.0008416  p(Satt)=0.0008388 (df=66)
  honest per-animal random slope (log-day): F(1,29.1)=11.46  p=0.00205
Cohen's f (interaction, partial eta^2=0.030) = 0.176  (small-medium; f: .10 small, .25 medium, .40 large)

--- power simulation (log-day ground truth: stim:logday=+0.08873, ratSD=0.04364, resSD=0.07673) ---

 true stim:log(day) = +0.08873 (100% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.71            |  0.97  <- observed
  5        |  1.00            |  1.00
  8        |  1.00            |  1.00
  12       |  1.00            |  1.00
  16       |  1.00            |  1.00
  24       |  1.00            |  1.00

 true stim:log(day) = +0.04437 (50% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.26            |  0.52  <- observed
  5        |  0.57            |  0.72
  8        |  0.85            |  0.90
  12       |  0.95            |  0.96
  16       |  1.00            |  1.00
  24       |  1.00            |  1.00

Read per-animal as the honest power; LME matches the paper's power code (optimistic).