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LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d6_10   [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=2   nrep=120, alpha=0.05

--- fitted on real data ---
stim x log(day) interaction: F(1,21)=0.027  p(resid)=0.8704  p(Satt)=0.8705 (df=20)
  honest per-animal random slope (log-day): F(1,5.0)=0.01  p=0.9117
Cohen's f (interaction, partial eta^2=0.001) = 0.023  (small; f: .10 small, .25 medium, .40 large)

--- power simulation (log-day ground truth: stim:logday=+0.02357, ratSD=0.03992, resSD=0.05586) ---

 true stim:log(day) = +0.02357 (100% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.03            |  0.03  <- observed
  5        |  0.03            |  0.06
  8        |  0.06            |  0.07
  12       |  0.08            |  0.09
  16       |  0.05            |  0.06
  24       |  0.08            |  0.06

 true stim:log(day) = +0.01179 (50% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.03            |  0.08  <- observed
  5        |  0.07            |  0.07
  8        |  0.05            |  0.05
  12       |  0.07            |  0.07
  16       |  0.07            |  0.07
  24       |  0.06            |  0.04

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