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LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d6_13   [metric: # successes (count)]
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model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = # successes (count))
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
observed groups: stim n=4, control n=2   nrep=120, alpha=0.05

--- fitted on real data ---
stim x log(day) interaction: F(1,38)=5.774  p(resid)=0.02125  p(Satt)=0.02133 (df=37)
  honest per-animal random slope (log-day): F(1,0.0)=3.19  p=NaN
Cohen's f (interaction, partial eta^2=0.036) = 0.194  (small-medium; f: .10 small, .25 medium, .40 large)

--- power simulation (log-day ground truth: stim:logday=+28.42, ratSD=6.482, resSD=7.389) ---

 true stim:log(day) = +28.42 (100% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.57            |  0.86
  5        |  0.93            |  0.98
  8        |  1.00            |  1.00
  12       |  1.00            |  1.00
  16       |  1.00            |  1.00
  24       |  1.00            |  1.00

 true stim:log(day) = +14.21 (50% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.17            |  0.30
  5        |  0.37            |  0.55
  8        |  0.62            |  0.68
  12       |  0.81            |  0.85
  16       |  0.90            |  0.92
  24       |  1.00            |  1.00

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