analysis(matlab): rate + count outputs, variation batch 3
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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@@ -1,7 +1,7 @@
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==============================================================================
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LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_10
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LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_10 [metric: # successes (count)]
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==============================================================================
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model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
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model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = # successes (count))
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log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
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observed groups: stim n=3, control n=7 nrep=120, alpha=0.05
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@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,46)=0.613 p(resid)=0.4376 p(Satt)=0.4382 (df=
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honest per-animal random slope (log-day): F(1,10.0)=0.43 p=0.5247
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Cohen's f (interaction, partial eta^2=0.005) = 0.069 (small; f: .10 small, .25 medium, .40 large)
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--- power simulation (log-day ground truth: stim:logday=-15.28, ratSD=11.24, resSD=10.10) ---
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--- power simulation (log-day ground truth: stim:logday=-15.28, ratSD=11.24, resSD=10.1) ---
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true stim:log(day) = -15.28 (100% of observed)
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N/group | per-animal power | LME power
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@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.005) = 0.069 (small; f: .10 small, .25
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16 | 0.10 | 0.11
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24 | 0.13 | 0.14
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Read the per-animal column as the honest power; the LME column matches the
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paper's power code (anova interaction p, observation-level DF) and is optimistic.
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Read per-animal as the honest power; LME matches the paper's power code (optimistic).
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