analysis(matlab): rate + count outputs, variation batch 5
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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==============================================================================
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LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d6_13 [metric: success RATE]
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==============================================================================
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model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = success RATE)
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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=2 nrep=120, alpha=0.05
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--- fitted on real data ---
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stim x log(day) interaction: F(1,30)=1.838 p(resid)=0.1853 p(Satt)=0.1853 (df=30)
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honest per-animal random slope (log-day): F(1,3.6)=0.01 p=0.9233
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Cohen's f (interaction, partial eta^2=0.016) = 0.129 (small-medium; f: .10 small, .25 medium, .40 large)
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--- power simulation (log-day ground truth: stim:logday=+0.1165, ratSD=0.04832, resSD=0.05061) ---
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true stim:log(day) = +0.1165 (100% of observed)
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N/group | per-animal power | LME power
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------------------------------------------
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3 | 0.28 | 0.46 <- observed
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5 | 0.58 | 0.70
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8 | 0.78 | 0.83
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12 | 0.90 | 0.89
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16 | 0.99 | 1.00
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24 | 1.00 | 1.00
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true stim:log(day) = +0.05823 (50% of observed)
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N/group | per-animal power | LME power
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------------------------------------------
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3 | 0.08 | 0.17 <- observed
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5 | 0.18 | 0.27
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8 | 0.23 | 0.32
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12 | 0.34 | 0.38
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16 | 0.47 | 0.51
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24 | 0.79 | 0.82
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Read per-animal as the honest power; LME matches the paper's power code (optimistic).
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