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analysis(matlab): add per-variation power simulation (powersim.m + power_result.txt)

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Add variation_power.m (self-contained Monte-Carlo power for the paper's
stim x day interaction, using each folder's own data as ground truth; scores
per-animal cluster-honest power + LME power across N=[3..24] and effect
multipliers 1/0.5) and make_variation_power.m, which drops powersim.m into
every variations/<name>/ folder and runs it, writing power_result.txt beside
the existing data.csv/analyze.m/result.txt. Named powersim (not power) to
avoid shadowing the MATLAB builtin. All 28 folders processed.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Experiments DB Dev
2026-07-23 13:57:25 -04:00
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analysis/matlab/variations/boxa_b2_d0_5/power_result.txt
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==============================================================================
POWER SIMULATION -- boxa_b2_d0_5
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (success COUNT; day within-window)
observed groups: stim n=4, control n=3 nrep=120, alpha=0.05
ground truth: stim:day=+4.71/day, rat SD=5.87, residual SD=11.30, days=6
true stim:day interaction = +4.71 (100% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.27 | 0.59 <- observed
5 | 0.63 | 0.72
8 | 0.91 | 0.93
12 | 1.00 | 1.00
16 | 0.99 | 0.99
24 | 1.00 | 1.00
true stim:day interaction = +2.35 (50% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.10 | 0.20 <- observed
5 | 0.20 | 0.24
8 | 0.31 | 0.33
12 | 0.52 | 0.54
16 | 0.62 | 0.62
24 | 0.85 | 0.87
Read the per-animal column as the honest power. At the observed N this study
is typically underpowered; per-animal power reaches ~0.8 only at larger N.