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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/right_only_d6_10/power_result.txt
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==============================================================================
POWER SIMULATION -- right_only_d6_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (success COUNT; day within-window)
observed groups: stim n=4, control n=2 nrep=120, alpha=0.05
ground truth: stim:day=+2.42/day, rat SD=5.71, residual SD=8.18, days=5
true stim:day interaction = +2.42 (100% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.06 | 0.25
5 | 0.25 | 0.33
8 | 0.38 | 0.44
12 | 0.53 | 0.57
16 | 0.71 | 0.74
24 | 0.94 | 0.94
true stim:day interaction = +1.21 (50% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.07 | 0.12
5 | 0.11 | 0.14
8 | 0.09 | 0.14
12 | 0.18 | 0.20
16 | 0.23 | 0.23
24 | 0.40 | 0.47
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.