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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
parent e1af1af7f4
commit bb5e2d88c0
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analysis/matlab/variations/naive_boxa_d0_10/power_result.txt
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==============================================================================
POWER SIMULATION -- naive_boxa_d0_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (success COUNT; day within-window)
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
ground truth: stim:day=+1.18/day, rat SD=8.67, residual SD=13.71, days=11
true stim:day interaction = +1.18 (100% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.07 | 0.17 <- observed
5 | 0.20 | 0.22
8 | 0.32 | 0.38 <- observed
12 | 0.58 | 0.62
16 | 0.68 | 0.70
24 | 0.88 | 0.88
true stim:day interaction = +0.59 (50% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.05 | 0.07 <- observed
5 | 0.04 | 0.07
8 | 0.09 | 0.12 <- observed
12 | 0.16 | 0.17
16 | 0.28 | 0.27
24 | 0.28 | 0.29
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.