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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_a2_d6_10/power_result.txt
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==============================================================================
POWER SIMULATION -- naive_a2_d6_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (success COUNT; day within-window)
observed groups: stim n=3, control n=6 nrep=120, alpha=0.05
ground truth: stim:day=-1.80/day, rat SD=10.99, residual SD=10.55, days=5
true stim:day interaction = -1.80 (100% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.06 | 0.09 <- observed
5 | 0.06 | 0.07
8 | 0.17 | 0.24
12 | 0.30 | 0.30
16 | 0.33 | 0.34
24 | 0.45 | 0.49
true stim:day interaction = -0.90 (50% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.03 | 0.07 <- observed
5 | 0.07 | 0.07
8 | 0.05 | 0.12
12 | 0.07 | 0.08
16 | 0.11 | 0.11
24 | 0.13 | 0.14
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.