Experiments DB Dev
969c0205e8
Add make_variations.m + variation_analyze.m generating 20 self-contained subfolders under analysis/matlab/variations/, one per grouping x day-window, each with exactly three files: data.csv (curated subset), analyze.m (a simple standalone script fitting behavior ~ stim + day + stim:day + (1|rat) on the success COUNT), and result.txt (its output). Plus variations/SUMMARY.csv. Groupings (stim=1 / stim=0, other groups dropped): unmerge B2 vs A2 right_only B2+Right vs A2 (Box-A dropped) naive_a2 B2 vs A2+Naive (Right, Box-A dropped) naive_boxa B2 vs A2+Naive+Box-A (Right dropped) Windows: 0-10, 0-13, 0-5, 6-10, 6-13. All 20 have equal day coverage. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
66 lines
2.5 KiB
Plaintext
66 lines
2.5 KiB
Plaintext
==============================================================================
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VARIATION: naive_a2_d0_5
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==============================================================================
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model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
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day = training day within window (0 = first analyzed day)
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treatment (stim=1): Electrode-Box-B2
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control (stim=0): Electrode-Box-A2, Naive
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N = 10 rats, 59 sessions raw day coverage: treat 0..5, control 0..5
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(equal day coverage over this window)
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==============================================================================
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FULL MODEL SUMMARY -- fitlme
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==============================================================================
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Linear mixed-effects model fit by ML
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Model information:
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Number of observations 59
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Fixed effects coefficients 4
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Random effects coefficients 10
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Covariance parameters 2
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Formula:
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behavior ~ 1 + day*stim + (1 | rat)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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489.28 501.74 -238.64 477.28
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 9.0584 5.0267 1.8021 55 0.077018
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{'day' } 8.2568 1.1279 7.3206 55 1.1254e-09
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{'stim' } 0.44958 9.048 0.049688 55 0.96055
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{'day:stim' } 6.9622 2.0162 3.4532 55 0.0010733
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Lower Upper
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-1.0153 19.132
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5.9965 10.517
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-17.683 18.582
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2.9217 11.003
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Random effects covariance parameters (95% CIs):
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Group: rat (10 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 9.6432
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Lower Upper
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5.5002 16.907
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 12.109 9.9317 14.763
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effect t (df) F (df1) p
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------------------------------------------------------------------
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stim x day (interaction) t(55)= 3.45 F(1)= 11.924 p=0.001073
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day (learning) t(55)= 7.32 F(1)= 53.591 p=1.125e-09
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stim (main, window start) t(55)= 0.05 F(1)= 0.002 p=0.9606
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interaction 95% CI: [+2.92, +11.00]
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INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.001073, slope diff=+6.96)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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