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: right_only_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, Right-Electrode
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control (stim=0): Electrode-Box-A2
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N = 7 rats, 42 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 42
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Fixed effects coefficients 4
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Random effects coefficients 7
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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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331.16 341.59 -159.58 319.16
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 12.524 5.1493 2.4321 38 0.019831
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{'day' } 9.8571 1.3751 7.1681 38 1.4583e-08
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{'stim' } -4.9762 6.8119 -0.73051 38 0.46956
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{'day:stim' } 5.3071 1.8191 2.9174 38 0.0058973
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Lower Upper
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2.0996 22.948
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7.0733 12.641
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-18.766 8.8138
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1.6245 8.9898
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Random effects covariance parameters (95% CIs):
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Group: rat (7 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 5.2482
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Lower Upper
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2.2427 12.282
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 9.9638 7.8829 12.594
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effect t (df) F (df1) p
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------------------------------------------------------------------
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stim x day (interaction) t(38)= 2.92 F(1)= 8.511 p=0.005897
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day (learning) t(38)= 7.17 F(1)= 51.382 p=1.458e-08
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stim (main, window start) t(38)= -0.73 F(1)= 0.534 p=0.4696
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interaction 95% CI: [+1.62, +8.99]
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INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.005897, slope diff=+5.31)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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