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_d6_13
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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 = 9 rats, 65 sessions raw day coverage: treat 6..13, control 6..13
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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 65
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Fixed effects coefficients 4
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Random effects coefficients 9
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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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535.5 548.54 -261.75 523.5
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 66.813 5.1278 13.03 61 2.7998e-19
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{'day' } 2.8262 0.83395 3.3889 61 0.0012345
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{'stim' } 21.913 8.8928 2.4641 61 0.016568
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{'day:stim' } 1.1553 1.4855 0.77769 61 0.43976
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Lower Upper
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56.56 77.067
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1.1586 4.4937
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4.1309 39.695
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-1.8152 4.1258
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Random effects covariance parameters (95% CIs):
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Group: rat (9 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 9.7589
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Lower Upper
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5.6191 16.948
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 12.033 10.004 14.473
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effect t (df) F (df1) p
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------------------------------------------------------------------
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stim x day (interaction) t(61)= 0.78 F(1)= 0.605 p=0.4398
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day (learning) t(61)= 3.39 F(1)= 11.485 p=0.001235
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stim (main, window start) t(61)= 2.46 F(1)= 6.072 p=0.01657
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interaction 95% CI: [-1.82, +4.13]
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INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4398, slope diff=+1.16)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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