analysis(matlab): matched-effort comparison (prev _f full vs current 0-3)
Add make_matched_effort.m: pick the current-study day cutoff whose per-animal cumulative attempts best match the previous (_f) study's full-span total (~405 attempts/animal -> current days 0-3), then fit the paper LME on both. Two variation folders (data.csv + analyze.m + result.txt): variations/prev_f_full/ b2_f vs a2_f, days 0-9 (24 rats) variations/matched_current_d0_3/ Box-B2 vs Box-A2, 0-3 (6 rats) At matched cumulative effort both show a significant positive stim x day interaction (prev p=0.008 +0.56/day; current-0-3 p=0.004 +10.2/day) -- the tDCS acceleration replicates at equal practice, though the count slopes are not directly comparable across datasets given differing attempts/session. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Experiments DB Dev
@@ -0,0 +1,74 @@
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% Variation analysis -- the paper's linear mixed model on the successful-reach
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% COUNT, fit on this folder's curated data subset.
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%
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% model: behavior ~ stim + day + stim:day + (1|rat)
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% behavior = successful reaches (count per session)
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% stim = 1 for the treatment group(s), 0 for the control group(s)
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% day = training day within this window (0 = first analyzed day)
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% rat = subject (random intercept)
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%
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% Self-contained: reads data.csv beside this script and writes result.txt.
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% Run headless from this folder with: matlab -batch "analyze"
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% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
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here = fileparts(mfilename('fullpath'));
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if isempty(here); here = pwd; end
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vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
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D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
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tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
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'VariableNames', {'behavior', 'day', 'stim', 'rat'});
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m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
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C = m.Coefficients; A = anova(m); ci = coefCI(m);
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gi = @(t) find(strcmp(C.Name, t), 1);
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ga = @(t) find(strcmp(A.Term, t), 1);
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row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
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C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
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maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
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if abs(maxT - maxC) > 2
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cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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else
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cov = '(equal day coverage over this window)';
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end
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ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
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if pI >= 0.05
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verdict = 'n.s. -- slopes parallel (no differential learning rate)';
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elseif eI > 0
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verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
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else
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verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
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end
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bar = repmat('=', 1, 78);
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raw = regexprep(evalc('disp(m)'), '</?strong>', '');
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s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
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s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)\n')];
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s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
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s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
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s = [s sprintf('control (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
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s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
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numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
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s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
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s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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s = [s row('stim x day (interaction)', 'day:stim')];
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s = [s row('day (learning)', 'day')];
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s = [s row('stim (main, window start)', 'stim')];
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s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
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s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
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s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
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fprintf('%s', s);
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fid = fopen(fullfile(here, 'result.txt'), 'w');
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fprintf(fid, '%s', s);
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fclose(fid);
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% Machine-readable handoff for the summary table (see make_variations.m).
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VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
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'nObs', height(D), 'interP', pI, 'interEst', eI, ...
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'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
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'covEqual', abs(maxT - maxC) <= 2);
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@@ -0,0 +1,25 @@
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subject,group,day,success,total,stim
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Banh-mi-1,Electrode-Box-B2,0,13,84,1
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Banh-mi-1,Electrode-Box-B2,1,35,86,1
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Banh-mi-1,Electrode-Box-B2,2,47,110,1
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Banh-mi-1,Electrode-Box-B2,3,65,140,1
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Banh-mi-2,Electrode-Box-A2,0,25,97,0
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Banh-mi-2,Electrode-Box-A2,1,22,101,0
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Banh-mi-2,Electrode-Box-A2,2,22,119,0
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Banh-mi-2,Electrode-Box-A2,3,29,118,0
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Egg-tart-1,Electrode-Box-B2,0,7,56,1
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Egg-tart-1,Electrode-Box-B2,1,16,78,1
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Egg-tart-1,Electrode-Box-B2,2,23,103,1
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Egg-tart-1,Electrode-Box-B2,3,63,120,1
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Egg-tart-2,Electrode-Box-A2,0,9,32,0
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Egg-tart-2,Electrode-Box-A2,1,2,38,0
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Egg-tart-2,Electrode-Box-A2,2,31,93,0
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Egg-tart-2,Electrode-Box-A2,3,44,101,0
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Root-beer-1,Electrode-Box-B2,0,11,85,1
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Root-beer-1,Electrode-Box-B2,1,18,76,1
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Root-beer-1,Electrode-Box-B2,2,40,105,1
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Root-beer-1,Electrode-Box-B2,3,55,134,1
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Root-beer-2,Electrode-Box-A2,0,22,74,0
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Root-beer-2,Electrode-Box-A2,1,31,87,0
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Root-beer-2,Electrode-Box-A2,2,49,134,0
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Root-beer-2,Electrode-Box-A2,3,31,89,0
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==============================================================================
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VARIATION: matched_current_d0_3
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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
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N = 6 rats, 24 sessions raw day coverage: treat 0..3, control 0..3
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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 24
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Fixed effects coefficients 4
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Random effects coefficients 6
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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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185.35 192.42 -86.676 173.35
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 16.867 4.4554 3.7856 20 0.0011608
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{'day' } 6.3667 2.2049 2.8875 20 0.0091051
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{'stim' } -8.9667 6.3009 -1.4231 20 0.17013
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{'day:stim' } 10.2 3.1182 3.2711 20 0.0038217
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Lower Upper
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7.5728 26.161
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1.7673 10.966
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-22.11 4.1769
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3.6955 16.705
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Random effects covariance parameters (95% CIs):
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Group: rat (6 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 2.9163
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Lower Upper
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0.43116 19.725
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 8.5397 6.1599 11.839
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effect t (df) F (df1) p
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------------------------------------------------------------------
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stim x day (interaction) t(20)= 3.27 F(1)= 10.700 p=0.003822
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day (learning) t(20)= 2.89 F(1)= 8.337 p=0.009105
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stim (main, window start) t(20)= -1.42 F(1)= 2.025 p=0.1701
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interaction 95% CI: [+3.70, +16.70]
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INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.003822, slope diff=+10.20)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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@@ -0,0 +1,74 @@
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% Variation analysis -- the paper's linear mixed model on the successful-reach
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% COUNT, fit on this folder's curated data subset.
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%
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% model: behavior ~ stim + day + stim:day + (1|rat)
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% behavior = successful reaches (count per session)
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% stim = 1 for the treatment group(s), 0 for the control group(s)
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% day = training day within this window (0 = first analyzed day)
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% rat = subject (random intercept)
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%
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% Self-contained: reads data.csv beside this script and writes result.txt.
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% Run headless from this folder with: matlab -batch "analyze"
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% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
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here = fileparts(mfilename('fullpath'));
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if isempty(here); here = pwd; end
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vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
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D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
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tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
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'VariableNames', {'behavior', 'day', 'stim', 'rat'});
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m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
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C = m.Coefficients; A = anova(m); ci = coefCI(m);
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gi = @(t) find(strcmp(C.Name, t), 1);
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ga = @(t) find(strcmp(A.Term, t), 1);
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row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
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C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
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maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
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if abs(maxT - maxC) > 2
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cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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else
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cov = '(equal day coverage over this window)';
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end
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ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
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if pI >= 0.05
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verdict = 'n.s. -- slopes parallel (no differential learning rate)';
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elseif eI > 0
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verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
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else
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verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
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end
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bar = repmat('=', 1, 78);
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raw = regexprep(evalc('disp(m)'), '</?strong>', '');
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s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
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s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)\n')];
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s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
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s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
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s = [s sprintf('control (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
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s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
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numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
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s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
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s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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s = [s row('stim x day (interaction)', 'day:stim')];
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s = [s row('day (learning)', 'day')];
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s = [s row('stim (main, window start)', 'stim')];
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s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
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s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
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s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
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fprintf('%s', s);
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fid = fopen(fullfile(here, 'result.txt'), 'w');
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fprintf(fid, '%s', s);
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fclose(fid);
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% Machine-readable handoff for the summary table (see make_variations.m).
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VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
|
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'nObs', height(D), 'interP', pI, 'interEst', eI, ...
|
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'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
|
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'covEqual', abs(maxT - maxC) <= 2);
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@@ -0,0 +1,232 @@
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subject,group,day,success,total,stim
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Afrasyab,a2_f,0,1,13,0
|
||||
Afrasyab,a2_f,1,8,24,0
|
||||
Afrasyab,a2_f,2,13,35,0
|
||||
Afrasyab,a2_f,3,8,29,0
|
||||
Afrasyab,a2_f,4,21,43,0
|
||||
Afrasyab,a2_f,5,14,42,0
|
||||
Afrasyab,a2_f,6,18,46,0
|
||||
Afrasyab,a2_f,7,21,52,0
|
||||
Afrasyab,a2_f,8,21,41,0
|
||||
Afrasyab,a2_f,9,21,55,0
|
||||
Arash,b2_f,0,9,34,1
|
||||
Arash,b2_f,1,12,41,1
|
||||
Arash,b2_f,2,15,43,1
|
||||
Arash,b2_f,3,18,44,1
|
||||
Arash,b2_f,4,16,33,1
|
||||
Arash,b2_f,5,18,47,1
|
||||
Arash,b2_f,6,13,48,1
|
||||
Arash,b2_f,7,9,38,1
|
||||
Arash,b2_f,8,18,43,1
|
||||
Arash,b2_f,9,15,47,1
|
||||
Ashkas,a2_f,0,2,30,0
|
||||
Ashkas,a2_f,1,11,35,0
|
||||
Ashkas,a2_f,2,10,30,0
|
||||
Ashkas,a2_f,3,9,33,0
|
||||
Ashkas,a2_f,4,9,41,0
|
||||
Ashkas,a2_f,5,10,36,0
|
||||
Ashkas,a2_f,6,13,46,0
|
||||
Ashkas,a2_f,7,9,42,0
|
||||
Ashkas,a2_f,8,6,36,0
|
||||
Ashkas,a2_f,9,15,48,0
|
||||
Fariborz,a2_f,0,8,32,0
|
||||
Fariborz,a2_f,1,10,32,0
|
||||
Fariborz,a2_f,2,9,34,0
|
||||
Fariborz,a2_f,3,11,35,0
|
||||
Fariborz,a2_f,4,13,35,0
|
||||
Fariborz,a2_f,5,5,38,0
|
||||
Fariborz,a2_f,6,12,44,0
|
||||
Garsivaz,b2_f,0,10,34,1
|
||||
Garsivaz,b2_f,1,12,30,1
|
||||
Garsivaz,b2_f,2,14,35,1
|
||||
Garsivaz,b2_f,3,27,56,1
|
||||
Garsivaz,b2_f,4,22,41,1
|
||||
Garsivaz,b2_f,5,30,57,1
|
||||
Garsivaz,b2_f,6,34,52,1
|
||||
Garsivaz,b2_f,7,25,49,1
|
||||
Garsivaz,b2_f,8,29,46,1
|
||||
Garsivaz,b2_f,9,25,44,1
|
||||
Iraj,b2_f,0,6,29,1
|
||||
Iraj,b2_f,1,13,37,1
|
||||
Iraj,b2_f,2,15,32,1
|
||||
Iraj,b2_f,3,20,49,1
|
||||
Iraj,b2_f,4,17,47,1
|
||||
Iraj,b2_f,5,21,47,1
|
||||
Iraj,b2_f,6,24,49,1
|
||||
Khosrow,b2_f,0,6,23,1
|
||||
Khosrow,b2_f,1,7,27,1
|
||||
Khosrow,b2_f,2,10,27,1
|
||||
Khosrow,b2_f,3,10,26,1
|
||||
Khosrow,b2_f,4,14,35,1
|
||||
Khosrow,b2_f,5,20,41,1
|
||||
Khosrow,b2_f,6,11,37,1
|
||||
Khosrow,b2_f,7,13,34,1
|
||||
Khosrow,b2_f,8,13,30,1
|
||||
Khosrow,b2_f,9,15,40,1
|
||||
Kiyanoush,b2_f,0,11,19,1
|
||||
Kiyanoush,b2_f,1,22,52,1
|
||||
Kiyanoush,b2_f,2,34,58,1
|
||||
Kiyanoush,b2_f,3,30,52,1
|
||||
Kiyanoush,b2_f,4,27,56,1
|
||||
Kiyanoush,b2_f,5,46,65,1
|
||||
Kiyanoush,b2_f,6,32,58,1
|
||||
Kiyanoush,b2_f,7,47,61,1
|
||||
Kiyanoush,b2_f,8,49,68,1
|
||||
Kiyanoush,b2_f,9,41,49,1
|
||||
Manuchehr,b2_f,0,1,22,1
|
||||
Manuchehr,b2_f,1,8,18,1
|
||||
Manuchehr,b2_f,2,9,41,1
|
||||
Manuchehr,b2_f,3,14,47,1
|
||||
Manuchehr,b2_f,4,25,60,1
|
||||
Manuchehr,b2_f,5,22,70,1
|
||||
Manuchehr,b2_f,6,37,74,1
|
||||
Manuchehr,b2_f,7,40,75,1
|
||||
Manuchehr,b2_f,8,32,55,1
|
||||
Manuchehr,b2_f,9,29,69,1
|
||||
Mehrab,a2_f,0,8,28,0
|
||||
Mehrab,a2_f,1,13,26,0
|
||||
Mehrab,a2_f,2,16,45,0
|
||||
Mehrab,a2_f,3,17,34,0
|
||||
Mehrab,a2_f,4,21,45,0
|
||||
Mehrab,a2_f,5,21,55,0
|
||||
Mehrab,a2_f,6,22,51,0
|
||||
Mehrab,a2_f,7,21,48,0
|
||||
Mehrab,a2_f,8,21,48,0
|
||||
Mehrab,a2_f,9,23,45,0
|
||||
Merdas,a2_f,0,7,30,0
|
||||
Merdas,a2_f,1,9,36,0
|
||||
Merdas,a2_f,2,8,33,0
|
||||
Merdas,a2_f,3,17,48,0
|
||||
Merdas,a2_f,4,21,45,0
|
||||
Merdas,a2_f,5,13,39,0
|
||||
Merdas,a2_f,6,19,43,0
|
||||
Merdas,a2_f,7,10,48,0
|
||||
Merdas,a2_f,8,19,52,0
|
||||
Merdas,a2_f,9,20,47,0
|
||||
Nozar,a2_f,0,4,20,0
|
||||
Nozar,a2_f,1,5,23,0
|
||||
Nozar,a2_f,2,8,43,0
|
||||
Nozar,a2_f,3,8,48,0
|
||||
Nozar,a2_f,4,11,40,0
|
||||
Nozar,a2_f,5,13,53,0
|
||||
Nozar,a2_f,6,15,50,0
|
||||
Nozar,a2_f,7,19,46,0
|
||||
Nozar,a2_f,8,18,56,0
|
||||
Nozar,a2_f,9,20,47,0
|
||||
Pashang,b2_f,0,5,32,1
|
||||
Pashang,b2_f,1,7,51,1
|
||||
Pashang,b2_f,2,18,49,1
|
||||
Pashang,b2_f,3,16,43,1
|
||||
Pashang,b2_f,4,21,36,1
|
||||
Pashang,b2_f,5,16,53,1
|
||||
Pashang,b2_f,6,18,42,1
|
||||
Pashang,b2_f,7,23,44,1
|
||||
Pashang,b2_f,8,20,43,1
|
||||
Pashang,b2_f,9,34,57,1
|
||||
Pashin,a2_f,0,7,27,0
|
||||
Pashin,a2_f,1,9,22,0
|
||||
Pashin,a2_f,2,11,41,0
|
||||
Pashin,a2_f,3,14,40,0
|
||||
Pashin,a2_f,4,12,36,0
|
||||
Pashin,a2_f,5,14,43,0
|
||||
Pashin,a2_f,6,11,42,0
|
||||
Pashin,a2_f,7,14,47,0
|
||||
Pashin,a2_f,8,13,50,0
|
||||
Pashin,a2_f,9,18,45,0
|
||||
Rostam,b2_f,0,9,29,1
|
||||
Rostam,b2_f,1,12,36,1
|
||||
Rostam,b2_f,2,14,42,1
|
||||
Rostam,b2_f,3,18,41,1
|
||||
Rostam,b2_f,4,15,45,1
|
||||
Rostam,b2_f,5,20,49,1
|
||||
Rostam,b2_f,6,21,51,1
|
||||
Rostam,b2_f,7,24,48,1
|
||||
Rostam,b2_f,8,19,46,1
|
||||
Rostam,b2_f,9,23,48,1
|
||||
Salm,a2_f,0,7,28,0
|
||||
Salm,a2_f,1,10,36,0
|
||||
Salm,a2_f,2,16,50,0
|
||||
Salm,a2_f,3,18,49,0
|
||||
Salm,a2_f,4,21,62,0
|
||||
Salm,a2_f,5,26,60,0
|
||||
Salm,a2_f,6,27,53,0
|
||||
Salm,a2_f,7,32,62,0
|
||||
Salm,a2_f,8,16,40,0
|
||||
Salm,a2_f,9,16,43,0
|
||||
Sam,b2_f,0,8,28,1
|
||||
Sam,b2_f,1,11,32,1
|
||||
Sam,b2_f,2,10,26,1
|
||||
Sam,b2_f,3,11,39,1
|
||||
Sam,b2_f,4,12,36,1
|
||||
Sam,b2_f,5,14,34,1
|
||||
Sam,b2_f,6,15,43,1
|
||||
Sam,b2_f,7,9,31,1
|
||||
Sam,b2_f,8,23,51,1
|
||||
Sam,b2_f,9,19,45,1
|
||||
Siavash,a2_f,0,10,21,0
|
||||
Siavash,a2_f,1,3,25,0
|
||||
Siavash,a2_f,2,7,39,0
|
||||
Siavash,a2_f,3,13,36,0
|
||||
Siavash,a2_f,4,12,35,0
|
||||
Siavash,a2_f,5,18,32,0
|
||||
Siavash,a2_f,6,18,45,0
|
||||
Siavash,a2_f,7,22,37,0
|
||||
Siavash,a2_f,8,18,36,0
|
||||
Siavash,a2_f,9,17,34,0
|
||||
Sohrab,b2_f,0,8,23,1
|
||||
Sohrab,b2_f,1,11,34,1
|
||||
Sohrab,b2_f,2,15,45,1
|
||||
Sohrab,b2_f,3,19,47,1
|
||||
Sohrab,b2_f,4,18,50,1
|
||||
Sohrab,b2_f,5,18,45,1
|
||||
Sohrab,b2_f,6,16,45,1
|
||||
Sohrab,b2_f,7,20,49,1
|
||||
Sohrab,b2_f,8,14,32,1
|
||||
Sohrab,b2_f,9,23,43,1
|
||||
Tahmasb,b2_f,0,7,32,1
|
||||
Tahmasb,b2_f,1,11,38,1
|
||||
Tahmasb,b2_f,2,8,37,1
|
||||
Tahmasb,b2_f,3,7,26,1
|
||||
Tahmasb,b2_f,4,12,35,1
|
||||
Tahmasb,b2_f,5,13,39,1
|
||||
Tahmasb,b2_f,6,11,41,1
|
||||
Tahmasb,b2_f,7,15,52,1
|
||||
Tahmasb,b2_f,8,23,61,1
|
||||
Tahmasb,b2_f,9,22,61,1
|
||||
Tur,a2_f,0,6,39,0
|
||||
Tur,a2_f,1,11,36,0
|
||||
Tur,a2_f,2,9,35,0
|
||||
Tur,a2_f,3,16,45,0
|
||||
Tur,a2_f,4,19,52,0
|
||||
Tur,a2_f,5,14,47,0
|
||||
Tur,a2_f,6,21,46,0
|
||||
Tus,b2_f,0,4,32,1
|
||||
Tus,b2_f,1,9,39,1
|
||||
Tus,b2_f,2,5,40,1
|
||||
Tus,b2_f,3,15,42,1
|
||||
Tus,b2_f,4,21,50,1
|
||||
Tus,b2_f,5,16,52,1
|
||||
Tus,b2_f,6,19,59,1
|
||||
Tus,b2_f,7,28,57,1
|
||||
Tus,b2_f,8,27,62,1
|
||||
Tus,b2_f,9,27,60,1
|
||||
Zal,a2_f,0,5,34,0
|
||||
Zal,a2_f,1,12,36,0
|
||||
Zal,a2_f,2,16,37,0
|
||||
Zal,a2_f,3,18,40,0
|
||||
Zal,a2_f,4,14,34,0
|
||||
Zal,a2_f,5,15,41,0
|
||||
Zal,a2_f,6,16,46,0
|
||||
Zal,a2_f,7,15,44,0
|
||||
Zal,a2_f,8,20,46,0
|
||||
Zal,a2_f,9,18,50,0
|
||||
Zav,a2_f,0,4,28,0
|
||||
Zav,a2_f,1,7,37,0
|
||||
Zav,a2_f,2,7,41,0
|
||||
Zav,a2_f,3,4,32,0
|
||||
Zav,a2_f,4,8,37,0
|
||||
Zav,a2_f,5,4,37,0
|
||||
Zav,a2_f,6,7,36,0
|
||||
Zav,a2_f,7,15,56,0
|
||||
Zav,a2_f,8,19,54,0
|
||||
Zav,a2_f,9,21,49,0
|
||||
|
@@ -0,0 +1,65 @@
|
||||
==============================================================================
|
||||
VARIATION: prev_f_full
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
|
||||
day = training day within window (0 = first analyzed day)
|
||||
treatment (stim=1): b2_f
|
||||
control (stim=0): a2_f
|
||||
N = 24 rats, 231 sessions raw day coverage: treat 0..9, control 0..9
|
||||
(equal day coverage over this window)
|
||||
|
||||
==============================================================================
|
||||
FULL MODEL SUMMARY -- fitlme
|
||||
==============================================================================
|
||||
|
||||
Linear mixed-effects model fit by ML
|
||||
|
||||
Model information:
|
||||
Number of observations 231
|
||||
Fixed effects coefficients 4
|
||||
Random effects coefficients 24
|
||||
Covariance parameters 2
|
||||
|
||||
Formula:
|
||||
behavior ~ 1 + day*stim + (1 | rat)
|
||||
|
||||
Model fit statistics:
|
||||
AIC BIC LogLikelihood Deviance
|
||||
1416.2 1436.9 -702.11 1404.2
|
||||
|
||||
Fixed effects coefficients (95% CIs):
|
||||
Name Estimate SE tStat DF pValue
|
||||
{'(Intercept)'} 7.7376 1.4647 5.2828 227 2.9782e-07
|
||||
{'day' } 1.3487 0.15094 8.9352 227 1.4298e-16
|
||||
{'stim' } 1.8961 2.0698 0.9161 227 0.36059
|
||||
{'day:stim' } 0.56023 0.21043 2.6623 227 0.0083151
|
||||
|
||||
|
||||
Lower Upper
|
||||
4.8515 10.624
|
||||
1.0513 1.6461
|
||||
-2.1823 5.9745
|
||||
0.14559 0.97488
|
||||
|
||||
Random effects covariance parameters (95% CIs):
|
||||
Group: rat (24 Levels)
|
||||
Name1 Name2 Type Estimate
|
||||
{'(Intercept)'} {'(Intercept)'} {'std'} 4.3164
|
||||
|
||||
|
||||
Lower Upper
|
||||
3.1535 5.9081
|
||||
|
||||
Group: Error
|
||||
Name Estimate Lower Upper
|
||||
{'Res Std'} 4.4892 4.0771 4.9429
|
||||
|
||||
|
||||
effect t (df) F (df1) p
|
||||
------------------------------------------------------------------
|
||||
stim x day (interaction) t(227)= 2.66 F(1)= 7.088 p=0.008315
|
||||
day (learning) t(227)= 8.94 F(1)= 79.838 p=1.43e-16
|
||||
stim (main, window start) t(227)= 0.92 F(1)= 0.839 p=0.3606
|
||||
interaction 95% CI: [+0.15, +0.97]
|
||||
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.008315, slope diff=+0.56)
|
||||
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
Reference in New Issue
Block a user