Experiments DB Dev
8a18c894dd
Every fitlme-based report (lme_*, paper_*, phase_*, and the variations' analyze.m) now shows, per effect: residual-DF p, Satterthwaite-DF p, and -- for the interaction -- an HONEST test from a per-animal random-SLOPE model (day|rat), whose Satterthwaite DF collapses toward the animal count. New: tdcs_random_slope_interaction.m (shared helper). Wired into tdcs_lme, tdcs_paper_lme, tdcs_phase_lme, variation_analyze; SUMMARY.csv gains interaction_p_satt / interaction_p_rs. Regenerated all results/, variations/, matched-effort outputs. Key point this surfaces: Satterthwaite ~= residual on the random-INTERCEPT model (the slope's error is at session level), so it does NOT fix pseudoreplication; the random-slope model does. Effect: full-range mergeA2 interaction 0.009 -> 0.75 (collapses); unmerge_d0_5 0.015 -> 0.13 (n.s.); the pooled-control early windows survive honestly (naive_a2_d0_5 0.001 -> 0.028; naive_boxa_d0_5 0.003 -> 0.036). Suite 42/42. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
97 lines
4.2 KiB
Matlab
97 lines
4.2 KiB
Matlab
function P = tdcs_phase_lme(mergeKey, cfg, phases)
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%TDCS_PHASE_LME Refit the days x tDCS LME within learning phases (Box-B2 vs A2).
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% P = TDCS_PHASE_LME(MERGEKEY, CFG) fits the same linear mixed model as the
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% lme_* scenarios -- success ~ dayp*tDCS + (1|subject) -- SEPARATELY within
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% each learning phase, for the Box-B2 (tDCS=1) vs Box-A2 (tDCS=0) subset of
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% the MERGEKEY grouping ('unmerged' | 'mergeA2' | 'mergeB2'). `dayp` is `day`
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% centered at each phase's start, so the tDCS main effect reads as the group
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% difference on the phase's first day (the interaction and slopes are
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% invariant to this centering). Prints a per-phase table and writes
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% results/phase_<MERGEKEY>.txt.
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%
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% P = TDCS_PHASE_LME(MERGEKEY, CFG, PHASES) uses custom phase windows
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% (default {[0 5],[6 10],[6 13]}), each a [loDay hiDay] pair.
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%
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% P.phases{k} has: .phase .nSub .a2Slope .b2Slope .dayP (learning) .tDCSlevelP
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% (between-group level at phase start) .interP .interEst .interCI (the
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% between-group learning-rate difference and its 95% CI).
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if nargin < 3 || isempty(phases)
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phases = {[0 5], [6 10], [6 13]};
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end
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Sfull = tdcs_scenario_data([mergeKey '_full']);
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T = Sfull(ismember(Sfull.group, {cfg.anchorLow, cfg.anchorHigh}), :);
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P = struct('mergeKey', mergeKey, 'phases', {cell(1, numel(phases))});
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bar = repmat('=', 1, 78);
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s = sprintf('%s\n', bar);
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s = [s sprintf('PHASED days x tDCS LME -- %s (Box-B2 vs Box-A2)\n', mergeKey)];
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s = [s sprintf('%s\n', bar)];
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s = [s sprintf('model per phase: success ~ dayp*tDCS + (1|subject) [dayp = day - phaseStart]\n\n')];
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s = [s sprintf('%-8s N A2slp B2slp day p tDCS p intP(res) intP(Satt) intP(RS) slopeDiff [95%% CI]\n', 'phase')];
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s = [s sprintf('%s\n', repmat('-', 1, 104))];
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rawSummaries = '';
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for k = 1:numel(phases)
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ph = phases{k};
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Tp = T(T.day >= ph(1) & T.day <= ph(2), :);
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Tp.dayp = Tp.day - ph(1);
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Tp.tDCS = double(Tp.group == cfg.anchorHigh);
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lme = fitlme(Tp, 'success ~ dayp*tDCS + (1|subject)');
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C = lme.Coefficients; A = anova(lme); ci = coefCI(lme);
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As = anova(lme, 'DFMethod', 'satterthwaite');
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rs = tdcs_random_slope_interaction(Tp, ...
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'success ~ dayp*tDCS + (dayp|subject)', 'dayp:tDCS');
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ii = strcmp(C.Name, 'dayp:tDCS');
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di = strcmp(C.Name, 'dayp');
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e = struct();
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e.phase = ph;
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e.nSub = numel(unique(Tp.subject));
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e.a2Slope = C.Estimate(di);
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e.b2Slope = C.Estimate(di) + C.Estimate(ii);
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e.dayP = A.pValue(strcmp(A.Term, 'dayp'));
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e.tDCSlevelP = A.pValue(strcmp(A.Term, 'tDCS'));
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e.interP = A.pValue(strcmp(A.Term, 'dayp:tDCS'));
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e.interPsatt = As.pValue(strcmp(As.Term, 'dayp:tDCS')); % Satterthwaite DF
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e.interPrs = rs.p; e.rsOk = rs.ok; % honest random-slope
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e.interEst = C.Estimate(ii);
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e.interCI = ci(ii, :);
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P.phases{k} = e;
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if rs.ok; rsStr = sprintf('%.3f', rs.p); else; rsStr = 'n/a'; end
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s = [s sprintf('%d-%-5d %d %5.2f %5.2f %-8.2g %-8.3f %-9.3f %-10.3f %-9s %+.2f [%+.2f,%+.2f]\n', ...
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ph(1), ph(2), e.nSub, e.a2Slope, e.b2Slope, e.dayP, e.tDCSlevelP, ...
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e.interP, e.interPsatt, rsStr, e.interEst, e.interCI(1), e.interCI(2))]; %#ok<AGROW>
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rawSummaries = [rawSummaries tdcs_model_summary(lme, ...
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sprintf('phase %d-%d: success ~ dayp*tDCS + (1|subject)', ph(1), ph(2))) ...
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sprintf('\n')]; %#ok<AGROW>
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end
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s = [s sprintf(['\nColumns: intP(res)=observation-level DF (anticonservative); intP(Satt)=Satterthwaite\n' ...
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'DF (~= residual on this random-intercept model); intP(RS)=per-animal random-slope\n' ...
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'(dayp|subject) model, the honest test (DF collapses toward the animal count; ''n/a'' if it\n' ...
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'did not converge). The early phase carries the Box-B2 faster-acquisition signal; late\n' ...
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'phases converge.\n'])];
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s = [s rawSummaries];
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fprintf('%s', s);
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localWrite(['phase_' mergeKey], s);
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end
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function localWrite(name, s)
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thisDir = fileparts(mfilename('fullpath'));
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resDir = fullfile(thisDir, 'results');
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if ~exist(resDir, 'dir'); mkdir(resDir); end
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fid = fopen(fullfile(resDir, [name '.txt']), 'w');
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if fid < 0; error('tdcs_phase_lme:fopen', 'Cannot open results file for "%s".', name); end
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cleanup = onCleanup(@() fclose(fid)); %#ok<NASGU>
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fprintf(fid, '%s', s);
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end
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