function tdcs_lme_report(L, scenario, cfg)
%TDCS_LME_REPORT Print + save the days x tDCS linear-mixed-model report.
%   TDCS_LME_REPORT(L, SCENARIO, CFG) formats the result of TDCS_LME to the
%   console and to analysis/matlab/results/<SCENARIO>.txt, in the style of the
%   published result (interaction, days, tDCS effects) plus interpretation and
%   the paper's reference numbers.

bar = repmat('=', 1, 78);
s = sprintf('%s\n', bar);
s = [s sprintf('LINEAR MIXED MODEL (days x tDCS) -- scenario: %s\n', scenario)];
s = [s sprintf('%s\n', bar)];
s = [s sprintf('model: success ~ day * tDCS + (1|subject)   [tDCS: %s = 1 vs %s = 0]\n', ...
    cfg.anchorHigh, cfg.anchorLow)];
s = [s sprintf('N = %d subjects, %d sessions\n', L.nSubjects, L.nObs)];
s = [s sprintf('day coverage: Box-B2 0..%d, Box-A2 0..%d\n', L.maxDayB, L.maxDayA)];
s = [s sprintf('(day is raw and 0-indexed: our day 0 = the paper''s "Day 1", so the tDCS\n')];
s = [s sprintf(' main effect below is the group difference on Day 1 -- comparable to the paper.)\n')];
if abs(L.maxDayB - L.maxDayA) > 2
    s = [s sprintf(['** WARNING: the groups'' day coverage is UNEQUAL (differ by %d days). The\n' ...
        '   interaction/slope over this window EXTRAPOLATES the shorter group''s line and is\n' ...
        '   CONFOUNDED -- prefer the _d0_%d window (both groups have data throughout). **\n'], ...
        abs(L.maxDayB - L.maxDayA), min(L.maxDayB, L.maxDayA))];
end
s = [s sprintf('\n')];

s = [s tdcs_model_summary(L.lme, 'fitlme: success ~ day*tDCS + (1|subject)') sprintf('\n')];

s = [s sprintf('%-27s %-20s %-12s %s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)')];
s = [s sprintf('%s\n', repmat('-', 1, 78))];
s = [s localRow('days x tDCS (interaction)', L.interaction)];
s = [s localRow('days (learning)', L.day)];
s = [s localRow('tDCS (main, at Day 1)', L.tDCS)];
s = [s localHonest(L.interRS)];

s = [s sprintf('\nINTERPRETATION\n')];
if L.interaction.p >= 0.05
    interTxt = 'slopes are parallel (no differential change over training)';
elseif L.interaction.estimate > 0
    interTxt = 'the tDCS (Box-B2) group improves FASTER -- benefit ACCUMULATES over training';
else
    interTxt = 'the tDCS (Box-B2) group improves SLOWER -- groups CONVERGE (Box-B2 is ahead early, the gap narrows)';
end
s = [s sprintf('  - days x tDCS interaction: %s (p=%.4f, slope diff=%.2f) -> %s.\n', ...
    localSig(L.interaction.p), L.interaction.p, L.interaction.estimate, interTxt)];
s = [s sprintf('  - days (learning): %s (p=%.2g) -> performance improves with training.\n', ...
    localSig(L.day.p), L.day.p)];
s = [s sprintf('  - tDCS main effect on Day 1 (our day 0): %s (p=%.4f) -> the groups %s on Day 1.\n', ...
    localSig(L.tDCS.p), L.tDCS.p, ...
    localPick(L.tDCS.p < 0.05, 'already DIFFER', 'are comparable (as in the paper)'))];

s = [s sprintf('\nPaper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;\n')];
s = [s sprintf('  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.\n')];
s = [s sprintf('  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)\n')];

fprintf('%s', s);

thisDir = fileparts(mfilename('fullpath'));
resDir = fullfile(thisDir, 'results');
if ~exist(resDir, 'dir'); mkdir(resDir); end
fid = fopen(fullfile(resDir, [scenario '.txt']), 'w');
if fid < 0
    error('tdcs_lme_report:fopen', 'Cannot open results file for "%s".', scenario);
end
cleanup = onCleanup(@() fclose(fid)); %#ok<NASGU>
fprintf(fid, '%s', s);

end

function r = localRow(name, e)
r = sprintf('%-27s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', ...
    name, e.df, e.t, e.df1, e.F, e.p, e.pSatt, e.dfSatt);
end

function s = localHonest(rs)
%LOCALHONEST Report the random-slope interaction (the honest LME test) + why
%   Satterthwaite on the random-intercept model above barely changes the DF.
if rs.ok
    s = sprintf(['\nHONEST LME -- per-animal random slope (day|subject): ' ...
        'interaction F(%d,%.1f)=%.2f, p=%.4g\n'], rs.df1, rs.df2, rs.F, rs.p);
else
    s = sprintf(['\nHONEST LME -- per-animal random slope (day|subject): ' ...
        'model did not converge for this window.\n']);
end
s = [s sprintf(['  Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual\n' ...
    '  (the slope''s error is at the session level), so it does NOT fix pseudoreplication.\n' ...
    '  Letting each animal have its OWN slope collapses the interaction DF toward the animal\n' ...
    '  count -- this, and the per-animal slope test, are the honest learning-rate inference.\n'])];
end

function t = localSig(p)
if p < 0.05; t = 'SIGNIFICANT'; else; t = 'n.s.'; end
end

function t = localPick(b, yes, no)
if b; t = yes; else; t = no; end
end