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feat(matlab): print full native fitlme/fitglme summary in every scenario

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Add tdcs_model_summary as the single place that renders a fitted model
verbatim (disp(model) with the Command-Window <strong> markup stripped),
and route every report through it so all scenarios emit the complete
model-fitting output:

- tdcs_report: adds the learning-rate GLMM's full summary (count/rate
  already had theirs); localCleanDisp now delegates to the shared helper.
- tdcs_lme_report / tdcs_paper_lme: embed the full fitlme summary before
  the curated effect table.
- tdcs_phase_lme: appends each phase's full fitlme summary.

Tests: tReport asserts 3 native summaries in a GLMM report; tLme asserts
the lme_*/paper_*/phase_* reports embed 1/1/3 summaries with markup
stripped. Suite 39/39.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Experiments DB Dev
2026-07-20 14:21:34 -04:00
parent d59ff2b659
commit 0e9263b958
41 changed files with 4637 additions and 7 deletions
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analysis/matlab/tdcs_report.m
+8 -7
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@@ -87,7 +87,9 @@ s = [s localCleanDisp(R.countGLME) sprintf('\n')];
s = [s localHeader('(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)')];
s = [s localCleanDisp(R.rateGLME) sprintf('\n')];
s = [s localHeader('(C) LEARNING RATE -- per-animal slope test (Box-B2 vs Box-A2)')];
s = [s localHeader('(C) LEARNING RATE -- Poisson GLMM (group x day interaction)')];
s = [s localCleanDisp(R.learnGLME) sprintf('\n')];
s = [s sprintf('Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:\n')];
if R.learn.slopeMWUp < 0.05
verdict = 'DIFFERENT learning rates';
else
@@ -103,12 +105,11 @@ s = [s sprintf(['(Reference only: the GLMM group x day_c joint F-test gives p=%.
end
function s = localCleanDisp(model)
%LOCALCLEANDISP disp(MODEL) captured to text, with the Command-Window-only
% <strong>...</strong> bold-markup tags (rendered by the interactive
% MATLAB terminal, but emitted as literal text by EVALC) stripped so the
% report/console/file output is plain text.
raw = evalc('disp(model)');
s = regexprep(raw, '</?strong>', '');
%LOCALCLEANDISP Full native model summary (disp(MODEL)) as plain text.
% Thin wrapper over the shared TDCS_MODEL_SUMMARY so the count, rate, and
% learning GLMMs here render the same verbatim summary used by the LME
% reports.
s = tdcs_model_summary(model);
end
% ------------------------------------------------------------ interpretation