Experiments DB Dev
110 lines
4.9 KiB
Markdown
110 lines
4.9 KiB
Markdown
# tDCS reaching study — MATLAB GLM port
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This directory is a MATLAB port of the Python tDCS GLM analysis
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(`analysis/tdcs_glm.py`, documented in `analysis/METHODS.md`), driven by raw
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app-export matrices rather than the pre-joined `tdcs_reach_data.csv`. It
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reproduces the same three models (count level, rate level, learning rate) and
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the same H2 anchor check / unknown-condition classification, but replaces the
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Python's population-average GEE with a `fitglme` GLMM (Poisson/Binomial,
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subject random intercept) — see "GLMM vs GEE" below.
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## Requirements
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- MATLAB R2025b + Statistics and Machine Learning Toolbox.
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- A license activated at `~/matlab/licenses/license.dat` (already in place in
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this environment). If MATLAB ever reports it is unlicensed, activate with:
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```
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~/matlab/bin/activate_matlab.sh
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```
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## Layout
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- `tdcs_config.m` — curated 12-animal group map, reference group, merge maps.
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- `tdcs_load_data.m` — parses `raw/raw_success.csv` and `raw/raw_total.csv`
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(app-export matrix format), joins them, and applies the curated group map.
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- `tdcs_scenario_data.m` — builds one of the 6 scenarios (merge map + day
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window), and saves the scenario table to `derived/<scenario>.csv`.
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- `tdcs_models.m` — fits the count/rate/learning GLMEs, the per-animal
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Welch/Mann-Whitney tests, the H2 anchor check, and (unmerged scenarios
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only) the unknown-condition classification.
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- `tdcs_report.m` — prints descriptives, the raw GLME fixed-effects tables,
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and an interpretation section (H2 verdict, per-animal p-values,
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classification, caveats); also writes `results/<scenario>.txt`.
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- `tdcs_glm.m` — the entry point: `tdcs_glm(scenario)` runs
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`tdcs_scenario_data` → `tdcs_models` → `tdcs_report` for one named
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scenario, via an explicit `switch/case` over all 6 scenario names.
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- `run_all.m` — runs all 6 scenarios in one script.
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- `run_tests.m` — runs the `matlab.unittest` suite in `tests/` and exits
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nonzero on any failure.
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- `tests/` — `matlab.unittest` test classes (`tLoadData`, `tScenarioData`,
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`tModels`) with golden values cross-checked against the Python.
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- `raw/` — pre-generated app-export CSVs (input; not modified by this code).
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- `derived/` — scenario tables written by `tdcs_scenario_data` (generated).
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- `results/` — report text files written by `tdcs_report`/`tdcs_glm`
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(generated).
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## Running
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Run one scenario (valid names: `unmerged_full`, `unmerged_d0_10`,
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`mergeA2_full`, `mergeA2_d0_10`, `mergeB2_full`, `mergeB2_d0_10`):
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```
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cd analysis/matlab
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~/matlab/bin/matlab -batch "addpath(pwd); tdcs_glm('mergeB2_full')"
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```
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This prints the full report to the console and writes it to
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`results/mergeB2_full.txt`.
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Run all 6 scenarios:
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```
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~/matlab/bin/matlab -batch "addpath(pwd); run_all"
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```
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Run the test suite:
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```
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~/matlab/bin/matlab -batch "addpath(pwd); run_tests"
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```
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(`run_tests` calls `error(...)` on any failure, so the `matlab -batch`
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process exits with a nonzero status — suitable for CI.)
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## Validation against the Python
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Per-animal statistics (per-subject Welch t-test / one-sided Mann-Whitney U,
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Box-B2 vs Box-A2) are pure stats independent of the GLM choice, and were
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matched to the Python's golden values to 3 decimals across all 6 scenarios
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(see `docs/superpowers/plans/2026-07-20-matlab-tdcs-analysis.md`, Task 3).
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## GLMM vs GEE (read this before comparing numbers to the Python)
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The Python reference uses **GEE** (population-average effects, exchangeable
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working correlation, robust/sandwich SEs) for the count and learning-rate
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models, and a cluster-robust Binomial GLM for the rate model. This MATLAB
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port instead uses **GLMM** (`fitglme`, subject-specific effects via a
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`(1|subject)` random intercept, Laplace/PL approximation) for all three
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models, because MATLAB's Statistics and Machine Learning Toolbox has no GEE
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implementation.
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GLMM and GEE estimate related but distinct quantities (subject-specific vs.
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population-average effects), so **exact coefficients, ratios, and p-values
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will differ** between the two implementations — sometimes by a fair amount
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for the small, unbalanced groups in this study. What should agree is the
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**interpretation**: the direction of the H2 anchor effect, which unknown
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condition classifies as A2-like/B2-like/ambiguous, and whether the learning
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rates differ across groups. Treat the MATLAB numbers as a sensitivity check
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on the Python's conclusions, not as a numerically-identical reproduction.
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## Scope vs. the Python
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The MATLAB port reproduces the Python's three core models (count, rate,
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learning-rate), per-animal tests, unknown-group classification, and the H2
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verdict (read off the main model's A2-vs-B2 term). The Python's two auxiliary
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sensitivity sections — `report_anchor_only` (a two-anchor-only refit) and
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`report_mixed` (a Bayesian MAP random-intercept model) — are intentionally
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**not** ported: the anchor-only refit is closely tracked by the main-model H2
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term, and MATLAB's native `fitglme` already *is* the random-intercept model.
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