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feat(matlab): verbatim paper LME (behavior ~ stim+day+stim:day+(1|rat)) + power sim on it

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Adds tdcs_paper_lme replicating the paper's exact formula/variable names (paper_*
switch cases): mergeA2 reproduces the interaction F(1)=7.09 vs 7.12, p=0.009 vs
0.008, with a coverage-confound warning. Rewrites tdcs_power_sim to fit that same
formula (interaction term canonicalized to day:stim). Adds a replication test.
Suite 37/37.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Experiments DB Dev
2026-07-20 12:40:26 -04:00
parent 800189371b
commit 7b5c27df8c
6 changed files with 178 additions and 47 deletions
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analysis/matlab/tdcs_power_sim.m
+50 -46
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@@ -1,60 +1,64 @@
function PW = tdcs_power_sim(mergeKey, phase, Ns, effMuls, nrep, cfg)
%TDCS_POWER_SIM Monte-Carlo power for the phased days x tDCS interaction.
% PW = TDCS_POWER_SIM(MERGEKEY, PHASE, NS, EFFMULS, NREP, CFG) estimates the
% power to detect the day x tDCS interaction of the phased LME
% (success ~ dayp*tDCS + (1|subject)). The fitted model for MERGEKEY over the
% PHASE = [lo hi] window is used as ground truth (its fixed effects, subject
% random-intercept SD, and residual SD); NREP datasets are simulated at each
% subjects-per-group in NS, for each true-effect multiplier in EFFMULS (e.g.
% [1 0.5] = observed and half the observed interaction). Each dataset is
% scored two ways at alpha = 0.05:
% - per-animal (cluster-honest): per-subject slope, Welch t (Box-B2 vs A2)
% - LME: the fitlme dayp:tDCS interaction p (observation-level DF)
% PW is a table (effMul, N, powerPerAnimal, powerLME); it is also printed.
% A fixed RNG seed makes the estimate reproducible.
function PW = tdcs_power_sim(mergeKey, window, Ns, effMuls, nrep, cfg)
%TDCS_POWER_SIM Monte-Carlo power for the paper's stim x day interaction.
% Uses the paper's exact model -- fitlme(tbl, 'behavior ~ stim + day +
% stim:day + (1|rat)') -- throughout. The fitted model for MERGEKEY over the
% WINDOW = [lo hi] day range (Box-B2 = stim = 1 vs Box-A2 = stim = 0) is the
% ground truth (its fixed effects, rat random-intercept SD, residual SD);
% NREP datasets are simulated at each rats-per-group in NS, for each
% true-effect multiplier in EFFMULS (e.g. [1 0.5] = observed and half the
% observed stim:day slope). Each dataset is scored at alpha = 0.05 two ways:
% - per-animal (cluster-honest): per-rat behavior~day slope, Welch t (stim)
% - LME: the fitlme stim:day interaction p (observation-level DF)
% PW is a table (effMul, N, powerPerAnimal, powerLME); also printed. A fixed
% RNG seed makes the estimate reproducible.
%
% Defaults: PHASE=[0 5], NS=[3 5 8 12 16 24 30], EFFMULS=[1 0.5], NREP=200.
% Defaults: WINDOW=[0 5] (early phase), NS=[3 5 8 12 16 24 30],
% EFFMULS=[1 0.5], NREP=200.
if nargin < 1 || isempty(mergeKey); mergeKey = 'mergeA2'; end
if nargin < 2 || isempty(phase); phase = [0 5]; end
if nargin < 2 || isempty(window); window = [0 5]; end
if nargin < 3 || isempty(Ns); Ns = [3 5 8 12 16 24 30]; end
if nargin < 4 || isempty(effMuls); effMuls = [1 0.5]; end
if nargin < 5 || isempty(nrep); nrep = 200; end
if nargin < 6 || isempty(cfg); cfg = tdcs_config(); end
warnState = warning('off', 'all'); cleanupW = onCleanup(@() warning(warnState)); %#ok<NASGU>
rng(1); % reproducible
rng(1);
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
% Ground truth = fitted phased LME.
% Ground truth = fitted paper model over the window.
Sfull = tdcs_scenario_data([mergeKey '_full']);
T = Sfull(ismember(Sfull.group, {cfg.anchorLow, cfg.anchorHigh}), :);
Tp = T(T.day >= phase(1) & T.day <= phase(2), :);
Tp.dayp = Tp.day - phase(1);
Tp.tDCS = double(Tp.group == cfg.anchorHigh);
lme = fitlme(Tp, 'success ~ dayp*tDCS + (1|subject)');
A = Sfull(ismember(Sfull.group, {cfg.anchorLow, cfg.anchorHigh}), :);
A = A(A.day >= window(1) & A.day <= window(2), :);
tbl0 = table();
tbl0.behavior = A.success;
tbl0.day = A.day - window(1); % 0-based within the window
tbl0.stim = double(A.group == cfg.anchorHigh);
tbl0.rat = A.subject;
lme = fitlme(tbl0, FORMULA);
cn = lme.CoefficientNames; be = lme.fixedEffects;
b0 = be(strcmp(cn,'(Intercept)')); bDay = be(strcmp(cn,'dayp'));
bT = be(strcmp(cn,'tDCS')); bInt = be(strcmp(cn,'dayp:tDCS'));
psi = covarianceParameters(lme); sSub = sqrt(psi{1}); sRes = sqrt(lme.MSE);
days = (phase(1):phase(2))' - phase(1);
b0 = be(strcmp(cn,'(Intercept)')); bStim = be(strcmp(cn,'stim'));
bDay = be(strcmp(cn,'day')); bInt = be(strcmp(cn,'day:stim'));
psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
days = (window(1):window(2))' - window(1);
fprintf('Power sim: truth=%s phase %d-%d | interaction=%.2f subjSD=%.2f resSD=%.2f | nrep=%d\n', ...
mergeKey, phase(1), phase(2), bInt, sSub, sRes, nrep);
fprintf('Power sim (paper formula): truth=%s window %d-%d | stim:day=%.2f ratSD=%.2f resSD=%.2f | nrep=%d\n', ...
mergeKey, window(1), window(2), bInt, sRat, sRes, nrep);
rows = {};
for eMul = effMuls
bI = bInt * eMul;
fprintf('\n true interaction = %+.2f (%.0f%% of observed)\n', bI, eMul*100);
fprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul*100);
fprintf(' %-8s | per-animal power | LME power\n', 'N/group');
for N = Ns
sigPA = 0; sigL = 0;
for r = 1:nrep
tbl = localSim(N, days, b0, bDay, bT, bI, sSub, sRes);
tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
sigPA = sigPA + (localPerAnimalP(tbl) < 0.05);
try
m = fitlme(tbl, 'success ~ dayp*tDCS + (1|subject)');
m = fitlme(tbl, FORMULA);
Cm = m.Coefficients;
sigL = sigL + (Cm.pValue(strcmp(Cm.Name,'dayp:tDCS')) < 0.05);
sigL = sigL + (Cm.pValue(strcmp(Cm.Name,'day:stim')) < 0.05);
catch
end
end
@@ -67,31 +71,31 @@ PW = cell2table(rows, 'VariableNames', {'effMul','N','powerPerAnimal','powerLME'
end
function tbl = localSim(N, days, b0, bDay, bT, bI, sSub, sRes)
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
nd = numel(days); rows = 2*N*nd;
subj = strings(rows,1); dayp = zeros(rows,1); tDCS = zeros(rows,1); success = zeros(rows,1);
rat = strings(rows,1); day = zeros(rows,1); stim = zeros(rows,1); behavior = zeros(rows,1);
k = 0;
for g = 0:1
for sIdx = 1:N
re = sSub * randn;
sid = sprintf('g%d_s%d', g, sIdx);
re = sRat * randn;
rid = sprintf('g%d_r%d', g, sIdx);
for d = 1:nd
k = k+1;
subj(k) = sid; dayp(k) = days(d); tDCS(k) = g;
success(k) = b0 + bDay*days(d) + bT*g + bI*days(d)*g + re + sRes*randn;
rat(k) = rid; day(k) = days(d); stim(k) = g;
behavior(k) = b0 + bStim*g + bDay*days(d) + bI*days(d)*g + re + sRes*randn;
end
end
end
tbl = table(categorical(subj), dayp, tDCS, success, ...
'VariableNames', {'subject','dayp','tDCS','success'});
tbl = table(categorical(rat), day, stim, behavior, ...
'VariableNames', {'rat','day','stim','behavior'});
end
function p = localPerAnimalP(tbl)
subs = unique(tbl.subject); sl = zeros(numel(subs),1); gr = zeros(numel(subs),1);
for i = 1:numel(subs)
r = tbl.subject == subs(i);
c = polyfit(tbl.dayp(r), tbl.success(r), 1); sl(i) = c(1);
gr(i) = tbl.tDCS(find(r,1));
rats = unique(tbl.rat); sl = zeros(numel(rats),1); gr = zeros(numel(rats),1);
for i = 1:numel(rats)
r = tbl.rat == rats(i);
c = polyfit(tbl.day(r), tbl.behavior(r), 1); sl(i) = c(1);
gr(i) = tbl.stim(find(r,1));
end
[~, p] = ttest2(sl(gr==1), sl(gr==0), 'Vartype', 'unequal');
end