analysis(matlab): add log(day) model + Cohen's f + power to every variation
Add variation_logpower.m (self-contained) + make_variation_logpower.m, dropping logpowersim.m + logpower_result.txt into all 28 variation folders. Matches the paper's power code: models behavior ~ stim + log(day) + stim:log(day) + (1|rat) (log(day+1), since our day 0 = paper Day 1), reports Cohen's f (partial eta^2 of the interaction) and the interaction under residual/Satterthwaite/honest random-slope DF, then runs the Monte-Carlo power sim on the log-day ground truth (per-animal cluster-honest + LME power). Notable: under log(day) the accumulating divergence is captured more sharply, so several full-window scenarios reach honest significance that were n.s. under raw day (e.g. right_only_d0_13 honest p=0.003, unmerge_d0_13 0.021, unmerge_d0_10 0.036); Cohen's f is small-medium (~0.10-0.34). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Experiments DB Dev
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function make_variation_logpower()
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%MAKE_VARIATION_LOGPOWER Add the log-day model + Cohen's f + power to each variation.
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% MAKE_VARIATION_LOGPOWER() copies variation_logpower.m into every
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% variations/<name>/ folder with a data.csv (as logpowersim.m) and runs it,
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% producing logpower_result.txt. Re-runnable; picks up new folders.
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thisDir = fileparts(mfilename('fullpath'));
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template = fullfile(thisDir, 'variation_logpower.m');
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root = fullfile(thisDir, 'variations');
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d = dir(root);
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n = 0;
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for i = 1:numel(d)
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if ~d(i).isdir || ismember(d(i).name, {'.', '..'}); continue; end
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folder = fullfile(root, d(i).name);
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if ~exist(fullfile(folder, 'data.csv'), 'file'); continue; end
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copyfile(template, fullfile(folder, 'logpowersim.m'));
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fprintf('\n### %s ###\n', d(i).name);
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localRun(fullfile(folder, 'logpowersim.m'));
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n = n + 1;
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end
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fprintf('\nAdded logpowersim.m + logpower_result.txt to %d variation folders.\n', n);
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end
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function localRun(scriptPath)
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run(scriptPath);
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end
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@@ -0,0 +1,148 @@
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% Variation log-day analysis + Cohen's f + power simulation.
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%
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% The paper's power code models behavior against LOG training day, not raw day:
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% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
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% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
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% "Day 1"), so log(day + 1) reproduces their transform exactly.
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%
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% This script (a) refits that log-day model on data.csv, (b) reports the
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% interaction (residual DF, Satterthwaite DF, and the honest per-animal
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% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
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% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
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% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
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% scoring per-animal (cluster-honest) and LME power across N.
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% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
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% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
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here = fileparts(mfilename('fullpath'));
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if isempty(here); here = pwd; end
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vname = regexprep(here, '.*[/\\]', '');
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D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
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FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
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NS = [3 5 8 12 16 24];
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EFFMULS = [1 0.5];
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NREP = 120;
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warnState = warning('off', 'all');
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rng(1);
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logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
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tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
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'VariableNames', {'behavior', 'day', 'stim', 'rat'});
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nStim = numel(unique(D.subject(D.stim == 1)));
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nCtrl = numel(unique(D.subject(D.stim == 0)));
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bar = repmat('=', 1, 78);
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s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
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s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
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s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
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s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
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if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
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s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
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localFinish(s, here); warning(warnState); return
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end
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% ---- fitted model on the real data ----
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full = fitlme(tbl0, FORMULA);
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An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
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ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
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reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
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eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
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cohenf = sqrt(eta2part / (1 - eta2part));
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% honest per-animal random-slope interaction
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rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
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try
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mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
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Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
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rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
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catch
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end
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if cohenf < 0.10; mag = 'small';
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elseif cohenf < 0.25; mag = 'small-medium';
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elseif cohenf < 0.40; mag = 'medium';
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else; mag = 'large'; end
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s = [s sprintf('\n--- fitted on real data ---\n')];
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s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
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An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
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if rsOk
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s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
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else
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s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
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end
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s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
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eta2part, cohenf, mag)];
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% ---- power simulation on the log-day ground truth ----
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cn = full.CoefficientNames; be = full.fixedEffects;
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b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
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bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
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psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
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days = unique(tbl0.day); % the log(day) grid
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s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
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bInt, sRat, sRes)];
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for eMul = EFFMULS
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bI = bInt * eMul;
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s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
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s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
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for N = NS
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sigPA = 0; sigL = 0;
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for r = 1:NREP
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tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
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sigPA = sigPA + (localPAp(tb) < 0.05);
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try
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m = fitlme(tb, FORMULA); Cm = m.Coefficients;
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sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
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catch
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end
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end
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star = '';
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if N == nStim || N == nCtrl; star = ' <- observed'; end
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s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
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end
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end
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s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
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'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
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localFinish(s, here);
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warning(warnState);
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% ---------------------------------------------------------------- helpers
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function localFinish(s, here)
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fprintf('%s', s);
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fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
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fprintf(fid, '%s', s); fclose(fid);
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end
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function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
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nd = numel(days); rows = 2 * N * nd;
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rat = strings(rows, 1); day = zeros(rows, 1); stim = zeros(rows, 1); behavior = zeros(rows, 1);
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k = 0;
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for g = 0:1
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for sIdx = 1:N
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re = sRat * randn;
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rid = sprintf('g%d_r%d', g, sIdx);
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for d = 1:nd
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k = k + 1;
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rat(k) = rid; day(k) = days(d); stim(k) = g;
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behavior(k) = b0 + bStim * g + bDay * days(d) + bI * days(d) * g + re + sRes * randn;
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end
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end
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end
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tbl = table(categorical(rat), day, stim, behavior, ...
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'VariableNames', {'rat', 'day', 'stim', 'behavior'});
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end
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function p = localPAp(tbl)
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rats = unique(tbl.rat); sl = zeros(numel(rats), 1); gr = zeros(numel(rats), 1);
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for i = 1:numel(rats)
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r = tbl.rat == rats(i);
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c = polyfit(tbl.day(r), tbl.behavior(r), 1); sl(i) = c(1);
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gr(i) = tbl.stim(find(r, 1));
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end
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[~, p] = ttest2(sl(gr == 1), sl(gr == 0), 'Vartype', 'unequal');
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end
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@@ -0,0 +1,36 @@
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==============================================================================
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LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d0_10
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==============================================================================
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model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
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log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
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observed groups: stim n=3, control n=4 nrep=120, alpha=0.05
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--- fitted on real data ---
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stim x log(day) interaction: F(1,68)=3.821 p(resid)=0.05474 p(Satt)=0.05451 (df=72)
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honest per-animal random slope (log-day): F(1,14.6)=3.22 p=0.09348
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Cohen's f (interaction, partial eta^2=0.009) = 0.097 (small; f: .10 small, .25 medium, .40 large)
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--- power simulation (log-day ground truth: stim:logday=+8.69, ratSD=0.00, resSD=13.42) ---
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true stim:log(day) = +8.69 (100% of observed)
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N/group | per-animal power | LME power
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------------------------------------------
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3 | 0.23 | 0.47 <- observed
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5 | 0.57 | 0.72
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8 | 0.83 | 0.87
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12 | 0.98 | 0.99
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16 | 0.99 | 1.00
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24 | 1.00 | 1.00
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true stim:log(day) = +4.34 (50% of observed)
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N/group | per-animal power | LME power
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------------------------------------------
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3 | 0.07 | 0.12 <- observed
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5 | 0.11 | 0.19
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8 | 0.30 | 0.38
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12 | 0.34 | 0.40
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16 | 0.62 | 0.64
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24 | 0.75 | 0.78
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Read the per-animal column as the honest power; the LME column matches the
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paper's power code (anova interaction p, observation-level DF) and is optimistic.
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@@ -0,0 +1,148 @@
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% Variation log-day analysis + Cohen's f + power simulation.
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%
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% The paper's power code models behavior against LOG training day, not raw day:
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% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
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% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
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% "Day 1"), so log(day + 1) reproduces their transform exactly.
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%
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% This script (a) refits that log-day model on data.csv, (b) reports the
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% interaction (residual DF, Satterthwaite DF, and the honest per-animal
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% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
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% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
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% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
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% scoring per-animal (cluster-honest) and LME power across N.
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% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
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% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
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here = fileparts(mfilename('fullpath'));
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if isempty(here); here = pwd; end
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vname = regexprep(here, '.*[/\\]', '');
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D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
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FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
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NS = [3 5 8 12 16 24];
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EFFMULS = [1 0.5];
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NREP = 120;
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warnState = warning('off', 'all');
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rng(1);
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logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
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tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
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'VariableNames', {'behavior', 'day', 'stim', 'rat'});
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nStim = numel(unique(D.subject(D.stim == 1)));
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nCtrl = numel(unique(D.subject(D.stim == 0)));
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bar = repmat('=', 1, 78);
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s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
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s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
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s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
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s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
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if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
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s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
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localFinish(s, here); warning(warnState); return
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end
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% ---- fitted model on the real data ----
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full = fitlme(tbl0, FORMULA);
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An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
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ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
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reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
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eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
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cohenf = sqrt(eta2part / (1 - eta2part));
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% honest per-animal random-slope interaction
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rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
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try
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mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
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Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
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rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
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catch
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end
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if cohenf < 0.10; mag = 'small';
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elseif cohenf < 0.25; mag = 'small-medium';
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elseif cohenf < 0.40; mag = 'medium';
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else; mag = 'large'; end
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s = [s sprintf('\n--- fitted on real data ---\n')];
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s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
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An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
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if rsOk
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s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
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else
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s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
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end
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s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
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eta2part, cohenf, mag)];
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% ---- power simulation on the log-day ground truth ----
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cn = full.CoefficientNames; be = full.fixedEffects;
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b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
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bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
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psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
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days = unique(tbl0.day); % the log(day) grid
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s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
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bInt, sRat, sRes)];
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for eMul = EFFMULS
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bI = bInt * eMul;
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s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
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s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
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for N = NS
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sigPA = 0; sigL = 0;
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for r = 1:NREP
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tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
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sigPA = sigPA + (localPAp(tb) < 0.05);
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try
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m = fitlme(tb, FORMULA); Cm = m.Coefficients;
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sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
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catch
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end
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end
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star = '';
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if N == nStim || N == nCtrl; star = ' <- observed'; end
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s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
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end
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end
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s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
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'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d0_5
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=4 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,38)=4.320 p(resid)=0.04447 p(Satt)=0.04382 (df=42)
|
||||
honest per-animal random slope (log-day): F(1,10.2)=3.63 p=0.0853
|
||||
Cohen's f (interaction, partial eta^2=0.032) = 0.181 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+15.16, ratSD=0.00, resSD=14.15) ---
|
||||
|
||||
true stim:log(day) = +15.16 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.22 | 0.51 <- observed
|
||||
5 | 0.55 | 0.68
|
||||
8 | 0.81 | 0.85
|
||||
12 | 0.95 | 0.97
|
||||
16 | 1.00 | 0.99
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +7.58 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.08 | 0.23 <- observed
|
||||
5 | 0.17 | 0.23
|
||||
8 | 0.24 | 0.27
|
||||
12 | 0.42 | 0.47
|
||||
16 | 0.55 | 0.58
|
||||
24 | 0.85 | 0.85
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d6_10
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=3 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,26)=0.252 p(resid)=0.6199 p(Satt)=0.6202 (df=24)
|
||||
honest per-animal random slope (log-day): F(1,6.0)=0.19 p=0.6783
|
||||
Cohen's f (interaction, partial eta^2=0.004) = 0.065 (small; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+9.52, ratSD=6.32, resSD=8.30) ---
|
||||
|
||||
true stim:log(day) = +9.52 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.02 | 0.06 <- observed
|
||||
5 | 0.09 | 0.13
|
||||
8 | 0.12 | 0.12
|
||||
12 | 0.11 | 0.17
|
||||
16 | 0.14 | 0.16
|
||||
24 | 0.32 | 0.34
|
||||
|
||||
true stim:log(day) = +4.76 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.06 | 0.10 <- observed
|
||||
5 | 0.07 | 0.07
|
||||
8 | 0.05 | 0.07
|
||||
12 | 0.09 | 0.10
|
||||
16 | 0.09 | 0.12
|
||||
24 | 0.12 | 0.15
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_d0_10
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=4, control n=3 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,68)=5.859 p(resid)=0.01817 p(Satt)=0.0182 (df=67)
|
||||
honest per-animal random slope (log-day): F(1,21.8)=5.17 p=0.03322
|
||||
Cohen's f (interaction, partial eta^2=0.013) = 0.115 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+10.40, ratSD=5.52, resSD=12.53) ---
|
||||
|
||||
true stim:log(day) = +10.40 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.32 | 0.62 <- observed
|
||||
5 | 0.78 | 0.87
|
||||
8 | 0.93 | 0.97
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +5.20 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.10 | 0.20 <- observed
|
||||
5 | 0.17 | 0.31
|
||||
8 | 0.45 | 0.53
|
||||
12 | 0.57 | 0.60
|
||||
16 | 0.86 | 0.85
|
||||
24 | 0.93 | 0.93
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_d0_5
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=4, control n=3 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,38)=3.165 p(resid)=0.08322 p(Satt)=0.08246 (df=42)
|
||||
honest per-animal random slope (log-day): F(1,9.4)=2.59 p=0.1407
|
||||
Cohen's f (interaction, partial eta^2=0.026) = 0.163 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+13.68, ratSD=0.00, resSD=14.91) ---
|
||||
|
||||
true stim:log(day) = +13.68 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.18 | 0.42 <- observed
|
||||
5 | 0.43 | 0.59
|
||||
8 | 0.68 | 0.75
|
||||
12 | 0.81 | 0.88
|
||||
16 | 0.96 | 0.97
|
||||
24 | 0.98 | 0.99
|
||||
|
||||
true stim:log(day) = +6.84 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.14 <- observed
|
||||
5 | 0.12 | 0.19
|
||||
8 | 0.17 | 0.19
|
||||
12 | 0.31 | 0.39
|
||||
16 | 0.43 | 0.49
|
||||
24 | 0.70 | 0.75
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_d6_10
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=4, control n=2 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,26)=0.850 p(resid)=0.365 p(Satt)=0.3657 (df=24)
|
||||
honest per-animal random slope (log-day): F(1,6.0)=0.67 p=0.4431
|
||||
Cohen's f (interaction, partial eta^2=0.014) = 0.118 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+18.32, ratSD=6.21, resSD=8.20) ---
|
||||
|
||||
true stim:log(day) = +18.32 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.06 | 0.17
|
||||
5 | 0.17 | 0.26
|
||||
8 | 0.27 | 0.36
|
||||
12 | 0.41 | 0.42
|
||||
16 | 0.57 | 0.63
|
||||
24 | 0.75 | 0.78
|
||||
|
||||
true stim:log(day) = +9.16 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.11
|
||||
5 | 0.10 | 0.12
|
||||
8 | 0.07 | 0.13
|
||||
12 | 0.16 | 0.15
|
||||
16 | 0.20 | 0.20
|
||||
24 | 0.31 | 0.31
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- matched_current_d0_3
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=3 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,20)=7.043 p(resid)=0.01524 p(Satt)=0.0139 (df=24)
|
||||
honest per-animal random slope (log-day): F(1,18.5)=7.24 p=0.01475
|
||||
Cohen's f (interaction, partial eta^2=0.102) = 0.336 (medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+20.79, ratSD=0.00, resSD=9.99) ---
|
||||
|
||||
true stim:log(day) = +20.79 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.39 | 0.77 <- observed
|
||||
5 | 0.84 | 0.93
|
||||
8 | 0.97 | 0.99
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +10.40 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.11 | 0.23 <- observed
|
||||
5 | 0.33 | 0.47
|
||||
8 | 0.49 | 0.59
|
||||
12 | 0.80 | 0.79
|
||||
16 | 0.79 | 0.84
|
||||
24 | 0.95 | 0.97
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_10
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=7 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,100)=4.633 p(resid)=0.03378 p(Satt)=0.03391 (df=95)
|
||||
honest per-animal random slope (log-day): F(1,7.2)=2.24 p=0.177
|
||||
Cohen's f (interaction, partial eta^2=0.010) = 0.101 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+9.59, ratSD=8.17, resSD=14.84) ---
|
||||
|
||||
true stim:log(day) = +9.59 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.23 | 0.44 <- observed
|
||||
5 | 0.57 | 0.68
|
||||
8 | 0.83 | 0.86
|
||||
12 | 0.97 | 0.98
|
||||
16 | 0.99 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +4.79 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.10 <- observed
|
||||
5 | 0.11 | 0.17
|
||||
8 | 0.30 | 0.37
|
||||
12 | 0.34 | 0.38
|
||||
16 | 0.62 | 0.64
|
||||
24 | 0.75 | 0.78
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_13
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=7 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,120)=6.786 p(resid)=0.01035 p(Satt)=0.01039 (df=116)
|
||||
honest per-animal random slope (log-day): F(1,5.5)=5.06 p=0.06966
|
||||
Cohen's f (interaction, partial eta^2=0.011) = 0.104 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+9.94, ratSD=8.36, resSD=14.40) ---
|
||||
|
||||
true stim:log(day) = +9.94 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.27 | 0.71 <- observed
|
||||
5 | 0.68 | 0.82
|
||||
8 | 0.97 | 0.97
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +4.97 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.12 | 0.25 <- observed
|
||||
5 | 0.27 | 0.31
|
||||
8 | 0.51 | 0.57
|
||||
12 | 0.62 | 0.68
|
||||
16 | 0.80 | 0.85
|
||||
24 | 0.92 | 0.93
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_5
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=7 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,55)=9.364 p(resid)=0.003417 p(Satt)=0.003585 (df=49)
|
||||
honest per-animal random slope (log-day): F(1,13.5)=7.67 p=0.01548
|
||||
Cohen's f (interaction, partial eta^2=0.057) = 0.245 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+19.33, ratSD=9.47, resSD=13.37) ---
|
||||
|
||||
true stim:log(day) = +19.33 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.38 | 0.73 <- observed
|
||||
5 | 0.76 | 0.92
|
||||
8 | 0.98 | 1.00
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +9.67 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.12 | 0.28 <- observed
|
||||
5 | 0.31 | 0.40
|
||||
8 | 0.46 | 0.53
|
||||
12 | 0.67 | 0.68
|
||||
16 | 0.83 | 0.84
|
||||
24 | 0.95 | 0.96
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d6_10
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=6 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,41)=0.662 p(resid)=0.4205 p(Satt)=0.4211 (df=36)
|
||||
honest per-animal random slope (log-day): F(1,9.0)=0.46 p=0.5139
|
||||
Cohen's f (interaction, partial eta^2=0.006) = 0.076 (small; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=-16.93, ratSD=10.99, resSD=10.51) ---
|
||||
|
||||
true stim:log(day) = -16.93 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.12 <- observed
|
||||
5 | 0.07 | 0.07
|
||||
8 | 0.20 | 0.27
|
||||
12 | 0.30 | 0.34
|
||||
16 | 0.34 | 0.36
|
||||
24 | 0.51 | 0.53
|
||||
|
||||
true stim:log(day) = -8.46 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.03 | 0.07 <- observed
|
||||
5 | 0.07 | 0.07
|
||||
8 | 0.08 | 0.11
|
||||
12 | 0.07 | 0.08
|
||||
16 | 0.11 | 0.12
|
||||
24 | 0.13 | 0.14
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d6_13
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=6 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,61)=0.405 p(resid)=0.5267 p(Satt)=0.5268 (df=58)
|
||||
honest per-animal random slope (log-day): F(1,8.2)=0.20 p=0.663
|
||||
Cohen's f (interaction, partial eta^2=0.002) = 0.047 (small; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+9.35, ratSD=9.78, resSD=11.90) ---
|
||||
|
||||
true stim:log(day) = +9.35 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.10 <- observed
|
||||
5 | 0.19 | 0.19
|
||||
8 | 0.17 | 0.19
|
||||
12 | 0.25 | 0.25
|
||||
16 | 0.30 | 0.32
|
||||
24 | 0.42 | 0.42
|
||||
|
||||
true stim:log(day) = +4.68 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.05 | 0.09 <- observed
|
||||
5 | 0.05 | 0.07
|
||||
8 | 0.07 | 0.10
|
||||
12 | 0.11 | 0.09
|
||||
16 | 0.07 | 0.07
|
||||
24 | 0.17 | 0.17
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_10
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,111)=3.705 p(resid)=0.05681 p(Satt)=0.05696 (df=105)
|
||||
honest per-animal random slope (log-day): F(1,8.2)=2.03 p=0.1913
|
||||
Cohen's f (interaction, partial eta^2=0.007) = 0.086 (small; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+8.43, ratSD=8.13, resSD=14.95) ---
|
||||
|
||||
true stim:log(day) = +8.43 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.17 | 0.35 <- observed
|
||||
5 | 0.44 | 0.56
|
||||
8 | 0.68 | 0.69 <- observed
|
||||
12 | 0.91 | 0.92
|
||||
16 | 0.95 | 0.96
|
||||
24 | 0.99 | 1.00
|
||||
|
||||
true stim:log(day) = +4.21 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.07 <- observed
|
||||
5 | 0.08 | 0.14
|
||||
8 | 0.23 | 0.29 <- observed
|
||||
12 | 0.28 | 0.28
|
||||
16 | 0.55 | 0.53
|
||||
24 | 0.63 | 0.62
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_13
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,134)=4.865 p(resid)=0.02911 p(Satt)=0.02918 (df=129)
|
||||
honest per-animal random slope (log-day): F(1,7.9)=3.22 p=0.1109
|
||||
Cohen's f (interaction, partial eta^2=0.007) = 0.083 (small; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+8.27, ratSD=8.74, resSD=14.51) ---
|
||||
|
||||
true stim:log(day) = +8.27 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.21 | 0.55 <- observed
|
||||
5 | 0.51 | 0.65
|
||||
8 | 0.88 | 0.90 <- observed
|
||||
12 | 0.97 | 1.00
|
||||
16 | 0.98 | 0.98
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +4.13 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.08 | 0.17 <- observed
|
||||
5 | 0.19 | 0.27
|
||||
8 | 0.42 | 0.42 <- observed
|
||||
12 | 0.47 | 0.51
|
||||
16 | 0.64 | 0.68
|
||||
24 | 0.76 | 0.78
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_5
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,61)=8.016 p(resid)=0.006273 p(Satt)=0.006505 (df=54)
|
||||
honest per-animal random slope (log-day): F(1,15.5)=6.72 p=0.01995
|
||||
Cohen's f (interaction, partial eta^2=0.048) = 0.223 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+18.30, ratSD=8.82, resSD=13.99) ---
|
||||
|
||||
true stim:log(day) = +18.30 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.33 | 0.66 <- observed
|
||||
5 | 0.73 | 0.82
|
||||
8 | 0.95 | 0.98 <- observed
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +9.15 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.11 | 0.24 <- observed
|
||||
5 | 0.23 | 0.31
|
||||
8 | 0.37 | 0.43 <- observed
|
||||
12 | 0.56 | 0.63
|
||||
16 | 0.74 | 0.78
|
||||
24 | 0.93 | 0.94
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_10
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=7 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,46)=0.613 p(resid)=0.4376 p(Satt)=0.4382 (df=40)
|
||||
honest per-animal random slope (log-day): F(1,10.0)=0.43 p=0.5247
|
||||
Cohen's f (interaction, partial eta^2=0.005) = 0.069 (small; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=-15.28, ratSD=11.24, resSD=10.10) ---
|
||||
|
||||
true stim:log(day) = -15.28 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.06 | 0.08 <- observed
|
||||
5 | 0.07 | 0.07
|
||||
8 | 0.18 | 0.21
|
||||
12 | 0.28 | 0.30
|
||||
16 | 0.30 | 0.35
|
||||
24 | 0.45 | 0.49
|
||||
|
||||
true stim:log(day) = -7.64 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.03 | 0.09 <- observed
|
||||
5 | 0.07 | 0.07
|
||||
8 | 0.07 | 0.11
|
||||
12 | 0.07 | 0.08
|
||||
16 | 0.10 | 0.11
|
||||
24 | 0.13 | 0.14
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_13
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=7 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,69)=0.239 p(resid)=0.6263 p(Satt)=0.6264 (df=65)
|
||||
honest per-animal random slope (log-day): F(1,9.1)=0.07 p=0.8013
|
||||
Cohen's f (interaction, partial eta^2=0.001) = 0.034 (small; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+6.70, ratSD=10.78, resSD=11.38) ---
|
||||
|
||||
true stim:log(day) = +6.70 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.05 | 0.07 <- observed
|
||||
5 | 0.13 | 0.15
|
||||
8 | 0.11 | 0.17
|
||||
12 | 0.15 | 0.12
|
||||
16 | 0.23 | 0.25
|
||||
24 | 0.23 | 0.24
|
||||
|
||||
true stim:log(day) = +3.35 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.05 | 0.08 <- observed
|
||||
5 | 0.04 | 0.07
|
||||
8 | 0.06 | 0.10
|
||||
12 | 0.05 | 0.05
|
||||
16 | 0.06 | 0.07
|
||||
24 | 0.12 | 0.11
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- prev_f_full
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=12, control n=12 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,227)=8.356 p(resid)=0.004218 p(Satt)=0.004253 (df=208)
|
||||
honest per-animal random slope (log-day): F(1,24.3)=3.32 p=0.08079
|
||||
Cohen's f (interaction, partial eta^2=0.012) = 0.112 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+2.39, ratSD=4.33, resSD=4.34) ---
|
||||
|
||||
true stim:log(day) = +2.39 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.15 | 0.28
|
||||
5 | 0.33 | 0.46
|
||||
8 | 0.61 | 0.65
|
||||
12 | 0.82 | 0.83 <- observed
|
||||
16 | 0.92 | 0.94
|
||||
24 | 0.99 | 0.98
|
||||
|
||||
true stim:log(day) = +1.19 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.05 | 0.09
|
||||
5 | 0.12 | 0.17
|
||||
8 | 0.20 | 0.26
|
||||
12 | 0.33 | 0.32 <- observed
|
||||
16 | 0.46 | 0.49
|
||||
24 | 0.46 | 0.49
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d0_10
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=4, control n=3 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,68)=8.420 p(resid)=0.004998 p(Satt)=0.005011 (df=67)
|
||||
honest per-animal random slope (log-day): F(1,22.8)=7.55 p=0.01151
|
||||
Cohen's f (interaction, partial eta^2=0.016) = 0.126 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+11.36, ratSD=4.91, resSD=11.42) ---
|
||||
|
||||
true stim:log(day) = +11.36 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.43 | 0.79 <- observed
|
||||
5 | 0.88 | 0.95
|
||||
8 | 0.98 | 1.00
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +5.68 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.13 | 0.29 <- observed
|
||||
5 | 0.30 | 0.42
|
||||
8 | 0.65 | 0.62
|
||||
12 | 0.78 | 0.82
|
||||
16 | 0.93 | 0.93
|
||||
24 | 0.99 | 0.99
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d0_13
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=4, control n=3 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,80)=13.182 p(resid)=0.0004969 p(Satt)=0.0004951 (df=81)
|
||||
honest per-animal random slope (log-day): F(1,18.9)=11.35 p=0.003237
|
||||
Cohen's f (interaction, partial eta^2=0.017) = 0.131 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+12.18, ratSD=5.40, resSD=10.82) ---
|
||||
|
||||
true stim:log(day) = +12.18 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.68 | 0.97 <- observed
|
||||
5 | 1.00 | 1.00
|
||||
8 | 1.00 | 1.00
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +6.09 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.25 | 0.47 <- observed
|
||||
5 | 0.53 | 0.69
|
||||
8 | 0.84 | 0.87
|
||||
12 | 0.93 | 0.96
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d0_5
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=4, control n=3 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,38)=5.667 p(resid)=0.02241 p(Satt)=0.0219 (df=42)
|
||||
honest per-animal random slope (log-day): F(1,9.0)=4.64 p=0.05966
|
||||
Cohen's f (interaction, partial eta^2=0.036) = 0.194 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+16.20, ratSD=0.00, resSD=13.20) ---
|
||||
|
||||
true stim:log(day) = +16.20 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.33 | 0.65 <- observed
|
||||
5 | 0.66 | 0.78
|
||||
8 | 0.93 | 0.97
|
||||
12 | 0.99 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +8.10 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.09 | 0.24 <- observed
|
||||
5 | 0.21 | 0.29
|
||||
8 | 0.29 | 0.38
|
||||
12 | 0.50 | 0.60
|
||||
16 | 0.69 | 0.69
|
||||
24 | 0.92 | 0.92
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d6_10
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=4, control n=2 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,26)=1.327 p(resid)=0.2599 p(Satt)=0.2608 (df=24)
|
||||
honest per-animal random slope (log-day): F(1,6.0)=0.96 p=0.3641
|
||||
Cohen's f (interaction, partial eta^2=0.021) = 0.145 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+23.04, ratSD=5.69, resSD=8.25) ---
|
||||
|
||||
true stim:log(day) = +23.04 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.28
|
||||
5 | 0.28 | 0.38
|
||||
8 | 0.44 | 0.50
|
||||
12 | 0.61 | 0.61
|
||||
16 | 0.76 | 0.78
|
||||
24 | 0.97 | 0.97
|
||||
|
||||
true stim:log(day) = +11.52 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.13
|
||||
5 | 0.11 | 0.14
|
||||
8 | 0.10 | 0.15
|
||||
12 | 0.19 | 0.23
|
||||
16 | 0.25 | 0.25
|
||||
24 | 0.45 | 0.52
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d6_13
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=4, control n=2 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,38)=5.774 p(resid)=0.02125 p(Satt)=0.02133 (df=37)
|
||||
honest per-animal random slope (log-day): F(1,0.0)=3.19 p=NaN
|
||||
Cohen's f (interaction, partial eta^2=0.036) = 0.194 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+28.42, ratSD=6.48, resSD=7.39) ---
|
||||
|
||||
true stim:log(day) = +28.42 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.57 | 0.86
|
||||
5 | 0.93 | 0.98
|
||||
8 | 1.00 | 1.00
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +14.21 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.17 | 0.30
|
||||
5 | 0.37 | 0.55
|
||||
8 | 0.62 | 0.68
|
||||
12 | 0.81 | 0.85
|
||||
16 | 0.90 | 0.92
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d0_10
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=3 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,57)=6.230 p(resid)=0.01548 p(Satt)=0.01528 (df=61)
|
||||
honest per-animal random slope (log-day): F(1,15.0)=5.28 p=0.0363
|
||||
Cohen's f (interaction, partial eta^2=0.017) = 0.131 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+11.60, ratSD=0.00, resSD=12.93) ---
|
||||
|
||||
true stim:log(day) = +11.60 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.34 | 0.69 <- observed
|
||||
5 | 0.82 | 0.92
|
||||
8 | 0.97 | 0.97
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +5.80 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.10 | 0.25 <- observed
|
||||
5 | 0.26 | 0.40
|
||||
8 | 0.53 | 0.58
|
||||
12 | 0.61 | 0.72
|
||||
16 | 0.90 | 0.90
|
||||
24 | 0.97 | 0.97
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d0_13
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=3 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,66)=8.712 p(resid)=0.004373 p(Satt)=0.004357 (df=67)
|
||||
honest per-animal random slope (log-day): F(1,12.9)=6.96 p=0.02062
|
||||
Cohen's f (interaction, partial eta^2=0.015) = 0.124 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+11.06, ratSD=5.68, resSD=11.36) ---
|
||||
|
||||
true stim:log(day) = +11.06 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.53 | 0.91 <- observed
|
||||
5 | 0.99 | 1.00
|
||||
8 | 1.00 | 1.00
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +5.53 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.18 | 0.38 <- observed
|
||||
5 | 0.41 | 0.54
|
||||
8 | 0.78 | 0.80
|
||||
12 | 0.86 | 0.91
|
||||
16 | 0.97 | 0.98
|
||||
24 | 0.98 | 0.98
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d0_5
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=3 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,32)=4.849 p(resid)=0.03498 p(Satt)=0.03414 (df=36)
|
||||
honest per-animal random slope (log-day): F(1,7.2)=3.65 p=0.09636
|
||||
Cohen's f (interaction, partial eta^2=0.039) = 0.200 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+16.48, ratSD=0.00, resSD=13.58) ---
|
||||
|
||||
true stim:log(day) = +16.48 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.33 | 0.64 <- observed
|
||||
5 | 0.66 | 0.78
|
||||
8 | 0.93 | 0.97
|
||||
12 | 0.99 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +8.24 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.09 | 0.24 <- observed
|
||||
5 | 0.21 | 0.29
|
||||
8 | 0.28 | 0.38
|
||||
12 | 0.47 | 0.59
|
||||
16 | 0.69 | 0.69
|
||||
24 | 0.90 | 0.92
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d6_10
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=2 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,21)=0.581 p(resid)=0.4545 p(Satt)=0.4549 (df=20)
|
||||
honest per-animal random slope (log-day): F(1,5.0)=0.44 p=0.5382
|
||||
Cohen's f (interaction, partial eta^2=0.011) = 0.106 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+16.98, ratSD=6.08, resSD=8.72) ---
|
||||
|
||||
true stim:log(day) = +16.98 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.06 | 0.11 <- observed
|
||||
5 | 0.17 | 0.23
|
||||
8 | 0.22 | 0.31
|
||||
12 | 0.33 | 0.33
|
||||
16 | 0.47 | 0.50
|
||||
24 | 0.63 | 0.66
|
||||
|
||||
true stim:log(day) = +8.49 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.11 <- observed
|
||||
5 | 0.09 | 0.11
|
||||
8 | 0.06 | 0.09
|
||||
12 | 0.12 | 0.14
|
||||
16 | 0.17 | 0.17
|
||||
24 | 0.26 | 0.26
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
@@ -0,0 +1,36 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d6_13
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
|
||||
observed groups: stim n=3, control n=2 nrep=120, alpha=0.05
|
||||
|
||||
--- fitted on real data ---
|
||||
stim x log(day) interaction: F(1,30)=3.007 p(resid)=0.09315 p(Satt)=0.0931 (df=30)
|
||||
honest per-animal random slope (log-day): F(1,12.8)=2.47 p=0.1406
|
||||
Cohen's f (interaction, partial eta^2=0.025) = 0.161 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+22.97, ratSD=7.14, resSD=7.81) ---
|
||||
|
||||
true stim:log(day) = +22.97 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.47 | 0.64 <- observed
|
||||
5 | 0.79 | 0.90
|
||||
8 | 0.96 | 0.99
|
||||
12 | 0.99 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +11.49 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.12 | 0.22 <- observed
|
||||
5 | 0.26 | 0.36
|
||||
8 | 0.41 | 0.47
|
||||
12 | 0.53 | 0.58
|
||||
16 | 0.71 | 0.74
|
||||
24 | 0.90 | 0.93
|
||||
|
||||
Read the per-animal column as the honest power; the LME column matches the
|
||||
paper's power code (anova interaction p, observation-level DF) and is optimistic.
|
||||
@@ -0,0 +1,148 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
|
||||
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
|
||||
% "Day 1"), so log(day + 1) reproduces their transform exactly.
|
||||
%
|
||||
% This script (a) refits that log-day model on data.csv, (b) reports the
|
||||
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
|
||||
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
|
||||
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
|
||||
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
|
||||
% scoring per-animal (cluster-honest) and LME power across N.
|
||||
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
|
||||
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
nStim = numel(unique(D.subject(D.stim == 1)));
|
||||
nCtrl = numel(unique(D.subject(D.stim == 0)));
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
|
||||
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
|
||||
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
|
||||
|
||||
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
|
||||
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
end
|
||||
|
||||
% ---- fitted model on the real data ----
|
||||
full = fitlme(tbl0, FORMULA);
|
||||
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
|
||||
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
|
||||
reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
|
||||
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
|
||||
cohenf = sqrt(eta2part / (1 - eta2part));
|
||||
|
||||
% honest per-animal random-slope interaction
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
try
|
||||
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
|
||||
Ar = anova(mr, 'DFMethod', 'satterthwaite'); ri = strcmp(Ar.Term, 'day:stim');
|
||||
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
|
||||
catch
|
||||
end
|
||||
if cohenf < 0.10; mag = 'small';
|
||||
elseif cohenf < 0.25; mag = 'small-medium';
|
||||
elseif cohenf < 0.40; mag = 'medium';
|
||||
else; mag = 'large'; end
|
||||
|
||||
s = [s sprintf('\n--- fitted on real data ---\n')];
|
||||
s = [s sprintf('stim x log(day) interaction: F(1,%d)=%.3f p(resid)=%.4g p(Satt)=%.4g (df=%.0f)\n', ...
|
||||
An.DF2(ii), An.FStat(ii), An.pValue(ii), Asatt.pValue(is), Asatt.DF2(is))];
|
||||
if rsOk
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): F(1,%.1f)=%.2f p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
s = [s sprintf(' honest per-animal random slope (log-day): did not converge\n')];
|
||||
end
|
||||
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
|
||||
eta2part, cohenf, mag)];
|
||||
|
||||
% ---- power simulation on the log-day ground truth ----
|
||||
cn = full.CoefficientNames; be = full.fixedEffects;
|
||||
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
|
||||
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
|
||||
days = unique(tbl0.day); % the log(day) grid
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
bInt, sRat, sRes)];
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
tb = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
|
||||
sigPA = sigPA + (localPAp(tb) < 0.05);
|
||||
try
|
||||
m = fitlme(tb, FORMULA); Cm = m.Coefficients;
|
||||
sigL = sigL + (Cm.pValue(strcmp(Cm.Name, 'day:stim')) < 0.05);
|
||||
catch
|
||||
end
|
||||
end
|
||||
star = '';
|
||||
if N == nStim || N == nCtrl; star = ' <- observed'; end
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
|
||||
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
warning(warnState);
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
|
||||
nd = numel(days); rows = 2 * N * nd;
|
||||
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 = sRat * randn;
|
||||
rid = sprintf('g%d_r%d', g, sIdx);
|
||||
for d = 1:nd
|
||||
k = k + 1;
|
||||
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(rat), day, stim, behavior, ...
|
||||
'VariableNames', {'rat', 'day', 'stim', 'behavior'});
|
||||
end
|
||||
|
||||
function p = localPAp(tbl)
|
||||
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
|
||||
Reference in New Issue
Block a user