analysis(matlab): rate + count outputs, variation batch 3
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Experiments DB Dev
@@ -1,30 +1,58 @@
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% Variation analysis -- the paper's linear mixed model on the successful-reach
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% COUNT, fit on this folder's curated data subset.
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% Variation analysis -- the paper's linear mixed model on this folder's data,
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% for BOTH metrics:
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% metric = count : behavior = # successes -> result.txt
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% metric = rate : behavior = success / attempts -> result_rate.txt
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% (rate uses only sessions with attempts > 0)
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%
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% model: behavior ~ stim + day + stim:day + (1|rat)
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% behavior = successful reaches (count per session)
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% stim = 1 for the treatment group(s), 0 for the control group(s)
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% day = training day within this window (0 = first analyzed day)
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% rat = subject (random intercept)
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%
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% Self-contained: reads data.csv beside this script and writes result.txt.
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% Run headless from this folder with: matlab -batch "analyze"
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% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
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% stim = 1 treatment / 0 control; day = training day within window (0 =
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% first analyzed day); rat = subject (random intercept).
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% For the interaction we report residual DF, Satterthwaite DF, and the honest
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% per-animal random-slope test. Self-contained: reads data.csv beside this
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% script. Run headless with: matlab -batch "analyze"
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% (Copy of analysis/matlab/variation_analyze.m; see make_variations.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, '.*[/\\]', ''); % folder name = variation id
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vname = regexprep(here, '.*[/\\]', '');
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D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
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tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
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Rc = localAnalyze(D, 'count', here, vname);
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Rr = localAnalyze(D, 'rate', here, vname);
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% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
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VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
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'interP', Rc.interP, 'interEst', Rc.interEst, ...
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'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
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'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
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'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
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% ------------------------------------------------------------------ helper
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function R = localAnalyze(D, metric, here, vname)
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if strcmp(metric, 'rate')
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D = D(D.total > 0, :);
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beh = D.success ./ D.total;
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mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
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else
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beh = D.success;
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mlabel = 'success COUNT'; suffix = '';
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end
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R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
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'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
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'nObs', height(D), 'covEqual', false);
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if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
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localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
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vname, mlabel), here, suffix);
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return
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end
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tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
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'VariableNames', {'behavior', 'day', 'stim', 'rat'});
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m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
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C = m.Coefficients; A = anova(m); ci = coefCI(m);
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As = anova(m, 'DFMethod', 'satterthwaite'); % Satterthwaite denominator DF
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As = anova(m, 'DFMethod', 'satterthwaite');
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% Honest test: refit with a per-animal random SLOPE so the interaction DF
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% collapses toward the animal count (guarded -- may not converge in short windows).
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rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
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wst = warning('off', 'all');
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try
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@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f
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C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
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As.pValue(gs(t)), As.DF2(gs(t)));
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ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
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maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
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maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
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if abs(maxT - maxC) > 2
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@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
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else
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cov = '(equal day coverage over this window)';
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end
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ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
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if pI >= 0.05
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verdict = 'n.s. -- slopes parallel (no differential learning rate)';
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elseif eI > 0
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verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
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else
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verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
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end
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if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
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elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
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else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
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bar = repmat('=', 1, 78);
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raw = regexprep(evalc('disp(m)'), '</?strong>', '');
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s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
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s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)\n')];
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s = sprintf('%s\nVARIATION: %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
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s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = %s)\n', mlabel)];
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s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
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s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
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s = [s sprintf('control (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
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@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
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s = [s row('stim x day (interaction)', 'day:stim')];
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s = [s row('day (learning)', 'day')];
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s = [s row('stim (main, window start)', 'stim')];
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s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
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s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
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if rsOk
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s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
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else
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@@ -82,17 +105,18 @@ else
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end
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s = [s sprintf([' (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
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' model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
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s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
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s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
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s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
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s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
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localWrite(s, here, suffix);
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R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
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'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
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'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
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end
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function localWrite(s, here, suffix)
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fprintf('%s', s);
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fid = fopen(fullfile(here, 'result.txt'), 'w');
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fprintf(fid, '%s', s);
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fclose(fid);
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% Machine-readable handoff for the summary table (see make_variations.m).
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VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
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'nObs', height(D), 'interP', pI, 'interEst', eI, ...
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'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
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'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
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'covEqual', abs(maxT - maxC) <= 2);
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fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
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fprintf(fid, '%s', s); fclose(fid);
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end
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@@ -1,7 +1,7 @@
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==============================================================================
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LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_10
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LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_10 [metric: # successes (count)]
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==============================================================================
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model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
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model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = # successes (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=8 nrep=120, alpha=0.05
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@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,111)=3.705 p(resid)=0.05681 p(Satt)=0.05696 (
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honest per-animal random slope (log-day): F(1,8.2)=2.03 p=0.1913
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Cohen's f (interaction, partial eta^2=0.007) = 0.086 (small; f: .10 small, .25 medium, .40 large)
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--- power simulation (log-day ground truth: stim:logday=+8.43, ratSD=8.13, resSD=14.95) ---
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--- power simulation (log-day ground truth: stim:logday=+8.428, ratSD=8.126, resSD=14.95) ---
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true stim:log(day) = +8.43 (100% of observed)
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true stim:log(day) = +8.428 (100% of observed)
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N/group | per-animal power | LME power
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------------------------------------------
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3 | 0.17 | 0.35 <- observed
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@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.007) = 0.086 (small; f: .10 small, .25
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16 | 0.95 | 0.96
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24 | 0.99 | 1.00
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true stim:log(day) = +4.21 (50% of observed)
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true stim:log(day) = +4.214 (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 | 0.07 <- observed
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@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.007) = 0.086 (small; f: .10 small, .25
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16 | 0.55 | 0.53
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24 | 0.63 | 0.62
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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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Read per-animal as the honest power; LME matches the paper's power code (optimistic).
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@@ -0,0 +1,35 @@
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==============================================================================
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LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_10 [metric: success RATE]
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==============================================================================
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model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = success RATE)
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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=8 nrep=120, alpha=0.05
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--- fitted on real data ---
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stim x log(day) interaction: F(1,109)=4.842 p(resid)=0.02988 p(Satt)=0.03001 (df=103)
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honest per-animal random slope (log-day): F(1,7.9)=3.23 p=0.1106
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Cohen's f (interaction, partial eta^2=0.013) = 0.116 (small-medium; f: .10 small, .25 medium, .40 large)
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--- power simulation (log-day ground truth: stim:logday=+0.06771, ratSD=0.04956, resSD=0.1034) ---
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true stim:log(day) = +0.06771 (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 | 0.71
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8 | 0.85 | 0.87 <- observed
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12 | 0.98 | 0.99
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16 | 1.00 | 1.00
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24 | 1.00 | 1.00
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true stim:log(day) = +0.03385 (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 | 0.12 | 0.17
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8 | 0.32 | 0.39 <- observed
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12 | 0.36 | 0.39
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16 | 0.63 | 0.66
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24 | 0.75 | 0.78
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Read per-animal as the honest power; LME matches the paper's power code (optimistic).
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@@ -1,50 +1,52 @@
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% Variation log-day analysis + Cohen's f + power simulation.
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% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
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% metric = count : behavior = # successes -> logpower_result.txt
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% metric = rate : behavior = success / attempts -> logpower_result_rate.txt
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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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% The paper's power code models behavior against LOG training 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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% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
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% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
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% refits that model, reports the interaction (residual / Satterthwaite / honest
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% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
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% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
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% 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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localLogPower(D, 'count', here, vname);
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localLogPower(D, 'rate', here, vname);
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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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% ---------------------------------------------------------------- per metric
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function localLogPower(D, metric, here, vname)
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NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
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FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' = log(day+1)
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if strcmp(metric, 'rate')
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D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
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else
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beh = D.success; mlabel = '# successes (count)'; suffix = '';
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end
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warnState = warning('off', 'all'); rng(1);
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logday = log(D.day + 1);
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tbl0 = table(beh, 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 = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
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s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = %s)\n', mlabel)];
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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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s = [s sprintf('\nInsufficient data for this analysis.\n')];
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localFinish(s, here, suffix); 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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@@ -52,7 +54,6 @@ 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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@@ -76,19 +77,18 @@ 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', ...
|
||||
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
|
||||
days = unique(tbl0.day);
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\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>
|
||||
s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -102,19 +102,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
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);
|
||||
s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,11 +1,12 @@
|
||||
% Variation learning-curve plot, in the style of the paper:
|
||||
% Variation learning-curve plots, in the style of the paper:
|
||||
% "Lines indicate mean (and SEM) across animals in the anodal (red) and
|
||||
% control (blue) groups."
|
||||
% Plots mean +/- SEM successful reaches per training day for the treatment /
|
||||
% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
|
||||
% this folder's data.csv and saving learning_curve.png. The per-group N is read
|
||||
% from the data (each variation pools different groups), so the legend shows the
|
||||
% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
|
||||
% Produces TWO figures from this folder's data.csv:
|
||||
% learning_curve.png # successes (count) per training day
|
||||
% learning_curve_rate.png success rate (success/attempts) per training day
|
||||
% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
|
||||
% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
|
||||
% the x-axis tick labels are drawn vertically.
|
||||
% Run: matlab -batch "plotcurve"
|
||||
% (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
|
||||
|
||||
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
days = unique(D.day); % 0-indexed
|
||||
days = unique(D.day);
|
||||
xd = days + 1; % plot as 1-indexed training day (paper axis)
|
||||
|
||||
red = [0.85 0.10 0.10];
|
||||
blue = [0.10 0.30 0.85];
|
||||
|
||||
[Ma, Sa, na] = localCurve(D, 1, days); % anodal / treatment (stim = 1)
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days); % control (stim = 0)
|
||||
localPlot(D, days, xd, 'count', '# successes', ...
|
||||
fullfile(here, 'learning_curve.png'), vname, red, blue);
|
||||
localPlot(D, days, xd, 'rate', 'success rate', ...
|
||||
fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
|
||||
|
||||
% ------------------------------------------------------------------ helpers
|
||||
function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
|
||||
[Ma, Sa, na] = localCurve(D, 1, days, metric); % anodal / treatment
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days, metric); % control
|
||||
fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
|
||||
hold on
|
||||
e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
|
||||
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
|
||||
hold off
|
||||
legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
|
||||
'Location', 'northwest', 'Box', 'off');
|
||||
xlabel('training day');
|
||||
ylabel('# successes');
|
||||
xlabel('training day'); ylabel(ylab);
|
||||
title(vname, 'Interpreter', 'none');
|
||||
set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
|
||||
|
||||
outFile = fullfile(here, 'learning_curve.png');
|
||||
xtickangle(90); % vertical x-axis tick labels
|
||||
exportgraphics(fig, outFile, 'Resolution', 150);
|
||||
close(fig);
|
||||
fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
|
||||
fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
|
||||
end
|
||||
|
||||
% ------------------------------------------------------------------ helper
|
||||
function [M, S, n] = localCurve(D, stimVal, days)
|
||||
%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
|
||||
function [M, S, n] = localCurve(D, stimVal, days, metric)
|
||||
%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
|
||||
subs = unique(D.subject(D.stim == stimVal));
|
||||
n = numel(subs);
|
||||
X = nan(numel(days), n);
|
||||
for j = 1:n
|
||||
for i = 1:numel(days)
|
||||
r = D.subject == subs(j) & D.day == days(i);
|
||||
if any(r); X(i, j) = mean(D.success(r)); end
|
||||
if ~any(r); continue; end
|
||||
if strcmp(metric, 'rate')
|
||||
tot = sum(D.total(r));
|
||||
if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
|
||||
else
|
||||
X(i, j) = mean(D.success(r));
|
||||
end
|
||||
end
|
||||
end
|
||||
M = mean(X, 2, 'omitnan');
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- naive_boxa_d0_10
|
||||
POWER SIMULATION -- naive_boxa_d0_10 [metric: # successes (count)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (success COUNT; day within-window)
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = # successes (count); day within-window)
|
||||
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=+1.18/day, rat SD=8.67, residual SD=13.71, days=11
|
||||
ground truth: stim:day=+1.178/day, rat SD=8.671, residual SD=13.71, days=11
|
||||
|
||||
true stim:day interaction = +1.18 (100% of observed)
|
||||
true stim:day interaction = +1.178 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.17 <- observed
|
||||
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.18/day, rat SD=8.67, residual SD=13.71, days=11
|
||||
16 | 0.68 | 0.70
|
||||
24 | 0.88 | 0.88
|
||||
|
||||
true stim:day interaction = +0.59 (50% of observed)
|
||||
true stim:day interaction = +0.589 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.05 | 0.07 <- observed
|
||||
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.18/day, rat SD=8.67, residual SD=13.71, days=11
|
||||
16 | 0.28 | 0.27
|
||||
24 | 0.28 | 0.29
|
||||
|
||||
Read the per-animal column as the honest power. At the observed N this study
|
||||
is typically underpowered; per-animal power reaches ~0.8 only at larger N.
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
|
||||
@@ -0,0 +1,28 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- naive_boxa_d0_10 [metric: success RATE]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE; day within-window)
|
||||
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=+0.008893/day, rat SD=0.05104, residual SD=0.09808, days=11
|
||||
|
||||
true stim:day interaction = +0.008893 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.08 | 0.16 <- observed
|
||||
5 | 0.21 | 0.25
|
||||
8 | 0.36 | 0.43 <- observed
|
||||
12 | 0.62 | 0.68
|
||||
16 | 0.72 | 0.73
|
||||
24 | 0.88 | 0.88
|
||||
|
||||
true stim:day interaction = +0.004446 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.08 <- observed
|
||||
5 | 0.05 | 0.08
|
||||
8 | 0.11 | 0.11 <- observed
|
||||
12 | 0.17 | 0.19
|
||||
16 | 0.30 | 0.29
|
||||
24 | 0.33 | 0.35
|
||||
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
@@ -1,45 +1,49 @@
|
||||
% Variation power simulation -- Monte-Carlo power for the paper's stim x day
|
||||
% interaction, using THIS folder's data as the ground truth.
|
||||
% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> power_result.txt
|
||||
% metric = rate : behavior = success / attempts -> power_result_rate.txt
|
||||
%
|
||||
% Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
|
||||
% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
|
||||
% and residual SD generate NREP synthetic datasets at each rats-per-group N and
|
||||
% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
|
||||
% scored at alpha = 0.05 two ways:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
|
||||
% honest power, matching the random-slope / per-animal inference)
|
||||
% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
|
||||
% generate NREP synthetic datasets at each rats-per-group N and each true-effect
|
||||
% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
|
||||
% LME : the fitlme stim:day p (observation-level DF -- optimistic)
|
||||
% Writes power_result.txt beside this script. Run: matlab -batch "powersim"
|
||||
% Writes power_result[_rate].txt. Run: matlab -batch "powersim"
|
||||
% (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
|
||||
localPower(D, 'count', here, vname);
|
||||
localPower(D, 'rate', here, vname);
|
||||
|
||||
% ---------------------------------------------------------------- per metric
|
||||
function localPower(D, metric, here, vname)
|
||||
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success; mlabel = '# successes (count)'; suffix = '';
|
||||
end
|
||||
warnState = warning('off', 'all'); rng(1);
|
||||
day0 = min(D.day);
|
||||
tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
|
||||
tbl0 = table(beh, D.day - day0, 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\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: %s (success COUNT; day within-window)\n', FORMULA)];
|
||||
s = sprintf('%s\nPOWER SIMULATION -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: %s (behavior = %s; day within-window)\n', FORMULA, mlabel)];
|
||||
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 a power simulation (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
s = [s sprintf('\nInsufficient data for a power simulation.\n')];
|
||||
localFinish(s, here, suffix); warning(warnState); return
|
||||
end
|
||||
|
||||
lme = fitlme(tbl0, FORMULA);
|
||||
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
|
||||
days = (0:max(tbl0.day))';
|
||||
|
||||
s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
|
||||
s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
|
||||
bInt, sRat, sRes, numel(days))];
|
||||
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
|
||||
s = [s sprintf(' %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -70,20 +73,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
end
|
||||
end
|
||||
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
|
||||
'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'power_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
==============================================================================
|
||||
VARIATION: naive_boxa_d0_10
|
||||
VARIATION: naive_boxa_d0_10 [metric: success COUNT]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
|
||||
day = training day within window (0 = first analyzed day)
|
||||
@@ -60,9 +60,9 @@ effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
stim x day (interaction) t(111)= 1.31 F(1)= 1.707 p=0.1941 p=0.1943 (df=104)
|
||||
day (learning) t(111)= 16.91 F(1)=285.824 p=1.743e-32 p=6.758e-32 (df=106)
|
||||
stim (main, window start) t(111)= 1.55 F(1)= 2.392 p=0.1248 p=0.1361 (df=22)
|
||||
interaction 95% CI: [-0.61, +2.96]
|
||||
interaction 95% CI: [-0.6088, +2.965]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,10.1)=0.83, p=0.3836
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1941, slope diff=+1.18)
|
||||
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1941, slope diff=+1.178)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
|
||||
@@ -0,0 +1,68 @@
|
||||
==============================================================================
|
||||
VARIATION: naive_boxa_d0_10 [metric: success RATE (success/attempts)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE (success/attempts))
|
||||
day = training day within window (0 = first analyzed day)
|
||||
treatment (stim=1): Electrode-Box-B2
|
||||
control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
|
||||
N = 11 rats, 113 sessions raw day coverage: treat 0..10, control 0..10
|
||||
(equal day coverage over this window)
|
||||
|
||||
==============================================================================
|
||||
FULL MODEL SUMMARY -- fitlme
|
||||
==============================================================================
|
||||
|
||||
Linear mixed-effects model fit by ML
|
||||
|
||||
Model information:
|
||||
Number of observations 113
|
||||
Fixed effects coefficients 4
|
||||
Random effects coefficients 11
|
||||
Covariance parameters 2
|
||||
|
||||
Formula:
|
||||
behavior ~ 1 + day*stim + (1 | rat)
|
||||
|
||||
Model fit statistics:
|
||||
AIC BIC LogLikelihood Deviance
|
||||
-177.53 -161.16 94.764 -189.53
|
||||
|
||||
Fixed effects coefficients (95% CIs):
|
||||
Name Estimate SE tStat DF pValue
|
||||
{'(Intercept)'} 0.19077 0.027729 6.8796 109 3.9606e-10
|
||||
{'day' } 0.044032 0.0036365 12.108 109 6.709e-22
|
||||
{'stim' } 0.064146 0.051551 1.2443 109 0.21605
|
||||
{'day:stim' } 0.0088928 0.0065096 1.3661 109 0.17471
|
||||
|
||||
|
||||
Lower Upper
|
||||
0.13581 0.24572
|
||||
0.036824 0.051239
|
||||
-0.038027 0.16632
|
||||
-0.0040089 0.021795
|
||||
|
||||
Random effects covariance parameters (95% CIs):
|
||||
Group: rat (11 Levels)
|
||||
Name1 Name2 Type Estimate
|
||||
{'(Intercept)'} {'(Intercept)'} {'std'} 0.05104
|
||||
|
||||
|
||||
Lower Upper
|
||||
0.028096 0.092721
|
||||
|
||||
Group: Error
|
||||
Name Estimate Lower Upper
|
||||
{'Res Std'} 0.09808 0.085453 0.11257
|
||||
|
||||
|
||||
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
----------------------------------------------------------------------------
|
||||
stim x day (interaction) t(109)= 1.37 F(1)= 1.866 p=0.1747 p=0.1749 (df=103)
|
||||
day (learning) t(109)= 12.11 F(1)=146.612 p=6.709e-22 p=1.131e-21 (df=105)
|
||||
stim (main, window start) t(109)= 1.24 F(1)= 1.548 p=0.2161 p=0.2244 (df=26)
|
||||
interaction 95% CI: [-0.004009, +0.02179]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,9.6)=1.38, p=0.2681
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1747, slope diff=+0.008893)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
@@ -1,30 +1,58 @@
|
||||
% Variation analysis -- the paper's linear mixed model on the successful-reach
|
||||
% COUNT, fit on this folder's curated data subset.
|
||||
% Variation analysis -- the paper's linear mixed model on this folder's data,
|
||||
% for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> result.txt
|
||||
% metric = rate : behavior = success / attempts -> result_rate.txt
|
||||
% (rate uses only sessions with attempts > 0)
|
||||
%
|
||||
% model: behavior ~ stim + day + stim:day + (1|rat)
|
||||
% behavior = successful reaches (count per session)
|
||||
% stim = 1 for the treatment group(s), 0 for the control group(s)
|
||||
% day = training day within this window (0 = first analyzed day)
|
||||
% rat = subject (random intercept)
|
||||
%
|
||||
% Self-contained: reads data.csv beside this script and writes result.txt.
|
||||
% Run headless from this folder with: matlab -batch "analyze"
|
||||
% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
|
||||
% stim = 1 treatment / 0 control; day = training day within window (0 =
|
||||
% first analyzed day); rat = subject (random intercept).
|
||||
% For the interaction we report residual DF, Satterthwaite DF, and the honest
|
||||
% per-animal random-slope test. Self-contained: reads data.csv beside this
|
||||
% script. Run headless with: matlab -batch "analyze"
|
||||
% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
|
||||
tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
|
||||
Rc = localAnalyze(D, 'count', here, vname);
|
||||
Rr = localAnalyze(D, 'rate', here, vname);
|
||||
|
||||
% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
|
||||
VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
|
||||
'interP', Rc.interP, 'interEst', Rc.interEst, ...
|
||||
'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
|
||||
'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
|
||||
'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
|
||||
|
||||
% ------------------------------------------------------------------ helper
|
||||
function R = localAnalyze(D, metric, here, vname)
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :);
|
||||
beh = D.success ./ D.total;
|
||||
mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success;
|
||||
mlabel = 'success COUNT'; suffix = '';
|
||||
end
|
||||
R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
|
||||
'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
|
||||
'nObs', height(D), 'covEqual', false);
|
||||
if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
|
||||
localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
|
||||
vname, mlabel), here, suffix);
|
||||
return
|
||||
end
|
||||
|
||||
tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
|
||||
C = m.Coefficients; A = anova(m); ci = coefCI(m);
|
||||
As = anova(m, 'DFMethod', 'satterthwaite'); % Satterthwaite denominator DF
|
||||
As = anova(m, 'DFMethod', 'satterthwaite');
|
||||
|
||||
% Honest test: refit with a per-animal random SLOPE so the interaction DF
|
||||
% collapses toward the animal count (guarded -- may not converge in short windows).
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
wst = warning('off', 'all');
|
||||
try
|
||||
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f
|
||||
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
|
||||
As.pValue(gs(t)), As.DF2(gs(t)));
|
||||
|
||||
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
|
||||
maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
|
||||
maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
|
||||
if abs(maxT - maxC) > 2
|
||||
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
|
||||
else
|
||||
cov = '(equal day coverage over this window)';
|
||||
end
|
||||
|
||||
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
|
||||
if pI >= 0.05
|
||||
verdict = 'n.s. -- slopes parallel (no differential learning rate)';
|
||||
elseif eI > 0
|
||||
verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
|
||||
else
|
||||
verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
|
||||
end
|
||||
if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
|
||||
elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
|
||||
else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
raw = regexprep(evalc('disp(m)'), '</?strong>', '');
|
||||
s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)\n')];
|
||||
s = sprintf('%s\nVARIATION: %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = %s)\n', mlabel)];
|
||||
s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
|
||||
s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
|
||||
s = [s sprintf('control (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
|
||||
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
|
||||
s = [s row('stim x day (interaction)', 'day:stim')];
|
||||
s = [s row('day (learning)', 'day')];
|
||||
s = [s row('stim (main, window start)', 'stim')];
|
||||
s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
|
||||
s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
|
||||
if rsOk
|
||||
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
@@ -82,17 +105,18 @@ else
|
||||
end
|
||||
s = [s sprintf([' (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
|
||||
' model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
|
||||
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
|
||||
s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
|
||||
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
|
||||
s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
|
||||
|
||||
localWrite(s, here, suffix);
|
||||
|
||||
R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
|
||||
'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
|
||||
'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
|
||||
end
|
||||
|
||||
function localWrite(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'result.txt'), 'w');
|
||||
fprintf(fid, '%s', s);
|
||||
fclose(fid);
|
||||
|
||||
% Machine-readable handoff for the summary table (see make_variations.m).
|
||||
VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
|
||||
'nObs', height(D), 'interP', pI, 'interEst', eI, ...
|
||||
'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
|
||||
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
|
||||
'covEqual', abs(maxT - maxC) <= 2);
|
||||
fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 43 KiB After Width: | Height: | Size: 43 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 41 KiB |
@@ -1,7 +1,7 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_13
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_13 [metric: # successes (count)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = # successes (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
|
||||
|
||||
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,134)=4.865 p(resid)=0.02911 p(Satt)=0.02918 (
|
||||
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) ---
|
||||
--- power simulation (log-day ground truth: stim:logday=+8.265, ratSD=8.74, resSD=14.51) ---
|
||||
|
||||
true stim:log(day) = +8.27 (100% of observed)
|
||||
true stim:log(day) = +8.265 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.21 | 0.55 <- observed
|
||||
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.007) = 0.083 (small; f: .10 small, .25
|
||||
16 | 0.98 | 0.98
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +4.13 (50% of observed)
|
||||
true stim:log(day) = +4.133 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.08 | 0.17 <- observed
|
||||
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.007) = 0.083 (small; f: .10 small, .25
|
||||
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.
|
||||
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
|
||||
|
||||
@@ -0,0 +1,35 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_13 [metric: success RATE]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = success RATE)
|
||||
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(1,132)=5.963 p(resid)=0.01593 p(Satt)=0.01598 (df=127)
|
||||
honest per-animal random slope (log-day): F(1,8.1)=4.16 p=0.07502
|
||||
Cohen's f (interaction, partial eta^2=0.012) = 0.111 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+0.06399, ratSD=0.05139, resSD=0.1) ---
|
||||
|
||||
true stim:log(day) = +0.06399 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.26 | 0.66 <- observed
|
||||
5 | 0.57 | 0.78
|
||||
8 | 0.93 | 0.96 <- observed
|
||||
12 | 1.00 | 1.00
|
||||
16 | 0.99 | 0.99
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +0.032 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.09 | 0.23 <- observed
|
||||
5 | 0.23 | 0.28
|
||||
8 | 0.48 | 0.53 <- observed
|
||||
12 | 0.55 | 0.61
|
||||
16 | 0.76 | 0.78
|
||||
24 | 0.85 | 0.82
|
||||
|
||||
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
|
||||
@@ -1,50 +1,52 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> logpower_result.txt
|
||||
% metric = rate : behavior = success / attempts -> logpower_result_rate.txt
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% The paper's power code models behavior against LOG training 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"
|
||||
% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
|
||||
% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
|
||||
% refits that model, reports the interaction (residual / Satterthwaite / honest
|
||||
% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
|
||||
% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
|
||||
% 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);
|
||||
localLogPower(D, 'count', here, vname);
|
||||
localLogPower(D, 'rate', here, vname);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
% ---------------------------------------------------------------- per metric
|
||||
function localLogPower(D, metric, here, vname)
|
||||
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' = log(day+1)
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success; mlabel = '# successes (count)'; suffix = '';
|
||||
end
|
||||
warnState = warning('off', 'all'); rng(1);
|
||||
logday = log(D.day + 1);
|
||||
tbl0 = table(beh, 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 = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = %s)\n', mlabel)];
|
||||
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
|
||||
s = [s sprintf('\nInsufficient data for this analysis.\n')];
|
||||
localFinish(s, here, suffix); 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');
|
||||
@@ -52,7 +54,6 @@ 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)');
|
||||
@@ -76,19 +77,18 @@ 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
|
||||
days = unique(tbl0.day);
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\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>
|
||||
s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -102,19 +102,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
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);
|
||||
s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,11 +1,12 @@
|
||||
% Variation learning-curve plot, in the style of the paper:
|
||||
% Variation learning-curve plots, in the style of the paper:
|
||||
% "Lines indicate mean (and SEM) across animals in the anodal (red) and
|
||||
% control (blue) groups."
|
||||
% Plots mean +/- SEM successful reaches per training day for the treatment /
|
||||
% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
|
||||
% this folder's data.csv and saving learning_curve.png. The per-group N is read
|
||||
% from the data (each variation pools different groups), so the legend shows the
|
||||
% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
|
||||
% Produces TWO figures from this folder's data.csv:
|
||||
% learning_curve.png # successes (count) per training day
|
||||
% learning_curve_rate.png success rate (success/attempts) per training day
|
||||
% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
|
||||
% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
|
||||
% the x-axis tick labels are drawn vertically.
|
||||
% Run: matlab -batch "plotcurve"
|
||||
% (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
|
||||
|
||||
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
days = unique(D.day); % 0-indexed
|
||||
days = unique(D.day);
|
||||
xd = days + 1; % plot as 1-indexed training day (paper axis)
|
||||
|
||||
red = [0.85 0.10 0.10];
|
||||
blue = [0.10 0.30 0.85];
|
||||
|
||||
[Ma, Sa, na] = localCurve(D, 1, days); % anodal / treatment (stim = 1)
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days); % control (stim = 0)
|
||||
localPlot(D, days, xd, 'count', '# successes', ...
|
||||
fullfile(here, 'learning_curve.png'), vname, red, blue);
|
||||
localPlot(D, days, xd, 'rate', 'success rate', ...
|
||||
fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
|
||||
|
||||
% ------------------------------------------------------------------ helpers
|
||||
function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
|
||||
[Ma, Sa, na] = localCurve(D, 1, days, metric); % anodal / treatment
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days, metric); % control
|
||||
fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
|
||||
hold on
|
||||
e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
|
||||
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
|
||||
hold off
|
||||
legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
|
||||
'Location', 'northwest', 'Box', 'off');
|
||||
xlabel('training day');
|
||||
ylabel('# successes');
|
||||
xlabel('training day'); ylabel(ylab);
|
||||
title(vname, 'Interpreter', 'none');
|
||||
set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
|
||||
|
||||
outFile = fullfile(here, 'learning_curve.png');
|
||||
xtickangle(90); % vertical x-axis tick labels
|
||||
exportgraphics(fig, outFile, 'Resolution', 150);
|
||||
close(fig);
|
||||
fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
|
||||
fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
|
||||
end
|
||||
|
||||
% ------------------------------------------------------------------ helper
|
||||
function [M, S, n] = localCurve(D, stimVal, days)
|
||||
%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
|
||||
function [M, S, n] = localCurve(D, stimVal, days, metric)
|
||||
%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
|
||||
subs = unique(D.subject(D.stim == stimVal));
|
||||
n = numel(subs);
|
||||
X = nan(numel(days), n);
|
||||
for j = 1:n
|
||||
for i = 1:numel(days)
|
||||
r = D.subject == subs(j) & D.day == days(i);
|
||||
if any(r); X(i, j) = mean(D.success(r)); end
|
||||
if ~any(r); continue; end
|
||||
if strcmp(metric, 'rate')
|
||||
tot = sum(D.total(r));
|
||||
if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
|
||||
else
|
||||
X(i, j) = mean(D.success(r));
|
||||
end
|
||||
end
|
||||
end
|
||||
M = mean(X, 2, 'omitnan');
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- naive_boxa_d0_13
|
||||
POWER SIMULATION -- naive_boxa_d0_13 [metric: # successes (count)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (success COUNT; day within-window)
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = # successes (count); day within-window)
|
||||
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=+1.31/day, rat SD=8.76, residual SD=15.18, days=14
|
||||
ground truth: stim:day=+1.306/day, rat SD=8.76, residual SD=15.18, days=14
|
||||
|
||||
true stim:day interaction = +1.31 (100% of observed)
|
||||
true stim:day interaction = +1.306 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.12 | 0.32 <- observed
|
||||
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.31/day, rat SD=8.76, residual SD=15.18, days=14
|
||||
16 | 0.95 | 0.96
|
||||
24 | 0.97 | 0.98
|
||||
|
||||
true stim:day interaction = +0.65 (50% of observed)
|
||||
true stim:day interaction = +0.6532 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.17 <- observed
|
||||
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.31/day, rat SD=8.76, residual SD=15.18, days=14
|
||||
16 | 0.42 | 0.51
|
||||
24 | 0.62 | 0.60
|
||||
|
||||
Read the per-animal column as the honest power. At the observed N this study
|
||||
is typically underpowered; per-animal power reaches ~0.8 only at larger N.
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
|
||||
@@ -0,0 +1,28 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- naive_boxa_d0_13 [metric: success RATE]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE; day within-window)
|
||||
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=+0.009461/day, rat SD=0.04773, residual SD=0.1046, days=14
|
||||
|
||||
true stim:day interaction = +0.009461 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.13 | 0.37 <- observed
|
||||
5 | 0.40 | 0.52
|
||||
8 | 0.70 | 0.72 <- observed
|
||||
12 | 0.96 | 0.96
|
||||
16 | 0.97 | 0.97
|
||||
24 | 0.99 | 0.99
|
||||
|
||||
true stim:day interaction = +0.00473 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.18 <- observed
|
||||
5 | 0.18 | 0.20
|
||||
8 | 0.34 | 0.32 <- observed
|
||||
12 | 0.38 | 0.39
|
||||
16 | 0.48 | 0.53
|
||||
24 | 0.64 | 0.63
|
||||
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
@@ -1,45 +1,49 @@
|
||||
% Variation power simulation -- Monte-Carlo power for the paper's stim x day
|
||||
% interaction, using THIS folder's data as the ground truth.
|
||||
% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> power_result.txt
|
||||
% metric = rate : behavior = success / attempts -> power_result_rate.txt
|
||||
%
|
||||
% Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
|
||||
% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
|
||||
% and residual SD generate NREP synthetic datasets at each rats-per-group N and
|
||||
% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
|
||||
% scored at alpha = 0.05 two ways:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
|
||||
% honest power, matching the random-slope / per-animal inference)
|
||||
% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
|
||||
% generate NREP synthetic datasets at each rats-per-group N and each true-effect
|
||||
% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
|
||||
% LME : the fitlme stim:day p (observation-level DF -- optimistic)
|
||||
% Writes power_result.txt beside this script. Run: matlab -batch "powersim"
|
||||
% Writes power_result[_rate].txt. Run: matlab -batch "powersim"
|
||||
% (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
|
||||
localPower(D, 'count', here, vname);
|
||||
localPower(D, 'rate', here, vname);
|
||||
|
||||
% ---------------------------------------------------------------- per metric
|
||||
function localPower(D, metric, here, vname)
|
||||
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success; mlabel = '# successes (count)'; suffix = '';
|
||||
end
|
||||
warnState = warning('off', 'all'); rng(1);
|
||||
day0 = min(D.day);
|
||||
tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
|
||||
tbl0 = table(beh, D.day - day0, 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\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: %s (success COUNT; day within-window)\n', FORMULA)];
|
||||
s = sprintf('%s\nPOWER SIMULATION -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: %s (behavior = %s; day within-window)\n', FORMULA, mlabel)];
|
||||
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 a power simulation (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
s = [s sprintf('\nInsufficient data for a power simulation.\n')];
|
||||
localFinish(s, here, suffix); warning(warnState); return
|
||||
end
|
||||
|
||||
lme = fitlme(tbl0, FORMULA);
|
||||
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
|
||||
days = (0:max(tbl0.day))';
|
||||
|
||||
s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
|
||||
s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
|
||||
bInt, sRat, sRes, numel(days))];
|
||||
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
|
||||
s = [s sprintf(' %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -70,20 +73,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
end
|
||||
end
|
||||
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
|
||||
'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'power_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
==============================================================================
|
||||
VARIATION: naive_boxa_d0_13
|
||||
VARIATION: naive_boxa_d0_13 [metric: success COUNT]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
|
||||
day = training day within window (0 = first analyzed day)
|
||||
@@ -60,9 +60,9 @@ effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
stim x day (interaction) t(134)= 1.74 F(1)= 3.015 p=0.08481 p=0.08489 (df=129)
|
||||
day (learning) t(134)= 16.74 F(1)=280.201 p=1.213e-34 p=2.161e-34 (df=131)
|
||||
stim (main, window start) t(134)= 1.48 F(1)= 2.198 p=0.1405 p=0.1519 (df=23)
|
||||
interaction 95% CI: [-0.18, +2.79]
|
||||
interaction 95% CI: [-0.1817, +2.794]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,25.3)=2.32, p=0.1403
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.08481, slope diff=+1.31)
|
||||
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.08481, slope diff=+1.306)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
|
||||
@@ -0,0 +1,68 @@
|
||||
==============================================================================
|
||||
VARIATION: naive_boxa_d0_13 [metric: success RATE (success/attempts)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE (success/attempts))
|
||||
day = training day within window (0 = first analyzed day)
|
||||
treatment (stim=1): Electrode-Box-B2
|
||||
control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
|
||||
N = 11 rats, 136 sessions raw day coverage: treat 0..13, control 0..13
|
||||
(equal day coverage over this window)
|
||||
|
||||
==============================================================================
|
||||
FULL MODEL SUMMARY -- fitlme
|
||||
==============================================================================
|
||||
|
||||
Linear mixed-effects model fit by ML
|
||||
|
||||
Model information:
|
||||
Number of observations 136
|
||||
Fixed effects coefficients 4
|
||||
Random effects coefficients 11
|
||||
Covariance parameters 2
|
||||
|
||||
Formula:
|
||||
behavior ~ 1 + day*stim + (1 | rat)
|
||||
|
||||
Model fit statistics:
|
||||
AIC BIC LogLikelihood Deviance
|
||||
-202.35 -184.87 107.17 -214.35
|
||||
|
||||
Fixed effects coefficients (95% CIs):
|
||||
Name Estimate SE tStat DF pValue
|
||||
{'(Intercept)'} 0.22695 0.026364 8.6084 132 1.9298e-14
|
||||
{'day' } 0.034099 0.0028279 12.058 132 4.9136e-23
|
||||
{'stim' } 0.061777 0.049323 1.2525 132 0.2126
|
||||
{'day:stim' } 0.009461 0.0052125 1.8151 132 0.071786
|
||||
|
||||
|
||||
Lower Upper
|
||||
0.1748 0.2791
|
||||
0.028506 0.039693
|
||||
-0.035788 0.15934
|
||||
-0.00084987 0.019772
|
||||
|
||||
Random effects covariance parameters (95% CIs):
|
||||
Group: rat (11 Levels)
|
||||
Name1 Name2 Type Estimate
|
||||
{'(Intercept)'} {'(Intercept)'} {'std'} 0.047732
|
||||
|
||||
|
||||
Lower Upper
|
||||
0.026198 0.086966
|
||||
|
||||
Group: Error
|
||||
Name Estimate Lower Upper
|
||||
{'Res Std'} 0.10455 0.092342 0.11837
|
||||
|
||||
|
||||
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
----------------------------------------------------------------------------
|
||||
stim x day (interaction) t(132)= 1.82 F(1)= 3.294 p=0.07179 p=0.07185 (df=128)
|
||||
day (learning) t(132)= 12.06 F(1)=145.397 p=4.914e-23 p=5.237e-23 (df=131)
|
||||
stim (main, window start) t(132)= 1.25 F(1)= 1.569 p=0.2126 p=0.2207 (df=28)
|
||||
interaction 95% CI: [-0.0008499, +0.01977]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,10.0)=2.86, p=0.1216
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.07179, slope diff=+0.009461)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
@@ -1,30 +1,58 @@
|
||||
% Variation analysis -- the paper's linear mixed model on the successful-reach
|
||||
% COUNT, fit on this folder's curated data subset.
|
||||
% Variation analysis -- the paper's linear mixed model on this folder's data,
|
||||
% for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> result.txt
|
||||
% metric = rate : behavior = success / attempts -> result_rate.txt
|
||||
% (rate uses only sessions with attempts > 0)
|
||||
%
|
||||
% model: behavior ~ stim + day + stim:day + (1|rat)
|
||||
% behavior = successful reaches (count per session)
|
||||
% stim = 1 for the treatment group(s), 0 for the control group(s)
|
||||
% day = training day within this window (0 = first analyzed day)
|
||||
% rat = subject (random intercept)
|
||||
%
|
||||
% Self-contained: reads data.csv beside this script and writes result.txt.
|
||||
% Run headless from this folder with: matlab -batch "analyze"
|
||||
% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
|
||||
% stim = 1 treatment / 0 control; day = training day within window (0 =
|
||||
% first analyzed day); rat = subject (random intercept).
|
||||
% For the interaction we report residual DF, Satterthwaite DF, and the honest
|
||||
% per-animal random-slope test. Self-contained: reads data.csv beside this
|
||||
% script. Run headless with: matlab -batch "analyze"
|
||||
% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
|
||||
tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
|
||||
Rc = localAnalyze(D, 'count', here, vname);
|
||||
Rr = localAnalyze(D, 'rate', here, vname);
|
||||
|
||||
% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
|
||||
VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
|
||||
'interP', Rc.interP, 'interEst', Rc.interEst, ...
|
||||
'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
|
||||
'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
|
||||
'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
|
||||
|
||||
% ------------------------------------------------------------------ helper
|
||||
function R = localAnalyze(D, metric, here, vname)
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :);
|
||||
beh = D.success ./ D.total;
|
||||
mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success;
|
||||
mlabel = 'success COUNT'; suffix = '';
|
||||
end
|
||||
R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
|
||||
'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
|
||||
'nObs', height(D), 'covEqual', false);
|
||||
if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
|
||||
localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
|
||||
vname, mlabel), here, suffix);
|
||||
return
|
||||
end
|
||||
|
||||
tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
|
||||
C = m.Coefficients; A = anova(m); ci = coefCI(m);
|
||||
As = anova(m, 'DFMethod', 'satterthwaite'); % Satterthwaite denominator DF
|
||||
As = anova(m, 'DFMethod', 'satterthwaite');
|
||||
|
||||
% Honest test: refit with a per-animal random SLOPE so the interaction DF
|
||||
% collapses toward the animal count (guarded -- may not converge in short windows).
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
wst = warning('off', 'all');
|
||||
try
|
||||
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f
|
||||
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
|
||||
As.pValue(gs(t)), As.DF2(gs(t)));
|
||||
|
||||
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
|
||||
maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
|
||||
maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
|
||||
if abs(maxT - maxC) > 2
|
||||
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
|
||||
else
|
||||
cov = '(equal day coverage over this window)';
|
||||
end
|
||||
|
||||
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
|
||||
if pI >= 0.05
|
||||
verdict = 'n.s. -- slopes parallel (no differential learning rate)';
|
||||
elseif eI > 0
|
||||
verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
|
||||
else
|
||||
verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
|
||||
end
|
||||
if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
|
||||
elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
|
||||
else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
raw = regexprep(evalc('disp(m)'), '</?strong>', '');
|
||||
s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)\n')];
|
||||
s = sprintf('%s\nVARIATION: %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = %s)\n', mlabel)];
|
||||
s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
|
||||
s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
|
||||
s = [s sprintf('control (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
|
||||
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
|
||||
s = [s row('stim x day (interaction)', 'day:stim')];
|
||||
s = [s row('day (learning)', 'day')];
|
||||
s = [s row('stim (main, window start)', 'stim')];
|
||||
s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
|
||||
s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
|
||||
if rsOk
|
||||
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
@@ -82,17 +105,18 @@ else
|
||||
end
|
||||
s = [s sprintf([' (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
|
||||
' model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
|
||||
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
|
||||
s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
|
||||
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
|
||||
s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
|
||||
|
||||
localWrite(s, here, suffix);
|
||||
|
||||
R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
|
||||
'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
|
||||
'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
|
||||
end
|
||||
|
||||
function localWrite(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'result.txt'), 'w');
|
||||
fprintf(fid, '%s', s);
|
||||
fclose(fid);
|
||||
|
||||
% Machine-readable handoff for the summary table (see make_variations.m).
|
||||
VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
|
||||
'nObs', height(D), 'interP', pI, 'interEst', eI, ...
|
||||
'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
|
||||
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
|
||||
'covEqual', abs(maxT - maxC) <= 2);
|
||||
fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 37 KiB After Width: | Height: | Size: 36 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 37 KiB |
@@ -1,7 +1,7 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_5
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_5 [metric: # successes (count)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = # successes (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
|
||||
|
||||
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,61)=8.016 p(resid)=0.006273 p(Satt)=0.006505
|
||||
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) ---
|
||||
--- power simulation (log-day ground truth: stim:logday=+18.3, ratSD=8.823, resSD=13.99) ---
|
||||
|
||||
true stim:log(day) = +18.30 (100% of observed)
|
||||
true stim:log(day) = +18.3 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.33 | 0.66 <- observed
|
||||
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.048) = 0.223 (small-medium; f: .10 smal
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +9.15 (50% of observed)
|
||||
true stim:log(day) = +9.151 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.11 | 0.24 <- observed
|
||||
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.048) = 0.223 (small-medium; f: .10 smal
|
||||
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.
|
||||
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
|
||||
|
||||
@@ -0,0 +1,35 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_5 [metric: success RATE]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = success RATE)
|
||||
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(1,59)=12.241 p(resid)=0.0008959 p(Satt)=0.000969 (df=52)
|
||||
honest per-animal random slope (log-day): F(1,13.9)=10.72 p=0.005603
|
||||
Cohen's f (interaction, partial eta^2=0.104) = 0.340 (medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+0.1706, ratSD=0.04897, resSD=0.1038) ---
|
||||
|
||||
true stim:log(day) = +0.1706 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.45 | 0.84 <- observed
|
||||
5 | 0.87 | 0.96
|
||||
8 | 1.00 | 1.00 <- observed
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:log(day) = +0.08531 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.12 | 0.31 <- observed
|
||||
5 | 0.36 | 0.48
|
||||
8 | 0.58 | 0.67 <- observed
|
||||
12 | 0.76 | 0.78
|
||||
16 | 0.89 | 0.91
|
||||
24 | 0.99 | 0.99
|
||||
|
||||
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
|
||||
@@ -1,50 +1,52 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> logpower_result.txt
|
||||
% metric = rate : behavior = success / attempts -> logpower_result_rate.txt
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% The paper's power code models behavior against LOG training 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"
|
||||
% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
|
||||
% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
|
||||
% refits that model, reports the interaction (residual / Satterthwaite / honest
|
||||
% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
|
||||
% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
|
||||
% 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);
|
||||
localLogPower(D, 'count', here, vname);
|
||||
localLogPower(D, 'rate', here, vname);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
% ---------------------------------------------------------------- per metric
|
||||
function localLogPower(D, metric, here, vname)
|
||||
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' = log(day+1)
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success; mlabel = '# successes (count)'; suffix = '';
|
||||
end
|
||||
warnState = warning('off', 'all'); rng(1);
|
||||
logday = log(D.day + 1);
|
||||
tbl0 = table(beh, 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 = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = %s)\n', mlabel)];
|
||||
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
|
||||
s = [s sprintf('\nInsufficient data for this analysis.\n')];
|
||||
localFinish(s, here, suffix); 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');
|
||||
@@ -52,7 +54,6 @@ 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)');
|
||||
@@ -76,19 +77,18 @@ 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
|
||||
days = unique(tbl0.day);
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\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>
|
||||
s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -102,19 +102,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
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);
|
||||
s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,11 +1,12 @@
|
||||
% Variation learning-curve plot, in the style of the paper:
|
||||
% Variation learning-curve plots, in the style of the paper:
|
||||
% "Lines indicate mean (and SEM) across animals in the anodal (red) and
|
||||
% control (blue) groups."
|
||||
% Plots mean +/- SEM successful reaches per training day for the treatment /
|
||||
% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
|
||||
% this folder's data.csv and saving learning_curve.png. The per-group N is read
|
||||
% from the data (each variation pools different groups), so the legend shows the
|
||||
% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
|
||||
% Produces TWO figures from this folder's data.csv:
|
||||
% learning_curve.png # successes (count) per training day
|
||||
% learning_curve_rate.png success rate (success/attempts) per training day
|
||||
% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
|
||||
% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
|
||||
% the x-axis tick labels are drawn vertically.
|
||||
% Run: matlab -batch "plotcurve"
|
||||
% (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
|
||||
|
||||
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
days = unique(D.day); % 0-indexed
|
||||
days = unique(D.day);
|
||||
xd = days + 1; % plot as 1-indexed training day (paper axis)
|
||||
|
||||
red = [0.85 0.10 0.10];
|
||||
blue = [0.10 0.30 0.85];
|
||||
|
||||
[Ma, Sa, na] = localCurve(D, 1, days); % anodal / treatment (stim = 1)
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days); % control (stim = 0)
|
||||
localPlot(D, days, xd, 'count', '# successes', ...
|
||||
fullfile(here, 'learning_curve.png'), vname, red, blue);
|
||||
localPlot(D, days, xd, 'rate', 'success rate', ...
|
||||
fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
|
||||
|
||||
% ------------------------------------------------------------------ helpers
|
||||
function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
|
||||
[Ma, Sa, na] = localCurve(D, 1, days, metric); % anodal / treatment
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days, metric); % control
|
||||
fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
|
||||
hold on
|
||||
e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
|
||||
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
|
||||
hold off
|
||||
legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
|
||||
'Location', 'northwest', 'Box', 'off');
|
||||
xlabel('training day');
|
||||
ylabel('# successes');
|
||||
xlabel('training day'); ylabel(ylab);
|
||||
title(vname, 'Interpreter', 'none');
|
||||
set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
|
||||
|
||||
outFile = fullfile(here, 'learning_curve.png');
|
||||
xtickangle(90); % vertical x-axis tick labels
|
||||
exportgraphics(fig, outFile, 'Resolution', 150);
|
||||
close(fig);
|
||||
fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
|
||||
fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
|
||||
end
|
||||
|
||||
% ------------------------------------------------------------------ helper
|
||||
function [M, S, n] = localCurve(D, stimVal, days)
|
||||
%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
|
||||
function [M, S, n] = localCurve(D, stimVal, days, metric)
|
||||
%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
|
||||
subs = unique(D.subject(D.stim == stimVal));
|
||||
n = numel(subs);
|
||||
X = nan(numel(days), n);
|
||||
for j = 1:n
|
||||
for i = 1:numel(days)
|
||||
r = D.subject == subs(j) & D.day == days(i);
|
||||
if any(r); X(i, j) = mean(D.success(r)); end
|
||||
if ~any(r); continue; end
|
||||
if strcmp(metric, 'rate')
|
||||
tot = sum(D.total(r));
|
||||
if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
|
||||
else
|
||||
X(i, j) = mean(D.success(r));
|
||||
end
|
||||
end
|
||||
end
|
||||
M = mean(X, 2, 'omitnan');
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- naive_boxa_d0_5
|
||||
POWER SIMULATION -- naive_boxa_d0_5 [metric: # successes (count)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (success COUNT; day within-window)
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = # successes (count); day within-window)
|
||||
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=+6.40/day, rat SD=9.10, residual SD=12.48, days=6
|
||||
ground truth: stim:day=+6.403/day, rat SD=9.103, residual SD=12.48, days=6
|
||||
|
||||
true stim:day interaction = +6.40 (100% of observed)
|
||||
true stim:day interaction = +6.403 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.36 | 0.77 <- observed
|
||||
@@ -15,7 +15,7 @@ ground truth: stim:day=+6.40/day, rat SD=9.10, residual SD=12.48, days=6
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:day interaction = +3.20 (50% of observed)
|
||||
true stim:day interaction = +3.202 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.11 | 0.24 <- observed
|
||||
@@ -25,5 +25,4 @@ ground truth: stim:day=+6.40/day, rat SD=9.10, residual SD=12.48, days=6
|
||||
16 | 0.82 | 0.84
|
||||
24 | 0.95 | 0.94
|
||||
|
||||
Read the per-animal column as the honest power. At the observed N this study
|
||||
is typically underpowered; per-animal power reaches ~0.8 only at larger N.
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
|
||||
@@ -0,0 +1,28 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- naive_boxa_d0_5 [metric: success RATE]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE; day within-window)
|
||||
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=+0.05713/day, rat SD=0.05041, residual SD=0.09994, days=6
|
||||
|
||||
true stim:day interaction = +0.05713 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.42 | 0.85 <- observed
|
||||
5 | 0.86 | 0.96
|
||||
8 | 1.00 | 1.00 <- observed
|
||||
12 | 1.00 | 1.00
|
||||
16 | 1.00 | 1.00
|
||||
24 | 1.00 | 1.00
|
||||
|
||||
true stim:day interaction = +0.02856 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.12 | 0.32 <- observed
|
||||
5 | 0.32 | 0.44
|
||||
8 | 0.57 | 0.66 <- observed
|
||||
12 | 0.74 | 0.79
|
||||
16 | 0.89 | 0.88
|
||||
24 | 0.97 | 0.99
|
||||
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
@@ -1,45 +1,49 @@
|
||||
% Variation power simulation -- Monte-Carlo power for the paper's stim x day
|
||||
% interaction, using THIS folder's data as the ground truth.
|
||||
% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> power_result.txt
|
||||
% metric = rate : behavior = success / attempts -> power_result_rate.txt
|
||||
%
|
||||
% Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
|
||||
% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
|
||||
% and residual SD generate NREP synthetic datasets at each rats-per-group N and
|
||||
% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
|
||||
% scored at alpha = 0.05 two ways:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
|
||||
% honest power, matching the random-slope / per-animal inference)
|
||||
% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
|
||||
% generate NREP synthetic datasets at each rats-per-group N and each true-effect
|
||||
% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
|
||||
% LME : the fitlme stim:day p (observation-level DF -- optimistic)
|
||||
% Writes power_result.txt beside this script. Run: matlab -batch "powersim"
|
||||
% Writes power_result[_rate].txt. Run: matlab -batch "powersim"
|
||||
% (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
|
||||
localPower(D, 'count', here, vname);
|
||||
localPower(D, 'rate', here, vname);
|
||||
|
||||
% ---------------------------------------------------------------- per metric
|
||||
function localPower(D, metric, here, vname)
|
||||
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success; mlabel = '# successes (count)'; suffix = '';
|
||||
end
|
||||
warnState = warning('off', 'all'); rng(1);
|
||||
day0 = min(D.day);
|
||||
tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
|
||||
tbl0 = table(beh, D.day - day0, 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\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: %s (success COUNT; day within-window)\n', FORMULA)];
|
||||
s = sprintf('%s\nPOWER SIMULATION -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: %s (behavior = %s; day within-window)\n', FORMULA, mlabel)];
|
||||
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 a power simulation (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
s = [s sprintf('\nInsufficient data for a power simulation.\n')];
|
||||
localFinish(s, here, suffix); warning(warnState); return
|
||||
end
|
||||
|
||||
lme = fitlme(tbl0, FORMULA);
|
||||
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
|
||||
days = (0:max(tbl0.day))';
|
||||
|
||||
s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
|
||||
s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
|
||||
bInt, sRat, sRes, numel(days))];
|
||||
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
|
||||
s = [s sprintf(' %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -70,20 +73,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
end
|
||||
end
|
||||
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
|
||||
'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'power_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
==============================================================================
|
||||
VARIATION: naive_boxa_d0_5
|
||||
VARIATION: naive_boxa_d0_5 [metric: success COUNT]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
|
||||
day = training day within window (0 = first analyzed day)
|
||||
@@ -60,9 +60,9 @@ effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
stim x day (interaction) t(61)= 3.15 F(1)= 9.909 p=0.002545 p=0.002679 (df=54)
|
||||
day (learning) t(61)= 8.14 F(1)= 66.293 p=2.506e-11 p=5.765e-11 (df=54)
|
||||
stim (main, window start) t(61)= 0.15 F(1)= 0.022 p=0.8814 p=0.8822 (df=24)
|
||||
interaction 95% CI: [+2.34, +10.47]
|
||||
interaction 95% CI: [+2.336, +10.47]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,10.5)=5.80, p=0.03561
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.002545, slope diff=+6.40)
|
||||
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.002545, slope diff=+6.403)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
|
||||
@@ -0,0 +1,68 @@
|
||||
==============================================================================
|
||||
VARIATION: naive_boxa_d0_5 [metric: success RATE (success/attempts)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE (success/attempts))
|
||||
day = training day within window (0 = first analyzed day)
|
||||
treatment (stim=1): Electrode-Box-B2
|
||||
control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
|
||||
N = 11 rats, 63 sessions raw day coverage: treat 0..5, control 0..5
|
||||
(equal day coverage over this window)
|
||||
|
||||
==============================================================================
|
||||
FULL MODEL SUMMARY -- fitlme
|
||||
==============================================================================
|
||||
|
||||
Linear mixed-effects model fit by ML
|
||||
|
||||
Model information:
|
||||
Number of observations 63
|
||||
Fixed effects coefficients 4
|
||||
Random effects coefficients 11
|
||||
Covariance parameters 2
|
||||
|
||||
Formula:
|
||||
behavior ~ 1 + day*stim + (1 | rat)
|
||||
|
||||
Model fit statistics:
|
||||
AIC BIC LogLikelihood Deviance
|
||||
-89.552 -76.693 50.776 -101.55
|
||||
|
||||
Fixed effects coefficients (95% CIs):
|
||||
Name Estimate SE tStat DF
|
||||
{'(Intercept)'} 0.20761 0.03353 6.1919 59
|
||||
{'day' } 0.035262 0.0090474 3.8975 59
|
||||
{'stim' } -0.042773 0.060953 -0.70173 59
|
||||
{'day:stim' } 0.057126 0.016495 3.4631 59
|
||||
|
||||
|
||||
pValue Lower Upper
|
||||
6.1978e-08 0.14052 0.27471
|
||||
0.000251 0.017158 0.053365
|
||||
0.4856 -0.16474 0.079194
|
||||
0.0010002 0.024119 0.090133
|
||||
|
||||
Random effects covariance parameters (95% CIs):
|
||||
Group: rat (11 Levels)
|
||||
Name1 Name2 Type Estimate
|
||||
{'(Intercept)'} {'(Intercept)'} {'std'} 0.050413
|
||||
|
||||
|
||||
Lower Upper
|
||||
0.02388 0.10643
|
||||
|
||||
Group: Error
|
||||
Name Estimate Lower Upper
|
||||
{'Res Std'} 0.099938 0.082348 0.12129
|
||||
|
||||
|
||||
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
----------------------------------------------------------------------------
|
||||
stim x day (interaction) t(59)= 3.46 F(1)= 11.993 p=0.001 p=0.00108 (df=52)
|
||||
day (learning) t(59)= 3.90 F(1)= 15.190 p=0.000251 p=0.0002756 (df=53)
|
||||
stim (main, window start) t(59)= -0.70 F(1)= 0.492 p=0.4856 p=0.4879 (df=32)
|
||||
interaction 95% CI: [+0.02412, +0.09013]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,12.0)=9.63, p=0.009127
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.001, slope diff=+0.05713)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
@@ -1,30 +1,58 @@
|
||||
% Variation analysis -- the paper's linear mixed model on the successful-reach
|
||||
% COUNT, fit on this folder's curated data subset.
|
||||
% Variation analysis -- the paper's linear mixed model on this folder's data,
|
||||
% for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> result.txt
|
||||
% metric = rate : behavior = success / attempts -> result_rate.txt
|
||||
% (rate uses only sessions with attempts > 0)
|
||||
%
|
||||
% model: behavior ~ stim + day + stim:day + (1|rat)
|
||||
% behavior = successful reaches (count per session)
|
||||
% stim = 1 for the treatment group(s), 0 for the control group(s)
|
||||
% day = training day within this window (0 = first analyzed day)
|
||||
% rat = subject (random intercept)
|
||||
%
|
||||
% Self-contained: reads data.csv beside this script and writes result.txt.
|
||||
% Run headless from this folder with: matlab -batch "analyze"
|
||||
% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
|
||||
% stim = 1 treatment / 0 control; day = training day within window (0 =
|
||||
% first analyzed day); rat = subject (random intercept).
|
||||
% For the interaction we report residual DF, Satterthwaite DF, and the honest
|
||||
% per-animal random-slope test. Self-contained: reads data.csv beside this
|
||||
% script. Run headless with: matlab -batch "analyze"
|
||||
% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
|
||||
tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
|
||||
Rc = localAnalyze(D, 'count', here, vname);
|
||||
Rr = localAnalyze(D, 'rate', here, vname);
|
||||
|
||||
% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
|
||||
VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
|
||||
'interP', Rc.interP, 'interEst', Rc.interEst, ...
|
||||
'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
|
||||
'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
|
||||
'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
|
||||
|
||||
% ------------------------------------------------------------------ helper
|
||||
function R = localAnalyze(D, metric, here, vname)
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :);
|
||||
beh = D.success ./ D.total;
|
||||
mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success;
|
||||
mlabel = 'success COUNT'; suffix = '';
|
||||
end
|
||||
R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
|
||||
'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
|
||||
'nObs', height(D), 'covEqual', false);
|
||||
if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
|
||||
localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
|
||||
vname, mlabel), here, suffix);
|
||||
return
|
||||
end
|
||||
|
||||
tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
|
||||
C = m.Coefficients; A = anova(m); ci = coefCI(m);
|
||||
As = anova(m, 'DFMethod', 'satterthwaite'); % Satterthwaite denominator DF
|
||||
As = anova(m, 'DFMethod', 'satterthwaite');
|
||||
|
||||
% Honest test: refit with a per-animal random SLOPE so the interaction DF
|
||||
% collapses toward the animal count (guarded -- may not converge in short windows).
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
wst = warning('off', 'all');
|
||||
try
|
||||
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f
|
||||
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
|
||||
As.pValue(gs(t)), As.DF2(gs(t)));
|
||||
|
||||
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
|
||||
maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
|
||||
maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
|
||||
if abs(maxT - maxC) > 2
|
||||
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
|
||||
else
|
||||
cov = '(equal day coverage over this window)';
|
||||
end
|
||||
|
||||
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
|
||||
if pI >= 0.05
|
||||
verdict = 'n.s. -- slopes parallel (no differential learning rate)';
|
||||
elseif eI > 0
|
||||
verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
|
||||
else
|
||||
verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
|
||||
end
|
||||
if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
|
||||
elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
|
||||
else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
raw = regexprep(evalc('disp(m)'), '</?strong>', '');
|
||||
s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)\n')];
|
||||
s = sprintf('%s\nVARIATION: %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = %s)\n', mlabel)];
|
||||
s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
|
||||
s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
|
||||
s = [s sprintf('control (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
|
||||
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
|
||||
s = [s row('stim x day (interaction)', 'day:stim')];
|
||||
s = [s row('day (learning)', 'day')];
|
||||
s = [s row('stim (main, window start)', 'stim')];
|
||||
s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
|
||||
s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
|
||||
if rsOk
|
||||
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
@@ -82,17 +105,18 @@ else
|
||||
end
|
||||
s = [s sprintf([' (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
|
||||
' model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
|
||||
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
|
||||
s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
|
||||
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
|
||||
s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
|
||||
|
||||
localWrite(s, here, suffix);
|
||||
|
||||
R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
|
||||
'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
|
||||
'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
|
||||
end
|
||||
|
||||
function localWrite(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'result.txt'), 'w');
|
||||
fprintf(fid, '%s', s);
|
||||
fclose(fid);
|
||||
|
||||
% Machine-readable handoff for the summary table (see make_variations.m).
|
||||
VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
|
||||
'nObs', height(D), 'interP', pI, 'interEst', eI, ...
|
||||
'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
|
||||
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
|
||||
'covEqual', abs(maxT - maxC) <= 2);
|
||||
fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 36 KiB After Width: | Height: | Size: 36 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 36 KiB |
@@ -1,7 +1,7 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_10
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_10 [metric: # successes (count)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = # successes (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
|
||||
|
||||
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,46)=0.613 p(resid)=0.4376 p(Satt)=0.4382 (df=
|
||||
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) ---
|
||||
--- power simulation (log-day ground truth: stim:logday=-15.28, ratSD=11.24, resSD=10.1) ---
|
||||
|
||||
true stim:log(day) = -15.28 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.005) = 0.069 (small; f: .10 small, .25
|
||||
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.
|
||||
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
|
||||
|
||||
@@ -0,0 +1,35 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_10 [metric: success RATE]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = success RATE)
|
||||
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)=2.393 p(resid)=0.1287 p(Satt)=0.1297 (df=40)
|
||||
honest per-animal random slope (log-day): F(1,10.0)=1.69 p=0.2224
|
||||
Cohen's f (interaction, partial eta^2=0.021) = 0.147 (small-medium; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=-0.1921, ratSD=0.0649, resSD=0.06429) ---
|
||||
|
||||
true stim:log(day) = -0.1921 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.12 | 0.25 <- observed
|
||||
5 | 0.24 | 0.36
|
||||
8 | 0.49 | 0.56
|
||||
12 | 0.78 | 0.80
|
||||
16 | 0.88 | 0.89
|
||||
24 | 0.97 | 0.95
|
||||
|
||||
true stim:log(day) = -0.09607 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.03 | 0.09 <- observed
|
||||
5 | 0.07 | 0.12
|
||||
8 | 0.17 | 0.21
|
||||
12 | 0.17 | 0.17
|
||||
16 | 0.33 | 0.37
|
||||
24 | 0.36 | 0.38
|
||||
|
||||
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
|
||||
@@ -1,50 +1,52 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> logpower_result.txt
|
||||
% metric = rate : behavior = success / attempts -> logpower_result_rate.txt
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% The paper's power code models behavior against LOG training 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"
|
||||
% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
|
||||
% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
|
||||
% refits that model, reports the interaction (residual / Satterthwaite / honest
|
||||
% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
|
||||
% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
|
||||
% 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);
|
||||
localLogPower(D, 'count', here, vname);
|
||||
localLogPower(D, 'rate', here, vname);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
% ---------------------------------------------------------------- per metric
|
||||
function localLogPower(D, metric, here, vname)
|
||||
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' = log(day+1)
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success; mlabel = '# successes (count)'; suffix = '';
|
||||
end
|
||||
warnState = warning('off', 'all'); rng(1);
|
||||
logday = log(D.day + 1);
|
||||
tbl0 = table(beh, 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 = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = %s)\n', mlabel)];
|
||||
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
|
||||
s = [s sprintf('\nInsufficient data for this analysis.\n')];
|
||||
localFinish(s, here, suffix); 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');
|
||||
@@ -52,7 +54,6 @@ 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)');
|
||||
@@ -76,19 +77,18 @@ 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
|
||||
days = unique(tbl0.day);
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\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>
|
||||
s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -102,19 +102,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
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);
|
||||
s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,11 +1,12 @@
|
||||
% Variation learning-curve plot, in the style of the paper:
|
||||
% Variation learning-curve plots, in the style of the paper:
|
||||
% "Lines indicate mean (and SEM) across animals in the anodal (red) and
|
||||
% control (blue) groups."
|
||||
% Plots mean +/- SEM successful reaches per training day for the treatment /
|
||||
% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
|
||||
% this folder's data.csv and saving learning_curve.png. The per-group N is read
|
||||
% from the data (each variation pools different groups), so the legend shows the
|
||||
% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
|
||||
% Produces TWO figures from this folder's data.csv:
|
||||
% learning_curve.png # successes (count) per training day
|
||||
% learning_curve_rate.png success rate (success/attempts) per training day
|
||||
% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
|
||||
% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
|
||||
% the x-axis tick labels are drawn vertically.
|
||||
% Run: matlab -batch "plotcurve"
|
||||
% (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
|
||||
|
||||
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
days = unique(D.day); % 0-indexed
|
||||
days = unique(D.day);
|
||||
xd = days + 1; % plot as 1-indexed training day (paper axis)
|
||||
|
||||
red = [0.85 0.10 0.10];
|
||||
blue = [0.10 0.30 0.85];
|
||||
|
||||
[Ma, Sa, na] = localCurve(D, 1, days); % anodal / treatment (stim = 1)
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days); % control (stim = 0)
|
||||
localPlot(D, days, xd, 'count', '# successes', ...
|
||||
fullfile(here, 'learning_curve.png'), vname, red, blue);
|
||||
localPlot(D, days, xd, 'rate', 'success rate', ...
|
||||
fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
|
||||
|
||||
% ------------------------------------------------------------------ helpers
|
||||
function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
|
||||
[Ma, Sa, na] = localCurve(D, 1, days, metric); % anodal / treatment
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days, metric); % control
|
||||
fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
|
||||
hold on
|
||||
e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
|
||||
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
|
||||
hold off
|
||||
legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
|
||||
'Location', 'northwest', 'Box', 'off');
|
||||
xlabel('training day');
|
||||
ylabel('# successes');
|
||||
xlabel('training day'); ylabel(ylab);
|
||||
title(vname, 'Interpreter', 'none');
|
||||
set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
|
||||
|
||||
outFile = fullfile(here, 'learning_curve.png');
|
||||
xtickangle(90); % vertical x-axis tick labels
|
||||
exportgraphics(fig, outFile, 'Resolution', 150);
|
||||
close(fig);
|
||||
fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
|
||||
fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
|
||||
end
|
||||
|
||||
% ------------------------------------------------------------------ helper
|
||||
function [M, S, n] = localCurve(D, stimVal, days)
|
||||
%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
|
||||
function [M, S, n] = localCurve(D, stimVal, days, metric)
|
||||
%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
|
||||
subs = unique(D.subject(D.stim == stimVal));
|
||||
n = numel(subs);
|
||||
X = nan(numel(days), n);
|
||||
for j = 1:n
|
||||
for i = 1:numel(days)
|
||||
r = D.subject == subs(j) & D.day == days(i);
|
||||
if any(r); X(i, j) = mean(D.success(r)); end
|
||||
if ~any(r); continue; end
|
||||
if strcmp(metric, 'rate')
|
||||
tot = sum(D.total(r));
|
||||
if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
|
||||
else
|
||||
X(i, j) = mean(D.success(r));
|
||||
end
|
||||
end
|
||||
end
|
||||
M = mean(X, 2, 'omitnan');
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- naive_boxa_d6_10
|
||||
POWER SIMULATION -- naive_boxa_d6_10 [metric: # successes (count)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (success COUNT; day within-window)
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = # successes (count); day within-window)
|
||||
observed groups: stim n=3, control n=7 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=-1.62/day, rat SD=11.24, residual SD=10.14, days=5
|
||||
ground truth: stim:day=-1.619/day, rat SD=11.24, residual SD=10.14, days=5
|
||||
|
||||
true stim:day interaction = -1.62 (100% of observed)
|
||||
true stim:day interaction = -1.619 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.05 | 0.08 <- observed
|
||||
@@ -15,7 +15,7 @@ ground truth: stim:day=-1.62/day, rat SD=11.24, residual SD=10.14, days=5
|
||||
16 | 0.28 | 0.30
|
||||
24 | 0.41 | 0.45
|
||||
|
||||
true stim:day interaction = -0.81 (50% of observed)
|
||||
true stim:day interaction = -0.8095 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.03 | 0.08 <- observed
|
||||
@@ -25,5 +25,4 @@ ground truth: stim:day=-1.62/day, rat SD=11.24, residual SD=10.14, days=5
|
||||
16 | 0.11 | 0.09
|
||||
24 | 0.12 | 0.13
|
||||
|
||||
Read the per-animal column as the honest power. At the observed N this study
|
||||
is typically underpowered; per-animal power reaches ~0.8 only at larger N.
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
|
||||
@@ -0,0 +1,28 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- naive_boxa_d6_10 [metric: success RATE]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE; day within-window)
|
||||
observed groups: stim n=3, control n=7 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=-0.02112/day, rat SD=0.06482, residual SD=0.06465, days=5
|
||||
|
||||
true stim:day interaction = -0.02112 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.10 | 0.23 <- observed
|
||||
5 | 0.21 | 0.33
|
||||
8 | 0.47 | 0.53
|
||||
12 | 0.73 | 0.78
|
||||
16 | 0.87 | 0.87
|
||||
24 | 0.93 | 0.93
|
||||
|
||||
true stim:day interaction = -0.01056 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.03 | 0.10 <- observed
|
||||
5 | 0.07 | 0.11
|
||||
8 | 0.16 | 0.21
|
||||
12 | 0.15 | 0.17
|
||||
16 | 0.32 | 0.38
|
||||
24 | 0.33 | 0.35
|
||||
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
@@ -1,45 +1,49 @@
|
||||
% Variation power simulation -- Monte-Carlo power for the paper's stim x day
|
||||
% interaction, using THIS folder's data as the ground truth.
|
||||
% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> power_result.txt
|
||||
% metric = rate : behavior = success / attempts -> power_result_rate.txt
|
||||
%
|
||||
% Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
|
||||
% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
|
||||
% and residual SD generate NREP synthetic datasets at each rats-per-group N and
|
||||
% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
|
||||
% scored at alpha = 0.05 two ways:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
|
||||
% honest power, matching the random-slope / per-animal inference)
|
||||
% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
|
||||
% generate NREP synthetic datasets at each rats-per-group N and each true-effect
|
||||
% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
|
||||
% LME : the fitlme stim:day p (observation-level DF -- optimistic)
|
||||
% Writes power_result.txt beside this script. Run: matlab -batch "powersim"
|
||||
% Writes power_result[_rate].txt. Run: matlab -batch "powersim"
|
||||
% (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
|
||||
localPower(D, 'count', here, vname);
|
||||
localPower(D, 'rate', here, vname);
|
||||
|
||||
% ---------------------------------------------------------------- per metric
|
||||
function localPower(D, metric, here, vname)
|
||||
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success; mlabel = '# successes (count)'; suffix = '';
|
||||
end
|
||||
warnState = warning('off', 'all'); rng(1);
|
||||
day0 = min(D.day);
|
||||
tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
|
||||
tbl0 = table(beh, D.day - day0, 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\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: %s (success COUNT; day within-window)\n', FORMULA)];
|
||||
s = sprintf('%s\nPOWER SIMULATION -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: %s (behavior = %s; day within-window)\n', FORMULA, mlabel)];
|
||||
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 a power simulation (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
s = [s sprintf('\nInsufficient data for a power simulation.\n')];
|
||||
localFinish(s, here, suffix); warning(warnState); return
|
||||
end
|
||||
|
||||
lme = fitlme(tbl0, FORMULA);
|
||||
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
|
||||
days = (0:max(tbl0.day))';
|
||||
|
||||
s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
|
||||
s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
|
||||
bInt, sRat, sRes, numel(days))];
|
||||
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
|
||||
s = [s sprintf(' %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -70,20 +73,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
end
|
||||
end
|
||||
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
|
||||
'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'power_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
==============================================================================
|
||||
VARIATION: naive_boxa_d6_10
|
||||
VARIATION: naive_boxa_d6_10 [metric: success COUNT]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
|
||||
day = training day within window (0 = first analyzed day)
|
||||
@@ -60,9 +60,9 @@ effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
stim x day (interaction) t(46)= -0.73 F(1)= 0.535 p=0.4682 p=0.4687 (df=40)
|
||||
day (learning) t(46)= 4.77 F(1)= 22.779 p=1.877e-05 p=2.436e-05 (df=40)
|
||||
stim (main, window start) t(46)= 2.54 F(1)= 6.429 p=0.01469 p=0.02198 (df=16)
|
||||
interaction 95% CI: [-6.07, +2.84]
|
||||
interaction 95% CI: [-6.074, +2.836]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,10.0)=0.41, p=0.5344
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4682, slope diff=-1.62)
|
||||
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4682, slope diff=-1.619)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
|
||||
@@ -0,0 +1,68 @@
|
||||
==============================================================================
|
||||
VARIATION: naive_boxa_d6_10 [metric: success RATE (success/attempts)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE (success/attempts))
|
||||
day = training day within window (0 = first analyzed day)
|
||||
treatment (stim=1): Electrode-Box-B2
|
||||
control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
|
||||
N = 10 rats, 50 sessions raw day coverage: treat 6..10, control 6..10
|
||||
(equal day coverage over this window)
|
||||
|
||||
==============================================================================
|
||||
FULL MODEL SUMMARY -- fitlme
|
||||
==============================================================================
|
||||
|
||||
Linear mixed-effects model fit by ML
|
||||
|
||||
Model information:
|
||||
Number of observations 50
|
||||
Fixed effects coefficients 4
|
||||
Random effects coefficients 10
|
||||
Covariance parameters 2
|
||||
|
||||
Formula:
|
||||
behavior ~ 1 + day*stim + (1 | rat)
|
||||
|
||||
Model fit statistics:
|
||||
AIC BIC LogLikelihood Deviance
|
||||
-102.02 -90.545 57.008 -114.02
|
||||
|
||||
Fixed effects coefficients (95% CIs):
|
||||
Name Estimate SE tStat DF pValue
|
||||
{'(Intercept)'} 0.4775 0.030961 15.422 46 8.4559e-20
|
||||
{'day' } 0.033319 0.0077275 4.3117 46 8.4717e-05
|
||||
{'stim' } 0.1661 0.056527 2.9385 46 0.0051416
|
||||
{'day:stim' } -0.021119 0.014108 -1.4969 46 0.14124
|
||||
|
||||
|
||||
Lower Upper
|
||||
0.41518 0.53982
|
||||
0.017764 0.048873
|
||||
0.052321 0.27989
|
||||
-0.049518 0.0072795
|
||||
|
||||
Random effects covariance parameters (95% CIs):
|
||||
Group: rat (10 Levels)
|
||||
Name1 Name2 Type Estimate
|
||||
{'(Intercept)'} {'(Intercept)'} {'std'} 0.064825
|
||||
|
||||
|
||||
Lower Upper
|
||||
0.038261 0.10983
|
||||
|
||||
Group: Error
|
||||
Name Estimate Lower Upper
|
||||
{'Res Std'} 0.064653 0.05193 0.080492
|
||||
|
||||
|
||||
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
----------------------------------------------------------------------------
|
||||
stim x day (interaction) t(46)= -1.50 F(1)= 2.241 p=0.1412 p=0.1423 (df=40)
|
||||
day (learning) t(46)= 4.31 F(1)= 18.591 p=8.472e-05 p=0.0001028 (df=40)
|
||||
stim (main, window start) t(46)= 2.94 F(1)= 8.635 p=0.005142 p=0.009071 (df=17)
|
||||
interaction 95% CI: [-0.04952, +0.00728]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,10.0)=1.71, p=0.2198
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1412, slope diff=-0.02112)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
@@ -1,30 +1,58 @@
|
||||
% Variation analysis -- the paper's linear mixed model on the successful-reach
|
||||
% COUNT, fit on this folder's curated data subset.
|
||||
% Variation analysis -- the paper's linear mixed model on this folder's data,
|
||||
% for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> result.txt
|
||||
% metric = rate : behavior = success / attempts -> result_rate.txt
|
||||
% (rate uses only sessions with attempts > 0)
|
||||
%
|
||||
% model: behavior ~ stim + day + stim:day + (1|rat)
|
||||
% behavior = successful reaches (count per session)
|
||||
% stim = 1 for the treatment group(s), 0 for the control group(s)
|
||||
% day = training day within this window (0 = first analyzed day)
|
||||
% rat = subject (random intercept)
|
||||
%
|
||||
% Self-contained: reads data.csv beside this script and writes result.txt.
|
||||
% Run headless from this folder with: matlab -batch "analyze"
|
||||
% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
|
||||
% stim = 1 treatment / 0 control; day = training day within window (0 =
|
||||
% first analyzed day); rat = subject (random intercept).
|
||||
% For the interaction we report residual DF, Satterthwaite DF, and the honest
|
||||
% per-animal random-slope test. Self-contained: reads data.csv beside this
|
||||
% script. Run headless with: matlab -batch "analyze"
|
||||
% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
|
||||
tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
|
||||
Rc = localAnalyze(D, 'count', here, vname);
|
||||
Rr = localAnalyze(D, 'rate', here, vname);
|
||||
|
||||
% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
|
||||
VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
|
||||
'interP', Rc.interP, 'interEst', Rc.interEst, ...
|
||||
'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
|
||||
'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
|
||||
'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
|
||||
|
||||
% ------------------------------------------------------------------ helper
|
||||
function R = localAnalyze(D, metric, here, vname)
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :);
|
||||
beh = D.success ./ D.total;
|
||||
mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success;
|
||||
mlabel = 'success COUNT'; suffix = '';
|
||||
end
|
||||
R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
|
||||
'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
|
||||
'nObs', height(D), 'covEqual', false);
|
||||
if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
|
||||
localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
|
||||
vname, mlabel), here, suffix);
|
||||
return
|
||||
end
|
||||
|
||||
tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
|
||||
C = m.Coefficients; A = anova(m); ci = coefCI(m);
|
||||
As = anova(m, 'DFMethod', 'satterthwaite'); % Satterthwaite denominator DF
|
||||
As = anova(m, 'DFMethod', 'satterthwaite');
|
||||
|
||||
% Honest test: refit with a per-animal random SLOPE so the interaction DF
|
||||
% collapses toward the animal count (guarded -- may not converge in short windows).
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
wst = warning('off', 'all');
|
||||
try
|
||||
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f
|
||||
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
|
||||
As.pValue(gs(t)), As.DF2(gs(t)));
|
||||
|
||||
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
|
||||
maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
|
||||
maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
|
||||
if abs(maxT - maxC) > 2
|
||||
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
|
||||
else
|
||||
cov = '(equal day coverage over this window)';
|
||||
end
|
||||
|
||||
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
|
||||
if pI >= 0.05
|
||||
verdict = 'n.s. -- slopes parallel (no differential learning rate)';
|
||||
elseif eI > 0
|
||||
verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
|
||||
else
|
||||
verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
|
||||
end
|
||||
if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
|
||||
elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
|
||||
else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
raw = regexprep(evalc('disp(m)'), '</?strong>', '');
|
||||
s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)\n')];
|
||||
s = sprintf('%s\nVARIATION: %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = %s)\n', mlabel)];
|
||||
s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
|
||||
s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
|
||||
s = [s sprintf('control (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
|
||||
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
|
||||
s = [s row('stim x day (interaction)', 'day:stim')];
|
||||
s = [s row('day (learning)', 'day')];
|
||||
s = [s row('stim (main, window start)', 'stim')];
|
||||
s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
|
||||
s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
|
||||
if rsOk
|
||||
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
@@ -82,17 +105,18 @@ else
|
||||
end
|
||||
s = [s sprintf([' (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
|
||||
' model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
|
||||
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
|
||||
s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
|
||||
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
|
||||
s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
|
||||
|
||||
localWrite(s, here, suffix);
|
||||
|
||||
R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
|
||||
'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
|
||||
'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
|
||||
end
|
||||
|
||||
function localWrite(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'result.txt'), 'w');
|
||||
fprintf(fid, '%s', s);
|
||||
fclose(fid);
|
||||
|
||||
% Machine-readable handoff for the summary table (see make_variations.m).
|
||||
VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
|
||||
'nObs', height(D), 'interP', pI, 'interEst', eI, ...
|
||||
'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
|
||||
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
|
||||
'covEqual', abs(maxT - maxC) <= 2);
|
||||
fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 40 KiB After Width: | Height: | Size: 40 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 39 KiB |
@@ -1,7 +1,7 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_13
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_13 [metric: # successes (count)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = # successes (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
|
||||
|
||||
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,69)=0.239 p(resid)=0.6263 p(Satt)=0.6264 (df=
|
||||
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) ---
|
||||
--- power simulation (log-day ground truth: stim:logday=+6.701, ratSD=10.78, resSD=11.38) ---
|
||||
|
||||
true stim:log(day) = +6.70 (100% of observed)
|
||||
true stim:log(day) = +6.701 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.05 | 0.07 <- observed
|
||||
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.001) = 0.034 (small; f: .10 small, .25
|
||||
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.
|
||||
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
|
||||
|
||||
@@ -0,0 +1,35 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_13 [metric: success RATE]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = success RATE)
|
||||
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)=0.000 p(resid)=0.9874 p(Satt)=0.9874 (df=65)
|
||||
honest per-animal random slope (log-day): F(1,8.6)=0.03 p=0.8653
|
||||
Cohen's f (interaction, partial eta^2=0.000) = 0.000 (small; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+0.001382, ratSD=0.06213, resSD=0.07275) ---
|
||||
|
||||
true stim:log(day) = +0.001382 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.03 | 0.06 <- observed
|
||||
5 | 0.08 | 0.07
|
||||
8 | 0.01 | 0.02
|
||||
12 | 0.03 | 0.05
|
||||
16 | 0.05 | 0.07
|
||||
24 | 0.02 | 0.03
|
||||
|
||||
true stim:log(day) = +0.0006912 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.04 | 0.06 <- observed
|
||||
5 | 0.04 | 0.04
|
||||
8 | 0.08 | 0.04
|
||||
12 | 0.05 | 0.04
|
||||
16 | 0.05 | 0.04
|
||||
24 | 0.05 | 0.04
|
||||
|
||||
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
|
||||
@@ -1,50 +1,52 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> logpower_result.txt
|
||||
% metric = rate : behavior = success / attempts -> logpower_result_rate.txt
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% The paper's power code models behavior against LOG training 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"
|
||||
% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
|
||||
% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
|
||||
% refits that model, reports the interaction (residual / Satterthwaite / honest
|
||||
% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
|
||||
% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
|
||||
% 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);
|
||||
localLogPower(D, 'count', here, vname);
|
||||
localLogPower(D, 'rate', here, vname);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
% ---------------------------------------------------------------- per metric
|
||||
function localLogPower(D, metric, here, vname)
|
||||
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' = log(day+1)
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success; mlabel = '# successes (count)'; suffix = '';
|
||||
end
|
||||
warnState = warning('off', 'all'); rng(1);
|
||||
logday = log(D.day + 1);
|
||||
tbl0 = table(beh, 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 = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = %s)\n', mlabel)];
|
||||
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
|
||||
s = [s sprintf('\nInsufficient data for this analysis.\n')];
|
||||
localFinish(s, here, suffix); 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');
|
||||
@@ -52,7 +54,6 @@ 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)');
|
||||
@@ -76,19 +77,18 @@ 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
|
||||
days = unique(tbl0.day);
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\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>
|
||||
s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -102,19 +102,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
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);
|
||||
s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,11 +1,12 @@
|
||||
% Variation learning-curve plot, in the style of the paper:
|
||||
% Variation learning-curve plots, in the style of the paper:
|
||||
% "Lines indicate mean (and SEM) across animals in the anodal (red) and
|
||||
% control (blue) groups."
|
||||
% Plots mean +/- SEM successful reaches per training day for the treatment /
|
||||
% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
|
||||
% this folder's data.csv and saving learning_curve.png. The per-group N is read
|
||||
% from the data (each variation pools different groups), so the legend shows the
|
||||
% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
|
||||
% Produces TWO figures from this folder's data.csv:
|
||||
% learning_curve.png # successes (count) per training day
|
||||
% learning_curve_rate.png success rate (success/attempts) per training day
|
||||
% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
|
||||
% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
|
||||
% the x-axis tick labels are drawn vertically.
|
||||
% Run: matlab -batch "plotcurve"
|
||||
% (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
|
||||
|
||||
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
days = unique(D.day); % 0-indexed
|
||||
days = unique(D.day);
|
||||
xd = days + 1; % plot as 1-indexed training day (paper axis)
|
||||
|
||||
red = [0.85 0.10 0.10];
|
||||
blue = [0.10 0.30 0.85];
|
||||
|
||||
[Ma, Sa, na] = localCurve(D, 1, days); % anodal / treatment (stim = 1)
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days); % control (stim = 0)
|
||||
localPlot(D, days, xd, 'count', '# successes', ...
|
||||
fullfile(here, 'learning_curve.png'), vname, red, blue);
|
||||
localPlot(D, days, xd, 'rate', 'success rate', ...
|
||||
fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
|
||||
|
||||
% ------------------------------------------------------------------ helpers
|
||||
function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
|
||||
[Ma, Sa, na] = localCurve(D, 1, days, metric); % anodal / treatment
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days, metric); % control
|
||||
fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
|
||||
hold on
|
||||
e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
|
||||
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
|
||||
hold off
|
||||
legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
|
||||
'Location', 'northwest', 'Box', 'off');
|
||||
xlabel('training day');
|
||||
ylabel('# successes');
|
||||
xlabel('training day'); ylabel(ylab);
|
||||
title(vname, 'Interpreter', 'none');
|
||||
set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
|
||||
|
||||
outFile = fullfile(here, 'learning_curve.png');
|
||||
xtickangle(90); % vertical x-axis tick labels
|
||||
exportgraphics(fig, outFile, 'Resolution', 150);
|
||||
close(fig);
|
||||
fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
|
||||
fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
|
||||
end
|
||||
|
||||
% ------------------------------------------------------------------ helper
|
||||
function [M, S, n] = localCurve(D, stimVal, days)
|
||||
%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
|
||||
function [M, S, n] = localCurve(D, stimVal, days, metric)
|
||||
%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
|
||||
subs = unique(D.subject(D.stim == stimVal));
|
||||
n = numel(subs);
|
||||
X = nan(numel(days), n);
|
||||
for j = 1:n
|
||||
for i = 1:numel(days)
|
||||
r = D.subject == subs(j) & D.day == days(i);
|
||||
if any(r); X(i, j) = mean(D.success(r)); end
|
||||
if ~any(r); continue; end
|
||||
if strcmp(metric, 'rate')
|
||||
tot = sum(D.total(r));
|
||||
if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
|
||||
else
|
||||
X(i, j) = mean(D.success(r));
|
||||
end
|
||||
end
|
||||
end
|
||||
M = mean(X, 2, 'omitnan');
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- naive_boxa_d6_13
|
||||
POWER SIMULATION -- naive_boxa_d6_13 [metric: # successes (count)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (success COUNT; day within-window)
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = # successes (count); day within-window)
|
||||
observed groups: stim n=3, control n=7 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=+0.87/day, rat SD=10.75, residual SD=11.53, days=8
|
||||
ground truth: stim:day=+0.8737/day, rat SD=10.75, residual SD=11.53, days=8
|
||||
|
||||
true stim:day interaction = +0.87 (100% of observed)
|
||||
true stim:day interaction = +0.8737 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.07 | 0.11 <- observed
|
||||
@@ -15,7 +15,7 @@ ground truth: stim:day=+0.87/day, rat SD=10.75, residual SD=11.53, days=8
|
||||
16 | 0.28 | 0.33
|
||||
24 | 0.38 | 0.41
|
||||
|
||||
true stim:day interaction = +0.44 (50% of observed)
|
||||
true stim:day interaction = +0.4368 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.06 | 0.10 <- observed
|
||||
@@ -25,5 +25,4 @@ ground truth: stim:day=+0.87/day, rat SD=10.75, residual SD=11.53, days=8
|
||||
16 | 0.07 | 0.07
|
||||
24 | 0.16 | 0.15
|
||||
|
||||
Read the per-animal column as the honest power. At the observed N this study
|
||||
is typically underpowered; per-animal power reaches ~0.8 only at larger N.
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
|
||||
@@ -0,0 +1,28 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- naive_boxa_d6_13 [metric: success RATE]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE; day within-window)
|
||||
observed groups: stim n=3, control n=7 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=+0.001651/day, rat SD=0.06197, residual SD=0.07346, days=8
|
||||
|
||||
true stim:day interaction = +0.001651 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.03 | 0.04 <- observed
|
||||
5 | 0.10 | 0.08
|
||||
8 | 0.05 | 0.03
|
||||
12 | 0.04 | 0.05
|
||||
16 | 0.06 | 0.07
|
||||
24 | 0.03 | 0.06
|
||||
|
||||
true stim:day interaction = +0.0008253 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.05 | 0.07 <- observed
|
||||
5 | 0.04 | 0.06
|
||||
8 | 0.07 | 0.05
|
||||
12 | 0.03 | 0.05
|
||||
16 | 0.03 | 0.05
|
||||
24 | 0.07 | 0.07
|
||||
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
@@ -1,45 +1,49 @@
|
||||
% Variation power simulation -- Monte-Carlo power for the paper's stim x day
|
||||
% interaction, using THIS folder's data as the ground truth.
|
||||
% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> power_result.txt
|
||||
% metric = rate : behavior = success / attempts -> power_result_rate.txt
|
||||
%
|
||||
% Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
|
||||
% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
|
||||
% and residual SD generate NREP synthetic datasets at each rats-per-group N and
|
||||
% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
|
||||
% scored at alpha = 0.05 two ways:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
|
||||
% honest power, matching the random-slope / per-animal inference)
|
||||
% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
|
||||
% generate NREP synthetic datasets at each rats-per-group N and each true-effect
|
||||
% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
|
||||
% LME : the fitlme stim:day p (observation-level DF -- optimistic)
|
||||
% Writes power_result.txt beside this script. Run: matlab -batch "powersim"
|
||||
% Writes power_result[_rate].txt. Run: matlab -batch "powersim"
|
||||
% (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
|
||||
localPower(D, 'count', here, vname);
|
||||
localPower(D, 'rate', here, vname);
|
||||
|
||||
% ---------------------------------------------------------------- per metric
|
||||
function localPower(D, metric, here, vname)
|
||||
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success; mlabel = '# successes (count)'; suffix = '';
|
||||
end
|
||||
warnState = warning('off', 'all'); rng(1);
|
||||
day0 = min(D.day);
|
||||
tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
|
||||
tbl0 = table(beh, D.day - day0, 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\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: %s (success COUNT; day within-window)\n', FORMULA)];
|
||||
s = sprintf('%s\nPOWER SIMULATION -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: %s (behavior = %s; day within-window)\n', FORMULA, mlabel)];
|
||||
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 a power simulation (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
s = [s sprintf('\nInsufficient data for a power simulation.\n')];
|
||||
localFinish(s, here, suffix); warning(warnState); return
|
||||
end
|
||||
|
||||
lme = fitlme(tbl0, FORMULA);
|
||||
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
|
||||
days = (0:max(tbl0.day))';
|
||||
|
||||
s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
|
||||
s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
|
||||
bInt, sRat, sRes, numel(days))];
|
||||
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
|
||||
s = [s sprintf(' %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -70,20 +73,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
end
|
||||
end
|
||||
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
|
||||
'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'power_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
==============================================================================
|
||||
VARIATION: naive_boxa_d6_13
|
||||
VARIATION: naive_boxa_d6_13 [metric: success COUNT]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
|
||||
day = training day within window (0 = first analyzed day)
|
||||
@@ -60,9 +60,9 @@ effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
stim x day (interaction) t(69)= 0.63 F(1)= 0.397 p=0.5308 p=0.5309 (df=65)
|
||||
day (learning) t(69)= 4.26 F(1)= 18.109 p=6.446e-05 p=6.931e-05 (df=64)
|
||||
stim (main, window start) t(69)= 2.21 F(1)= 4.886 p=0.03039 p=0.0412 (df=17)
|
||||
interaction 95% CI: [-1.89, +3.64]
|
||||
interaction 95% CI: [-1.893, +3.641]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,8.5)=0.16, p=0.6966
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.5308, slope diff=+0.87)
|
||||
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.5308, slope diff=+0.8737)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
|
||||
@@ -0,0 +1,68 @@
|
||||
==============================================================================
|
||||
VARIATION: naive_boxa_d6_13 [metric: success RATE (success/attempts)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE (success/attempts))
|
||||
day = training day within window (0 = first analyzed day)
|
||||
treatment (stim=1): Electrode-Box-B2
|
||||
control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
|
||||
N = 10 rats, 73 sessions raw day coverage: treat 6..13, control 6..13
|
||||
(equal day coverage over this window)
|
||||
|
||||
==============================================================================
|
||||
FULL MODEL SUMMARY -- fitlme
|
||||
==============================================================================
|
||||
|
||||
Linear mixed-effects model fit by ML
|
||||
|
||||
Model information:
|
||||
Number of observations 73
|
||||
Fixed effects coefficients 4
|
||||
Random effects coefficients 10
|
||||
Covariance parameters 2
|
||||
|
||||
Formula:
|
||||
behavior ~ 1 + day*stim + (1 | rat)
|
||||
|
||||
Model fit statistics:
|
||||
AIC BIC LogLikelihood Deviance
|
||||
-143.92 -130.17 77.958 -155.92
|
||||
|
||||
Fixed effects coefficients (95% CIs):
|
||||
Name Estimate SE tStat DF pValue
|
||||
{'(Intercept)'} 0.50639 0.029658 17.074 69 1.7857e-26
|
||||
{'day' } 0.013881 0.0046444 2.9887 69 0.0038784
|
||||
{'stim' } 0.13179 0.05426 2.4289 69 0.017756
|
||||
{'day:stim' } 0.0016506 0.0088296 0.18693 69 0.85226
|
||||
|
||||
|
||||
Lower Upper
|
||||
0.44722 0.56555
|
||||
0.0046157 0.023146
|
||||
0.023544 0.24004
|
||||
-0.015964 0.019265
|
||||
|
||||
Random effects covariance parameters (95% CIs):
|
||||
Group: rat (10 Levels)
|
||||
Name1 Name2 Type Estimate
|
||||
{'(Intercept)'} {'(Intercept)'} {'std'} 0.061972
|
||||
|
||||
|
||||
Lower Upper
|
||||
0.036939 0.10397
|
||||
|
||||
Group: Error
|
||||
Name Estimate Lower Upper
|
||||
{'Res Std'} 0.073458 0.061716 0.087434
|
||||
|
||||
|
||||
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
----------------------------------------------------------------------------
|
||||
stim x day (interaction) t(69)= 0.19 F(1)= 0.035 p=0.8523 p=0.8523 (df=65)
|
||||
day (learning) t(69)= 2.99 F(1)= 8.933 p=0.003878 p=0.003964 (df=64)
|
||||
stim (main, window start) t(69)= 2.43 F(1)= 5.899 p=0.01776 p=0.02572 (df=18)
|
||||
interaction 95% CI: [-0.01596, +0.01927]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,8.1)=0.00, p=0.9836
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.8523, slope diff=+0.001651)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
@@ -1,30 +1,58 @@
|
||||
% Variation analysis -- the paper's linear mixed model on the successful-reach
|
||||
% COUNT, fit on this folder's curated data subset.
|
||||
% Variation analysis -- the paper's linear mixed model on this folder's data,
|
||||
% for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> result.txt
|
||||
% metric = rate : behavior = success / attempts -> result_rate.txt
|
||||
% (rate uses only sessions with attempts > 0)
|
||||
%
|
||||
% model: behavior ~ stim + day + stim:day + (1|rat)
|
||||
% behavior = successful reaches (count per session)
|
||||
% stim = 1 for the treatment group(s), 0 for the control group(s)
|
||||
% day = training day within this window (0 = first analyzed day)
|
||||
% rat = subject (random intercept)
|
||||
%
|
||||
% Self-contained: reads data.csv beside this script and writes result.txt.
|
||||
% Run headless from this folder with: matlab -batch "analyze"
|
||||
% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
|
||||
% stim = 1 treatment / 0 control; day = training day within window (0 =
|
||||
% first analyzed day); rat = subject (random intercept).
|
||||
% For the interaction we report residual DF, Satterthwaite DF, and the honest
|
||||
% per-animal random-slope test. Self-contained: reads data.csv beside this
|
||||
% script. Run headless with: matlab -batch "analyze"
|
||||
% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
|
||||
tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
|
||||
Rc = localAnalyze(D, 'count', here, vname);
|
||||
Rr = localAnalyze(D, 'rate', here, vname);
|
||||
|
||||
% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
|
||||
VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
|
||||
'interP', Rc.interP, 'interEst', Rc.interEst, ...
|
||||
'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
|
||||
'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
|
||||
'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
|
||||
|
||||
% ------------------------------------------------------------------ helper
|
||||
function R = localAnalyze(D, metric, here, vname)
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :);
|
||||
beh = D.success ./ D.total;
|
||||
mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success;
|
||||
mlabel = 'success COUNT'; suffix = '';
|
||||
end
|
||||
R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
|
||||
'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
|
||||
'nObs', height(D), 'covEqual', false);
|
||||
if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
|
||||
localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
|
||||
vname, mlabel), here, suffix);
|
||||
return
|
||||
end
|
||||
|
||||
tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
|
||||
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
|
||||
m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
|
||||
C = m.Coefficients; A = anova(m); ci = coefCI(m);
|
||||
As = anova(m, 'DFMethod', 'satterthwaite'); % Satterthwaite denominator DF
|
||||
As = anova(m, 'DFMethod', 'satterthwaite');
|
||||
|
||||
% Honest test: refit with a per-animal random SLOPE so the interaction DF
|
||||
% collapses toward the animal count (guarded -- may not converge in short windows).
|
||||
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
|
||||
wst = warning('off', 'all');
|
||||
try
|
||||
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f
|
||||
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
|
||||
As.pValue(gs(t)), As.DF2(gs(t)));
|
||||
|
||||
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
|
||||
maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
|
||||
maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
|
||||
if abs(maxT - maxC) > 2
|
||||
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
|
||||
else
|
||||
cov = '(equal day coverage over this window)';
|
||||
end
|
||||
|
||||
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
|
||||
if pI >= 0.05
|
||||
verdict = 'n.s. -- slopes parallel (no differential learning rate)';
|
||||
elseif eI > 0
|
||||
verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
|
||||
else
|
||||
verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
|
||||
end
|
||||
if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
|
||||
elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
|
||||
else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
|
||||
|
||||
bar = repmat('=', 1, 78);
|
||||
raw = regexprep(evalc('disp(m)'), '</?strong>', '');
|
||||
s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)\n')];
|
||||
s = sprintf('%s\nVARIATION: %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = %s)\n', mlabel)];
|
||||
s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
|
||||
s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
|
||||
s = [s sprintf('control (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
|
||||
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
|
||||
s = [s row('stim x day (interaction)', 'day:stim')];
|
||||
s = [s row('day (learning)', 'day')];
|
||||
s = [s row('stim (main, window start)', 'stim')];
|
||||
s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
|
||||
s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
|
||||
if rsOk
|
||||
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
|
||||
else
|
||||
@@ -82,17 +105,18 @@ else
|
||||
end
|
||||
s = [s sprintf([' (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
|
||||
' model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
|
||||
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
|
||||
s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
|
||||
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
|
||||
s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
|
||||
|
||||
localWrite(s, here, suffix);
|
||||
|
||||
R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
|
||||
'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
|
||||
'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
|
||||
end
|
||||
|
||||
function localWrite(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'result.txt'), 'w');
|
||||
fprintf(fid, '%s', s);
|
||||
fclose(fid);
|
||||
|
||||
% Machine-readable handoff for the summary table (see make_variations.m).
|
||||
VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
|
||||
'nObs', height(D), 'interP', pI, 'interEst', eI, ...
|
||||
'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
|
||||
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
|
||||
'covEqual', abs(maxT - maxC) <= 2);
|
||||
fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 39 KiB After Width: | Height: | Size: 39 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 40 KiB |
@@ -1,7 +1,7 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- prev_f_full
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- prev_f_full [metric: # successes (count)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = # successes (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
|
||||
|
||||
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,227)=8.356 p(resid)=0.004218 p(Satt)=0.004253
|
||||
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) ---
|
||||
--- power simulation (log-day ground truth: stim:logday=+2.389, ratSD=4.328, resSD=4.338) ---
|
||||
|
||||
true stim:log(day) = +2.39 (100% of observed)
|
||||
true stim:log(day) = +2.389 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.15 | 0.28
|
||||
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.012) = 0.112 (small-medium; f: .10 smal
|
||||
16 | 0.92 | 0.94
|
||||
24 | 0.99 | 0.98
|
||||
|
||||
true stim:log(day) = +1.19 (50% of observed)
|
||||
true stim:log(day) = +1.195 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.05 | 0.09
|
||||
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.012) = 0.112 (small-medium; f: .10 smal
|
||||
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.
|
||||
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
|
||||
|
||||
@@ -0,0 +1,35 @@
|
||||
==============================================================================
|
||||
LOG-DAY MODEL + COHEN'S f + POWER -- prev_f_full [metric: success RATE]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = success RATE)
|
||||
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)=2.888 p(resid)=0.0906 p(Satt)=0.09072 (df=208)
|
||||
honest per-animal random slope (log-day): F(1,23.7)=2.05 p=0.1655
|
||||
Cohen's f (interaction, partial eta^2=0.006) = 0.079 (small; f: .10 small, .25 medium, .40 large)
|
||||
|
||||
--- power simulation (log-day ground truth: stim:logday=+0.02595, ratSD=0.06838, resSD=0.08017) ---
|
||||
|
||||
true stim:log(day) = +0.02595 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.06 | 0.07
|
||||
5 | 0.17 | 0.18
|
||||
8 | 0.33 | 0.38
|
||||
12 | 0.38 | 0.47 <- observed
|
||||
16 | 0.55 | 0.62
|
||||
24 | 0.64 | 0.60
|
||||
|
||||
true stim:log(day) = +0.01298 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.04 | 0.04
|
||||
5 | 0.07 | 0.11
|
||||
8 | 0.10 | 0.12
|
||||
12 | 0.17 | 0.17 <- observed
|
||||
16 | 0.10 | 0.15
|
||||
24 | 0.15 | 0.17
|
||||
|
||||
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
|
||||
@@ -1,50 +1,52 @@
|
||||
% Variation log-day analysis + Cohen's f + power simulation.
|
||||
% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> logpower_result.txt
|
||||
% metric = rate : behavior = success / attempts -> logpower_result_rate.txt
|
||||
%
|
||||
% The paper's power code models behavior against LOG training day, not raw day:
|
||||
% The paper's power code models behavior against LOG training 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"
|
||||
% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
|
||||
% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
|
||||
% refits that model, reports the interaction (residual / Satterthwaite / honest
|
||||
% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
|
||||
% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
|
||||
% 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);
|
||||
localLogPower(D, 'count', here, vname);
|
||||
localLogPower(D, 'rate', here, vname);
|
||||
|
||||
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
|
||||
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
|
||||
% ---------------------------------------------------------------- per metric
|
||||
function localLogPower(D, metric, here, vname)
|
||||
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' = log(day+1)
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success; mlabel = '# successes (count)'; suffix = '';
|
||||
end
|
||||
warnState = warning('off', 'all'); rng(1);
|
||||
logday = log(D.day + 1);
|
||||
tbl0 = table(beh, 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 = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = %s)\n', mlabel)];
|
||||
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
|
||||
s = [s sprintf('\nInsufficient data for this analysis.\n')];
|
||||
localFinish(s, here, suffix); 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');
|
||||
@@ -52,7 +54,6 @@ 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)');
|
||||
@@ -76,19 +77,18 @@ 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
|
||||
days = unique(tbl0.day);
|
||||
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
|
||||
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\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>
|
||||
s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -102,19 +102,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
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);
|
||||
s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,11 +1,12 @@
|
||||
% Variation learning-curve plot, in the style of the paper:
|
||||
% Variation learning-curve plots, in the style of the paper:
|
||||
% "Lines indicate mean (and SEM) across animals in the anodal (red) and
|
||||
% control (blue) groups."
|
||||
% Plots mean +/- SEM successful reaches per training day for the treatment /
|
||||
% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
|
||||
% this folder's data.csv and saving learning_curve.png. The per-group N is read
|
||||
% from the data (each variation pools different groups), so the legend shows the
|
||||
% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
|
||||
% Produces TWO figures from this folder's data.csv:
|
||||
% learning_curve.png # successes (count) per training day
|
||||
% learning_curve_rate.png success rate (success/attempts) per training day
|
||||
% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
|
||||
% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
|
||||
% the x-axis tick labels are drawn vertically.
|
||||
% Run: matlab -batch "plotcurve"
|
||||
% (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
|
||||
|
||||
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
days = unique(D.day); % 0-indexed
|
||||
days = unique(D.day);
|
||||
xd = days + 1; % plot as 1-indexed training day (paper axis)
|
||||
|
||||
red = [0.85 0.10 0.10];
|
||||
blue = [0.10 0.30 0.85];
|
||||
|
||||
[Ma, Sa, na] = localCurve(D, 1, days); % anodal / treatment (stim = 1)
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days); % control (stim = 0)
|
||||
localPlot(D, days, xd, 'count', '# successes', ...
|
||||
fullfile(here, 'learning_curve.png'), vname, red, blue);
|
||||
localPlot(D, days, xd, 'rate', 'success rate', ...
|
||||
fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
|
||||
|
||||
% ------------------------------------------------------------------ helpers
|
||||
function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
|
||||
[Ma, Sa, na] = localCurve(D, 1, days, metric); % anodal / treatment
|
||||
[Mc, Sc, nc] = localCurve(D, 0, days, metric); % control
|
||||
fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
|
||||
hold on
|
||||
e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
|
||||
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
|
||||
hold off
|
||||
legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
|
||||
'Location', 'northwest', 'Box', 'off');
|
||||
xlabel('training day');
|
||||
ylabel('# successes');
|
||||
xlabel('training day'); ylabel(ylab);
|
||||
title(vname, 'Interpreter', 'none');
|
||||
set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
|
||||
|
||||
outFile = fullfile(here, 'learning_curve.png');
|
||||
xtickangle(90); % vertical x-axis tick labels
|
||||
exportgraphics(fig, outFile, 'Resolution', 150);
|
||||
close(fig);
|
||||
fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
|
||||
fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
|
||||
end
|
||||
|
||||
% ------------------------------------------------------------------ helper
|
||||
function [M, S, n] = localCurve(D, stimVal, days)
|
||||
%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
|
||||
function [M, S, n] = localCurve(D, stimVal, days, metric)
|
||||
%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
|
||||
subs = unique(D.subject(D.stim == stimVal));
|
||||
n = numel(subs);
|
||||
X = nan(numel(days), n);
|
||||
for j = 1:n
|
||||
for i = 1:numel(days)
|
||||
r = D.subject == subs(j) & D.day == days(i);
|
||||
if any(r); X(i, j) = mean(D.success(r)); end
|
||||
if ~any(r); continue; end
|
||||
if strcmp(metric, 'rate')
|
||||
tot = sum(D.total(r));
|
||||
if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
|
||||
else
|
||||
X(i, j) = mean(D.success(r));
|
||||
end
|
||||
end
|
||||
end
|
||||
M = mean(X, 2, 'omitnan');
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- prev_f_full
|
||||
POWER SIMULATION -- prev_f_full [metric: # successes (count)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (success COUNT; day within-window)
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = # successes (count); day within-window)
|
||||
observed groups: stim n=12, control n=12 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=+0.56/day, rat SD=4.32, residual SD=4.49, days=10
|
||||
ground truth: stim:day=+0.5602/day, rat SD=4.316, residual SD=4.489, days=10
|
||||
|
||||
true stim:day interaction = +0.56 (100% of observed)
|
||||
true stim:day interaction = +0.5602 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.13 | 0.27
|
||||
@@ -15,7 +15,7 @@ ground truth: stim:day=+0.56/day, rat SD=4.32, residual SD=4.49, days=10
|
||||
16 | 0.91 | 0.93
|
||||
24 | 0.98 | 0.98
|
||||
|
||||
true stim:day interaction = +0.28 (50% of observed)
|
||||
true stim:day interaction = +0.2801 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.04 | 0.07
|
||||
@@ -25,5 +25,4 @@ ground truth: stim:day=+0.56/day, rat SD=4.32, residual SD=4.49, days=10
|
||||
16 | 0.33 | 0.37
|
||||
24 | 0.45 | 0.47
|
||||
|
||||
Read the per-animal column as the honest power. At the observed N this study
|
||||
is typically underpowered; per-animal power reaches ~0.8 only at larger N.
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
|
||||
@@ -0,0 +1,28 @@
|
||||
==============================================================================
|
||||
POWER SIMULATION -- prev_f_full [metric: success RATE]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE; day within-window)
|
||||
observed groups: stim n=12, control n=12 nrep=120, alpha=0.05
|
||||
ground truth: stim:day=+0.006292/day, rat SD=0.06824, residual SD=0.08152, days=10
|
||||
|
||||
true stim:day interaction = +0.006292 (100% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.06 | 0.09
|
||||
5 | 0.17 | 0.16
|
||||
8 | 0.28 | 0.35
|
||||
12 | 0.37 | 0.41 <- observed
|
||||
16 | 0.55 | 0.62
|
||||
24 | 0.62 | 0.62
|
||||
|
||||
true stim:day interaction = +0.003146 (50% of observed)
|
||||
N/group | per-animal power | LME power
|
||||
------------------------------------------
|
||||
3 | 0.03 | 0.04
|
||||
5 | 0.07 | 0.05
|
||||
8 | 0.12 | 0.15
|
||||
12 | 0.17 | 0.18 <- observed
|
||||
16 | 0.12 | 0.17
|
||||
24 | 0.15 | 0.13
|
||||
|
||||
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
|
||||
@@ -1,45 +1,49 @@
|
||||
% Variation power simulation -- Monte-Carlo power for the paper's stim x day
|
||||
% interaction, using THIS folder's data as the ground truth.
|
||||
% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
|
||||
% metric = count : behavior = # successes -> power_result.txt
|
||||
% metric = rate : behavior = success / attempts -> power_result_rate.txt
|
||||
%
|
||||
% Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
|
||||
% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
|
||||
% and residual SD generate NREP synthetic datasets at each rats-per-group N and
|
||||
% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
|
||||
% scored at alpha = 0.05 two ways:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
|
||||
% honest power, matching the random-slope / per-animal inference)
|
||||
% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
|
||||
% generate NREP synthetic datasets at each rats-per-group N and each true-effect
|
||||
% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
|
||||
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
|
||||
% LME : the fitlme stim:day p (observation-level DF -- optimistic)
|
||||
% Writes power_result.txt beside this script. Run: matlab -batch "powersim"
|
||||
% Writes power_result[_rate].txt. Run: matlab -batch "powersim"
|
||||
% (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
|
||||
|
||||
here = fileparts(mfilename('fullpath'));
|
||||
if isempty(here); here = pwd; end
|
||||
vname = regexprep(here, '.*[/\\]', '');
|
||||
|
||||
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
|
||||
|
||||
localPower(D, 'count', here, vname);
|
||||
localPower(D, 'rate', here, vname);
|
||||
|
||||
% ---------------------------------------------------------------- per metric
|
||||
function localPower(D, metric, here, vname)
|
||||
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
|
||||
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
|
||||
NS = [3 5 8 12 16 24];
|
||||
EFFMULS = [1 0.5];
|
||||
NREP = 120;
|
||||
|
||||
warnState = warning('off', 'all');
|
||||
rng(1);
|
||||
|
||||
if strcmp(metric, 'rate')
|
||||
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
|
||||
else
|
||||
beh = D.success; mlabel = '# successes (count)'; suffix = '';
|
||||
end
|
||||
warnState = warning('off', 'all'); rng(1);
|
||||
day0 = min(D.day);
|
||||
tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
|
||||
tbl0 = table(beh, D.day - day0, 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\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
|
||||
s = [s sprintf('model: %s (success COUNT; day within-window)\n', FORMULA)];
|
||||
s = sprintf('%s\nPOWER SIMULATION -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
|
||||
s = [s sprintf('model: %s (behavior = %s; day within-window)\n', FORMULA, mlabel)];
|
||||
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 a power simulation (need >=2 animals/group and >=2 days).\n')];
|
||||
localFinish(s, here); warning(warnState); return
|
||||
s = [s sprintf('\nInsufficient data for a power simulation.\n')];
|
||||
localFinish(s, here, suffix); warning(warnState); return
|
||||
end
|
||||
|
||||
lme = fitlme(tbl0, FORMULA);
|
||||
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
|
||||
psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
|
||||
days = (0:max(tbl0.day))';
|
||||
|
||||
s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
|
||||
s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
|
||||
bInt, sRat, sRes, numel(days))];
|
||||
|
||||
for eMul = EFFMULS
|
||||
bI = bInt * eMul;
|
||||
s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
|
||||
s = [s sprintf(' %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
|
||||
s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
|
||||
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
|
||||
for N = NS
|
||||
sigPA = 0; sigL = 0;
|
||||
for r = 1:NREP
|
||||
@@ -70,20 +73,18 @@ for eMul = EFFMULS
|
||||
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>
|
||||
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
|
||||
end
|
||||
end
|
||||
|
||||
s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
|
||||
'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
|
||||
|
||||
localFinish(s, here);
|
||||
s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
|
||||
localFinish(s, here, suffix);
|
||||
warning(warnState);
|
||||
end
|
||||
|
||||
% ---------------------------------------------------------------- helpers
|
||||
function localFinish(s, here)
|
||||
function localFinish(s, here, suffix)
|
||||
fprintf('%s', s);
|
||||
fid = fopen(fullfile(here, 'power_result.txt'), 'w');
|
||||
fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
|
||||
fprintf(fid, '%s', s); fclose(fid);
|
||||
end
|
||||
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
==============================================================================
|
||||
VARIATION: prev_f_full
|
||||
VARIATION: prev_f_full [metric: success COUNT]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
|
||||
day = training day within window (0 = first analyzed day)
|
||||
@@ -60,9 +60,9 @@ effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
stim x day (interaction) t(227)= 2.66 F(1)= 7.088 p=0.008315 p=0.008365 (df=208)
|
||||
day (learning) t(227)= 8.94 F(1)= 79.838 p=1.43e-16 p=2.16e-16 (df=209)
|
||||
stim (main, window start) t(227)= 0.92 F(1)= 0.839 p=0.3606 p=0.3656 (df=37)
|
||||
interaction 95% CI: [+0.15, +0.97]
|
||||
interaction 95% CI: [+0.1456, +0.9749]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,24.0)=3.01, p=0.09541
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.008315, slope diff=+0.56)
|
||||
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.008315, slope diff=+0.5602)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
|
||||
@@ -0,0 +1,68 @@
|
||||
==============================================================================
|
||||
VARIATION: prev_f_full [metric: success RATE (success/attempts)]
|
||||
==============================================================================
|
||||
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE (success/attempts))
|
||||
day = training day within window (0 = first analyzed day)
|
||||
treatment (stim=1): b2_f
|
||||
control (stim=0): a2_f
|
||||
N = 24 rats, 231 sessions raw day coverage: treat 0..9, control 0..9
|
||||
(equal day coverage over this window)
|
||||
|
||||
==============================================================================
|
||||
FULL MODEL SUMMARY -- fitlme
|
||||
==============================================================================
|
||||
|
||||
Linear mixed-effects model fit by ML
|
||||
|
||||
Model information:
|
||||
Number of observations 231
|
||||
Fixed effects coefficients 4
|
||||
Random effects coefficients 24
|
||||
Covariance parameters 2
|
||||
|
||||
Formula:
|
||||
behavior ~ 1 + day*stim + (1 | rat)
|
||||
|
||||
Model fit statistics:
|
||||
AIC BIC LogLikelihood Deviance
|
||||
-441.62 -420.96 226.81 -453.62
|
||||
|
||||
Fixed effects coefficients (95% CIs):
|
||||
Name Estimate SE tStat DF pValue
|
||||
{'(Intercept)'} 0.25306 0.024155 10.477 227 3.3213e-21
|
||||
{'day' } 0.017111 0.0027396 6.246 227 2.0536e-09
|
||||
{'stim' } 0.040889 0.034129 1.1981 227 0.23214
|
||||
{'day:stim' } 0.006292 0.0038198 1.6472 227 0.1009
|
||||
|
||||
|
||||
Lower Upper
|
||||
0.20547 0.30066
|
||||
0.011713 0.02251
|
||||
-0.026361 0.10814
|
||||
-0.0012348 0.013819
|
||||
|
||||
Random effects covariance parameters (95% CIs):
|
||||
Group: rat (24 Levels)
|
||||
Name1 Name2 Type Estimate
|
||||
{'(Intercept)'} {'(Intercept)'} {'std'} 0.06824
|
||||
|
||||
|
||||
Lower Upper
|
||||
0.049364 0.094332
|
||||
|
||||
Group: Error
|
||||
Name Estimate Lower Upper
|
||||
{'Res Std'} 0.081522 0.07404 0.08976
|
||||
|
||||
|
||||
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
|
||||
----------------------------------------------------------------------------
|
||||
stim x day (interaction) t(227)= 1.65 F(1)= 2.713 p=0.1009 p=0.101 (df=209)
|
||||
day (learning) t(227)= 6.25 F(1)= 39.012 p=2.054e-09 p=2.316e-09 (df=209)
|
||||
stim (main, window start) t(227)= 1.20 F(1)= 1.435 p=0.2321 p=0.2378 (df=41)
|
||||
interaction 95% CI: [-0.001235, +0.01382]
|
||||
HONEST LME (per-animal random slope, day|rat): interaction F(1,22.9)=1.78, p=0.1956
|
||||
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
|
||||
model above is the honest learning-rate test -- DF collapses toward the animal count.)
|
||||
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1009, slope diff=+0.006292)
|
||||
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
|
||||
Reference in New Issue
Block a user