analysis(matlab): rate + count outputs, variation batch 4

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

analysis/matlab/variations/right_only_d0_10/analyze.m

@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
+% Variation analysis -- the paper's linear mixed model on this folder's data,
-% COUNT, fit on this folder's curated data subset.
+% 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 treatment / 0 control; day = training day within window (0 =
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
+%     first analyzed day); rat = subject (random intercept).
-%     day      = training day within this window (0 = first analyzed day)
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
-%     rat      = subject (random intercept)
+% per-animal random-slope test. Self-contained: reads data.csv beside this
-%
+% script. Run headless with:  matlab -batch "analyze"
-% Self-contained: reads data.csv beside this script and writes result.txt.
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a 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
-
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-if pI >= 0.05
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
-    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 = 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 = success COUNT)\n')];
+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('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\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('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');
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
-fprintf(fid, '%s', s);
+fprintf(fid, '%s', s); fclose(fid);
-fclose(fid);
+end
-
-% 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);

analysis/matlab/variations/right_only_d0_10/logpower_result.txt

@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d0_10
+LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d0_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=4, control n=3   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,68)=8.420  p(resid)=0.004998  p(Satt)=0.005011
   honest per-animal random slope (log-day): F(1,22.8)=7.55  p=0.01151
 Cohen's f (interaction, partial eta^2=0.016) = 0.126  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+11.36, ratSD=4.91, resSD=11.42) ---
+--- power simulation (log-day ground truth: stim:logday=+11.36, ratSD=4.911, resSD=11.42) ---
 
  true stim:log(day) = +11.36 (100% of observed)
   N/group  | per-animal power | LME power
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.016) = 0.126  (small-medium; f: .10 smal
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +5.68 (50% of observed)
+ true stim:log(day) = +5.682 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.13            |  0.29  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.016) = 0.126  (small-medium; f: .10 smal
   16       |  0.93            |  0.93
   24       |  0.99            |  0.99
 
-Read the per-animal column as the honest power; the LME column matches the
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
-paper's power code (anova interaction p, observation-level DF) and is optimistic.

analysis/matlab/variations/right_only_d0_10/logpower_result_rate.txt

@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d0_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=4, control n=3   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,68)=13.480  p(resid)=0.0004756  p(Satt)=0.0004808 (df=67)
+  honest per-animal random slope (log-day): F(1,62.4)=13.42  p=0.0005158
+Cohen's f (interaction, partial eta^2=0.033) = 0.185  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.095, ratSD=0.04259, resSD=0.07524) ---
+
+ true stim:log(day) = +0.095 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.63            |  0.95  <- observed
+  5        |  0.99            |  1.00
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.0475 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.23            |  0.42  <- observed
+  5        |  0.55            |  0.66
+  8        |  0.78            |  0.82
+  12       |  0.96            |  0.97
+  16       |  0.98            |  1.00
+  24       |  0.99            |  1.00
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).

analysis/matlab/variations/right_only_d0_10/logpowersim.m

@@ -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
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
-%
+% refits that model, reports the interaction (residual / Satterthwaite / honest
-% This script (a) refits that log-day model on data.csv, (b) reports the
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
+% Run:  matlab -batch "logpowersim"
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
+localLogPower(D, 'count', here, vname);
-rng(1);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
+% ---------------------------------------------------------------- per metric
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+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 = 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)   (success COUNT)\n')];
+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')];
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\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))]; %#ok<AGROW>
+    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' ...
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
+localFinish(s, here, suffix);
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/right_only_d0_10/plotcurve.m

@@ -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 /
+% Produces TWO figures from this folder's data.csv:
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
+%   learning_curve.png       # successes (count)   per training day
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
+%   learning_curve_rate.png  success rate (success/attempts) per training day
-% from the data (each variation pools different groups), so the legend shows the
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% 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)
+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)
+localPlot(D, days, xd, 'count', '# successes', ...
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+    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');
+xlabel('training day'); ylabel(ylab);
-ylabel('# successes');
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
+xtickangle(90);                  % vertical x-axis tick labels
-outFile = fullfile(here, 'learning_curve.png');
 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, metric)
-function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
 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');

analysis/matlab/variations/right_only_d0_10/power_result.txt

@@ -1,9 +1,9 @@
 ==============================================================================
-POWER SIMULATION -- right_only_d0_10
+POWER SIMULATION -- right_only_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=4, control n=3   nrep=120, alpha=0.05
-ground truth: stim:day=+1.73/day, rat SD=5.35, residual SD=11.96, days=11
+ground truth: stim:day=+1.73/day, rat SD=5.346, residual SD=11.96, days=11
 
  true stim:day interaction = +1.73 (100% of observed)
   N/group  | per-animal power | LME power
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.73/day, rat SD=5.35, residual SD=11.96, days=11
   16       |  0.98            |  0.98
   24       |  1.00            |  1.00
 
- true stim:day interaction = +0.87 (50% of observed)
+ true stim:day interaction = +0.865 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.12            |  0.12  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.73/day, rat SD=5.35, residual SD=11.96, days=11
   16       |  0.62            |  0.62
   24       |  0.74            |  0.71
 
-Read the per-animal column as the honest power. At the observed N this study
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.

analysis/matlab/variations/right_only_d0_10/power_result_rate.txt

@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- right_only_d0_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=4, control n=3   nrep=120, alpha=0.05
+ground truth: stim:day=+0.01393/day, rat SD=0.04403, residual SD=0.08402, days=11
+
+ true stim:day interaction = +0.01393 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.28            |  0.53  <- observed
+  5        |  0.65            |  0.81
+  8        |  0.89            |  0.90
+  12       |  0.99            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.006967 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.13            |  0.16  <- observed
+  5        |  0.15            |  0.23
+  8        |  0.36            |  0.44
+  12       |  0.43            |  0.48
+  16       |  0.72            |  0.72
+  24       |  0.84            |  0.87
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).

analysis/matlab/variations/right_only_d0_10/powersim.m

@@ -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,
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
-% scored at alpha = 0.05 two ways:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
 %   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];
+if strcmp(metric, 'rate')
-EFFMULS = [1 0.5];
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
-NREP = 120;
+else
-
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
-warnState = warning('off', 'all');
+end
-rng(1);
+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 = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+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')];
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
     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; LME is optimistic (obs-level DF).\n')];
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
+localFinish(s, here, suffix);
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/right_only_d0_10/result.txt

@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: right_only_d0_10
+VARIATION: right_only_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(68)=  1.83 F(1)=  3.336  p=0.07218    p=0.07218 (df=68)
 day (learning)             t(68)= 10.55 F(1)=111.222  p=5.943e-16    p=4.533e-16 (df=70)
 stim (main, window start)  t(68)=  0.37 F(1)=  0.134  p=0.7159    p=0.7187 (df=19)
-interaction 95% CI: [-0.16, +3.62]
+interaction 95% CI: [-0.1602, +3.62]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,20.5)=2.56, p=0.1246
   (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=0.07218, slope diff=+1.73)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.

analysis/matlab/variations/right_only_d0_10/result_rate.txt

@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: right_only_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, Right-Electrode
+control   (stim=0): Electrode-Box-A2
+N = 7 rats, 72 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              72
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -130.99    -117.33    71.495           -142.99 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate      SE           tStat       DF
+    {'(Intercept)'}           0.22977     0.037841      6.0719    68
+    {'day'        }          0.040867    0.0053407       7.652    68
+    {'stim'       }        0.00041563     0.049782    0.008349    68
+    {'day:stim'   }          0.013934     0.006676      2.0872    68
+
+
+    pValue        Lower         Upper   
+    6.3632e-08       0.15426     0.30528
+    9.3879e-11       0.03021    0.051524
+       0.99336     -0.098922    0.099754
+      0.040617    0.00061265    0.027256
+
+Random effects covariance parameters (95% CIs):
+Group: rat (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.044032
+
+
+    Lower       Upper  
+    0.021261    0.09119
+
+Group: Error
+    Name               Estimate    Lower       Upper   
+    {'Res Std'}        0.084023    0.070735    0.099809
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(68)=  2.09 F(1)=  4.357  p=0.04062    p=0.04064 (df=68)
+day (learning)             t(68)=  7.65 F(1)= 58.553  p=9.388e-11    p=8.647e-11 (df=69)
+stim (main, window start)  t(68)=  0.01 F(1)=  0.000  p=0.9934    p=0.9934 (df=17)
+interaction 95% CI: [+0.0006126, +0.02726]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,67.1)=4.40, p=0.03968
+  (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=0.04062, slope diff=+0.01393)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.

analysis/matlab/variations/right_only_d0_13/analyze.m

@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
+% Variation analysis -- the paper's linear mixed model on this folder's data,
-% COUNT, fit on this folder's curated data subset.
+% 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 treatment / 0 control; day = training day within window (0 =
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
+%     first analyzed day); rat = subject (random intercept).
-%     day      = training day within this window (0 = first analyzed day)
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
-%     rat      = subject (random intercept)
+% per-animal random-slope test. Self-contained: reads data.csv beside this
-%
+% script. Run headless with:  matlab -batch "analyze"
-% Self-contained: reads data.csv beside this script and writes result.txt.
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a 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
-
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-if pI >= 0.05
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
-    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 = 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 = success COUNT)\n')];
+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('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\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('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');
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
-fprintf(fid, '%s', s);
+fprintf(fid, '%s', s); fclose(fid);
-fclose(fid);
+end
-
-% 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);

analysis/matlab/variations/right_only_d0_13/logpower_result.txt

@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d0_13
+LOG-DAY MODEL + COHEN'S f + POWER -- right_only_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=4, control n=3   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,80)=13.182  p(resid)=0.0004969  p(Satt)=0.00049
   honest per-animal random slope (log-day): F(1,18.9)=11.35  p=0.003237
 Cohen's f (interaction, partial eta^2=0.017) = 0.131  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+12.18, ratSD=5.40, resSD=10.82) ---
+--- power simulation (log-day ground truth: stim:logday=+12.18, ratSD=5.4, resSD=10.82) ---
 
  true stim:log(day) = +12.18 (100% of observed)
   N/group  | per-animal power | LME power
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.017) = 0.131  (small-medium; f: .10 smal
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +6.09 (50% of observed)
+ true stim:log(day) = +6.089 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.25            |  0.47  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.017) = 0.131  (small-medium; f: .10 smal
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
-Read the per-animal column as the honest power; the LME column matches the
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
-paper's power code (anova interaction p, observation-level DF) and is optimistic.

analysis/matlab/variations/right_only_d0_13/logpower_result_rate.txt

@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- right_only_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=4, control n=3   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,80)=20.061  p(resid)=2.465e-05  p(Satt)=2.466e-05 (df=80)
+  honest per-animal random slope (log-day): F(1,71.2)=19.98  p=2.882e-05
+Cohen's f (interaction, partial eta^2=0.035) = 0.191  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.1007, ratSD=0.04245, resSD=0.07232) ---
+
+ true stim:log(day) = +0.1007 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.85            |  0.99  <- observed
+  5        |  1.00            |  1.00
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.05035 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.40            |  0.66  <- observed
+  5        |  0.74            |  0.86
+  8        |  0.97            |  0.99
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).

analysis/matlab/variations/right_only_d0_13/logpowersim.m

@@ -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
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
-%
+% refits that model, reports the interaction (residual / Satterthwaite / honest
-% This script (a) refits that log-day model on data.csv, (b) reports the
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
+% Run:  matlab -batch "logpowersim"
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
+localLogPower(D, 'count', here, vname);
-rng(1);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
+% ---------------------------------------------------------------- per metric
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+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 = 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)   (success COUNT)\n')];
+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')];
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\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))]; %#ok<AGROW>
+    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' ...
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
+localFinish(s, here, suffix);
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/right_only_d0_13/plotcurve.m

@@ -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 /
+% Produces TWO figures from this folder's data.csv:
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
+%   learning_curve.png       # successes (count)   per training day
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
+%   learning_curve_rate.png  success rate (success/attempts) per training day
-% from the data (each variation pools different groups), so the legend shows the
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% 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)
+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)
+localPlot(D, days, xd, 'count', '# successes', ...
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+    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');
+xlabel('training day'); ylabel(ylab);
-ylabel('# successes');
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
+xtickangle(90);                  % vertical x-axis tick labels
-outFile = fullfile(here, 'learning_curve.png');
 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, metric)
-function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
 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');

analysis/matlab/variations/right_only_d0_13/power_result.txt

@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- right_only_d0_13
+POWER SIMULATION -- right_only_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=4, control n=3   nrep=120, alpha=0.05
-ground truth: stim:day=+1.70/day, rat SD=0.00, residual SD=14.12, days=14
+ground truth: stim:day=+1.7/day, rat SD=3.135e-15, residual SD=14.12, days=14
 
- true stim:day interaction = +1.70 (100% of observed)
+ true stim:day interaction = +1.7 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.28            |  0.67  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.70/day, rat SD=0.00, residual SD=14.12, days=14
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:day interaction = +0.85 (50% of observed)
+ true stim:day interaction = +0.8501 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.15            |  0.24  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.70/day, rat SD=0.00, residual SD=14.12, days=14
   16       |  0.75            |  0.82
   24       |  0.87            |  0.85
 
-Read the per-animal column as the honest power. At the observed N this study
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.

analysis/matlab/variations/right_only_d0_13/power_result_rate.txt

@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- right_only_d0_13   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=4, control n=3   nrep=120, alpha=0.05
+ground truth: stim:day=+0.01351/day, rat SD=0.03866, residual SD=0.09009, days=14
+
+ true stim:day interaction = +0.01351 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.37            |  0.82  <- observed
+  5        |  0.85            |  0.93
+  8        |  0.99            |  0.99
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.006756 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.20            |  0.34  <- observed
+  5        |  0.33            |  0.39
+  8        |  0.66            |  0.69
+  12       |  0.78            |  0.82
+  16       |  0.93            |  0.94
+  24       |  0.95            |  0.94
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).

analysis/matlab/variations/right_only_d0_13/powersim.m

@@ -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,
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
-% scored at alpha = 0.05 two ways:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
 %   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];
+if strcmp(metric, 'rate')
-EFFMULS = [1 0.5];
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
-NREP = 120;
+else
-
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
-warnState = warning('off', 'all');
+end
-rng(1);
+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 = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+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')];
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
     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; LME is optimistic (obs-level DF).\n')];
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
+localFinish(s, here, suffix);
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/right_only_d0_13/result.txt

@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: right_only_d0_13
+VARIATION: right_only_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(80)=  2.01 F(1)=  4.031  p=0.04804    p=0.04788 (df=84)
 day (learning)             t(80)=  9.45 F(1)= 89.224  p=1.171e-14    p=7.518e-15 (df=84)
 stim (main, window start)  t(80)=  0.63 F(1)=  0.401  p=0.5285    p=0.5284 (df=84)
-interaction 95% CI: [+0.02, +3.39]
+interaction 95% CI: [+0.01501, +3.385]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,20.2)=2.92, p=0.1029
   (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=0.04804, slope diff=+1.70)
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.04804, slope diff=+1.7)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.

analysis/matlab/variations/right_only_d0_13/result_rate.txt

@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: right_only_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, Right-Electrode
+control   (stim=0): Electrode-Box-A2
+N = 7 rats, 84 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              84
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -145.93    -131.35    78.967           -157.93 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE           tStat      DF    pValue    
+    {'(Intercept)'}          0.25632     0.035875     7.1447    80    3.7358e-10
+    {'day'        }         0.032358    0.0046124     7.0154    80    6.6273e-10
+    {'stim'       }        0.0062182     0.046891    0.13261    80       0.89483
+    {'day:stim'   }         0.013512    0.0056067       2.41    80      0.018249
+
+
+    Lower        Upper   
+      0.18492     0.32771
+     0.023179    0.041537
+    -0.087097    0.099534
+    0.0023542     0.02467
+
+Random effects covariance parameters (95% CIs):
+Group: rat (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.038665
+
+
+    Lower       Upper   
+    0.017826    0.083864
+
+Group: Error
+    Name               Estimate    Lower       Upper 
+    {'Res Std'}        0.090086    0.076927    0.1055
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(80)=  2.41 F(1)=  5.808  p=0.01825    p=0.01818 (df=82)
+day (learning)             t(80)=  7.02 F(1)= 49.216  p=6.627e-10    p=5.568e-10 (df=83)
+stim (main, window start)  t(80)=  0.13 F(1)=  0.018  p=0.8948    p=0.8959 (df=19)
+interaction 95% CI: [+0.002354, +0.02467]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,81.6)=5.93, p=0.01709
+  (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=0.01825, slope diff=+0.01351)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.

analysis/matlab/variations/right_only_d0_5/analyze.m

@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
+% Variation analysis -- the paper's linear mixed model on this folder's data,
-% COUNT, fit on this folder's curated data subset.
+% 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 treatment / 0 control; day = training day within window (0 =
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
+%     first analyzed day); rat = subject (random intercept).
-%     day      = training day within this window (0 = first analyzed day)
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
-%     rat      = subject (random intercept)
+% per-animal random-slope test. Self-contained: reads data.csv beside this
-%
+% script. Run headless with:  matlab -batch "analyze"
-% Self-contained: reads data.csv beside this script and writes result.txt.
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a 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
-
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-if pI >= 0.05
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
-    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 = 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 = success COUNT)\n')];
+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('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\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('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');
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
-fprintf(fid, '%s', s);
+fprintf(fid, '%s', s); fclose(fid);
-fclose(fid);
+end
-
-% 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);

analysis/matlab/variations/right_only_d0_5/logpower_result.txt

@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d0_5
+LOG-DAY MODEL + COHEN'S f + POWER -- right_only_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=4, control n=3   nrep=120, alpha=0.05
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,38)=5.667  p(resid)=0.02241  p(Satt)=0.0219 (df
   honest per-animal random slope (log-day): F(1,9.0)=4.64  p=0.05966
 Cohen's f (interaction, partial eta^2=0.036) = 0.194  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+16.20, ratSD=0.00, resSD=13.20) ---
+--- power simulation (log-day ground truth: stim:logday=+16.2, ratSD=0, resSD=13.2) ---
 
- true stim:log(day) = +16.20 (100% of observed)
+ true stim:log(day) = +16.2 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.33            |  0.65  <- observed
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.036) = 0.194  (small-medium; f: .10 smal
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +8.10 (50% of observed)
+ true stim:log(day) = +8.101 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.09            |  0.24  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.036) = 0.194  (small-medium; f: .10 smal
   16       |  0.69            |  0.69
   24       |  0.92            |  0.92
 
-Read the per-animal column as the honest power; the LME column matches the
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
-paper's power code (anova interaction p, observation-level DF) and is optimistic.

analysis/matlab/variations/right_only_d0_5/logpower_result_rate.txt

@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- right_only_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=4, control n=3   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,38)=15.000  p(resid)=0.0004109  p(Satt)=0.0004502 (df=35)
+  honest per-animal random slope (log-day): F(1,10.8)=11.68  p=0.005855
+Cohen's f (interaction, partial eta^2=0.111) = 0.353  (medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.1583, ratSD=0.04781, resSD=0.07931) ---
+
+ true stim:log(day) = +0.1583 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.62            |  0.93  <- observed
+  5        |  0.95            |  0.98
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.07917 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.16            |  0.42  <- observed
+  5        |  0.52            |  0.62
+  8        |  0.81            |  0.82
+  12       |  0.93            |  0.92
+  16       |  0.98            |  0.98
+  24       |  1.00            |  1.00
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).

analysis/matlab/variations/right_only_d0_5/logpowersim.m

@@ -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
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
-%
+% refits that model, reports the interaction (residual / Satterthwaite / honest
-% This script (a) refits that log-day model on data.csv, (b) reports the
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
+% Run:  matlab -batch "logpowersim"
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
+localLogPower(D, 'count', here, vname);
-rng(1);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
+% ---------------------------------------------------------------- per metric
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+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 = 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)   (success COUNT)\n')];
+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')];
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\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))]; %#ok<AGROW>
+    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' ...
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
+localFinish(s, here, suffix);
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/right_only_d0_5/plotcurve.m

@@ -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 /
+% Produces TWO figures from this folder's data.csv:
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
+%   learning_curve.png       # successes (count)   per training day
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
+%   learning_curve_rate.png  success rate (success/attempts) per training day
-% from the data (each variation pools different groups), so the legend shows the
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% 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)
+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)
+localPlot(D, days, xd, 'count', '# successes', ...
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+    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');
+xlabel('training day'); ylabel(ylab);
-ylabel('# successes');
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
+xtickangle(90);                  % vertical x-axis tick labels
-outFile = fullfile(here, 'learning_curve.png');
 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, metric)
-function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
 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');

analysis/matlab/variations/right_only_d0_5/power_result.txt

@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- right_only_d0_5
+POWER SIMULATION -- right_only_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=4, control n=3   nrep=120, alpha=0.05
-ground truth: stim:day=+5.31/day, rat SD=5.25, residual SD=9.96, days=6
+ground truth: stim:day=+5.307/day, rat SD=5.248, residual SD=9.964, days=6
 
- true stim:day interaction = +5.31 (100% of observed)
+ true stim:day interaction = +5.307 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.38            |  0.80  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+5.31/day, rat SD=5.25, residual SD=9.96, days=6
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:day interaction = +2.65 (50% of observed)
+ true stim:day interaction = +2.654 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.11            |  0.28  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+5.31/day, rat SD=5.25, residual SD=9.96, days=6
   16       |  0.83            |  0.85
   24       |  0.95            |  0.97
 
-Read the per-animal column as the honest power. At the observed N this study
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.

analysis/matlab/variations/right_only_d0_5/power_result_rate.txt

@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- right_only_d0_5   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=4, control n=3   nrep=120, alpha=0.05
+ground truth: stim:day=+0.04981/day, rat SD=0.04929, residual SD=0.07366, days=6
+
+ true stim:day interaction = +0.04981 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.60            |  0.91  <- observed
+  5        |  0.97            |  0.98
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.0249 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.15            |  0.37  <- observed
+  5        |  0.49            |  0.55
+  8        |  0.73            |  0.81
+  12       |  0.88            |  0.89
+  16       |  0.97            |  0.97
+  24       |  1.00            |  1.00
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).

analysis/matlab/variations/right_only_d0_5/powersim.m

@@ -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,
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
-% scored at alpha = 0.05 two ways:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
 %   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];
+if strcmp(metric, 'rate')
-EFFMULS = [1 0.5];
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
-NREP = 120;
+else
-
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
-warnState = warning('off', 'all');
+end
-rng(1);
+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 = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+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')];
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
     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; LME is optimistic (obs-level DF).\n')];
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
+localFinish(s, here, suffix);
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/right_only_d0_5/result.txt

@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: right_only_d0_5
+VARIATION: right_only_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(38)=  2.92 F(1)=  8.511  p=0.005897    p=0.006128 (df=35)
 day (learning)             t(38)=  7.17 F(1)= 51.382  p=1.458e-08    p=2.321e-08 (df=35)
 stim (main, window start)  t(38)= -0.73 F(1)=  0.534  p=0.4696    p=0.4735 (df=20)
-interaction 95% CI: [+1.62, +8.99]
+interaction 95% CI: [+1.625, +8.99]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,7.0)=3.94, p=0.08763
   (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=0.005897, slope diff=+5.31)
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.005897, slope diff=+5.307)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.

analysis/matlab/variations/right_only_d0_5/result_rate.txt

@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: right_only_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, Right-Electrode
+control   (stim=0): Electrode-Box-A2
+N = 7 rats, 42 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              42
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -78.771    -68.345    45.385           -90.771 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE          tStat      DF    pValue    
+    {'(Intercept)'}           0.2186    0.041919     5.2147    38    6.7762e-06
+    {'day'        }         0.043366    0.010166     4.2656    38    0.00012743
+    {'stim'       }        -0.074496    0.055454    -1.3434    38       0.18712
+    {'day:stim'   }         0.049806    0.013449     3.7033    38    0.00067357
+
+
+    Lower       Upper   
+     0.13374     0.30346
+    0.022785    0.063946
+    -0.18676    0.037766
+     0.02258    0.077032
+
+Random effects covariance parameters (95% CIs):
+Group: rat (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.049289
+
+
+    Lower       Upper  
+    0.023894    0.10168
+
+Group: Error
+    Name               Estimate    Lower       Upper   
+    {'Res Std'}        0.073663    0.058279    0.093108
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(38)=  3.70 F(1)= 13.715  p=0.0006736    p=0.0007295 (df=35)
+day (learning)             t(38)=  4.27 F(1)= 18.195  p=0.0001274    p=0.0001438 (df=35)
+stim (main, window start)  t(38)= -1.34 F(1)=  1.805  p=0.1871    p=0.1975 (df=16)
+interaction 95% CI: [+0.02258, +0.07703]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,7.0)=9.42, p=0.0181
+  (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=0.0006736, slope diff=+0.04981)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.

analysis/matlab/variations/right_only_d6_10/analyze.m

@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
+% Variation analysis -- the paper's linear mixed model on this folder's data,
-% COUNT, fit on this folder's curated data subset.
+% 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 treatment / 0 control; day = training day within window (0 =
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
+%     first analyzed day); rat = subject (random intercept).
-%     day      = training day within this window (0 = first analyzed day)
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
-%     rat      = subject (random intercept)
+% per-animal random-slope test. Self-contained: reads data.csv beside this
-%
+% script. Run headless with:  matlab -batch "analyze"
-% Self-contained: reads data.csv beside this script and writes result.txt.
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a 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
-
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-if pI >= 0.05
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
-    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 = 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 = success COUNT)\n')];
+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('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\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('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');
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
-fprintf(fid, '%s', s);
+fprintf(fid, '%s', s); fclose(fid);
-fclose(fid);
+end
-
-% 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);

analysis/matlab/variations/right_only_d6_10/logpower_result.txt

@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d6_10
+LOG-DAY MODEL + COHEN'S f + POWER -- right_only_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=4, control n=2   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,26)=1.327  p(resid)=0.2599  p(Satt)=0.2608 (df=
   honest per-animal random slope (log-day): F(1,6.0)=0.96  p=0.3641
 Cohen's f (interaction, partial eta^2=0.021) = 0.145  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+23.04, ratSD=5.69, resSD=8.25) ---
+--- power simulation (log-day ground truth: stim:logday=+23.04, ratSD=5.689, resSD=8.25) ---
 
  true stim:log(day) = +23.04 (100% of observed)
   N/group  | per-animal power | LME power
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.021) = 0.145  (small-medium; f: .10 smal
   16       |  0.25            |  0.25
   24       |  0.45            |  0.52
 
-Read the per-animal column as the honest power; the LME column matches the
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
-paper's power code (anova interaction p, observation-level DF) and is optimistic.

analysis/matlab/variations/right_only_d6_10/logpower_result_rate.txt

@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- right_only_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=4, control n=2   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,26)=0.485  p(resid)=0.4924  p(Satt)=0.4929 (df=24)
+  honest per-animal random slope (log-day): F(1,6.0)=0.24  p=0.644
+Cohen's f (interaction, partial eta^2=0.009) = 0.094  (small; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.09523, ratSD=0.03826, resSD=0.05641) ---
+
+ true stim:log(day) = +0.09523 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.04            |  0.10
+  5        |  0.15            |  0.19
+  8        |  0.17            |  0.23
+  12       |  0.20            |  0.27
+  16       |  0.40            |  0.38
+  24       |  0.54            |  0.54
+
+ true stim:log(day) = +0.04761 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.06            |  0.11
+  5        |  0.09            |  0.09
+  8        |  0.06            |  0.07
+  12       |  0.12            |  0.12
+  16       |  0.14            |  0.14
+  24       |  0.22            |  0.22
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).

analysis/matlab/variations/right_only_d6_10/logpowersim.m

@@ -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
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
-%
+% refits that model, reports the interaction (residual / Satterthwaite / honest
-% This script (a) refits that log-day model on data.csv, (b) reports the
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
+% Run:  matlab -batch "logpowersim"
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
+localLogPower(D, 'count', here, vname);
-rng(1);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
+% ---------------------------------------------------------------- per metric
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+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 = 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)   (success COUNT)\n')];
+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')];
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\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))]; %#ok<AGROW>
+    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' ...
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
+localFinish(s, here, suffix);
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/right_only_d6_10/plotcurve.m

@@ -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 /
+% Produces TWO figures from this folder's data.csv:
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
+%   learning_curve.png       # successes (count)   per training day
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
+%   learning_curve_rate.png  success rate (success/attempts) per training day
-% from the data (each variation pools different groups), so the legend shows the
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% 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)
+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)
+localPlot(D, days, xd, 'count', '# successes', ...
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+    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');
+xlabel('training day'); ylabel(ylab);
-ylabel('# successes');
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
+xtickangle(90);                  % vertical x-axis tick labels
-outFile = fullfile(here, 'learning_curve.png');
 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, metric)
-function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
 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');

analysis/matlab/variations/right_only_d6_10/power_result.txt

@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- right_only_d6_10
+POWER SIMULATION -- right_only_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=4, control n=2   nrep=120, alpha=0.05
-ground truth: stim:day=+2.42/day, rat SD=5.71, residual SD=8.18, days=5
+ground truth: stim:day=+2.425/day, rat SD=5.709, residual SD=8.18, days=5
 
- true stim:day interaction = +2.42 (100% of observed)
+ true stim:day interaction = +2.425 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.06            |  0.25
@@ -15,7 +15,7 @@ ground truth: stim:day=+2.42/day, rat SD=5.71, residual SD=8.18, days=5
   16       |  0.71            |  0.74
   24       |  0.94            |  0.94
 
- true stim:day interaction = +1.21 (50% of observed)
+ true stim:day interaction = +1.212 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.12
@@ -25,5 +25,4 @@ ground truth: stim:day=+2.42/day, rat SD=5.71, residual SD=8.18, days=5
   16       |  0.23            |  0.23
   24       |  0.40            |  0.47
 
-Read the per-animal column as the honest power. At the observed N this study
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.

analysis/matlab/variations/right_only_d6_10/power_result_rate.txt

@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- right_only_d6_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=4, control n=2   nrep=120, alpha=0.05
+ground truth: stim:day=+0.0102/day, rat SD=0.03828, residual SD=0.05632, days=5
+
+ true stim:day interaction = +0.0102 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.09
+  5        |  0.13            |  0.18
+  8        |  0.16            |  0.24
+  12       |  0.19            |  0.24
+  16       |  0.36            |  0.36
+  24       |  0.47            |  0.47
+
+ true stim:day interaction = +0.0051 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.07            |  0.11
+  5        |  0.07            |  0.10
+  8        |  0.06            |  0.08
+  12       |  0.10            |  0.12
+  16       |  0.12            |  0.13
+  24       |  0.19            |  0.19
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).

analysis/matlab/variations/right_only_d6_10/powersim.m

@@ -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,
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
-% scored at alpha = 0.05 two ways:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
 %   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];
+if strcmp(metric, 'rate')
-EFFMULS = [1 0.5];
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
-NREP = 120;
+else
-
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
-warnState = warning('off', 'all');
+end
-rng(1);
+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 = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+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')];
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
     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; LME is optimistic (obs-level DF).\n')];
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
+localFinish(s, here, suffix);
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/right_only_d6_10/result.txt

@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: right_only_d6_10
+VARIATION: right_only_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)
@@ -64,5 +64,5 @@ interaction 95% CI: [-2.18, +7.03]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=0.90, p=0.3784
   (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=0.289, slope diff=+2.42)
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.289, slope diff=+2.425)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.

analysis/matlab/variations/right_only_d6_10/result_rate.txt

@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: right_only_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, Right-Electrode
+control   (stim=0): Electrode-Box-A2
+N = 6 rats, 30 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              30
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC        LogLikelihood    Deviance
+    -68.28    -59.872    40.14            -80.28  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE          tStat      DF    pValue    
+    {'(Intercept)'}          0.52619    0.041043      12.82    26    9.5461e-13
+    {'day'        }        0.0099391    0.012595    0.78916    26       0.43716
+    {'stim'       }         0.090314    0.050267     1.7967    26      0.084015
+    {'day:stim'   }           0.0102    0.015425    0.66127    26       0.51426
+
+
+    Lower        Upper   
+      0.44182     0.61055
+    -0.015949    0.035828
+    -0.013012     0.19364
+    -0.021507    0.041907
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.038283
+
+
+    Lower       Upper   
+    0.016862    0.086916
+
+Group: Error
+    Name               Estimate    Lower       Upper   
+    {'Res Std'}        0.056325    0.042446    0.074741
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(26)=  0.66 F(1)=  0.437  p=0.5143    p=0.5147 (df=24)
+day (learning)             t(26)=  0.79 F(1)=  0.623  p=0.4372    p=0.4377 (df=24)
+stim (main, window start)  t(26)=  1.80 F(1)=  3.228  p=0.08402    p=0.09376 (df=14)
+interaction 95% CI: [-0.02151, +0.04191]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=0.23, p=0.6495
+  (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=0.5143, slope diff=+0.0102)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.

analysis/matlab/variations/right_only_d6_13/analyze.m

@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
+% Variation analysis -- the paper's linear mixed model on this folder's data,
-% COUNT, fit on this folder's curated data subset.
+% 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 treatment / 0 control; day = training day within window (0 =
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
+%     first analyzed day); rat = subject (random intercept).
-%     day      = training day within this window (0 = first analyzed day)
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
-%     rat      = subject (random intercept)
+% per-animal random-slope test. Self-contained: reads data.csv beside this
-%
+% script. Run headless with:  matlab -batch "analyze"
-% Self-contained: reads data.csv beside this script and writes result.txt.
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a 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
-
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-if pI >= 0.05
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
-    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 = 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 = success COUNT)\n')];
+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('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\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('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');
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
-fprintf(fid, '%s', s);
+fprintf(fid, '%s', s); fclose(fid);
-fclose(fid);
+end
-
-% 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);

analysis/matlab/variations/right_only_d6_13/logpower_result.txt

@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- right_only_d6_13
+LOG-DAY MODEL + COHEN'S f + POWER -- right_only_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=4, control n=2   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,38)=5.774  p(resid)=0.02125  p(Satt)=0.02133 (d
   honest per-animal random slope (log-day): F(1,0.0)=3.19  p=NaN
 Cohen's f (interaction, partial eta^2=0.036) = 0.194  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+28.42, ratSD=6.48, resSD=7.39) ---
+--- power simulation (log-day ground truth: stim:logday=+28.42, ratSD=6.482, resSD=7.389) ---
 
  true stim:log(day) = +28.42 (100% of observed)
   N/group  | per-animal power | LME power
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.036) = 0.194  (small-medium; f: .10 smal
   16       |  0.90            |  0.92
   24       |  1.00            |  1.00
 
-Read the per-animal column as the honest power; the LME column matches the
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
-paper's power code (anova interaction p, observation-level DF) and is optimistic.

analysis/matlab/variations/right_only_d6_13/logpower_result_rate.txt

@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- right_only_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=4, control n=2   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,38)=4.975  p(resid)=0.0317  p(Satt)=0.03178 (df=38)
+  honest per-animal random slope (log-day): F(1,4.9)=0.25  p=0.6396
+Cohen's f (interaction, partial eta^2=0.039) = 0.201  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.1853, ratSD=0.04282, resSD=0.05196) ---
+
+ true stim:log(day) = +0.1853 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.53            |  0.80
+  5        |  0.88            |  0.96
+  8        |  0.99            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.09266 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.16            |  0.27
+  5        |  0.31            |  0.42
+  8        |  0.52            |  0.62
+  12       |  0.74            |  0.79
+  16       |  0.86            |  0.89
+  24       |  0.98            |  0.99
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).

analysis/matlab/variations/right_only_d6_13/logpowersim.m

@@ -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
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
-%
+% refits that model, reports the interaction (residual / Satterthwaite / honest
-% This script (a) refits that log-day model on data.csv, (b) reports the
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
+% Run:  matlab -batch "logpowersim"
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
+localLogPower(D, 'count', here, vname);
-rng(1);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
+% ---------------------------------------------------------------- per metric
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+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 = 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)   (success COUNT)\n')];
+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')];
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\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))]; %#ok<AGROW>
+    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' ...
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
+localFinish(s, here, suffix);
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/right_only_d6_13/plotcurve.m

@@ -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 /
+% Produces TWO figures from this folder's data.csv:
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
+%   learning_curve.png       # successes (count)   per training day
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
+%   learning_curve_rate.png  success rate (success/attempts) per training day
-% from the data (each variation pools different groups), so the legend shows the
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% 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)
+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)
+localPlot(D, days, xd, 'count', '# successes', ...
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+    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');
+xlabel('training day'); ylabel(ylab);
-ylabel('# successes');
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
+xtickangle(90);                  % vertical x-axis tick labels
-outFile = fullfile(here, 'learning_curve.png');
 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, metric)
-function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
 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');

analysis/matlab/variations/right_only_d6_13/power_result.txt

@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- right_only_d6_13
+POWER SIMULATION -- right_only_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=4, control n=2   nrep=120, alpha=0.05
-ground truth: stim:day=+2.76/day, rat SD=6.47, residual SD=7.37, days=8
+ground truth: stim:day=+2.758/day, rat SD=6.469, residual SD=7.367, days=8
 
- true stim:day interaction = +2.76 (100% of observed)
+ true stim:day interaction = +2.758 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.56            |  0.83
@@ -15,7 +15,7 @@ ground truth: stim:day=+2.76/day, rat SD=6.47, residual SD=7.37, days=8
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:day interaction = +1.38 (50% of observed)
+ true stim:day interaction = +1.379 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.15            |  0.28
@@ -25,5 +25,4 @@ ground truth: stim:day=+2.76/day, rat SD=6.47, residual SD=7.37, days=8
   16       |  0.93            |  0.93
   24       |  0.99            |  0.99
 
-Read the per-animal column as the honest power. At the observed N this study
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.

analysis/matlab/variations/right_only_d6_13/power_result_rate.txt

@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- right_only_d6_13   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=4, control n=2   nrep=120, alpha=0.05
+ground truth: stim:day=+0.01878/day, rat SD=0.04274, residual SD=0.05164, days=8
+
+ true stim:day interaction = +0.01878 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.54            |  0.82
+  5        |  0.92            |  0.97
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.009388 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.15            |  0.28
+  5        |  0.33            |  0.51
+  8        |  0.58            |  0.65
+  12       |  0.77            |  0.81
+  16       |  0.88            |  0.90
+  24       |  0.98            |  0.99
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).

analysis/matlab/variations/right_only_d6_13/powersim.m

@@ -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,
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
-% scored at alpha = 0.05 two ways:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
 %   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];
+if strcmp(metric, 'rate')
-EFFMULS = [1 0.5];
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
-NREP = 120;
+else
-
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
-warnState = warning('off', 'all');
+end
-rng(1);
+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 = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+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')];
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
     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; LME is optimistic (obs-level DF).\n')];
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
+localFinish(s, here, suffix);
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/right_only_d6_13/result.txt

@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: right_only_d6_13
+VARIATION: right_only_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(38)=  2.32 F(1)=  5.379  p=0.02585    p=0.02591 (df=38)
 day (learning)             t(38)=  1.70 F(1)=  2.885  p=0.09761    p=0.09766 (df=38)
 stim (main, window start)  t(38)=  1.64 F(1)=  2.685  p=0.1096    p=0.1304 (df=11)
-interaction 95% CI: [+0.35, +5.17]
+interaction 95% CI: [+0.3507, +5.165]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,21.2)=4.96, p=0.03697
   (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=0.02585, slope diff=+2.76)
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.02585, slope diff=+2.758)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.

analysis/matlab/variations/right_only_d6_13/result_rate.txt

@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: right_only_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, Right-Electrode
+control   (stim=0): Electrode-Box-A2
+N = 6 rats, 42 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              42
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -107.29    -96.867    59.646           -119.29 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE           tStat      DF    pValue    
+    {'(Intercept)'}          0.53543     0.038925     13.755    38    2.4576e-16
+    {'day'        }        0.0034157    0.0070712    0.48304    38       0.63184
+    {'stim'       }         0.077975     0.047524     1.6407    38        0.1091
+    {'day:stim'   }         0.018776    0.0083266     2.2549    38      0.029981
+
+
+    Lower        Upper   
+      0.45663     0.61423
+    -0.010899    0.017731
+    -0.018233     0.17418
+    0.0019195    0.035632
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.042741
+
+
+    Lower       Upper   
+    0.021774    0.083895
+
+Group: Error
+    Name               Estimate    Lower       Upper   
+    {'Res Std'}        0.051638    0.041022    0.065002
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(38)=  2.25 F(1)=  5.085  p=0.02998    p=0.03003 (df=38)
+day (learning)             t(38)=  0.48 F(1)=  0.233  p=0.6318    p=0.6318 (df=38)
+stim (main, window start)  t(38)=  1.64 F(1)=  2.692  p=0.1091    p=0.1287 (df=11)
+interaction 95% CI: [+0.00192, +0.03563]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,4.0)=0.24, p=0.6492
+  (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=0.02998, slope diff=+0.01878)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.

analysis/matlab/variations/unmerge_d0_10/analyze.m

@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
+% Variation analysis -- the paper's linear mixed model on this folder's data,
-% COUNT, fit on this folder's curated data subset.
+% 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 treatment / 0 control; day = training day within window (0 =
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
+%     first analyzed day); rat = subject (random intercept).
-%     day      = training day within this window (0 = first analyzed day)
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
-%     rat      = subject (random intercept)
+% per-animal random-slope test. Self-contained: reads data.csv beside this
-%
+% script. Run headless with:  matlab -batch "analyze"
-% Self-contained: reads data.csv beside this script and writes result.txt.
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a 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
-
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-if pI >= 0.05
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
-    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 = 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 = success COUNT)\n')];
+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('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\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('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');
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
-fprintf(fid, '%s', s);
+fprintf(fid, '%s', s); fclose(fid);
-fclose(fid);
+end
-
-% 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);

analysis/matlab/variations/unmerge_d0_10/logpower_result.txt

@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d0_10
+LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d0_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=3   nrep=120, alpha=0.05
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,57)=6.230  p(resid)=0.01548  p(Satt)=0.01528 (d
   honest per-animal random slope (log-day): F(1,15.0)=5.28  p=0.0363
 Cohen's f (interaction, partial eta^2=0.017) = 0.131  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+11.60, ratSD=0.00, resSD=12.93) ---
+--- power simulation (log-day ground truth: stim:logday=+11.6, ratSD=0, resSD=12.93) ---
 
- true stim:log(day) = +11.60 (100% of observed)
+ true stim:log(day) = +11.6 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.34            |  0.69  <- observed
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.017) = 0.131  (small-medium; f: .10 smal
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +5.80 (50% of observed)
+ true stim:log(day) = +5.801 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.10            |  0.25  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.017) = 0.131  (small-medium; f: .10 smal
   16       |  0.90            |  0.90
   24       |  0.97            |  0.97
 
-Read the per-animal column as the honest power; the LME column matches the
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
-paper's power code (anova interaction p, observation-level DF) and is optimistic.

analysis/matlab/variations/unmerge_d0_10/logpower_result_rate.txt

@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d0_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=3   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,57)=9.174  p(resid)=0.003684  p(Satt)=0.003695 (df=57)
+  honest per-animal random slope (log-day): F(1,35.1)=8.84  p=0.005302
+Cohen's f (interaction, partial eta^2=0.030) = 0.177  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.08869, ratSD=0.04059, resSD=0.08029) ---
+
+ true stim:log(day) = +0.08869 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.54            |  0.85  <- observed
+  5        |  0.95            |  0.98
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.04434 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.17            |  0.33  <- observed
+  5        |  0.40            |  0.52
+  8        |  0.69            |  0.68
+  12       |  0.89            |  0.90
+  16       |  0.95            |  0.96
+  24       |  0.99            |  0.99
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).

analysis/matlab/variations/unmerge_d0_10/logpowersim.m

@@ -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
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
-%
+% refits that model, reports the interaction (residual / Satterthwaite / honest
-% This script (a) refits that log-day model on data.csv, (b) reports the
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
+% Run:  matlab -batch "logpowersim"
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
+localLogPower(D, 'count', here, vname);
-rng(1);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
+% ---------------------------------------------------------------- per metric
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+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 = 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)   (success COUNT)\n')];
+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')];
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\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))]; %#ok<AGROW>
+    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' ...
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
+localFinish(s, here, suffix);
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/unmerge_d0_10/plotcurve.m

@@ -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 /
+% Produces TWO figures from this folder's data.csv:
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
+%   learning_curve.png       # successes (count)   per training day
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
+%   learning_curve_rate.png  success rate (success/attempts) per training day
-% from the data (each variation pools different groups), so the legend shows the
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% 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)
+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)
+localPlot(D, days, xd, 'count', '# successes', ...
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+    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');
+xlabel('training day'); ylabel(ylab);
-ylabel('# successes');
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
+xtickangle(90);                  % vertical x-axis tick labels
-outFile = fullfile(here, 'learning_curve.png');
 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, metric)
-function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
 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');

analysis/matlab/variations/unmerge_d0_10/power_result.txt

@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- unmerge_d0_10
+POWER SIMULATION -- unmerge_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=3   nrep=120, alpha=0.05
-ground truth: stim:day=+1.56/day, rat SD=5.35, residual SD=12.51, days=11
+ground truth: stim:day=+1.558/day, rat SD=5.354, residual SD=12.51, days=11
 
- true stim:day interaction = +1.56 (100% of observed)
+ true stim:day interaction = +1.558 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.15            |  0.31  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.56/day, rat SD=5.35, residual SD=12.51, days=11
   16       |  0.93            |  0.95
   24       |  0.99            |  1.00
 
- true stim:day interaction = +0.78 (50% of observed)
+ true stim:day interaction = +0.7788 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.11            |  0.11  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.56/day, rat SD=5.35, residual SD=12.51, days=11
   16       |  0.47            |  0.47
   24       |  0.63            |  0.60
 
-Read the per-animal column as the honest power. At the observed N this study
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.

analysis/matlab/variations/unmerge_d0_10/power_result_rate.txt

@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- unmerge_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=3   nrep=120, alpha=0.05
+ground truth: stim:day=+0.01218/day, rat SD=0.04284, residual SD=0.08704, days=11
+
+ true stim:day interaction = +0.01218 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.20            |  0.36  <- observed
+  5        |  0.51            |  0.64
+  8        |  0.77            |  0.79
+  12       |  0.96            |  0.97
+  16       |  0.97            |  0.97
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.006089 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.11  <- observed
+  5        |  0.11            |  0.18
+  8        |  0.28            |  0.31
+  12       |  0.36            |  0.41
+  16       |  0.58            |  0.62
+  24       |  0.71            |  0.72
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).

analysis/matlab/variations/unmerge_d0_10/powersim.m

@@ -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,
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
-% scored at alpha = 0.05 two ways:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
 %   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];
+if strcmp(metric, 'rate')
-EFFMULS = [1 0.5];
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
-NREP = 120;
+else
-
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
-warnState = warning('off', 'all');
+end
-rng(1);
+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 = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+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')];
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
-    localFinish(s, here); warning(warnState); return
+    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('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
     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; LME is optimistic (obs-level DF).\n')];
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
+localFinish(s, here, suffix);
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
 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
 

analysis/matlab/variations/unmerge_d0_10/result.txt

@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: unmerge_d0_10
+VARIATION: unmerge_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(57)=  1.49 F(1)=  2.208  p=0.1428    p=0.1428 (df=57)
 day (learning)             t(57)= 10.08 F(1)=101.556  p=2.832e-14    p=1.931e-14 (df=59)
 stim (main, window start)  t(57)=  0.68 F(1)=  0.466  p=0.4974    p=0.5037 (df=17)
-interaction 95% CI: [-0.54, +3.66]
+interaction 95% CI: [-0.5416, +3.657]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,13.0)=1.46, p=0.2479
   (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=0.1428, slope diff=+1.56)
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1428, slope diff=+1.558)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.

analysis/matlab/variations/unmerge_d0_10/result_rate.txt

@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: unmerge_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-A2
+N = 6 rats, 61 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              61
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -105.35    -92.689    58.677           -117.35 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE           tStat      DF    pValue    
+    {'(Intercept)'}         0.22998     0.038137     6.0304    57    1.2826e-07
+    {'day'        }        0.040747    0.0055227      7.378    57    7.4583e-10
+    {'stim'       }         0.02493     0.053569    0.46538    57       0.64343
+    {'day:stim'   }        0.012178    0.0073113     1.6656    57       0.10127
+
+
+    Lower         Upper   
+       0.15361     0.30635
+      0.029687    0.051806
+     -0.082339      0.1322
+    -0.0024627    0.026819
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.04284 
+
+
+    Lower      Upper   
+    0.01883    0.097462
+
+Group: Error
+    Name               Estimate    Lower       Upper  
+    {'Res Std'}        0.087035    0.072173    0.10496
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(57)=  1.67 F(1)=  2.774  p=0.1013    p=0.1013 (df=57)
+day (learning)             t(57)=  7.38 F(1)= 54.435  p=7.458e-10    p=6.427e-10 (df=59)
+stim (main, window start)  t(57)=  0.47 F(1)=  0.217  p=0.6434    p=0.6483 (df=15)
+interaction 95% CI: [-0.002463, +0.02682]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,40.5)=2.61, p=0.1136
+  (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=0.1013, slope diff=+0.01218)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.