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analysis(matlab): rate + count outputs, variation batch 3

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Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Experiments DB Dev
2026-07-24 02:32:52 -04:00
parent 6ba21a1a35
commit 0e16690c8f
72 changed files with 1875 additions and 897 deletions
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analysis/matlab/variations/naive_boxa_d0_10/analyze.m
+63 -39
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@@ -1,30 +1,58 @@
% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
% Variation analysis -- the paper's linear mixed model on this folder's data,
% for BOTH metrics:
% metric = count : behavior = # successes -> result.txt
% metric = rate : behavior = success / attempts -> result_rate.txt
% (rate uses only sessions with attempts > 0)
%
% model: behavior ~ stim + day + stim:day + (1|rat)
% behavior = successful reaches (count per session)
% stim = 1 for the treatment group(s), 0 for the control group(s)
% day = training day within this window (0 = first analyzed day)
% rat = subject (random intercept)
%
% Self-contained: reads data.csv beside this script and writes result.txt.
% Run headless from this folder with: matlab -batch "analyze"
% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
% stim = 1 treatment / 0 control; day = training day within window (0 =
% first analyzed day); rat = subject (random intercept).
% For the interaction we report residual DF, Satterthwaite DF, and the honest
% per-animal random-slope test. Self-contained: reads data.csv beside this
% script. Run headless with: matlab -batch "analyze"
% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
vname = regexprep(here, '.*[/\\]', '');
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
Rc = localAnalyze(D, 'count', here, vname);
Rr = localAnalyze(D, 'rate', here, vname);
% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
'interP', Rc.interP, 'interEst', Rc.interEst, ...
'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
% ------------------------------------------------------------------ helper
function R = localAnalyze(D, metric, here, vname)
if strcmp(metric, 'rate')
D = D(D.total > 0, :);
beh = D.success ./ D.total;
mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
else
beh = D.success;
mlabel = 'success COUNT'; suffix = '';
end
R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
'nObs', height(D), 'covEqual', false);
if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
vname, mlabel), here, suffix);
return
end
tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
C = m.Coefficients; A = anova(m); ci = coefCI(m);
As = anova(m, 'DFMethod', 'satterthwaite'); % Satterthwaite denominator DF
As = anova(m, 'DFMethod', 'satterthwaite');
% Honest test: refit with a per-animal random SLOPE so the interaction DF
% collapses toward the animal count (guarded -- may not converge in short windows).
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
wst = warning('off', 'all');
try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
As.pValue(gs(t)), As.DF2(gs(t)));
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
else
cov = '(equal day coverage over this window)';
end
ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
if pI >= 0.05
verdict = 'n.s. -- slopes parallel (no differential learning rate)';
elseif eI > 0
verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
else
verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
end
if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
bar = repmat('=', 1, 78);
raw = regexprep(evalc('disp(m)'), '</?strong>', '');
s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)\n')];
s = sprintf('%s\nVARIATION: %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat) (behavior = %s)\n', mlabel)];
s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
s = [s sprintf('control (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
s = [s row('stim x day (interaction)', 'day:stim')];
s = [s row('day (learning)', 'day')];
s = [s row('stim (main, window start)', 'stim')];
s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
if rsOk
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
else
@@ -82,17 +105,18 @@ else
end
s = [s sprintf([' (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
' model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
localWrite(s, here, suffix);
R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
end
function localWrite(s, here, suffix)
fprintf('%s', s);
fid = fopen(fullfile(here, 'result.txt'), 'w');
fprintf(fid, '%s', s);
fclose(fid);
% Machine-readable handoff for the summary table (see make_variations.m).
VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
'nObs', height(D), 'interP', pI, 'interEst', eI, ...
'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
fprintf(fid, '%s', s); fclose(fid);
end
analysis/matlab/variations/naive_boxa_d0_10/learning_curve.png
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analysis/matlab/variations/naive_boxa_d0_10/logpower_result.txt
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@@ -1,7 +1,7 @@
==============================================================================
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_10
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=8 nrep=120, alpha=0.05
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,111)=3.705 p(resid)=0.05681 p(Satt)=0.05696 (
honest per-animal random slope (log-day): F(1,8.2)=2.03 p=0.1913
Cohen's f (interaction, partial eta^2=0.007) = 0.086 (small; f: .10 small, .25 medium, .40 large)
--- power simulation (log-day ground truth: stim:logday=+8.43, ratSD=8.13, resSD=14.95) ---
--- power simulation (log-day ground truth: stim:logday=+8.428, ratSD=8.126, resSD=14.95) ---
true stim:log(day) = +8.43 (100% of observed)
true stim:log(day) = +8.428 (100% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.17 | 0.35 <- observed
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.007) = 0.086 (small; f: .10 small, .25
16 | 0.95 | 0.96
24 | 0.99 | 1.00
true stim:log(day) = +4.21 (50% of observed)
true stim:log(day) = +4.214 (50% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.07 | 0.07 <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.007) = 0.086 (small; f: .10 small, .25
16 | 0.55 | 0.53
24 | 0.63 | 0.62
Read the per-animal column as the honest power; the LME column matches the
paper's power code (anova interaction p, observation-level DF) and is optimistic.
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
analysis/matlab/variations/naive_boxa_d0_10/logpower_result_rate.txt
+35
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@@ -0,0 +1,35 @@
==============================================================================
LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=8 nrep=120, alpha=0.05
--- fitted on real data ---
stim x log(day) interaction: F(1,109)=4.842 p(resid)=0.02988 p(Satt)=0.03001 (df=103)
honest per-animal random slope (log-day): F(1,7.9)=3.23 p=0.1106
Cohen's f (interaction, partial eta^2=0.013) = 0.116 (small-medium; f: .10 small, .25 medium, .40 large)
--- power simulation (log-day ground truth: stim:logday=+0.06771, ratSD=0.04956, resSD=0.1034) ---
true stim:log(day) = +0.06771 (100% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.23 | 0.47 <- observed
5 | 0.57 | 0.71
8 | 0.85 | 0.87 <- observed
12 | 0.98 | 0.99
16 | 1.00 | 1.00
24 | 1.00 | 1.00
true stim:log(day) = +0.03385 (50% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.07 | 0.12 <- observed
5 | 0.12 | 0.17
8 | 0.32 | 0.39 <- observed
12 | 0.36 | 0.39
16 | 0.63 | 0.66
24 | 0.75 | 0.78
Read per-animal as the honest power; LME matches the paper's power code (optimistic).
analysis/matlab/variations/naive_boxa_d0_10/logpowersim.m
+38 -39
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@@ -1,50 +1,52 @@
% Variation log-day analysis + Cohen's f + power simulation.
% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
% metric = count : behavior = # successes -> logpower_result.txt
% metric = rate : behavior = success / attempts -> logpower_result_rate.txt
%
% The paper's power code models behavior against LOG training day, not raw day:
% The paper's power code models behavior against LOG training day:
% behavior ~ stim + log(day) + stim:log(day) + (1|rat).
% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
% "Day 1"), so log(day + 1) reproduces their transform exactly.
%
% This script (a) refits that log-day model on data.csv, (b) reports the
% interaction (residual DF, Satterthwaite DF, and the honest per-animal
% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
% scoring per-animal (cluster-honest) and LME power across N.
% Writes logpower_result.txt. Run: matlab -batch "logpowersim"
% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
% refits that model, reports the interaction (residual / Satterthwaite / honest
% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
% Run: matlab -batch "logpowersim"
% (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', '');
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' column = log(day+1)
NS = [3 5 8 12 16 24];
EFFMULS = [1 0.5];
NREP = 120;
warnState = warning('off', 'all');
rng(1);
localLogPower(D, 'count', here, vname);
localLogPower(D, 'rate', here, vname);
logday = log(D.day + 1); % 0-indexed day -> their log(1-indexed day)
tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
% ---------------------------------------------------------------- per metric
function localLogPower(D, metric, here, vname)
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)'; % 'day' = log(day+1)
if strcmp(metric, 'rate')
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
else
beh = D.success; mlabel = '# successes (count)'; suffix = '';
end
warnState = warning('off', 'all'); rng(1);
logday = log(D.day + 1);
tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
nStim = numel(unique(D.subject(D.stim == 1)));
nCtrl = numel(unique(D.subject(D.stim == 0)));
bar = repmat('=', 1, 78);
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (success COUNT)\n')];
s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat) (behavior = %s)\n', mlabel)];
s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
localFinish(s, here); warning(warnState); return
s = [s sprintf('\nInsufficient data for this analysis.\n')];
localFinish(s, here, suffix); warning(warnState); return
end
% ---- fitted model on the real data ----
full = fitlme(tbl0, FORMULA);
An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
cohenf = sqrt(eta2part / (1 - eta2part));
% honest per-animal random-slope interaction
rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
try
mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f (%s; f: .10 small, .25 medium, .40 large)\n', ...
eta2part, cohenf, mag)];
% ---- power simulation on the log-day ground truth ----
cn = full.CoefficientNames; be = full.fixedEffects;
b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
days = unique(tbl0.day); % the log(day) grid
days = unique(tbl0.day);
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
bInt, sRat, sRes)];
for eMul = EFFMULS
bI = bInt * eMul;
s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
for N = NS
sigPA = 0; sigL = 0;
for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
end
star = '';
if N == nStim || N == nCtrl; star = ' <- observed'; end
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
end
end
s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
localFinish(s, here);
s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
localFinish(s, here, suffix);
warning(warnState);
end
% ---------------------------------------------------------------- helpers
function localFinish(s, here)
function localFinish(s, here, suffix)
fprintf('%s', s);
fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
fprintf(fid, '%s', s); fclose(fid);
end
analysis/matlab/variations/naive_boxa_d0_10/plotcurve.m
+30 -20
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@@ -1,11 +1,12 @@
% Variation learning-curve plot, in the style of the paper:
% Variation learning-curve plots, in the style of the paper:
% "Lines indicate mean (and SEM) across animals in the anodal (red) and
% control (blue) groups."
% Plots mean +/- SEM successful reaches per training day for the treatment /
% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
% this folder's data.csv and saving learning_curve.png. The per-group N is read
% from the data (each variation pools different groups), so the legend shows the
% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
% Produces TWO figures from this folder's data.csv:
% learning_curve.png # successes (count) per training day
% learning_curve_rate.png success rate (success/attempts) per training day
% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
% the x-axis tick labels are drawn vertically.
% Run: matlab -batch "plotcurve"
% (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', '');
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
days = unique(D.day); % 0-indexed
xd = days + 1; % plot as 1-indexed training day (paper axis)
days = unique(D.day);
xd = days + 1; % plot as 1-indexed training day (paper axis)
red = [0.85 0.10 0.10];
blue = [0.10 0.30 0.85];
[Ma, Sa, na] = localCurve(D, 1, days); % anodal / treatment (stim = 1)
[Mc, Sc, nc] = localCurve(D, 0, days); % control (stim = 0)
localPlot(D, days, xd, 'count', '# successes', ...
fullfile(here, 'learning_curve.png'), vname, red, blue);
localPlot(D, days, xd, 'rate', 'success rate', ...
fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
% ------------------------------------------------------------------ helpers
function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
[Ma, Sa, na] = localCurve(D, 1, days, metric); % anodal / treatment
[Mc, Sc, nc] = localCurve(D, 0, days, metric); % control
fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
hold on
e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
hold off
legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
'Location', 'northwest', 'Box', 'off');
xlabel('training day');
ylabel('# successes');
xlabel('training day'); ylabel(ylab);
title(vname, 'Interpreter', 'none');
set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
outFile = fullfile(here, 'learning_curve.png');
xtickangle(90); % vertical x-axis tick labels
exportgraphics(fig, outFile, 'Resolution', 150);
close(fig);
fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
end
% ------------------------------------------------------------------ helper
function [M, S, n] = localCurve(D, stimVal, days)
%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
function [M, S, n] = localCurve(D, stimVal, days, metric)
%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
subs = unique(D.subject(D.stim == stimVal));
n = numel(subs);
X = nan(numel(days), n);
for j = 1:n
for i = 1:numel(days)
r = D.subject == subs(j) & D.day == days(i);
if any(r); X(i, j) = mean(D.success(r)); end
if ~any(r); continue; end
if strcmp(metric, 'rate')
tot = sum(D.total(r));
if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
else
X(i, j) = mean(D.success(r));
end
end
end
M = mean(X, 2, 'omitnan');
analysis/matlab/variations/naive_boxa_d0_10/power_result.txt
+6 -7
View File
@@ -1,11 +1,11 @@
==============================================================================
POWER SIMULATION -- naive_boxa_d0_10
POWER SIMULATION -- naive_boxa_d0_10 [metric: # successes (count)]
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (success COUNT; day within-window)
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = # successes (count); day within-window)
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
ground truth: stim:day=+1.18/day, rat SD=8.67, residual SD=13.71, days=11
ground truth: stim:day=+1.178/day, rat SD=8.671, residual SD=13.71, days=11
true stim:day interaction = +1.18 (100% of observed)
true stim:day interaction = +1.178 (100% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.07 | 0.17 <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.18/day, rat SD=8.67, residual SD=13.71, days=11
16 | 0.68 | 0.70
24 | 0.88 | 0.88
true stim:day interaction = +0.59 (50% of observed)
true stim:day interaction = +0.589 (50% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.05 | 0.07 <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.18/day, rat SD=8.67, residual SD=13.71, days=11
16 | 0.28 | 0.27
24 | 0.28 | 0.29
Read the per-animal column as the honest power. At the observed N this study
is typically underpowered; per-animal power reaches ~0.8 only at larger N.
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
analysis/matlab/variations/naive_boxa_d0_10/power_result_rate.txt
+28
View File
@@ -0,0 +1,28 @@
==============================================================================
POWER SIMULATION -- naive_boxa_d0_10 [metric: success RATE]
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE; day within-window)
observed groups: stim n=3, control n=8 nrep=120, alpha=0.05
ground truth: stim:day=+0.008893/day, rat SD=0.05104, residual SD=0.09808, days=11
true stim:day interaction = +0.008893 (100% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.08 | 0.16 <- observed
5 | 0.21 | 0.25
8 | 0.36 | 0.43 <- observed
12 | 0.62 | 0.68
16 | 0.72 | 0.73
24 | 0.88 | 0.88
true stim:day interaction = +0.004446 (50% of observed)
N/group | per-animal power | LME power
------------------------------------------
3 | 0.07 | 0.08 <- observed
5 | 0.05 | 0.08
8 | 0.11 | 0.11 <- observed
12 | 0.17 | 0.19
16 | 0.30 | 0.29
24 | 0.33 | 0.35
Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
analysis/matlab/variations/naive_boxa_d0_10/powersim.m
+35 -34
View File
@@ -1,45 +1,49 @@
% Variation power simulation -- Monte-Carlo power for the paper's stim x day
% interaction, using THIS folder's data as the ground truth.
% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
% metric = count : behavior = # successes -> power_result.txt
% metric = rate : behavior = success / attempts -> power_result_rate.txt
%
% Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
% and residual SD generate NREP synthetic datasets at each rats-per-group N and
% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
% scored at alpha = 0.05 two ways:
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
% honest power, matching the random-slope / per-animal inference)
% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
% generate NREP synthetic datasets at each rats-per-group N and each true-effect
% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
% per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
% LME : the fitlme stim:day p (observation-level DF -- optimistic)
% Writes power_result.txt beside this script. Run: matlab -batch "powersim"
% Writes power_result[_rate].txt. Run: matlab -batch "powersim"
% (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', '');
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
localPower(D, 'count', here, vname);
localPower(D, 'rate', here, vname);
% ---------------------------------------------------------------- per metric
function localPower(D, metric, here, vname)
NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
NS = [3 5 8 12 16 24];
EFFMULS = [1 0.5];
NREP = 120;
warnState = warning('off', 'all');
rng(1);
if strcmp(metric, 'rate')
D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
else
beh = D.success; mlabel = '# successes (count)'; suffix = '';
end
warnState = warning('off', 'all'); rng(1);
day0 = min(D.day);
tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
'VariableNames', {'behavior', 'day', 'stim', 'rat'});
nStim = numel(unique(D.subject(D.stim == 1)));
nCtrl = numel(unique(D.subject(D.stim == 0)));
bar = repmat('=', 1, 78);
s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
s = [s sprintf('model: %s (success COUNT; day within-window)\n', FORMULA)];
s = sprintf('%s\nPOWER SIMULATION -- %s [metric: %s]\n%s\n', bar, vname, mlabel, bar);
s = [s sprintf('model: %s (behavior = %s; day within-window)\n', FORMULA, mlabel)];
s = [s sprintf('observed groups: stim n=%d, control n=%d nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
localFinish(s, here); warning(warnState); return
s = [s sprintf('\nInsufficient data for a power simulation.\n')];
localFinish(s, here, suffix); warning(warnState); return
end
lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
days = (0:max(tbl0.day))';
s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
bInt, sRat, sRes, numel(days))];
for eMul = EFFMULS
bI = bInt * eMul;
s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
s = [s sprintf(' %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
s = [s sprintf(' %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
s = [s sprintf(' %-8s | per-animal power | LME power\n %s\n', 'N/group', repmat('-', 1, 42))];
for N = NS
sigPA = 0; sigL = 0;
for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
end
star = '';
if N == nStim || N == nCtrl; star = ' <- observed'; end
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
s = [s sprintf(' %-8d | %5.2f | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
end
end
s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
localFinish(s, here);
s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
localFinish(s, here, suffix);
warning(warnState);
end
% ---------------------------------------------------------------- helpers
function localFinish(s, here)
function localFinish(s, here, suffix)
fprintf('%s', s);
fid = fopen(fullfile(here, 'power_result.txt'), 'w');
fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
fprintf(fid, '%s', s); fclose(fid);
end
analysis/matlab/variations/naive_boxa_d0_10/result.txt
+4 -4
View File
@@ -1,5 +1,5 @@
==============================================================================
VARIATION: naive_boxa_d0_10
VARIATION: naive_boxa_d0_10 [metric: success COUNT]
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
stim x day (interaction) t(111)= 1.31 F(1)= 1.707 p=0.1941 p=0.1943 (df=104)
day (learning) t(111)= 16.91 F(1)=285.824 p=1.743e-32 p=6.758e-32 (df=106)
stim (main, window start) t(111)= 1.55 F(1)= 2.392 p=0.1248 p=0.1361 (df=22)
interaction 95% CI: [-0.61, +2.96]
interaction 95% CI: [-0.6088, +2.965]
HONEST LME (per-animal random slope, day|rat): interaction F(1,10.1)=0.83, p=0.3836
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
model above is the honest learning-rate test -- DF collapses toward the animal count.)
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1941, slope diff=+1.18)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1941, slope diff=+1.178)
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/naive_boxa_d0_10/result_rate.txt
+68
View File
@@ -0,0 +1,68 @@
==============================================================================
VARIATION: naive_boxa_d0_10 [metric: success RATE (success/attempts)]
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success RATE (success/attempts))
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
N = 11 rats, 113 sessions raw day coverage: treat 0..10, control 0..10
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 113
Fixed effects coefficients 4
Random effects coefficients 11
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
-177.53 -161.16 94.764 -189.53
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 0.19077 0.027729 6.8796 109 3.9606e-10
{'day' } 0.044032 0.0036365 12.108 109 6.709e-22
{'stim' } 0.064146 0.051551 1.2443 109 0.21605
{'day:stim' } 0.0088928 0.0065096 1.3661 109 0.17471
Lower Upper
0.13581 0.24572
0.036824 0.051239
-0.038027 0.16632
-0.0040089 0.021795
Random effects covariance parameters (95% CIs):
Group: rat (11 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 0.05104
Lower Upper
0.028096 0.092721
Group: Error
Name Estimate Lower Upper
{'Res Std'} 0.09808 0.085453 0.11257
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
----------------------------------------------------------------------------
stim x day (interaction) t(109)= 1.37 F(1)= 1.866 p=0.1747 p=0.1749 (df=103)
day (learning) t(109)= 12.11 F(1)=146.612 p=6.709e-22 p=1.131e-21 (df=105)
stim (main, window start) t(109)= 1.24 F(1)= 1.548 p=0.2161 p=0.2244 (df=26)
interaction 95% CI: [-0.004009, +0.02179]
HONEST LME (per-animal random slope, day|rat): interaction F(1,9.6)=1.38, p=0.2681
(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
model above is the honest learning-rate test -- DF collapses toward the animal count.)
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1747, slope diff=+0.008893)
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.