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analysis(matlab): Box-A pooling variations (b2 vs a2+a, b2+a vs a2)

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Add make_boxa_variations.m producing 6 variation folders (data.csv + analyze.m
+ result.txt) for the paper LME over windows 0-5, 6-10, 0-10:
  boxa_a2  B2         vs A2 + Box-A   (b2 vs a2+a)
  boxa_b2  B2 + Box-A vs A2           (b2+a vs a2)
plus variations/boxa_summary.csv (residual / Satterthwaite / random-slope
interaction p). Early window (0-5) is obs-level significant (res p~0.03-0.04)
but n.s. under the honest random-slope test (rs p~0.13); later windows n.s.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Experiments DB Dev
2026-07-23 13:39:01 -04:00
parent b03549982e
commit e1af1af7f4
20 changed files with 1370 additions and 0 deletions
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analysis/matlab/make_boxa_variations.m
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function make_boxa_variations()
%MAKE_BOXA_VARIATIONS Variation folders for the two Box-A pooling groupings.
% MAKE_BOXA_VARIATIONS() writes variations/<grouping>_<window>/ -- each with
% data.csv + analyze.m (a copy of variation_analyze.m) + result.txt, fitting
% the paper LME (behavior ~ stim + day + stim:day + (1|rat)) on the success
% COUNT -- for:
% boxa_a2 B2 vs A2 + Box-A ("b2 vs a2+a")
% boxa_b2 B2 + Box-A vs A2 ("b2+a vs a2")
% over windows d0_5 (0-5), d6_10 (6-10), d0_10 (0-10). Box-A is the
% single-animal Electrode-Box-A condition, pooled into the control (boxa_a2)
% or the treatment (boxa_b2). Also writes variations/boxa_summary.csv with the
% residual / Satterthwaite / honest random-slope interaction p-values.
thisDir = fileparts(mfilename('fullpath'));
template = fullfile(thisDir, 'variation_analyze.m');
root = fullfile(thisDir, 'variations');
if ~exist(root, 'dir'); mkdir(root); end
Tc = tdcs_load_data();
B2 = 'Electrode-Box-B2'; A2 = 'Electrode-Box-A2'; BOXA = 'Electrode-Box-A';
% grouping name | treatment groups (stim=1) | control groups (stim=0)
groupings = {
'boxa_a2', {B2}, {A2, BOXA}
'boxa_b2', {B2, BOXA}, {A2}
};
windows = {'d0_5', [0 5]; 'd6_10', [6 10]; 'd0_10', [0 10]};
allGroup = cellstr(Tc.group);
rows = {};
for gi = 1:size(groupings, 1)
gname = groupings{gi, 1};
inTreat = ismember(allGroup, groupings{gi, 2});
inCtrl = ismember(allGroup, groupings{gi, 3});
for wi = 1:size(windows, 1)
wname = windows{wi, 1}; w = windows{wi, 2};
sel = (inTreat | inCtrl) & Tc.day >= w(1) & Tc.day <= w(2);
D = Tc(sel, :);
stim = double(inTreat(sel));
Dout = table(string(D.subject), string(D.group), D.day, D.success, ...
D.total, stim, 'VariableNames', ...
{'subject', 'group', 'day', 'success', 'total', 'stim'});
vname = [gname '_' wname];
folder = fullfile(root, vname);
if ~exist(folder, 'dir'); mkdir(folder); end
writetable(Dout, fullfile(folder, 'data.csv'));
copyfile(template, fullfile(folder, 'analyze.m'));
r = localRun(fullfile(folder, 'analyze.m'));
rows(end + 1, :) = {vname, gname, wname, r.nRats, r.nObs, ...
r.interP, r.interPsatt, r.interPrs, r.interEst, r.stimP}; %#ok<AGROW>
fprintf(' %-14s N=%d obs=%3d int p: res=%.3f satt=%.3f rs=%-5s est=%+.2f\n', ...
vname, r.nRats, r.nObs, r.interP, r.interPsatt, localNum(r.interPrs), r.interEst);
end
end
S = cell2table(rows, 'VariableNames', {'variation', 'grouping', 'window', ...
'nRats', 'nObs', 'interaction_p', 'interaction_p_satt', 'interaction_p_rs', ...
'interaction_est', 'stim_p'});
writetable(S, fullfile(root, 'boxa_summary.csv'));
fprintf('\nWrote %d folders + variations/boxa_summary.csv\n', size(rows, 1));
end
function r = localRun(scriptPath)
run(scriptPath);
r = VARRESULT; %#ok<NODEF> (defined by the analyze.m script just run)
end
function s = localNum(x)
if isnan(x); s = 'n/a'; else; s = sprintf('%.3f', x); end
end
analysis/matlab/variations/boxa_a2_d0_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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
% 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
mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
Ar = anova(mr, 'DFMethod', 'satterthwaite');
ri = strcmp(Ar.Term, 'day:stim');
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
catch
end
warning(wst);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
gs = @(t) find(strcmp(As.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f)\n', nm, ...
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)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
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))];
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
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
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')];
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);
analysis/matlab/variations/boxa_a2_d0_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Khoai-tay-1,Electrode-Box-A,0,14,62,0
Khoai-tay-1,Electrode-Box-A,1,22,83,0
Khoai-tay-1,Electrode-Box-A,2,6,79,0
Khoai-tay-1,Electrode-Box-A,3,28,97,0
Khoai-tay-1,Electrode-Box-A,4,60,134,0
Khoai-tay-1,Electrode-Box-A,5,75,138,0
Khoai-tay-1,Electrode-Box-A,6,81,137,0
Khoai-tay-1,Electrode-Box-A,7,78,147,0
Khoai-tay-1,Electrode-Box-A,8,91,132,0
Khoai-tay-1,Electrode-Box-A,9,99,146,0
Khoai-tay-1,Electrode-Box-A,10,94,143,0
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-1 Electrode-Box-B2 6 90 131 1
9 Banh-mi-1 Electrode-Box-B2 7 109 148 1
10 Banh-mi-1 Electrode-Box-B2 8 104 137 1
11 Banh-mi-1 Electrode-Box-B2 9 119 150 1
12 Banh-mi-1 Electrode-Box-B2 10 121 158 1
13 Banh-mi-2 Electrode-Box-A2 0 25 97 0
14 Banh-mi-2 Electrode-Box-A2 1 22 101 0
15 Banh-mi-2 Electrode-Box-A2 2 22 119 0
16 Banh-mi-2 Electrode-Box-A2 3 29 118 0
17 Banh-mi-2 Electrode-Box-A2 4 27 136 0
18 Banh-mi-2 Electrode-Box-A2 5 43 146 0
19 Banh-mi-2 Electrode-Box-A2 6 74 146 0
20 Banh-mi-2 Electrode-Box-A2 7 70 148 0
21 Banh-mi-2 Electrode-Box-A2 8 65 130 0
22 Banh-mi-2 Electrode-Box-A2 9 79 151 0
23 Banh-mi-2 Electrode-Box-A2 10 93 152 0
24 Egg-tart-1 Electrode-Box-B2 0 7 56 1
25 Egg-tart-1 Electrode-Box-B2 1 16 78 1
26 Egg-tart-1 Electrode-Box-B2 2 23 103 1
27 Egg-tart-1 Electrode-Box-B2 3 63 120 1
28 Egg-tart-1 Electrode-Box-B2 4 69 132 1
29 Egg-tart-1 Electrode-Box-B2 5 83 136 1
30 Egg-tart-1 Electrode-Box-B2 6 71 142 1
31 Egg-tart-1 Electrode-Box-B2 7 79 138 1
32 Egg-tart-1 Electrode-Box-B2 8 98 142 1
33 Egg-tart-1 Electrode-Box-B2 9 89 139 1
34 Egg-tart-1 Electrode-Box-B2 10 96 143 1
35 Egg-tart-2 Electrode-Box-A2 0 9 32 0
36 Egg-tart-2 Electrode-Box-A2 1 2 38 0
37 Egg-tart-2 Electrode-Box-A2 2 31 93 0
38 Egg-tart-2 Electrode-Box-A2 3 44 101 0
39 Egg-tart-2 Electrode-Box-A2 4 54 131 0
40 Egg-tart-2 Electrode-Box-A2 5 84 139 0
41 Egg-tart-2 Electrode-Box-A2 6 85 145 0
42 Egg-tart-2 Electrode-Box-A2 7 79 143 0
43 Egg-tart-2 Electrode-Box-A2 8 76 131 0
44 Egg-tart-2 Electrode-Box-A2 9 88 149 0
45 Egg-tart-2 Electrode-Box-A2 10 81 151 0
46 Khoai-tay-1 Electrode-Box-A 0 14 62 0
47 Khoai-tay-1 Electrode-Box-A 1 22 83 0
48 Khoai-tay-1 Electrode-Box-A 2 6 79 0
49 Khoai-tay-1 Electrode-Box-A 3 28 97 0
50 Khoai-tay-1 Electrode-Box-A 4 60 134 0
51 Khoai-tay-1 Electrode-Box-A 5 75 138 0
52 Khoai-tay-1 Electrode-Box-A 6 81 137 0
53 Khoai-tay-1 Electrode-Box-A 7 78 147 0
54 Khoai-tay-1 Electrode-Box-A 8 91 132 0
55 Khoai-tay-1 Electrode-Box-A 9 99 146 0
56 Khoai-tay-1 Electrode-Box-A 10 94 143 0
57 Root-beer-1 Electrode-Box-B2 0 11 85 1
58 Root-beer-1 Electrode-Box-B2 1 18 76 1
59 Root-beer-1 Electrode-Box-B2 2 40 105 1
60 Root-beer-1 Electrode-Box-B2 3 55 134 1
61 Root-beer-1 Electrode-Box-B2 4 75 136 1
62 Root-beer-1 Electrode-Box-B2 5 64 133 1
63 Root-beer-1 Electrode-Box-B2 6 104 139 1
64 Root-beer-1 Electrode-Box-B2 7 98 148 1
65 Root-beer-1 Electrode-Box-B2 8 81 145 1
66 Root-beer-1 Electrode-Box-B2 9 89 156 1
67 Root-beer-1 Electrode-Box-B2 10 105 158 1
68 Root-beer-2 Electrode-Box-A2 0 22 74 0
69 Root-beer-2 Electrode-Box-A2 1 31 87 0
70 Root-beer-2 Electrode-Box-A2 2 49 134 0
71 Root-beer-2 Electrode-Box-A2 3 31 89 0
72 Root-beer-2 Electrode-Box-A2 4 60 140 0
73 Root-beer-2 Electrode-Box-A2 5 84 147 0
analysis/matlab/variations/boxa_a2_d0_10/result.txt
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==============================================================================
VARIATION: boxa_a2_d0_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A, 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
590.8 604.46 -289.4 578.8
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 15.599 3.8531 4.0485 68 0.00013444
{'day' } 8.3258 0.69175 12.036 68 1.6397e-18
{'stim' } 6.7494 5.8389 1.1559 68 0.25175
{'day:stim' } 1.1985 1.0141 1.1818 68 0.2414
Lower Upper
7.9104 23.288
6.9454 9.7061
-4.9019 18.401
-0.82514 3.2221
Random effects covariance parameters (95% CIs):
Group: rat (7 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 0
Lower Upper
NaN NaN
Group: Error
Name Estimate Lower Upper
{'Res Std'} 13.471 11.441 15.861
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
----------------------------------------------------------------------------
stim x day (interaction) t(68)= 1.18 F(1)= 1.397 p=0.2414 p=0.2412 (df=72)
day (learning) t(68)= 12.04 F(1)=144.862 p=1.64e-18 p=6.575e-19 (df=72)
stim (main, window start) t(68)= 1.16 F(1)= 1.336 p=0.2517 p=0.2515 (df=72)
interaction 95% CI: [-0.83, +3.22]
HONEST LME (per-animal random slope, day|rat): interaction F(1,14.0)=0.68, p=0.4245
(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=0.2414, slope diff=+1.20)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/boxa_a2_d0_5/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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
% 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
mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
Ar = anova(mr, 'DFMethod', 'satterthwaite');
ri = strcmp(Ar.Term, 'day:stim');
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
catch
end
warning(wst);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
gs = @(t) find(strcmp(As.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f)\n', nm, ...
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)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
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))];
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
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
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')];
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);
analysis/matlab/variations/boxa_a2_d0_5/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Khoai-tay-1,Electrode-Box-A,0,14,62,0
Khoai-tay-1,Electrode-Box-A,1,22,83,0
Khoai-tay-1,Electrode-Box-A,2,6,79,0
Khoai-tay-1,Electrode-Box-A,3,28,97,0
Khoai-tay-1,Electrode-Box-A,4,60,134,0
Khoai-tay-1,Electrode-Box-A,5,75,138,0
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-2 Electrode-Box-A2 0 25 97 0
9 Banh-mi-2 Electrode-Box-A2 1 22 101 0
10 Banh-mi-2 Electrode-Box-A2 2 22 119 0
11 Banh-mi-2 Electrode-Box-A2 3 29 118 0
12 Banh-mi-2 Electrode-Box-A2 4 27 136 0
13 Banh-mi-2 Electrode-Box-A2 5 43 146 0
14 Egg-tart-1 Electrode-Box-B2 0 7 56 1
15 Egg-tart-1 Electrode-Box-B2 1 16 78 1
16 Egg-tart-1 Electrode-Box-B2 2 23 103 1
17 Egg-tart-1 Electrode-Box-B2 3 63 120 1
18 Egg-tart-1 Electrode-Box-B2 4 69 132 1
19 Egg-tart-1 Electrode-Box-B2 5 83 136 1
20 Egg-tart-2 Electrode-Box-A2 0 9 32 0
21 Egg-tart-2 Electrode-Box-A2 1 2 38 0
22 Egg-tart-2 Electrode-Box-A2 2 31 93 0
23 Egg-tart-2 Electrode-Box-A2 3 44 101 0
24 Egg-tart-2 Electrode-Box-A2 4 54 131 0
25 Egg-tart-2 Electrode-Box-A2 5 84 139 0
26 Khoai-tay-1 Electrode-Box-A 0 14 62 0
27 Khoai-tay-1 Electrode-Box-A 1 22 83 0
28 Khoai-tay-1 Electrode-Box-A 2 6 79 0
29 Khoai-tay-1 Electrode-Box-A 3 28 97 0
30 Khoai-tay-1 Electrode-Box-A 4 60 134 0
31 Khoai-tay-1 Electrode-Box-A 5 75 138 0
32 Root-beer-1 Electrode-Box-B2 0 11 85 1
33 Root-beer-1 Electrode-Box-B2 1 18 76 1
34 Root-beer-1 Electrode-Box-B2 2 40 105 1
35 Root-beer-1 Electrode-Box-B2 3 55 134 1
36 Root-beer-1 Electrode-Box-B2 4 75 136 1
37 Root-beer-1 Electrode-Box-B2 5 64 133 1
38 Root-beer-2 Electrode-Box-A2 0 22 74 0
39 Root-beer-2 Electrode-Box-A2 1 31 87 0
40 Root-beer-2 Electrode-Box-A2 2 49 134 0
41 Root-beer-2 Electrode-Box-A2 3 31 89 0
42 Root-beer-2 Electrode-Box-A2 4 60 140 0
43 Root-beer-2 Electrode-Box-A2 5 84 147 0
analysis/matlab/variations/boxa_a2_d0_5/result.txt
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==============================================================================
VARIATION: boxa_a2_d0_5
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A, 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
339.96 350.39 -163.98 327.96
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 10.06 4.3445 2.3155 38 0.026083
{'day' } 10.543 1.4349 7.3472 38 8.3775e-09
{'stim' } -0.55159 6.6363 -0.083116 38 0.9342
{'day:stim' } 4.6762 2.1919 2.1334 38 0.03941
Lower Upper
1.2645 18.855
7.638 13.448
-13.986 12.883
0.2389 9.1135
Random effects covariance parameters (95% CIs):
Group: rat (7 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 0
Lower Upper
NaN NaN
Group: Error
Name Estimate Lower Upper
{'Res Std'} 12.006 9.6941 14.868
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
----------------------------------------------------------------------------
stim x day (interaction) t(38)= 2.13 F(1)= 4.551 p=0.03941 p=0.03878 (df=42)
day (learning) t(38)= 7.35 F(1)= 53.982 p=8.377e-09 p=4.651e-09 (df=42)
stim (main, window start) t(38)= -0.08 F(1)= 0.007 p=0.9342 p=0.9342 (df=42)
interaction 95% CI: [+0.24, +9.11]
HONEST LME (per-animal random slope, day|rat): interaction F(1,7.0)=2.87, p=0.1341
(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=0.03941, slope diff=+4.68)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/boxa_a2_d6_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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
% 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
mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
Ar = anova(mr, 'DFMethod', 'satterthwaite');
ri = strcmp(Ar.Term, 'day:stim');
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
catch
end
warning(wst);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
gs = @(t) find(strcmp(As.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f)\n', nm, ...
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)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
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))];
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
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
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')];
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);
analysis/matlab/variations/boxa_a2_d6_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Khoai-tay-1,Electrode-Box-A,6,81,137,0
Khoai-tay-1,Electrode-Box-A,7,78,147,0
Khoai-tay-1,Electrode-Box-A,8,91,132,0
Khoai-tay-1,Electrode-Box-A,9,99,146,0
Khoai-tay-1,Electrode-Box-A,10,94,143,0
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 6 90 131 1
3 Banh-mi-1 Electrode-Box-B2 7 109 148 1
4 Banh-mi-1 Electrode-Box-B2 8 104 137 1
5 Banh-mi-1 Electrode-Box-B2 9 119 150 1
6 Banh-mi-1 Electrode-Box-B2 10 121 158 1
7 Banh-mi-2 Electrode-Box-A2 6 74 146 0
8 Banh-mi-2 Electrode-Box-A2 7 70 148 0
9 Banh-mi-2 Electrode-Box-A2 8 65 130 0
10 Banh-mi-2 Electrode-Box-A2 9 79 151 0
11 Banh-mi-2 Electrode-Box-A2 10 93 152 0
12 Egg-tart-1 Electrode-Box-B2 6 71 142 1
13 Egg-tart-1 Electrode-Box-B2 7 79 138 1
14 Egg-tart-1 Electrode-Box-B2 8 98 142 1
15 Egg-tart-1 Electrode-Box-B2 9 89 139 1
16 Egg-tart-1 Electrode-Box-B2 10 96 143 1
17 Egg-tart-2 Electrode-Box-A2 6 85 145 0
18 Egg-tart-2 Electrode-Box-A2 7 79 143 0
19 Egg-tart-2 Electrode-Box-A2 8 76 131 0
20 Egg-tart-2 Electrode-Box-A2 9 88 149 0
21 Egg-tart-2 Electrode-Box-A2 10 81 151 0
22 Khoai-tay-1 Electrode-Box-A 6 81 137 0
23 Khoai-tay-1 Electrode-Box-A 7 78 147 0
24 Khoai-tay-1 Electrode-Box-A 8 91 132 0
25 Khoai-tay-1 Electrode-Box-A 9 99 146 0
26 Khoai-tay-1 Electrode-Box-A 10 94 143 0
27 Root-beer-1 Electrode-Box-B2 6 104 139 1
28 Root-beer-1 Electrode-Box-B2 7 98 148 1
29 Root-beer-1 Electrode-Box-B2 8 81 145 1
30 Root-beer-1 Electrode-Box-B2 9 89 156 1
31 Root-beer-1 Electrode-Box-B2 10 105 158 1
analysis/matlab/variations/boxa_a2_d6_10/result.txt
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==============================================================================
VARIATION: boxa_a2_d6_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A, 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
231.74 240.15 -109.87 219.74
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 75.867 5.1856 14.63 26 4.6141e-14
{'day' } 3.1667 1.4985 2.1132 26 0.044334
{'stim' } 12.667 7.3335 1.7272 26 0.095989
{'day:stim' } 1 2.1192 0.47188 26 0.64095
Lower Upper
65.208 86.526
0.086483 6.2469
-2.4075 27.741
-3.356 5.356
Random effects covariance parameters (95% CIs):
Group: rat (6 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 6.3444
Lower Upper
2.9639 13.581
Group: Error
Name Estimate Lower Upper
{'Res Std'} 8.2076 6.1852 10.891
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
----------------------------------------------------------------------------
stim x day (interaction) t(26)= 0.47 F(1)= 0.223 p=0.6409 p=0.6413 (df=24)
day (learning) t(26)= 2.11 F(1)= 4.466 p=0.04433 p=0.04517 (df=24)
stim (main, window start) t(26)= 1.73 F(1)= 2.983 p=0.09599 p=0.1083 (df=13)
interaction 95% CI: [-3.36, +5.36]
HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=0.18, p=0.6872
(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=0.6409, slope diff=+1.00)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/boxa_b2_d0_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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
% 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
mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
Ar = anova(mr, 'DFMethod', 'satterthwaite');
ri = strcmp(Ar.Term, 'day:stim');
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
catch
end
warning(wst);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
gs = @(t) find(strcmp(As.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f)\n', nm, ...
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)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
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))];
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
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
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')];
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);
analysis/matlab/variations/boxa_b2_d0_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Khoai-tay-1,Electrode-Box-A,0,14,62,1
Khoai-tay-1,Electrode-Box-A,1,22,83,1
Khoai-tay-1,Electrode-Box-A,2,6,79,1
Khoai-tay-1,Electrode-Box-A,3,28,97,1
Khoai-tay-1,Electrode-Box-A,4,60,134,1
Khoai-tay-1,Electrode-Box-A,5,75,138,1
Khoai-tay-1,Electrode-Box-A,6,81,137,1
Khoai-tay-1,Electrode-Box-A,7,78,147,1
Khoai-tay-1,Electrode-Box-A,8,91,132,1
Khoai-tay-1,Electrode-Box-A,9,99,146,1
Khoai-tay-1,Electrode-Box-A,10,94,143,1
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-1 Electrode-Box-B2 6 90 131 1
9 Banh-mi-1 Electrode-Box-B2 7 109 148 1
10 Banh-mi-1 Electrode-Box-B2 8 104 137 1
11 Banh-mi-1 Electrode-Box-B2 9 119 150 1
12 Banh-mi-1 Electrode-Box-B2 10 121 158 1
13 Banh-mi-2 Electrode-Box-A2 0 25 97 0
14 Banh-mi-2 Electrode-Box-A2 1 22 101 0
15 Banh-mi-2 Electrode-Box-A2 2 22 119 0
16 Banh-mi-2 Electrode-Box-A2 3 29 118 0
17 Banh-mi-2 Electrode-Box-A2 4 27 136 0
18 Banh-mi-2 Electrode-Box-A2 5 43 146 0
19 Banh-mi-2 Electrode-Box-A2 6 74 146 0
20 Banh-mi-2 Electrode-Box-A2 7 70 148 0
21 Banh-mi-2 Electrode-Box-A2 8 65 130 0
22 Banh-mi-2 Electrode-Box-A2 9 79 151 0
23 Banh-mi-2 Electrode-Box-A2 10 93 152 0
24 Egg-tart-1 Electrode-Box-B2 0 7 56 1
25 Egg-tart-1 Electrode-Box-B2 1 16 78 1
26 Egg-tart-1 Electrode-Box-B2 2 23 103 1
27 Egg-tart-1 Electrode-Box-B2 3 63 120 1
28 Egg-tart-1 Electrode-Box-B2 4 69 132 1
29 Egg-tart-1 Electrode-Box-B2 5 83 136 1
30 Egg-tart-1 Electrode-Box-B2 6 71 142 1
31 Egg-tart-1 Electrode-Box-B2 7 79 138 1
32 Egg-tart-1 Electrode-Box-B2 8 98 142 1
33 Egg-tart-1 Electrode-Box-B2 9 89 139 1
34 Egg-tart-1 Electrode-Box-B2 10 96 143 1
35 Egg-tart-2 Electrode-Box-A2 0 9 32 0
36 Egg-tart-2 Electrode-Box-A2 1 2 38 0
37 Egg-tart-2 Electrode-Box-A2 2 31 93 0
38 Egg-tart-2 Electrode-Box-A2 3 44 101 0
39 Egg-tart-2 Electrode-Box-A2 4 54 131 0
40 Egg-tart-2 Electrode-Box-A2 5 84 139 0
41 Egg-tart-2 Electrode-Box-A2 6 85 145 0
42 Egg-tart-2 Electrode-Box-A2 7 79 143 0
43 Egg-tart-2 Electrode-Box-A2 8 76 131 0
44 Egg-tart-2 Electrode-Box-A2 9 88 149 0
45 Egg-tart-2 Electrode-Box-A2 10 81 151 0
46 Khoai-tay-1 Electrode-Box-A 0 14 62 1
47 Khoai-tay-1 Electrode-Box-A 1 22 83 1
48 Khoai-tay-1 Electrode-Box-A 2 6 79 1
49 Khoai-tay-1 Electrode-Box-A 3 28 97 1
50 Khoai-tay-1 Electrode-Box-A 4 60 134 1
51 Khoai-tay-1 Electrode-Box-A 5 75 138 1
52 Khoai-tay-1 Electrode-Box-A 6 81 137 1
53 Khoai-tay-1 Electrode-Box-A 7 78 147 1
54 Khoai-tay-1 Electrode-Box-A 8 91 132 1
55 Khoai-tay-1 Electrode-Box-A 9 99 146 1
56 Khoai-tay-1 Electrode-Box-A 10 94 143 1
57 Root-beer-1 Electrode-Box-B2 0 11 85 1
58 Root-beer-1 Electrode-Box-B2 1 18 76 1
59 Root-beer-1 Electrode-Box-B2 2 40 105 1
60 Root-beer-1 Electrode-Box-B2 3 55 134 1
61 Root-beer-1 Electrode-Box-B2 4 75 136 1
62 Root-beer-1 Electrode-Box-B2 5 64 133 1
63 Root-beer-1 Electrode-Box-B2 6 104 139 1
64 Root-beer-1 Electrode-Box-B2 7 98 148 1
65 Root-beer-1 Electrode-Box-B2 8 81 145 1
66 Root-beer-1 Electrode-Box-B2 9 89 156 1
67 Root-beer-1 Electrode-Box-B2 10 105 158 1
68 Root-beer-2 Electrode-Box-A2 0 22 74 0
69 Root-beer-2 Electrode-Box-A2 1 31 87 0
70 Root-beer-2 Electrode-Box-A2 2 49 134 0
71 Root-beer-2 Electrode-Box-A2 3 31 89 0
72 Root-beer-2 Electrode-Box-A2 4 60 140 0
73 Root-beer-2 Electrode-Box-A2 5 84 147 0
analysis/matlab/variations/boxa_b2_d0_10/result.txt
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==============================================================================
VARIATION: boxa_b2_d0_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-A, Electrode-Box-B2
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
587.74 601.4 -287.87 575.74
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 17.308 5.4187 3.1941 68 0.0021263
{'day' } 8.0028 0.78816 10.154 68 2.9164e-15
{'stim' } 1.8623 7.1264 0.26132 68 0.79463
{'day:stim' } 1.604 0.98592 1.6269 68 0.10838
Lower Upper
6.4953 28.121
6.4301 9.5756
-12.358 16.083
-0.36337 3.5714
Random effects covariance parameters (95% CIs):
Group: rat (7 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 6.0474
Lower Upper
2.8281 12.931
Group: Error
Name Estimate Lower Upper
{'Res Std'} 12.425 10.46 14.759
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
----------------------------------------------------------------------------
stim x day (interaction) t(68)= 1.63 F(1)= 2.647 p=0.1084 p=0.1084 (df=68)
day (learning) t(68)= 10.15 F(1)=103.100 p=2.916e-15 p=2.368e-15 (df=69)
stim (main, window start) t(68)= 0.26 F(1)= 0.068 p=0.7946 p=0.7968 (df=18)
interaction 95% CI: [-0.36, +3.57]
HONEST LME (per-animal random slope, day|rat): interaction F(1,22.2)=2.05, p=0.1666
(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=0.1084, slope diff=+1.60)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/boxa_b2_d0_5/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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
% 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
mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
Ar = anova(mr, 'DFMethod', 'satterthwaite');
ri = strcmp(Ar.Term, 'day:stim');
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
catch
end
warning(wst);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
gs = @(t) find(strcmp(As.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f)\n', nm, ...
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)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
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))];
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
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
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')];
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);
analysis/matlab/variations/boxa_b2_d0_5/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Khoai-tay-1,Electrode-Box-A,0,14,62,1
Khoai-tay-1,Electrode-Box-A,1,22,83,1
Khoai-tay-1,Electrode-Box-A,2,6,79,1
Khoai-tay-1,Electrode-Box-A,3,28,97,1
Khoai-tay-1,Electrode-Box-A,4,60,134,1
Khoai-tay-1,Electrode-Box-A,5,75,138,1
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-2 Electrode-Box-A2 0 25 97 0
9 Banh-mi-2 Electrode-Box-A2 1 22 101 0
10 Banh-mi-2 Electrode-Box-A2 2 22 119 0
11 Banh-mi-2 Electrode-Box-A2 3 29 118 0
12 Banh-mi-2 Electrode-Box-A2 4 27 136 0
13 Banh-mi-2 Electrode-Box-A2 5 43 146 0
14 Egg-tart-1 Electrode-Box-B2 0 7 56 1
15 Egg-tart-1 Electrode-Box-B2 1 16 78 1
16 Egg-tart-1 Electrode-Box-B2 2 23 103 1
17 Egg-tart-1 Electrode-Box-B2 3 63 120 1
18 Egg-tart-1 Electrode-Box-B2 4 69 132 1
19 Egg-tart-1 Electrode-Box-B2 5 83 136 1
20 Egg-tart-2 Electrode-Box-A2 0 9 32 0
21 Egg-tart-2 Electrode-Box-A2 1 2 38 0
22 Egg-tart-2 Electrode-Box-A2 2 31 93 0
23 Egg-tart-2 Electrode-Box-A2 3 44 101 0
24 Egg-tart-2 Electrode-Box-A2 4 54 131 0
25 Egg-tart-2 Electrode-Box-A2 5 84 139 0
26 Khoai-tay-1 Electrode-Box-A 0 14 62 1
27 Khoai-tay-1 Electrode-Box-A 1 22 83 1
28 Khoai-tay-1 Electrode-Box-A 2 6 79 1
29 Khoai-tay-1 Electrode-Box-A 3 28 97 1
30 Khoai-tay-1 Electrode-Box-A 4 60 134 1
31 Khoai-tay-1 Electrode-Box-A 5 75 138 1
32 Root-beer-1 Electrode-Box-B2 0 11 85 1
33 Root-beer-1 Electrode-Box-B2 1 18 76 1
34 Root-beer-1 Electrode-Box-B2 2 40 105 1
35 Root-beer-1 Electrode-Box-B2 3 55 134 1
36 Root-beer-1 Electrode-Box-B2 4 75 136 1
37 Root-beer-1 Electrode-Box-B2 5 64 133 1
38 Root-beer-2 Electrode-Box-A2 0 22 74 0
39 Root-beer-2 Electrode-Box-A2 1 31 87 0
40 Root-beer-2 Electrode-Box-A2 2 49 134 0
41 Root-beer-2 Electrode-Box-A2 3 31 89 0
42 Root-beer-2 Electrode-Box-A2 4 60 140 0
43 Root-beer-2 Electrode-Box-A2 5 84 147 0
analysis/matlab/variations/boxa_b2_d0_5/result.txt
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==============================================================================
VARIATION: boxa_b2_d0_5
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-A, Electrode-Box-B2
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
341.62 352.04 -164.81 329.62
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 12.524 5.8127 2.1546 38 0.037599
{'day' } 9.8571 1.5596 6.3204 38 2.0711e-07
{'stim' } -4.7262 7.6895 -0.61463 38 0.54246
{'day:stim' } 4.7071 2.0631 2.2816 38 0.028206
Lower Upper
0.75663 24.291
6.6999 13.014
-20.293 10.84
0.53057 8.8837
Random effects covariance parameters (95% CIs):
Group: rat (7 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 5.8715
Lower Upper
2.486 13.868
Group: Error
Name Estimate Lower Upper
{'Res Std'} 11.3 8.9402 14.283
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
----------------------------------------------------------------------------
stim x day (interaction) t(38)= 2.28 F(1)= 5.206 p=0.02821 p=0.02871 (df=35)
day (learning) t(38)= 6.32 F(1)= 39.947 p=2.071e-07 p=2.928e-07 (df=35)
stim (main, window start) t(38)= -0.61 F(1)= 0.378 p=0.5425 p=0.5456 (df=20)
interaction 95% CI: [+0.53, +8.88]
HONEST LME (per-animal random slope, day|rat): interaction F(1,7.0)=2.92, p=0.131
(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=0.02821, slope diff=+4.71)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/boxa_b2_d6_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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
% 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
mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
Ar = anova(mr, 'DFMethod', 'satterthwaite');
ri = strcmp(Ar.Term, 'day:stim');
rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
catch
end
warning(wst);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
gs = @(t) find(strcmp(As.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f p=%.4g p=%.4g (df=%.0f)\n', nm, ...
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)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
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))];
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
s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
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')];
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);
analysis/matlab/variations/boxa_b2_d6_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Khoai-tay-1,Electrode-Box-A,6,81,137,1
Khoai-tay-1,Electrode-Box-A,7,78,147,1
Khoai-tay-1,Electrode-Box-A,8,91,132,1
Khoai-tay-1,Electrode-Box-A,9,99,146,1
Khoai-tay-1,Electrode-Box-A,10,94,143,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 6 90 131 1
3 Banh-mi-1 Electrode-Box-B2 7 109 148 1
4 Banh-mi-1 Electrode-Box-B2 8 104 137 1
5 Banh-mi-1 Electrode-Box-B2 9 119 150 1
6 Banh-mi-1 Electrode-Box-B2 10 121 158 1
7 Banh-mi-2 Electrode-Box-A2 6 74 146 0
8 Banh-mi-2 Electrode-Box-A2 7 70 148 0
9 Banh-mi-2 Electrode-Box-A2 8 65 130 0
10 Banh-mi-2 Electrode-Box-A2 9 79 151 0
11 Banh-mi-2 Electrode-Box-A2 10 93 152 0
12 Egg-tart-1 Electrode-Box-B2 6 71 142 1
13 Egg-tart-1 Electrode-Box-B2 7 79 138 1
14 Egg-tart-1 Electrode-Box-B2 8 98 142 1
15 Egg-tart-1 Electrode-Box-B2 9 89 139 1
16 Egg-tart-1 Electrode-Box-B2 10 96 143 1
17 Egg-tart-2 Electrode-Box-A2 6 85 145 0
18 Egg-tart-2 Electrode-Box-A2 7 79 143 0
19 Egg-tart-2 Electrode-Box-A2 8 76 131 0
20 Egg-tart-2 Electrode-Box-A2 9 88 149 0
21 Egg-tart-2 Electrode-Box-A2 10 81 151 0
22 Khoai-tay-1 Electrode-Box-A 6 81 137 1
23 Khoai-tay-1 Electrode-Box-A 7 78 147 1
24 Khoai-tay-1 Electrode-Box-A 8 91 132 1
25 Khoai-tay-1 Electrode-Box-A 9 99 146 1
26 Khoai-tay-1 Electrode-Box-A 10 94 143 1
27 Root-beer-1 Electrode-Box-B2 6 104 139 1
28 Root-beer-1 Electrode-Box-B2 7 98 148 1
29 Root-beer-1 Electrode-Box-B2 8 81 145 1
30 Root-beer-1 Electrode-Box-B2 9 89 156 1
31 Root-beer-1 Electrode-Box-B2 10 105 158 1
analysis/matlab/variations/boxa_b2_d6_10/result.txt
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==============================================================================
VARIATION: boxa_b2_d6_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-A, Electrode-Box-B2
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
231.05 239.46 -109.52 219.05
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 74.2 6.2618 11.85 26 5.5401e-12
{'day' } 2.4 1.8164 1.3213 26 0.1979
{'stim' } 12 7.6691 1.5647 26 0.12974
{'day:stim' } 1.9 2.2246 0.85409 26 0.40085
Lower Upper
61.329 87.071
-1.3336 6.1336
-3.764 27.764
-2.6727 6.4727
Random effects covariance parameters (95% CIs):
Group: rat (6 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 6.2314
Lower Upper
2.9021 13.38
Group: Error
Name Estimate Lower Upper
{'Res Std'} 8.123 6.1215 10.779
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
----------------------------------------------------------------------------
stim x day (interaction) t(26)= 0.85 F(1)= 0.729 p=0.4009 p=0.4015 (df=24)
day (learning) t(26)= 1.32 F(1)= 1.746 p=0.1979 p=0.1989 (df=24)
stim (main, window start) t(26)= 1.56 F(1)= 2.448 p=0.1297 p=0.142 (df=13)
interaction 95% CI: [-2.67, +6.47]
HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=0.61, p=0.4631
(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=0.4009, slope diff=+1.90)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/boxa_summary.csv
+7
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variation,grouping,window,nRats,nObs,interaction_p,interaction_p_satt,interaction_p_rs,interaction_est,stim_p
boxa_a2_d0_5,boxa_a2,d0_5,7,42,0.0394100933497468,0.0387789570155384,0.134104933837531,4.67619047619049,0.934195418405735
boxa_a2_d6_10,boxa_a2,d6_10,6,30,0.640948346908124,0.641274155570031,0.687193776070756,0.999999999999999,0.0959886716436125
boxa_a2_d0_10,boxa_a2,d0_10,7,72,0.241397580095809,0.241169749082988,0.42454522177293,1.1984817751552,0.251749522863234
boxa_b2_d0_5,boxa_b2,d0_5,7,42,0.0282058457878955,0.0287096264064777,0.131045386717085,4.70714285714286,0.542460948914174
boxa_b2_d6_10,boxa_b2,d6_10,6,30,0.400853858752755,0.401497745167433,0.463104837605515,1.9,0.129739412462252
boxa_b2_d0_10,boxa_b2,d0_10,7,72,0.108380977202764,0.108390578891594,0.166606323000689,1.60400880375615,0.794632800487035
1 variation grouping window nRats nObs interaction_p interaction_p_satt interaction_p_rs interaction_est stim_p
2 boxa_a2_d0_5 boxa_a2 d0_5 7 42 0.0394100933497468 0.0387789570155384 0.134104933837531 4.67619047619049 0.934195418405735
3 boxa_a2_d6_10 boxa_a2 d6_10 6 30 0.640948346908124 0.641274155570031 0.687193776070756 0.999999999999999 0.0959886716436125
4 boxa_a2_d0_10 boxa_a2 d0_10 7 72 0.241397580095809 0.241169749082988 0.42454522177293 1.1984817751552 0.251749522863234
5 boxa_b2_d0_5 boxa_b2 d0_5 7 42 0.0282058457878955 0.0287096264064777 0.131045386717085 4.70714285714286 0.542460948914174
6 boxa_b2_d6_10 boxa_b2 d6_10 6 30 0.400853858752755 0.401497745167433 0.463104837605515 1.9 0.129739412462252
7 boxa_b2_d0_10 boxa_b2 d0_10 7 72 0.108380977202764 0.108390578891594 0.166606323000689 1.60400880375615 0.794632800487035