Experiments DB Dev
08c14c476c
Adds 6 switch cases replicating the paper's linear mixed model success ~ day * tDCS + (1|subject) on the Box-B2(tDCS=1) vs Box-A2(tDCS=0) arm, reporting the days x tDCS interaction, days, and tDCS effects as t/F/p. mergeA2_full reproduces the paper's interaction (F=7.09 vs 7.12, p=0.009 vs 0.008); interpretation is sign-aware and honestly notes our data diverges from the paper (Box-B2 ahead on day 1, gap narrows -- not 'equal on day 1, accumulates'). Adds tLme tests. Suite 31/31. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
51 lines
2.0 KiB
Matlab
51 lines
2.0 KiB
Matlab
function L = tdcs_lme(S, cfg)
|
|
%TDCS_LME Linear mixed-effects "days x tDCS" model on the Box-B2 vs Box-A2 arm.
|
|
% L = TDCS_LME(S, CFG) subsets one scenario table S to the two anchor groups,
|
|
% codes a binary tDCS factor (Box-B2 = 1, Box-A2 = 0), and fits the LINEAR
|
|
% mixed model
|
|
%
|
|
% success ~ day * tDCS + (1|subject)
|
|
%
|
|
% replicating the published formulation (a linear mixed-effects model for the
|
|
% number of successful reaches with a days x tDCS interaction, a main effect
|
|
% of days, and a main effect of tDCS). `day` is used raw (not centered), so
|
|
% the tDCS main effect is the group difference on the first day ("Day 1").
|
|
%
|
|
% L fields:
|
|
% .lme the LinearMixedModel object
|
|
% .nSubjects number of subjects (Box-A2 + Box-B2)
|
|
% .nObs number of sessions
|
|
% .interaction / .day / .tDCS effect structs, each with .estimate .se
|
|
% .t .df .p (from Coefficients) and .F .df1 .df2 .Fp (from
|
|
% ANOVA). For these single-df terms F == t^2 and Fp == p.
|
|
%
|
|
% NOTE: this is a LINEAR mixed model (Gaussian on the raw count), matching
|
|
% the paper's method, unlike the Poisson/Binomial GLMMs in tdcs_models.
|
|
|
|
T = S(ismember(S.group, {cfg.anchorLow, cfg.anchorHigh}), :);
|
|
T.group = removecats(T.group);
|
|
T.tDCS = double(T.group == cfg.anchorHigh); % Box-B2 = 1, Box-A2 = 0
|
|
|
|
lme = fitlme(T, 'success ~ day*tDCS + (1|subject)');
|
|
C = lme.Coefficients;
|
|
A = anova(lme);
|
|
|
|
L.lme = lme;
|
|
L.nSubjects = numel(unique(T.subject));
|
|
L.nObs = height(T);
|
|
L.interaction = localTerm(C, A, 'day:tDCS');
|
|
L.day = localTerm(C, A, 'day');
|
|
L.tDCS = localTerm(C, A, 'tDCS');
|
|
|
|
end
|
|
|
|
function e = localTerm(C, A, name)
|
|
%LOCALTERM Pull one term's coefficient (t/df/p) and ANOVA (F/df/p) stats.
|
|
ci = strcmp(C.Name, name);
|
|
ai = strcmp(A.Term, name);
|
|
e = struct( ...
|
|
'estimate', C.Estimate(ci), 'se', C.SE(ci), ...
|
|
't', C.tStat(ci), 'df', C.DF(ci), 'p', C.pValue(ci), ...
|
|
'F', A.FStat(ai), 'df1', A.DF1(ai), 'df2', A.DF2(ai), 'Fp', A.pValue(ai));
|
|
end
|