Experiments DB Dev
f0ce978bd4
The model already evaluates the tDCS main effect at day 0, which is the paper's 'Day 1'; relabel the report/docstring to state the equivalence explicitly so the 'equal on Day 1' comparison is unambiguous. No model change (re-indexing day would move the main-effect evaluation point off Day 1). Suite 31/31. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
54 lines
2.1 KiB
Matlab
54 lines
2.1 KiB
Matlab
function L = tdcs_lme(S, cfg)
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%TDCS_LME Linear mixed-effects "days x tDCS" model on the Box-B2 vs Box-A2 arm.
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% L = TDCS_LME(S, CFG) subsets one scenario table S to the two anchor groups,
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% codes a binary tDCS factor (Box-B2 = 1, Box-A2 = 0), and fits the LINEAR
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% mixed model
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%
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% success ~ day * tDCS + (1|subject)
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%
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% replicating the published formulation (a linear mixed-effects model for the
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% number of successful reaches with a days x tDCS interaction, a main effect
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% of days, and a main effect of tDCS). `day` is raw and 0-indexed, and OUR
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% day 0 corresponds to the paper's "Day 1", so the tDCS main effect is the
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% group difference on Day 1 -- directly comparable to the paper's "equal on
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% Day 1" test. (Do not re-index day to 1-based: that would move the main
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% effect's evaluation point off Day 1.)
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%
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% L fields:
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% .lme the LinearMixedModel object
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% .nSubjects number of subjects (Box-A2 + Box-B2)
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% .nObs number of sessions
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% .interaction / .day / .tDCS effect structs, each with .estimate .se
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% .t .df .p (from Coefficients) and .F .df1 .df2 .Fp (from
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% ANOVA). For these single-df terms F == t^2 and Fp == p.
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%
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% NOTE: this is a LINEAR mixed model (Gaussian on the raw count), matching
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% the paper's method, unlike the Poisson/Binomial GLMMs in tdcs_models.
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T = S(ismember(S.group, {cfg.anchorLow, cfg.anchorHigh}), :);
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T.group = removecats(T.group);
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T.tDCS = double(T.group == cfg.anchorHigh); % Box-B2 = 1, Box-A2 = 0
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lme = fitlme(T, 'success ~ day*tDCS + (1|subject)');
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C = lme.Coefficients;
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A = anova(lme);
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L.lme = lme;
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L.nSubjects = numel(unique(T.subject));
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L.nObs = height(T);
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L.interaction = localTerm(C, A, 'day:tDCS');
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L.day = localTerm(C, A, 'day');
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L.tDCS = localTerm(C, A, 'tDCS');
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end
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function e = localTerm(C, A, name)
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%LOCALTERM Pull one term's coefficient (t/df/p) and ANOVA (F/df/p) stats.
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ci = strcmp(C.Name, name);
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ai = strcmp(A.Term, name);
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e = struct( ...
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'estimate', C.Estimate(ci), 'se', C.SE(ci), ...
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't', C.tStat(ci), 'df', C.DF(ci), 'p', C.pValue(ci), ...
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'F', A.FStat(ai), 'df1', A.DF1(ai), 'df2', A.DF2(ai), 'Fp', A.pValue(ai));
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end
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