Add variation_plot.m + make_variation_plot.m, dropping plotcurve.m +
learning_curve.png into all 28 variation folders. Each figure plots mean +/-
SEM successful reaches per training day for the anodal/treatment group (red)
vs control (blue), in the style of the paper ("Lines indicate mean (and SEM)
across animals in the anodal (red) and control (blue) groups"). Per-group N is
read from the data and shown in the legend; training day is 1-indexed (our day
0 = paper Day 1). Headless via exportgraphics. prev_f_full reproduces the
paper's own figure (anodal N=12 vs control N=12).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add variation_logpower.m (self-contained) + make_variation_logpower.m,
dropping logpowersim.m + logpower_result.txt into all 28 variation folders.
Matches the paper's power code: models behavior ~ stim + log(day) +
stim:log(day) + (1|rat) (log(day+1), since our day 0 = paper Day 1), reports
Cohen's f (partial eta^2 of the interaction) and the interaction under
residual/Satterthwaite/honest random-slope DF, then runs the Monte-Carlo power
sim on the log-day ground truth (per-animal cluster-honest + LME power).
Notable: under log(day) the accumulating divergence is captured more sharply,
so several full-window scenarios reach honest significance that were n.s. under
raw day (e.g. right_only_d0_13 honest p=0.003, unmerge_d0_13 0.021,
unmerge_d0_10 0.036); Cohen's f is small-medium (~0.10-0.34).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add variation_power.m (self-contained Monte-Carlo power for the paper's
stim x day interaction, using each folder's own data as ground truth; scores
per-animal cluster-honest power + LME power across N=[3..24] and effect
multipliers 1/0.5) and make_variation_power.m, which drops powersim.m into
every variations/<name>/ folder and runs it, writing power_result.txt beside
the existing data.csv/analyze.m/result.txt. Named powersim (not power) to
avoid shadowing the MATLAB builtin. All 28 folders processed.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Every fitlme-based report (lme_*, paper_*, phase_*, and the variations'
analyze.m) now shows, per effect: residual-DF p, Satterthwaite-DF p, and --
for the interaction -- an HONEST test from a per-animal random-SLOPE model
(day|rat), whose Satterthwaite DF collapses toward the animal count.
New: tdcs_random_slope_interaction.m (shared helper). Wired into tdcs_lme,
tdcs_paper_lme, tdcs_phase_lme, variation_analyze; SUMMARY.csv gains
interaction_p_satt / interaction_p_rs. Regenerated all results/, variations/,
matched-effort outputs.
Key point this surfaces: Satterthwaite ~= residual on the random-INTERCEPT
model (the slope's error is at session level), so it does NOT fix
pseudoreplication; the random-slope model does. Effect: full-range mergeA2
interaction 0.009 -> 0.75 (collapses); unmerge_d0_5 0.015 -> 0.13 (n.s.);
the pooled-control early windows survive honestly (naive_a2_d0_5 0.001 ->
0.028; naive_boxa_d0_5 0.003 -> 0.036). Suite 42/42.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add make_variations.m + variation_analyze.m generating 20 self-contained
subfolders under analysis/matlab/variations/, one per grouping x day-window,
each with exactly three files: data.csv (curated subset), analyze.m (a simple
standalone script fitting behavior ~ stim + day + stim:day + (1|rat) on the
success COUNT), and result.txt (its output). Plus variations/SUMMARY.csv.
Groupings (stim=1 / stim=0, other groups dropped):
unmerge B2 vs A2
right_only B2+Right vs A2 (Box-A dropped)
naive_a2 B2 vs A2+Naive (Right, Box-A dropped)
naive_boxa B2 vs A2+Naive+Box-A (Right dropped)
Windows: 0-10, 0-13, 0-5, 6-10, 6-13. All 20 have equal day coverage.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Experiments DB Dev
samsam2610