Overlap Correction with Linear Mixed Models

using Unfold
using UnfoldSim

using CategoricalArrays
using MixedModels
using UnfoldMakie, CairoMakie
using DataFrames

This notebook is similar to the Linear Model with Overlap Correction tutorial, but fits mixed models with overlap correction

Get some data

dat, evts = UnfoldSim.predef_2x2(; signalsize=20, n_items=16, n_subjects=16)

# We also need to fix the latencies, they are now relative to 1:size(data, 1), but we want a continuous long EEG.
subj_idx = [parse(Int, split(string(s), 'S')[2]) for s in evts.subject]
evts.latency .+= size(dat, 1) .* (subj_idx .- 1)

dat = dat[:] # we need all data concatenated over subjects
evts.subject  = categorical(Array(evts.subject))

Linear Mixed Model Continuous Time

Again we have 4 steps:

1. Specify a temporal basisfunction
2. Specify a formula
3. Fit a linear model for each channel (one model for all timepoints!)
4. Visualize the results.

1. Specify a temporal basisfunction

By default, we would want to use a FIR basis function. See Basis Functions for more details.

basisfunction = firbasis(τ=(-0.4, .8), sfreq=20, name="stimulus")

2. Specify the formula

Define the formula and specify a random effect.

f  = @formula 0 ~ 1 + A  *B + zerocorr(1 + A*B|subject);

3. Fit the model

bfDict = Dict(Any=>(f, basisfunction))
# Skipping this tutorial for now due to a significant error.
m = fit(UnfoldModel, bfDict, evts, dat)

results = coeftable(m)
first(results, 6)

4. Visualize results

plot_erp(results; mapping=(; col = :group))