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3024 lines (2489 loc) · 103 KB
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### A Pluto.jl notebook ###
# v0.20.6
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
#! format: off
return quote
local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
el
end
#! format: on
end
# ╔═╡ 56340884-cf55-4eb0-b1dc-0d6f4525a7ba
using SwarmMakie
# ╔═╡ 2789e7ef-1571-4a47-b343-2b8485c78e7f
using UnfoldMixedModels
# ╔═╡ 290ff8b5-6579-4786-84e7-3407b2e8fcd7
using UnfoldStats # hat-tip to Judith Schepers!
# ╔═╡ 3ecea8b3-73f2-4b7b-814a-542dd0232d7c
using ClusterDepth,MixedModelsPermutations # other tools we developed :)
# ╔═╡ 592b60e0-fb97-4c89-aa2e-f4806786028c
begin
using PlutoTeachingTools
using PlutoUI
using Unfold
using UnfoldMakie
using MakieThemes
using UnfoldSim
using CairoMakie
using MixedModels
using MixedModelsSim
using DataFrames
using DataFramesMeta
using Chain
using Statistics
using DisplayAs
using HypertextLiteral
using AlgebraOfGraphics
const AoG = AlgebraOfGraphics;
using HypothesisTests
end
# ╔═╡ dc644a89-b395-4256-83f7-383b9c9affb0
using Random
# ╔═╡ b3ac7b4f-4869-43f1-8870-09675a298db6
using CategoricalArrays
# ╔═╡ ec5b3848-2aa7-11f0-0ba0-95072a5a4b7b
md"""
# Lightning talk ⚡ UnfoldMixedModels.jl
"""
# ╔═╡ 518f364b-85c1-4fe5-8bb5-6a33e2120028
md"""
Slides & Toolbox: Benedikt Ehinger
"""
# ╔═╡ 58962381-1b1e-4464-b6d3-147115d5d423
md"""
# Why Linear Mixed Models?
"""
# ╔═╡ 4ba67ea1-8186-4a90-848e-1b77a25585d5
md"""
|fixed effect||
|---|---|
|condition|$(@bind β_cond PlutoUI.Slider(0:4,show_value=true))|
|random effect||
|---|---|
|condition\|subject |$(@bind s_cond PlutoUI.Slider(1:4,show_value=true))|
|condition\|item |$(@bind i_cond PlutoUI.Slider(1:4,show_value=true))|
"""
# ╔═╡ f23d6cfb-b613-41e4-a7bd-ff9ce8284bf3
# ╠═╡ show_logs = false
#form_analysis = @formula(y~1+condition + (1+condition|subject))
formula_analysis = @formula(y~1+condition + (1+condition|subject) + (1+condition|item));
# ╔═╡ d71b7260-21eb-4a4f-939f-039d310b53d1
begin
nitem = 11
nsub = 13
ntrials =4
# generate factorial design
d0 = factorproduct((;item=nlevels(nitem, "I")),(;subject=nlevels(nsub, "S")),(;condition=nlevels(2,"C")),(;trials=1:ntrials))|>DataFrame
d0.y .= rand(size(d0,1)) # dummy response
form_full = @formula(y~1+condition+(1+condition|subject) + (1+condition|item))
m0 = fit(MixedModel, form_full, d0,contrasts=Dict(:condition=>EffectsCoding()))
d_sim = deepcopy(d0)
update!(m0; subject=create_re(1,s_cond),item=create_re(1,i_cond))
simulate!(MersenneTwister(2),m0,σ=1,β=[0.,β_cond])
d_sim.y = m0.y
m_analysis = fit(MixedModel,formula_analysis,d_sim)
d_avg = groupby(d_sim,[:subject,:condition])|>x->combine(x,:y=>mean)
end;
# ╔═╡ ff949ccd-b0da-4a6a-94de-c81d9bc125aa
md"""
Basic dataset: Two level `condition` :🚲 vs. 🧐
(with $nsub subjects, $nitem items, $ntrials repetitions; in total $(2*nsub*nitem*ntrials) trials)
> --- wait, what the heck is an item?
**Items** are typically the different individual stimuli you use, different faces, different words, different sounds.
Potential Face-Stimuli: 👨🏼👩🏼🦰🤶🏼👩🏼🌾👩🏼🏭👨🏼🚒🙍🏼♀️🧚🏼♂️👨🏼✈️ - do each evoke the same ERP? No!
"""
# ╔═╡ 301514eb-3197-42ae-83a1-0e95faadd413
AoG.data(d_sim)*mapping(:condition,:y,color=:subject)*visual(Beeswarm,algorithm=QuasirandomJitter(jitter_width=15))|>draw;
# ╔═╡ 496d24a5-8e29-41f8-926b-6f41b2500cd0
begin
ix = d_avg.condition.=="C1"
diff_values = d_avg.y_mean[ix] .- d_avg.y_mean[.!ix]
pval_2stage = pvalue(OneSampleTTest(diff_values))
HTML(html(html"""<style>
#largetable table td {
font-size: 1.5em !important;
}
</style><div id="largetable">""")*html(md"""
|||
|---|---|
|2-stage t-test: p = | $(round(pval_2stage,sigdigits=2)) $(pval_2stage<=0.05 ? "✓" : "❎")|
|LMM: p = |$(round(coeftable(m_analysis).cols[4][2],sigdigits=2)) $(coeftable(m_analysis).cols[4][2]<=0.05 ? "✓" : "❎")|
""")*html(html"""</div>"""))
end
# ╔═╡ 77a7ace8-9689-44b4-9a08-8147177e25a4
md"""
## Item effects inflate type-I errors
"""
# ╔═╡ d43ba68b-7873-414e-8c06-61e46a2234e3
danger(md"Repeatedly measuring the same item introduces a dependency!")
# ╔═╡ 472775cc-7521-415a-bd0e-00464c978071
md"""
Besides the item-effects modelling, LMMs have **other benefits**:
- Missing data & unbalanced designs ✔
- All linear modeling features, but with repeated measures!
- Make your analysis more fancy 😉
"""
# ╔═╡ ce64aa76-c2df-4c4d-a80a-36d88cef611f
md"""
# Mass Univariate LMMs
"""
# ╔═╡ d8f6335e-a47d-4340-9d57-def15284ffaa
begin
data, evts = UnfoldSim.predef_eeg(10; return_epoched = true) # simulate 10 subjects
data = reshape(data, 1, size(data, 1), :) # concatenate the data into a long EEG
times = range(0, length = size(data, 2), step = 1 / 100)
rename!(evts,:continuous=>:cont,:condition=>:cond)
end;
# ╔═╡ f6afc96f-e407-44ca-a1f5-d74323962b36
md"""
`size(eeg-data)` = **1** ch x **45** timepoints x **1000** trials
"""
# ╔═╡ f7a3acf4-7131-4467-9950-1a3cfc43f725
evts
# ╔═╡ 39ca2d14-4d54-493d-b10e-5201d628f1cb
# ╠═╡ show_logs = false
f_lmm = @formula(0~1+cont+(1+cont|item)+(1+cont|subject));
# ╔═╡ 036982f3-673e-4f65-b392-9110d7a5858b
# ╠═╡ show_logs = false
m_lmm = fit(UnfoldModel,f_lmm,evts,data,times);
# ╔═╡ 64e34904-a336-4734-9ca3-6fcfa6f2403e
plot_erp( coeftable(m_lmm),
mapping=(;col=:group),
layout=(;show_legend=false))
# ╔═╡ b285ab15-eec6-4422-8eec-9291a5368e86
md"""
# What about p-values?
"""
# ╔═╡ bb271a40-0a4d-4032-9273-8bd5ad372778
# ╠═╡ show_logs = false
begin
f_0 = @formula(0~1+(1+cont|item)+(1+cont|subject))
m_lmm_0 = fit(UnfoldModel,f_0,evts,data,times)
end
# ╔═╡ d7f9cc69-f891-4c2e-be71-a9f2fbe33a24
pvalues = likelihoodratiotest(data,m_lmm_0,m_lmm) |> pvalue;
# ╔═╡ 02a32ebe-1a26-4edc-a982-c699075f459c
scatter(times,vcat(pvalues...);axis=(;yscale=log10,ylabel="p-value",xlabel="time"))
# ╔═╡ 62565204-6031-4895-af8c-17f585c4205f
md"""
# Multiple comparison correction?
"""
# ╔═╡ 2cd05a7c-a7c6-41ce-89f9-3c3005604296
danger(md"""
We are now deep in uncharted territory. The following methods are **active research**. **No waranty** - code will change - papers are 🐌 coming
""")
# ╔═╡ 768b2dc1-a025-460d-889d-6213bcb4a1d3
rng = MersenneTwister(1);
# ╔═╡ fb71d1fb-b72f-4ee5-8ace-5d0ec77bd5f7
pvalues_mcc=pvalue(rng,m_lmm,data,2;n_permutations=100,clusterforming_threshold=1.8);
# ╔═╡ 914c1214-eb5d-43b1-98b4-5cb73206f873
pvalues_mcc
# ╔═╡ 89e0b24b-e2da-4597-ad4b-5e5f62ccf77e
let
f,ax,h = scatter(times,pvalues_mcc[1,:],label="permutation corrected")#,axis=(;yscale=log10))
scatter!(times,vcat(pvalues...),label="likelihood ratio test")
Legend(f[0,1],ax,orientation=:horizontal)
f
end
# ╔═╡ 8af2a5d0-e44c-426f-bc80-4895b14be69e
md"""
# Conclusions
- LMMs can be **super** important
- Fitting mass-univariate LMMs in Unfold: ⚡🐆⚡
"""
# ╔═╡ 0a11ace9-88d6-4df5-8c77-60b9a6d345f3
PlutoTeachingTools.warning(md""""More research is needed"™ - contact me if interested!
""")
# ╔═╡ bc25131c-1ae5-4e5f-911d-0dee59749912
md"""
# Package loading 🤷
"""
# ╔═╡ a3907b8a-12dd-4633-bf8b-0f47436a35d3
let
t = ggthemr(:fresh)
t.fontsize = 15 # presentation fontsize
set_theme!(t)
end
# ╔═╡ 04f0b361-208e-411c-8a48-c0364712c741
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
AlgebraOfGraphics = "cbdf2221-f076-402e-a563-3d30da359d67"
CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
Chain = "8be319e6-bccf-4806-a6f7-6fae938471bc"
ClusterDepth = "c8d8bbfa-f476-4995-adff-2987f04015d1"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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MakieThemes = "e296ed71-da82-5faf-88ab-0034a9761098"
MixedModels = "ff71e718-51f3-5ec2-a782-8ffcbfa3c316"
MixedModelsPermutations = "647c4018-d7ef-4d03-a0cc-8889a722319e"
MixedModelsSim = "d5ae56c5-23ca-4a1f-b505-9fc4796fc1fe"
PlutoTeachingTools = "661c6b06-c737-4d37-b85c-46df65de6f69"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
SwarmMakie = "0b1c068e-6a84-4e66-8136-5c95cafa83ed"
Unfold = "181c99d8-e21b-4ff3-b70b-c233eddec679"
UnfoldMakie = "69a5ce3b-64fb-4f22-ae69-36dd4416af2a"
UnfoldMixedModels = "019ae9e0-8363-565c-86e5-97a5a2fe84f4"
UnfoldSim = "ed8ae6d2-84d3-44c6-ab46-0baf21700804"
UnfoldStats = "96fd419a-8306-4ce8-ba5b-cd907cb7647c"
[compat]
AlgebraOfGraphics = "~0.10.4"
CairoMakie = "~0.13.4"
CategoricalArrays = "~0.10.8"
Chain = "~0.6.0"
ClusterDepth = "~0.2.2"
DataFrames = "~1.7.0"
DataFramesMeta = "~0.15.4"
DisplayAs = "~0.1.6"
HypertextLiteral = "~0.9.5"
HypothesisTests = "~0.11.5"
MakieThemes = "~0.1.4"
MixedModels = "~4.34.1"
MixedModelsPermutations = "~0.2.0"
MixedModelsSim = "~0.2.11"
PlutoTeachingTools = "~0.3.1"
PlutoUI = "~0.7.62"
SwarmMakie = "~0.1.5"
Unfold = "~0.8.4"
UnfoldMakie = "~0.5.18"
UnfoldMixedModels = "~0.1.1"
UnfoldSim = "~0.4.0"
UnfoldStats = "~0.2.1"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
julia_version = "1.11.3"
manifest_format = "2.0"
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