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Copy path01_massunivariate.jl
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3077 lines (2517 loc) · 105 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
# ╔═╡ d9912a4c-5d3a-11ee-381e-03ad95d59994
begin
using Unfold # Formulas, fit, coefs, coeftable
using UnfoldSim # Simulation
using UnfoldMakie,CairoMakie # Plotting + Plotting-Backend
using PlutoUI, PlutoTeachingTools # Only needed for Sliders + pretty boxes
# make plotting look a tad better
using MakieThemes
set_theme!(ggthemr(:fresh))
end
# ╔═╡ d18def09-658d-47af-92e6-d729c9f83667
# load some PlutoUI + simulate_eeg utilities - externalized it to have the same functions in all worksheets
include(download("https://raw.githubusercontent.com/s-ccs/workshop_unfold_2025/refs/heads/main/workshop_utils_shared.jl"))
# ╔═╡ a49df28b-6587-4051-b777-26a56d339a2e
md"""
# Pluto.jl & Unfold.jl
[CC-By Benedikt Ehinger](https://www.s-ccs.de) - Unfold.jl Workshop
"""
# ╔═╡ 363cb188-97c7-4ece-b416-08256da0b5f9
md"""
What follows is some code to get to know `Pluto.jl` a little bit, implement some interactive sliders, and then continue on to explore your first Unfold.jl analyses
"""
# ╔═╡ d2eae77b-07be-405f-ad7c-d9a3c7f28acc
md"""
## Pluto 101
Different to Jupyter, Pluto keeps track of what cell depends on what other cell, and **automatically** updates dependent cells!
"""
# ╔═╡ 1e5244f9-91db-4f7c-bdf0-eb98d9efe0b1
md"""
Let's see that in action - but first, we will simulate some EEG-data:
"""
# ╔═╡ ab033cea-1eb9-4237-a595-3928cca0a0ca
aside(tip(md"**`n-repeats`** repeats one instance of a 2x5 design (10 events)\
**`min_overlap`** controls the minimal distance between adjacent events"),v_offset=-170)
# ╔═╡ 65befddb-2542-473e-8b3b-0b5c5b9fe839
question_box(md"""
Change the value of `n_repeats` in the cell below to `3`, and save (▶ button or `shift+s`).
1) Observe that Pluto automatically updates all dependent cells.
2) What would you think a realistic noise level is?
""")
# ╔═╡ d429f512-5e4b-4137-9fd4-f064f098dc57
aside(PlutoTeachingTools.tip(md"Press `F1` to see all Pluto shortcuts :)
"),v_offset=-175)
# ╔═╡ a851ef86-c830-4de3-b485-8997f9e5b81c
n_repeats = 1
# ╔═╡ 9d50b828-fafb-4ca3-b196-fed692476e22
noiselevel = 0
# ╔═╡ 3cbe1c9d-d3e3-4757-ba8d-2867e37b1dbe
my_tip("Pluto.jl",md"""
`Pluto.jl` puts the outputs on top of the cell, not below. Your code is the "caption" of the output :)
""")
# ╔═╡ 6cb8aed5-7133-4f77-8fe7-44fcb0d4a941
warning_box(md"""
Pluto keeps track of your variables, therefore:
1. Any variable name **cannot** be **defined in more than one cell**.
2. **Multi-line code** needs to be encapsulated in `begin ... end`.
3. **Inplace** operations (also known as side-effects, mutations), cannot be seen by Pluto; dangerous :)
""")
# ╔═╡ 8d0da37e-25bf-42e0-82cc-72cd24a5c82d
md"""
## Sliders
We can also use sliders instead of fixing the parameters.
A slider is defined like this:
```julia
@bind yourvariable PlutoUI.Slider(from:stepsize:to,show_value=true,default=1)
```
"""
# ╔═╡ 0b009035-d91a-4c46-a762-3ae33e5bae18
question_box(md"""
Now it is your turn
1. Replace the cell with `n_repeats` above with a slider!
2. Choose at least 5 repetitions (depending on your noise level), you might want to revisit this slider later.
""")
# ╔═╡ 2c278d2d-11b1-436b-abba-e67910e10ca2
answer_box(md"""
```julia
@bind n_repeats PlutoUI.Slider(1:1:10,show_value=true,default=3)
```
""")
# ╔═╡ ea71ee25-35bf-49bb-a869-db2cfe98c6f3
md"""
# Mass univariate rERP analysis
"""
# ╔═╡ 4a23c228-9494-4a59-8c3f-0b4d1621e322
md"""
## Experimental Design
We already started by simulating a **single-subject** design with a **2-level** factor `stimulation` (🚲 vs. 😊).
In the other exercises, we'll add a `continuous` effect from 0:15 (👀 sacccade amplitude).
"""
# ╔═╡ 5efdb07f-bff6-40bc-b25c-09b9b03c634e
PlutoTeachingTools.aside(md"""
The event table describes the experimental design (think `EEG.event`, `raw.annotations`). Each row is one event, `latency` describes the onset in samples. `stimulation` (or other columns) describe the conditions/covariates of that event.
""",v_offset = -200)
# ╔═╡ 12b81ec4-9111-4583-b5ce-1bfe3a7e4f61
md"""
## Preparation
The data are still continuous. For a mass-univariate analysis, we need to epoch them.
"""
# ╔═╡ 53ad0364-f7ad-4b27-90f8-f4e06bc26c22
md"""
## Analyze the data
Let's run a single-subject ERP analysis, extracting the intercept (condition = 🚲) and difference of condition (😊 - 🚲).
"""
# ╔═╡ 70e50157-289c-4d44-85b7-ed9fc3ba9dfb
md"""
### 1. Define a formula
A formula is an easy, succinct, but also formal description of your linear model. Some example formulas to get you started:
- `@formula(0~1)` - just an intercept (= the mean!)\
- `@formula(0~1+A)` - intercept + main/simple effect\
- `@formula(0~1+A+B)` - intercept + main A + main B\
- `@formula(0~1+A&B)` - intercept + interaction\
- `@formula(0~1+A*B)` - intercept + simple effects + interaction\
- `@formula(0~0+A)` - no intercept + main effect\
"""
# ╔═╡ 3c43e8d0-fe44-47cf-8b7e-25e9efabb82f
aside(md"""
Why does the left side have a `0` and not `ERP~1+A` or something? Just by convention!
""",v_offset=-150)
# ╔═╡ 033fe720-221a-45f8-b33c-d37aaad20083
PlutoTeachingTools.protip(
md"""
Glad you asked!
In the `fit` command below, you can define a `contrasts` Dictionary, defining your contrasts. Popular options:
```julia
contrasts = Dict("A"=>EffectsCoding())
contrasts = Dict("B"=>DummyCoding(base="levelZ"))
contrasts = Dict("B"=>DummyCoding(levels=["levelZ","levelA","levelX"]))
```
You don't know what contrasts are? No time in this course, but [imho this is the definitive guide](https://doi.org/10.1016/j.jml.2019.104038) to them! And if you want to learn more on the Julia side, [check out this documentation.](https://juliastats.org/StatsModels.jl/stable/contrasts/)
""",md"""
Advanced: What about contrasts?
""")
# ╔═╡ a643265c-e34f-4a77-939f-addce5d57117
question_box(md"""
Go ahead, define a formula for the `intercept` and for the `stimulation` main effect.
`f = @formula ...`
""")
# ╔═╡ 67e82140-5d2d-4abe-9e77-6866cd7104e7
f = missing; # <-- replace me
# ╔═╡ 4f822c2e-1e9b-400d-8738-c7603dcd7072
answer_box(md"""
```julia
f = @formula(0~1+stimulation)
```
""")
# ╔═╡ 852fbf20-9386-4c11-a000-04a38d2fa9e8
@check_response(f,Unfold.FormulaTerm)
# ╔═╡ 6ae533e8-ce36-48b2-8e4f-88a02e3089f2
md"""
### 2. Run the model
After you specified the formula, we are ready to run the model on all time-points (and all channels, but we only have one ;)).
"""
# ╔═╡ 2065acc9-0229-487d-9923-3553108bd3c2
# uncomment once `f` is defined
#m_erp = fit(UnfoldModel,f,events,eegdata_epochs,times)
# ╔═╡ f6457cbd-f164-4dcf-9dcf-de5b3f53fad4
PlutoTeachingTools.protip(md"""
In case you are wondering why `typeof(eegdata_epochs)` is not only `Float64`, but also contains `Missing`:
When epoching an event, the event-window might overlap with a region of data that does not have any data (e.g. the beginning/end of the recording, or data marked as artefactual). In most EEG libraries, such trials are then removed from further analysis (e.g. eeglab, mne). In `Unfold.jl`, we have (almost) full support to handle missings, that is, also "partial"-trials are used in the average!
This can be especially advantageous in population with lot's of blinks, movements etc. where each cleaned artefact removes a whole trial, like infants.
""","Advanced: Why does `data` have `Missing`?")
# ╔═╡ e71ad2e4-103c-4165-b0f5-eb7b15d96b97
md"""
### 3. Extract the coefficients
"""
# ╔═╡ 04a5809a-d1ed-4477-b3e5-5c19f3522d30
md"""
There are two main ways to extract coeffients:
1. Extract as a matrix (`coef`)
2. Extract as a 🧹tidy dataframe (`coeftable`)
"""
# ╔═╡ fc57a8a1-69b7-4dd0-aa56-8847f16a0253
question_box(md"""Let's start with `coef`, and see what we get. Add the correct command using `coef` and `m_erp` in the following cell.
**Hint:** you can evoke the help by typing `?coef` in a cell to automatically open the "Live Docs" of Pluto.jl - or click the button on the lower right!"""
)
# ╔═╡ f6c405a1-e900-461b-9eb3-802348e8f691
coefs = missing; # <-- replace me
# ╔═╡ 5155f9d7-9280-4fd7-a9e8-d9f557b16573
answer_box(md"""
```julia
coefs = coef(m_erp);
```
""")
# ╔═╡ c3ac0645-e8f5-4d88-8257-1271683d85db
@check_response(coefs,AbstractArray)
# ╔═╡ a0d28de1-5485-48e1-9309-f3de4549d404
# uncomment once coefs is defined
#series(coefs[1,:,:]')
# ╔═╡ dbbee49f-ccd0-40ce-b14e-ac8bd67d8908
hint(md"""
Cannot recognize anything? Try putting the `n_repeats` to >10 and the `noiselevel` lower.
""")
# ╔═╡ 35c94ce1-ef0e-4424-9e80-c948ec17e334
question_box(md"Next, define `coefs_df` via `coeftable` to receive a tidy dataframe")
# ╔═╡ 2fe64ff7-9d9e-47f2-8c26-4d5d27bd66cb
coefs_df = missing; # <-- replace me
# ╔═╡ aa7d2d15-c933-4f47-8e7b-4bd4420799c7
answer_box(md"""
```julia
coefs_df = coeftable(m_erp);
```
""")
# ╔═╡ 13f07084-c6e8-44ae-af07-17a2e4ea6ad5
@check_response(coefs_df,Unfold.AbstractDataFrame)
# ╔═╡ cdc4b2d3-4d9d-4b56-8153-7175cd86acc4
md"""
🧹Tidy dataframes allow us to plot them using some ggplot-type UnfoldMakie.jl magic (based on `AlgebraOfGraphics`)"""
# ╔═╡ 0253718e-f1bd-4c78-9b53-11314120eb29
# uncomment once coefs_df is defined
#plot_erp(coefs_df)
# ╔═╡ ba8a1905-97ba-46ae-aff3-d99aee4c1f0f
#n_repeats = 2
PlutoTeachingTools.aside(md"Min. overlap: $(@bind min_overlap PlutoUI.Slider(0.1:0.1:1,show_value=true,
default=0.5))",v_offset=-50)
# ╔═╡ 4de80b12-59c8-4cee-86d7-d13463fa263a
eegdata,events = simulate_eeg(;noiselevel,
n_repeats,
overlap=(min_overlap,0.2),
#twobytwo = true
);
# ╔═╡ 635c697a-771a-4228-bde1-e38e21698905
let # "let" enforces local scope, you cannot access variables from here outside this cell - but you can then re-use variable names :)
f,ax,h = lines(eegdata)
vlines!(events.latency,linestyle=:dash)
f
end
# ╔═╡ ec09f589-4d48-4780-b786-d1c9c115238d
first(events,10)
# ╔═╡ 6bd3bf55-947a-43d9-a367-e816a1c2d9c9
eegdata_epochs, times = Unfold.epoch(data = eegdata, tbl = events, τ = (-0.2, 0.8), sfreq = 100); # channel x timesteps x trials
# ╔═╡ f13f93bf-f9a9-414e-8061-9d27d54e0cc2
size(eegdata_epochs) # channels x times x trials
# ╔═╡ 8dbd8657-4396-4d32-affb-25387e775ded
size(eegdata) # continuous-time
# ╔═╡ f854a1e7-3f37-4f38-9637-ea4326d6352d
(;size_eeg=size(eegdata),n_repeats,min_overlap) # display some info parameters
# ╔═╡ 2bde3123-09a5-4faa-84ba-8ef579179511
question_box(md"""
Looking at this ERPs:
1. Do we have an effect of `stimulation`?
2. What is going on in the baseline? What parameter do you need to change, to decrease this mess?
""")
# ╔═╡ a69ed114-3539-40b0-b049-d8806dad7fb8
answer_box(md"""
1. Yes! `stimulation` shows an effect at ~170ms.
2. We are seeing here the overlap effect in action. Try changing the `min_overlap` slider to remove this mess :)
""")
# ╔═╡ 72bd7a31-e267-47fb-8b6b-596753226b90
@bind finished PlutoUI.CounterButton("All tasks finished? Click here!")
# ╔═╡ 4aae7a31-523a-4f1e-8e61-a18dcc30bebf
begin
finished > 0 ? confetti() : nothing
end
# ╔═╡ eae8bcda-5120-47cd-b349-c27551743ada
begin
md"""
# Extra Tasks
Bravo! You made it to the end!
If you still have time - ⚡🐆⚡ - you probably used Unfold before!
Here are some tasks you could enjoy:
"""
end
# ╔═╡ 62cd895c-b663-4e66-bea1-bf5c79a6026c
md"""
## 2x2 designs
"""
# ╔═╡ 41c335cc-2a0e-458c-b2ce-0806f0bf692b
question_box(md"""
Add `twobytwo=true` to the `simulate_eeg` command on top. This will add a second effect `size`. Add it to your formula (including an interaction) and re-run your code. Can you observe an interaction?
""")
# ╔═╡ 31b4cfbc-ae6a-4c44-9abf-416ee5a8ced0
answer_box(md"""
The formula should be: `0~1+stimulation*size`
And yes, there seems to be an interaction!
""")
# ╔═╡ f2898827-74d3-4e0c-88f2-02a68bedd9ab
md"""
## Under the hood
Let's inspect the designmatrix (the $X$ in $$y=Xb + e$$)
"""
# ╔═╡ 446e914e-0a40-4c84-8a57-d43c5399d900
# uncomment once `m_erp` runs successfully
#plot_designmatrix(designmatrix(m_erp),sort_data=false)
# ╔═╡ 91a3aac1-3f48-4e58-b617-fa353d8948f7
question_box(md"""
The designmatrix is currently sorted by trial-number. After you sort it, can you answer the question whether the design is balanced [^1]?
[^1]: All condition-combinations have equal number of trials
""")
# ╔═╡ f2b18e0a-7bb7-4a69-adf2-b3555303129f
answer_box(md"""
The design is indeed balanced, you can see that each simple-effect has the same amount of trials.
```julia
plot_designmatrix(designmatrix(m_erp),sort_data=true)
```
""")
# ╔═╡ ca86bfa1-16e7-414d-ba01-a7691c74d7d5
md"""
## Multichannel!
"""
# ╔═╡ b0af7e88-9eac-4842-8586-65d5904d7c61
question_box(md"""
Add `multichannel` to the `simulate_eeg` command above. This will simulate data based on 20 channels.
You need to either adapt your other code to support multi-channel, or accept that some code cells will throw errors ;-)
Can you adapt `plot_erp` to return useful results? You could try `mapping=(;layout=:channel=>nonnumeric`).
""")
# ╔═╡ f69faad9-886e-4806-a2e2-9c531f686538
answer_box(md"""
An alternative solution could be to use a butterfly plot:
```julia
plot_erp(coefs_df,mapping = (; group=:channel, color=:channel,layout=:coefname))
```
""")
# ╔═╡ ef78ffb1-f2f0-4937-80dc-e7b1c8271bb2
md"""
# Setup / Bookkeeping / Layout
Nothing to see here, move along ;-)
"""
# ╔═╡ d0bd417a-8ab7-46f8-8a98-92b3b7ff5765
TableOfContents() # add TOC to the side
# ╔═╡ 00000000-0000-0000-0000-000000000001
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