diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 6cfe42b..3d31817 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-11-20T20:14:10","documenter_version":"1.1.2"}} \ No newline at end of file +{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-11-27T11:24:19","documenter_version":"1.1.2"}} \ No newline at end of file diff --git a/dev/api/index.html b/dev/api/index.html index 73454f7..18c038b 100644 --- a/dev/api/index.html +++ b/dev/api/index.html @@ -5,26 +5,26 @@ β = [1.,2.], contrasts=Dict(:cond=>EffectsCoding()) ) -source
UnfoldSim.MixedModelComponent — Type

A component that adds a hierarchical relation between parameters according to a LMM defined via MixedModels.jl

  • basis: an object, if accessed, provides a 'basis-function', e.g. hanning(40), this defines the response at a single event. It will be weighted by the model-prediction
  • formula: Formula-Object in the style of MixedModels.jl e.g. @formula 0~1+cond + (1|subject) - left-handside is ignored
  • β Vector of betas, must fit the formula
  • σs Dict of random effect variances, e.g. Dict(:subject=>[0.5,0.4]) or to specify correlationmatrix Dict(:subject=>[0.5,0.4,I(2,2)],...). Technically, this will be passed to MixedModels.jl create_re function, which creates the θ matrices.
  • contrasts: Dict in the style of MixedModels.jl. Default is empty.

All arguments can be named, in that case contrasts is optional

Works best with MultiSubjectDesign

MixedModelComponent(;
+
source
UnfoldSim.MixedModelComponent — Type

A component that adds a hierarchical relation between parameters according to a LMM defined via MixedModels.jl

  • basis: an object, if accessed, provides a 'basis-function', e.g. hanning(40), this defines the response at a single event. It will be weighted by the model-prediction
  • formula: Formula-Object in the style of MixedModels.jl e.g. @formula 0~1+cond + (1|subject) - left-handside is ignored
  • β Vector of betas, must fit the formula
  • σs Dict of random effect variances, e.g. Dict(:subject=>[0.5,0.4]) or to specify correlationmatrix Dict(:subject=>[0.5,0.4,I(2,2)],...). Technically, this will be passed to MixedModels.jl create_re function, which creates the θ matrices.
  • contrasts: Dict in the style of MixedModels.jl. Default is empty.

All arguments can be named, in that case contrasts is optional

Works best with MultiSubjectDesign

MixedModelComponent(;
     basis=hanning(40),
     formula=@formula(0~1+cond+(1+cond|subject)),
     β = [1.,2.],
     σs= Dict(:subject=>[0.5,0.4]),
     contrasts=Dict(:cond=>EffectsCoding())
 )
-
source
UnfoldSim.MultiSubjectDesign — Type
  • n_subjects::Int -> number of subjects
  • n_items::Int -> number of items (sometimes ≈trials)
  • subjects_between = nothing -> effects between subjects, e.g. young vs old
  • items_between = nothing -> effects between items, e.g. natural vs artificial images, but shown to all subjects
  • both_within = nothing -> effects completly crossed
  • tableModifyFun = x->x; # can be used to sort, or x->shuffle(MersenneTwister(42),x) - be sure to fix/update the rng accordingly!!

tipp: check the resulting dataframe using generate(design)

# declaring same condition both sub-between and item-between results in a full between subject/item design
+
source
UnfoldSim.MultiSubjectDesign — Type
  • n_subjects::Int -> number of subjects
  • n_items::Int -> number of items (sometimes ≈trials)
  • subjects_between = nothing -> effects between subjects, e.g. young vs old
  • items_between = nothing -> effects between items, e.g. natural vs artificial images, but shown to all subjects
  • both_within = nothing -> effects completly crossed
  • tableModifyFun = x->x; # can be used to sort, or x->shuffle(MersenneTwister(42),x) - be sure to fix/update the rng accordingly!!

tipp: check the resulting dataframe using generate(design)

# declaring same condition both sub-between and item-between results in a full between subject/item design
 design = MultiSubjectDesignjectDesign(;
         n_items=10,
 		n_subjects = 30,
         subjects_between=Dict(:cond=>["levelA","levelB"]),
 		items_between =Dict(:cond=>["levelA","levelB"]),
-        );
source
UnfoldSim.MultichannelComponent — Type

Wrapper for an AbstractComponent to project it to multiple target-channels via projection. optional adds noise to the source prior to projection.

source
UnfoldSim.RepeatDesign — Type

repeat a design DataFrame multiple times to mimick repeatedly recorded trials

designOnce = MultiSubjectDesign(;
+        );
source
UnfoldSim.MultichannelComponent — Type

Wrapper for an AbstractComponent to project it to multiple target-channels via projection. optional adds noise to the source prior to projection.

source
UnfoldSim.RepeatDesign — Type

repeat a design DataFrame multiple times to mimick repeatedly recorded trials

designOnce = MultiSubjectDesign(;
         n_items=2,
 		n_subjects = 2,
         subjects_between =Dict(:cond=>["levelA","levelB"]),
 		items_between =Dict(:cond=>["levelA","levelB"]),
         );
 
-design = RepeatDesign(designOnce,4);
source
UnfoldSim.SingleSubjectDesign — Type
  • conditions = Dict of conditions, e.g. Dict(:A=>["a_small","a_big"],:B=>["b_tiny","b_large"])
  • tableModifyFun = x->x; # can be used to sort, or x->shuffle(MersenneTwister(42),x) - be sure to fix/update the rng accordingly!!

Number of trials / rows in generate(design) depend on the full factorial of your conditions.

To increase the number of repetitions simply use RepeatDesign(SingleSubjectDesign(...),5)

tipp: check the resulting dataframe using generate(design)

source
Base.size — Method

Returns dimension of experiment design

source
DSP.Windows.hanning — Method

generate a hanning window

duration: in s offset: in s, defines hanning peak sfreq: sampling rate in Hz

source
UnfoldSim.adderp! — Method

Helper function to add inplace the erps to the EEG, but for both 2D (1 channel) and 3D (X channel case)

source
UnfoldSim.closest_src — Method
closest_src(coords_list::AbstractVector{<:AbstractVector}, pos)
-closest_src(coords::Vector{<:Real}, pos)

Takes an array of 'm' target coordinate vector (size 3) (or vector of vectors) and a matrix (n-by-3) of all available positions, and returns an array of size 'm' containing the indices of the respective items in 'pos' that are nearest to each of the target coordinates.

source
UnfoldSim.closest_src — Method
closest_src(head::Hartmut,label::String)

Returns src-ix of the Headmodel Hartmut which is closest to the average of the label.

Important

We use the average in eucledean space, but the cortex is a curved surface. In most cases they will not overlap. Ideally we would calculate the average on the surface, but this is a bit more complex to do (you'd need to calculate the vertices etc.)

hartmut = headmodel()
-pos = closest_src(hartmut=>"Left Middle Temporal Gyrus, posterior division")
source
UnfoldSim.convert — Method

Function to convert output similar to unfold (data, evts)

source
UnfoldSim.gen_noise — Method
gen_noise(t::RealisticNoise, n::Int)

Generate noise of a given type t and length n

source
UnfoldSim.gen_noise — Method
gen_noise(t::Union{PinkNoise, RedNoise}, n::Int)

Generate noise of a given type t and length n

source
UnfoldSim.gen_noise — Method
gen_noise(t::WhiteNoise, n::Int)

Generate noise of a given type t and length n

source
UnfoldSim.generate — Method

Generates full factorial Dataframe according to MixedModelsSim.jl 's simdatcrossed function Note: nitems = you can think of it as trials or better, as stimuli

Note: No condition can be named dv which is used internally in MixedModelsSim / MixedModels as a dummy left-side

Afterwards applies expdesign.tableModifyFun. Could be used to duplicate trials, sort, subselect etc.

Finally it sorts by :subject

julia> d = MultiSubjectDesign(;nsubjects = 10,nitems=20,both_within= Dict(:A=>nlevels(5),:B=>nlevels(2))) julia> generate(d)

source
UnfoldSim.generate — Method

Generates full-factorial DataFrame of expdesign.conditions

Afterwards applies expdesign.tableModifyFun.

julia> d = SingleSubjectDesign(;conditions= Dict(:A=>nlevels(5),:B=>nlevels(2))) julia> generate(d)

source
UnfoldSim.hartmut_citation — Method

Returns citation-string for HArtMuT

source
UnfoldSim.headmodel — Method

Load a headmodel, using Artifacts.jl automatically downloads the required files

Currently only type="hartmut" is implemented

source
UnfoldSim.hrf — Method

Generate a HRF kernel.

TR = 1/sfreq default parameters taken from SPM

Code adapted from Unfold.jl

source
UnfoldSim.leadfield — Method

Returns the leadfield

source
UnfoldSim.magnitude — Method

Extracts magnitude of the orientation-including leadfield.

By default uses the orientation specified in the headmodel

Fallback: along the third dimension using norm - the maximal projection

source
UnfoldSim.magnitude — Method

Extract magnitude of 3-orientation-leadfield, type (default: "perpendicular") => uses the provided source-point orientations - otherwise falls back to norm

source
UnfoldSim.n_channels — Method

Returns the number of channels. By default = 1

source
UnfoldSim.n_channels — Method

for MultichannelComponent returns the length of the projection vector

source
UnfoldSim.padarray — Method

Pads array with specified value, length padarray(arr, len, val)

source
UnfoldSim.predef_2x2 — Method

todo

Careful if you modify nitems with nsubjects = 1, n_items has to be a multiple of 4 (or your equivalent conditions factorial, e.g. all combinations length)

source
UnfoldSim.predef_eeg — Method

Generates a P1/N1/P3 complex. predefeeg(;kwargs...) predefeeg(rng;kwargs...) predefeeg(rng,nsubjects;kwargs...)

In case of n_subjects, MixedModelComponents are generated

Default params: n_repeats=100 tableModifyFun = x->shuffle(deepcopy(rng),x # random trial order conditions = Dict(...),

component / signal

sfreq = 100, p1 = (p100(;sfreq=sfreq), @formula(0~1),[5],Dict()), # P1 amp 5, no effects n1 = (n170(;sfreq=sfreq), @formula(0~1+condition),[5,-3],Dict()), # N1 amp 5, dummycoded condition effect (levels "car", "face") of -3 p3 = (p300(;sfreq=sfreq), @formula(0~1+continuous),[5,1],Dict()), # P3 amp 5, continuous effect range [-5,5] with slope 1

noise

noiselevel = 0.2, noise = PinkNoise(;noiselevel=noiselevel),

onset

overlap = (0.5,0.2), # offset + width/length of Uniform noise. put offset to 1 for no overlap. put width to 0 for no jitter onset=UniformOnset(;offset=sfreq0.5overlap[1],width=sfreq0.5overlap[2]),

source
UnfoldSim.predef_eeg — Method

predefeeg(rng,nsubjects;kwargs...) Runs predefeeg(rng;kwargs...) nsubject times and concatenates the results.

source
UnfoldSim.simulate — Method

by default call simulate with ::Abstractcomponent,::AbstractDesign`, but allow for custom types

making use of other information in simulation

source
UnfoldSim.simulate — Method

simulate a linearModel

julia> c = UnfoldSim.LinearModelComponent([0,1,1,0],@formula(0~1+cond),[1,2],Dict()) julia> design = MultiSubjectDesign(;nsubjects=2,nitems=50,item_between=(;:cond=>["A","B"])) julia> simulate(StableRNG(1),c,design)

source
UnfoldSim.simulate — Method

simulate MixedModelComponent

julia> design = MultiSubjectDesign(;nsubjects=2,nitems=50,item_between=(;:cond=>["A","B"])) julia> c = UnfoldSim.MixedModelComponent([0.,1,1,0],@formula(0~1+cond+(1|subject)),[1,2],Dict(:subject=>[2],),Dict()) julia> simulate(StableRNG(1),c,design)

source
UnfoldSim.simulate — Method

Simulates erp data given the specified parameters

source
UnfoldSim.weight_σs — Method

Weights a σs Dict for MixedModels.jl by a Float64

Finally sales it by σ_lmm, as a trick to simulate noise-free LMMs

I anticipate a function function weight_σs(σs::Dict,b_σs::Dict,σ_lmm::Float64) where each σs entry can be weighted individually

source
+design = RepeatDesign(designOnce,4);source
UnfoldSim.SingleSubjectDesign — Type
  • conditions = Dict of conditions, e.g. Dict(:A=>["a_small","a_big"],:B=>["b_tiny","b_large"])
  • tableModifyFun = x->x; # can be used to sort, or x->shuffle(MersenneTwister(42),x) - be sure to fix/update the rng accordingly!!

Number of trials / rows in generate(design) depend on the full factorial of your conditions.

To increase the number of repetitions simply use RepeatDesign(SingleSubjectDesign(...),5)

tipp: check the resulting dataframe using generate(design)

source
Base.size — Method

Returns dimension of experiment design

source
DSP.Windows.hanning — Method

generate a hanning window

duration: in s offset: in s, defines hanning peak sfreq: sampling rate in Hz

source
UnfoldSim.adderp! — Method

Helper function to add inplace the erps to the EEG, but for both 2D (1 channel) and 3D (X channel case)

source
UnfoldSim.closest_src — Method
closest_src(coords_list::AbstractVector{<:AbstractVector}, pos)
+closest_src(coords::Vector{<:Real}, pos)

Takes an array of 'm' target coordinate vector (size 3) (or vector of vectors) and a matrix (n-by-3) of all available positions, and returns an array of size 'm' containing the indices of the respective items in 'pos' that are nearest to each of the target coordinates.

source
UnfoldSim.closest_src — Method
closest_src(head::Hartmut,label::String)

Returns src-ix of the Headmodel Hartmut which is closest to the average of the label.

Important

We use the average in eucledean space, but the cortex is a curved surface. In most cases they will not overlap. Ideally we would calculate the average on the surface, but this is a bit more complex to do (you'd need to calculate the vertices etc.)

hartmut = headmodel()
+pos = closest_src(hartmut=>"Left Middle Temporal Gyrus, posterior division")
source
UnfoldSim.convert — Method

Function to convert output similar to unfold (data, evts)

source
UnfoldSim.gen_noise — Method
gen_noise(t::RealisticNoise, n::Int)

Generate noise of a given type t and length n

source
UnfoldSim.gen_noise — Method
gen_noise(t::Union{PinkNoise, RedNoise}, n::Int)

Generate noise of a given type t and length n

source
UnfoldSim.gen_noise — Method
gen_noise(t::WhiteNoise, n::Int)

Generate noise of a given type t and length n

source
UnfoldSim.generate — Method

Generates full factorial Dataframe according to MixedModelsSim.jl 's simdatcrossed function Note: nitems = you can think of it as trials or better, as stimuli

Note: No condition can be named dv which is used internally in MixedModelsSim / MixedModels as a dummy left-side

Afterwards applies expdesign.tableModifyFun. Could be used to duplicate trials, sort, subselect etc.

Finally it sorts by :subject

julia> d = MultiSubjectDesign(;nsubjects = 10,nitems=20,both_within= Dict(:A=>nlevels(5),:B=>nlevels(2))) julia> generate(d)

source
UnfoldSim.generate — Method

Generates full-factorial DataFrame of expdesign.conditions

Afterwards applies expdesign.tableModifyFun.

julia> d = SingleSubjectDesign(;conditions= Dict(:A=>nlevels(5),:B=>nlevels(2))) julia> generate(d)

source
UnfoldSim.hartmut_citation — Method

Returns citation-string for HArtMuT

source
UnfoldSim.headmodel — Method

Load a headmodel, using Artifacts.jl automatically downloads the required files

Currently only type="hartmut" is implemented

source
UnfoldSim.hrf — Method

Generate a HRF kernel.

TR = 1/sfreq default parameters taken from SPM

Code adapted from Unfold.jl

source
UnfoldSim.leadfield — Method

Returns the leadfield

source
UnfoldSim.magnitude — Method

Extracts magnitude of the orientation-including leadfield.

By default uses the orientation specified in the headmodel

Fallback: along the third dimension using norm - the maximal projection

source
UnfoldSim.magnitude — Method

Extract magnitude of 3-orientation-leadfield, type (default: "perpendicular") => uses the provided source-point orientations - otherwise falls back to norm

source
UnfoldSim.n_channels — Method

Returns the number of channels. By default = 1

source
UnfoldSim.n_channels — Method

for MultichannelComponent returns the length of the projection vector

source
UnfoldSim.padarray — Method

Pads array with specified value, length padarray(arr, len, val)

source
UnfoldSim.predef_2x2 — Method

todo

Careful if you modify nitems with nsubjects = 1, n_items has to be a multiple of 4 (or your equivalent conditions factorial, e.g. all combinations length)

source
UnfoldSim.predef_eeg — Method

Generates a P1/N1/P3 complex. predefeeg(;kwargs...) predefeeg(rng;kwargs...) predefeeg(rng,nsubjects;kwargs...)

In case of n_subjects, MixedModelComponents are generated

Default params: n_repeats=100 tableModifyFun = x->shuffle(deepcopy(rng),x # random trial order conditions = Dict(...),

component / signal

sfreq = 100, p1 = (p100(;sfreq=sfreq), @formula(0~1),[5],Dict()), # P1 amp 5, no effects n1 = (n170(;sfreq=sfreq), @formula(0~1+condition),[5,-3],Dict()), # N1 amp 5, dummycoded condition effect (levels "car", "face") of -3 p3 = (p300(;sfreq=sfreq), @formula(0~1+continuous),[5,1],Dict()), # P3 amp 5, continuous effect range [-5,5] with slope 1

noise

noiselevel = 0.2, noise = PinkNoise(;noiselevel=noiselevel),

onset

overlap = (0.5,0.2), # offset + width/length of Uniform noise. put offset to 1 for no overlap. put width to 0 for no jitter onset=UniformOnset(;offset=sfreq0.5overlap[1],width=sfreq0.5overlap[2]),

source
UnfoldSim.predef_eeg — Method

predefeeg(rng,nsubjects;kwargs...) Runs predefeeg(rng;kwargs...) nsubject times and concatenates the results.

source
UnfoldSim.simulate — Method

by default call simulate with ::Abstractcomponent,::AbstractDesign`, but allow for custom types

making use of other information in simulation

source
UnfoldSim.simulate — Method

simulate a linearModel

julia> c = UnfoldSim.LinearModelComponent([0,1,1,0],@formula(0~1+cond),[1,2],Dict()) julia> design = MultiSubjectDesign(;nsubjects=2,nitems=50,item_between=(;:cond=>["A","B"])) julia> simulate(StableRNG(1),c,design)

source
UnfoldSim.simulate — Method

simulate MixedModelComponent

julia> design = MultiSubjectDesign(;nsubjects=2,nitems=50,item_between=(;:cond=>["A","B"])) julia> c = UnfoldSim.MixedModelComponent([0.,1,1,0],@formula(0~1+cond+(1|subject)),[1,2],Dict(:subject=>[2],),Dict()) julia> simulate(StableRNG(1),c,design)

source
UnfoldSim.simulate — Method

Simulates erp data given the specified parameters

source
UnfoldSim.weight_σs — Method

Weights a σs Dict for MixedModels.jl by a Float64

Finally sales it by σ_lmm, as a trick to simulate noise-free LMMs

I anticipate a function function weight_σs(σs::Dict,b_σs::Dict,σ_lmm::Float64) where each σs entry can be weighted individually

source
diff --git a/dev/generated/HowTo/multichannel/index.html b/dev/generated/HowTo/multichannel/index.html index c8993e5..9e6fa8f 100644 --- a/dev/generated/HowTo/multichannel/index.html +++ b/dev/generated/HowTo/multichannel/index.html @@ -26,4 +26,4 @@ plot_butterfly!(f[1,1:2],df;positions=pos2d) plot_topoplot!(f[2,1],df[df.time .== 28,:];positions=pos2d,visual=(;enlarge=0.5,label_scatter=false),axis=(;limits=((0,1),(0,0.9)))) plot_topoplot!(f[2,2],df[df.time .== 48,:];positions=pos2d,visual=(;enlarge=0.5,label_scatter=false),axis=(;limits=((0,1),(0,0.9)))) -fExample block output

This page was generated using Literate.jl.

+fExample block output

This page was generated using Literate.jl.

diff --git a/dev/generated/HowTo/newComponent/4f09f12b.png b/dev/generated/HowTo/newComponent/4f09f12b.png new file mode 100644 index 0000000..aaf80fe Binary files /dev/null and b/dev/generated/HowTo/newComponent/4f09f12b.png differ diff --git a/dev/generated/HowTo/newComponent/eb67b204.png b/dev/generated/HowTo/newComponent/eb67b204.png deleted file mode 100644 index b98cfb0..0000000 Binary files a/dev/generated/HowTo/newComponent/eb67b204.png and /dev/null differ diff --git a/dev/generated/HowTo/newComponent/index.html b/dev/generated/HowTo/newComponent/index.html index 53c65bc..a3fa5bd 100644 --- a/dev/generated/HowTo/newComponent/index.html +++ b/dev/generated/HowTo/newComponent/index.html @@ -34,4 +34,4 @@ erp = UnfoldSim.simulate(MersenneTwister(1),TimeVaryingComponent(basis_shiftduration,50),design) -heatmap(hcat(erp...))Example block output

This page was generated using Literate.jl.

+heatmap(hcat(erp...))Example block output

This page was generated using Literate.jl.

diff --git a/dev/generated/HowTo/newDesign/index.html b/dev/generated/HowTo/newDesign/index.html index efb2d0e..250cf5e 100644 --- a/dev/generated/HowTo/newDesign/index.html +++ b/dev/generated/HowTo/newDesign/index.html @@ -13,4 +13,4 @@ levels = vcat(repeat(["levelA"],nA),repeat(["levelB"],nB)) return DataFrame(Dict(:condition=>levels)) end;

Finally, we can test the function and see whether it returns a Design-DataFrame as we requested

design = ImbalanceSubjectDesign(;nTrials=6,balance=0.2)
-generate(design)
6×1 DataFrame
Rowcondition
String
1levelA
2levelB
3levelB
4levelB
5levelB
6levelB
Important

It is the users task to ensure that each run is reproducible. So if you have a random process (e.g. shuffling), be sure to safe a RNG object in your struct and use it in your generate function.

This page was generated using Literate.jl.

+generate(design)
6×1 DataFrame
Rowcondition
String
1levelA
2levelB
3levelB
4levelB
5levelB
6levelB
Important

It is the users task to ensure that each run is reproducible. So if you have a random process (e.g. shuffling), be sure to safe a RNG object in your struct and use it in your generate function.

This page was generated using Literate.jl.

diff --git a/dev/generated/HowTo/repeatTrials/index.html b/dev/generated/HowTo/repeatTrials/index.html index fca06d3..b7731dc 100644 --- a/dev/generated/HowTo/repeatTrials/index.html +++ b/dev/generated/HowTo/repeatTrials/index.html @@ -7,4 +7,4 @@ ); design = RepeatDesign(designOnce,4); -generate(design)
16×3 DataFrame
Rowsubjectconditem
StringStringString
1S1levelAI1
2S1levelBI2
3S1levelAI1
4S1levelBI2
5S1levelAI1
6S1levelBI2
7S1levelAI1
8S1levelBI2
9S2levelAI1
10S2levelBI2
11S2levelAI1
12S2levelBI2
13S2levelAI1
14S2levelBI2
15S2levelAI1
16S2levelBI2

As you can see, the design was simply repeated. As always, you can ignore the dv column, it is for internal consistency with MixedModelsSim.jl

Note

If you implemented your own AbstractDesign, you need to define the size function accordingly. E.g.: Base.size(design::RepeatDesign{SingleSubjectDesign}) = size(design.design).*design.repeat

This page was generated using Literate.jl.

+generate(design)
16×3 DataFrame
Rowsubjectconditem
StringStringString
1S1levelAI1
2S1levelBI2
3S1levelAI1
4S1levelBI2
5S1levelAI1
6S1levelBI2
7S1levelAI1
8S1levelBI2
9S2levelAI1
10S2levelBI2
11S2levelAI1
12S2levelBI2
13S2levelAI1
14S2levelBI2
15S2levelAI1
16S2levelBI2

As you can see, the design was simply repeated. As always, you can ignore the dv column, it is for internal consistency with MixedModelsSim.jl

Note

If you implemented your own AbstractDesign, you need to define the size function accordingly. E.g.: Base.size(design::RepeatDesign{SingleSubjectDesign}) = size(design.design).*design.repeat

This page was generated using Literate.jl.

diff --git a/dev/generated/reference/basistypes/index.html b/dev/generated/reference/basistypes/index.html index ee5adc2..ad354aa 100644 --- a/dev/generated/reference/basistypes/index.html +++ b/dev/generated/reference/basistypes/index.html @@ -45,4 +45,4 @@ axislegend(string(cfg[1]);merge=true,) end -fExample block output

This page was generated using Literate.jl.

+fExample block output

This page was generated using Literate.jl.

diff --git a/dev/generated/reference/noisetypes/index.html b/dev/generated/reference/noisetypes/index.html index cf6c238..226bbcc 100644 --- a/dev/generated/reference/noisetypes/index.html +++ b/dev/generated/reference/noisetypes/index.html @@ -27,4 +27,4 @@ end f[1:2,3] = Legend(f,ax_sig,"NoiseType") -fExample block output
Hint

We recommend for smaller signals the ExponentialNoise, maybe with a removed DC offset or a HighPass filter. For long signals, this Noise requires lots of memory though. Maybe Pinknoise is a better choice then.

This page was generated using Literate.jl.

+fExample block output
Hint

We recommend for smaller signals the ExponentialNoise, maybe with a removed DC offset or a HighPass filter. For long signals, this Noise requires lots of memory though. Maybe Pinknoise is a better choice then.

This page was generated using Literate.jl.

diff --git a/dev/generated/reference/overview/index.html b/dev/generated/reference/overview/index.html index a3313c9..2b21dce 100644 --- a/dev/generated/reference/overview/index.html +++ b/dev/generated/reference/overview/index.html @@ -15,4 +15,4 @@ RedNoise UnfoldSim.AutoRegressiveNoise UnfoldSim.RealisticNoise - WhiteNoise

This page was generated using Literate.jl.

+ WhiteNoise

This page was generated using Literate.jl.

diff --git a/dev/generated/tutorials/poweranalysis/index.html b/dev/generated/tutorials/poweranalysis/index.html index 51d2cfe..0b18418 100644 --- a/dev/generated/tutorials/poweranalysis/index.html +++ b/dev/generated/tutorials/poweranalysis/index.html @@ -25,4 +25,4 @@ # calculate a one-sided t-test pvals[seed] = pvalue(OneSampleTTest(y_big,y_small)) -end
 16.076927 seconds (25.67 M allocations: 3.371 GiB, 4.83% gc time, 94.32% compilation time: 9% of which was recompilation)

let's calculate the power

power = mean(pvals .<0.05)*100
60.0

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+end
 15.586743 seconds (25.80 M allocations: 3.379 GiB, 4.45% gc time, 94.12% compilation time: 9% of which was recompilation)

let's calculate the power

power = mean(pvals .<0.05)*100
60.0

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diff --git a/dev/generated/tutorials/quickstart/index.html b/dev/generated/tutorials/quickstart/index.html index 5f9ef70..27baca2 100644 --- a/dev/generated/tutorials/quickstart/index.html +++ b/dev/generated/tutorials/quickstart/index.html @@ -9,4 +9,4 @@ β = [1,0.5] );

Onsets and Noise

We will start with a uniform (but overlapping, offset < length(signal.basis)) onset-distribution

onset = UniformOnset(;width=20,offset=4);

And we will use some noise

noise = PinkNoise(;noiselevel=0.2);

Combine & Generate

We will put it all together in one Simulation type

simulation = Simulation(design, signal,  onset, noise);

finally, we will simulate some data

data,events = simulate(MersenneTwister(1),simulation);

Data is a n-sample Vector (but could be a Matrix for e.g. MultiSubjectDesign).

events is a DataFrame that contains a column latency with the onsets of events.

Plot them!

lines(data;color="black")
 vlines!(events.latency;color=["orange","teal"][1 .+ (events.condA.=="levelB")])
-current_figure()
Example block output

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+current_figure()Example block output

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diff --git a/dev/generated/tutorials/simulateERP/index.html b/dev/generated/tutorials/simulateERP/index.html index bf0071f..6e69d05 100644 --- a/dev/generated/tutorials/simulateERP/index.html +++ b/dev/generated/tutorials/simulateERP/index.html @@ -20,4 +20,4 @@ );

Now we can simply combine the components and simulate

components = [p1,n1,p3]
 data,evts = simulate(MersenneTwister(1),design,[p1,n1,p3],UniformOnset(;width=0,offset=1000),PinkNoise());

Analysis

Let's check that everything worked out well, by using Unfold

m = fit(UnfoldModel,Dict(Any=>(@formula(0~1+condition+continuous),firbasis(τ=[-0.1,1],sfreq=100,name="basis"))),evts,data);

first the "pure" beta/linear regression parameters

plot_erp(coeftable(m))
Example block output

and now beautifully visualized as marginal betas / predicted ERPs

plot_erp(effects(Dict(:condition=>["car","face"],:continuous=>-5:5),m);
         mapping=(:color=>:continuous,linestyle=:condition,group=:continuous),
-        extra=(;categoricalColor=false))
Example block output

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+ extra=(;categoricalColor=false))Example block output

This page was generated using Literate.jl.

diff --git a/dev/index.html b/dev/index.html index d7519da..61f33c3 100644 --- a/dev/index.html +++ b/dev/index.html @@ -7,4 +7,4 @@ vlines!(evts.latency;color=["orange","teal"][1 .+ (evts.condition .=="car")]) current_figure()Example block output

Or simulate epoched data directly

data,evts = UnfoldSim.predef_eeg(;n_repeats=20,noiselevel=0.8,return_epoched=true)
-heatmap(data[:,sortperm(evts,[:condition,:continuous])])
Example block output +heatmap(data[:,sortperm(evts,[:condition,:continuous])])Example block output