From bc3e4f17b15255c86d9b89e5f95c77a12968efa5 Mon Sep 17 00:00:00 2001 From: Dominique Makowski Date: Thu, 4 Jul 2024 22:44:16 +0100 Subject: [PATCH] add data --- .github/workflows/publish.yml | 2 - content/.jupyter_cache/__version__.txt | 1 + .../base.ipynb | 1915 +++ .../base.ipynb | 251 + content/.jupyter_cache/global.db | Bin 0 -> 28672 bytes .../1_introduction/execute-results/html.json | 16 + .../_freeze/4_rt/execute-results/html.json | 16 + .../4_rt/figure-html/cell-5-output-2.svg | 1561 +++ .../site_libs/clipboard/clipboard.min.js | 7 + content/.quarto/cites/index.json | 1 + content/.quarto/idx/1_introduction.qmd.json | 1 + content/.quarto/idx/2_predictors.qmd.json | 1 + content/.quarto/idx/3_scales.qmd.json | 1 + content/.quarto/idx/4_rt.qmd.json | 1 + content/.quarto/idx/5_individual.qmd.json | 1 + content/.quarto/idx/index.qmd.json | 1 + content/.quarto/idx/references.qmd.json | 1 + content/.quarto/xref/04307669 | 1 + content/.quarto/xref/15f266d2 | 1 + content/.quarto/xref/1a47137c | 1 + content/.quarto/xref/26afb962 | 1 + content/.quarto/xref/6cbe9151 | 1 + content/.quarto/xref/INDEX | 23 + content/.quarto/xref/ce37606d | 1 + content/.quarto/xref/efe17597 | 1 + .../1_introduction/execute-results/html.json | 16 + .../_freeze/4_rt/execute-results/html.json | 16 + .../4_rt/figure-html/cell-5-output-2.svg | 1561 +++ .../site_libs/clipboard/clipboard.min.js | 7 + content/_quarto.yml | 16 +- data/make_data.R | 21 + data/wagenmakers2008_speedaccuracy.csv | 10403 ++++++++++++++++ 32 files changed, 15837 insertions(+), 10 deletions(-) create mode 100644 content/.jupyter_cache/__version__.txt create mode 100644 content/.jupyter_cache/executed/7090acf5defd63a6a9d492b3789a7814/base.ipynb create mode 100644 content/.jupyter_cache/executed/acf8587a1fcb362872d5be8328ea7865/base.ipynb create mode 100644 content/.jupyter_cache/global.db create mode 100644 content/.quarto/_freeze/1_introduction/execute-results/html.json create mode 100644 content/.quarto/_freeze/4_rt/execute-results/html.json create mode 100644 content/.quarto/_freeze/4_rt/figure-html/cell-5-output-2.svg create mode 100644 content/.quarto/_freeze/site_libs/clipboard/clipboard.min.js create mode 100644 content/.quarto/cites/index.json create mode 100644 content/.quarto/idx/1_introduction.qmd.json create mode 100644 content/.quarto/idx/2_predictors.qmd.json create mode 100644 content/.quarto/idx/3_scales.qmd.json create mode 100644 content/.quarto/idx/4_rt.qmd.json create mode 100644 content/.quarto/idx/5_individual.qmd.json create mode 100644 content/.quarto/idx/index.qmd.json create mode 100644 content/.quarto/idx/references.qmd.json create mode 100644 content/.quarto/xref/04307669 create mode 100644 content/.quarto/xref/15f266d2 create mode 100644 content/.quarto/xref/1a47137c create mode 100644 content/.quarto/xref/26afb962 create mode 100644 content/.quarto/xref/6cbe9151 create mode 100644 content/.quarto/xref/INDEX create mode 100644 content/.quarto/xref/ce37606d create mode 100644 content/.quarto/xref/efe17597 create mode 100644 content/_freeze/1_introduction/execute-results/html.json create mode 100644 content/_freeze/4_rt/execute-results/html.json create mode 100644 content/_freeze/4_rt/figure-html/cell-5-output-2.svg create mode 100644 content/_freeze/site_libs/clipboard/clipboard.min.js create mode 100644 data/make_data.R create mode 100644 data/wagenmakers2008_speedaccuracy.csv diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 6be90e0..ce3a071 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -21,7 +21,6 @@ jobs: uses: actions/setup-python@v4 with: python-version: "3.12" - # cache: "pip" - name: Install dependencies run: | @@ -38,7 +37,6 @@ jobs: - name: Install Julia dependencies run: | - julia --project=@quarto -e 'import Pkg; Pkg.add("QuartoNotebookRunner")' julia -e 'import Pkg; Pkg.add("Plots")' julia -e '# import Pkg; Pkg.add("CSV"); Pkg.add("DataFrames")' julia -e '# import Pkg; Pkg.add("Turing"); Pkg.add("Distributions")' diff --git a/content/.jupyter_cache/__version__.txt b/content/.jupyter_cache/__version__.txt new file mode 100644 index 0000000..afaf360 --- /dev/null +++ b/content/.jupyter_cache/__version__.txt @@ -0,0 +1 @@ +1.0.0 \ No newline at end of file diff --git a/content/.jupyter_cache/executed/7090acf5defd63a6a9d492b3789a7814/base.ipynb b/content/.jupyter_cache/executed/7090acf5defd63a6a9d492b3789a7814/base.ipynb new file mode 100644 index 0000000..977ed38 --- /dev/null +++ b/content/.jupyter_cache/executed/7090acf5defd63a6a9d492b3789a7814/base.ipynb @@ -0,0 +1,1915 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "030e218a", + "metadata": {}, + "outputs": [], + "source": [ + "import IJulia\n", + "\n", + "# The julia kernel has built in support for Revise.jl, so this is the \n", + "# recommended approach for long-running sessions:\n", + "# https://github.com/JuliaLang/IJulia.jl/blob/9b10fa9b879574bbf720f5285029e07758e50a5e/src/kernel.jl#L46-L51\n", + "\n", + "# Users should enable revise within .julia/config/startup_ijulia.jl:\n", + "# https://timholy.github.io/Revise.jl/stable/config/#Using-Revise-automatically-within-Jupyter/IJulia-1\n", + "\n", + "# clear console history\n", + "IJulia.clear_history()\n", + "\n", + "fig_width = 7\n", + "fig_height = 5\n", + "fig_format = :retina\n", + "fig_dpi = 96\n", + "\n", + "# no retina format type, use svg for high quality type/marks\n", + "if fig_format == :retina\n", + " fig_format = :svg\n", + "elseif fig_format == :pdf\n", + " fig_dpi = 96\n", + " # Enable PDF support for IJulia\n", + " IJulia.register_mime(MIME(\"application/pdf\"))\n", + "end\n", + "\n", + "# convert inches to pixels\n", + "fig_width = fig_width * fig_dpi\n", + "fig_height = fig_height * fig_dpi\n", + "\n", + "# Intialize Plots w/ default fig width/height\n", + "try\n", + " import Plots\n", + "\n", + " # Plots.jl doesn't support PDF output for versions < 1.28.1\n", + " # so use png (if the DPI remains the default of 300 then set to 96)\n", + " if (Plots._current_plots_version < v\"1.28.1\") & (fig_format == :pdf)\n", + " Plots.gr(size=(fig_width, fig_height), fmt = :png, dpi = fig_dpi)\n", + " else\n", + " Plots.gr(size=(fig_width, fig_height), fmt = fig_format, dpi = fig_dpi)\n", + " end\n", + "catch e\n", + " # @warn \"Plots init\" exception=(e, catch_backtrace())\n", + "end\n", + "\n", + "# Initialize CairoMakie with default fig width/height\n", + "try\n", + " import CairoMakie\n", + "\n", + " # CairoMakie's display() in PDF format opens an interactive window\n", + " # instead of saving to the ipynb file, so we don't do that.\n", + " # https://github.com/quarto-dev/quarto-cli/issues/7548\n", + " if fig_format == :pdf\n", + " CairoMakie.activate!(type = \"png\")\n", + " else\n", + " CairoMakie.activate!(type = string(fig_format))\n", + " end\n", + " CairoMakie.update_theme!(resolution=(fig_width, fig_height))\n", + "catch e\n", + " # @warn \"CairoMakie init\" exception=(e, catch_backtrace())\n", + "end\n", + " \n", + "# Set run_path if specified\n", + "try\n", + " run_path = raw\"C:\\Users\\domma\\Dropbox\\Software\\CognitiveModels\\content\"\n", + " if !isempty(run_path)\n", + " cd(run_path)\n", + " end\n", + "catch e\n", + " @warn \"Run path init:\" exception=(e, catch_backtrace())\n", + "end\n", + "\n", + "\n", + "# emulate old Pkg.installed beahvior, see\n", + "# https://discourse.julialang.org/t/how-to-use-pkg-dependencies-instead-of-pkg-installed/36416/9\n", + "import Pkg\n", + "function isinstalled(pkg::String)\n", + " any(x -> x.name == pkg && x.is_direct_dep, values(Pkg.dependencies()))\n", + "end\n", + "\n", + "# ojs_define\n", + "if isinstalled(\"JSON\") && isinstalled(\"DataFrames\")\n", + " import JSON, DataFrames\n", + " global function ojs_define(; kwargs...)\n", + " convert(x) = x\n", + " convert(x::DataFrames.AbstractDataFrame) = Tables.rows(x)\n", + " content = Dict(\"contents\" => [Dict(\"name\" => k, \"value\" => convert(v)) for (k, v) in kwargs])\n", + " tag = \"\"\n", + " IJulia.display(MIME(\"text/html\"), tag)\n", + " end\n", + "elseif isinstalled(\"JSON\")\n", + " import JSON\n", + " global function ojs_define(; kwargs...)\n", + " content = Dict(\"contents\" => [Dict(\"name\" => k, \"value\" => v) for (k, v) in kwargs])\n", + " tag = \"\"\n", + " IJulia.display(MIME(\"text/html\"), tag)\n", + " end\n", + "else\n", + " global function ojs_define(; kwargs...)\n", + " @warn \"JSON package not available. Please install the JSON.jl package to use ojs_define.\"\n", + " end\n", + "end\n", + "\n", + "\n", + "# don't return kernel dependencies (b/c Revise should take care of dependencies)\n", + "nothing\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "718393b9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
10×9 DataFrame
RowRTISITrialParticipantSessionThresholdFeedbackFeedback_N_PositiveFeedback_N_Negative
Float64Float64Int64String7String3String7String15Int64Int64
10.5171.6381S002S1NAPositive00
20.4332.2822S002S10.475Positive10
30.5330.7393S002S10.494Negative20
40.350.6024S002S10.458Positive01
50.5171.9515S002S10.47Negative10
60.3830.9526S002S10.456Positive01
70.3830.8757S002S10.445Positive10
80.4172.6628S002S10.442Positive20
90.3672.89S002S10.433Positive30
100.4170.25810S002S10.432Positive40
" + ], + "text/latex": [ + "\\begin{tabular}{r|ccccccccc}\n", + "\t& RT & ISI & Trial & Participant & Session & Threshold & Feedback & Feedback\\_N\\_Positive & \\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64 & Int64 & String7 & String3 & String7 & String15 & Int64 & \\\\\n", + "\t\\hline\n", + "\t1 & 0.517 & 1.638 & 1 & S002 & S1 & NA & Positive & 0 & $\\dots$ \\\\\n", + "\t2 & 0.433 & 2.282 & 2 & S002 & S1 & 0.475 & Positive & 1 & $\\dots$ \\\\\n", + "\t3 & 0.533 & 0.739 & 3 & S002 & S1 & 0.494 & Negative & 2 & $\\dots$ \\\\\n", + "\t4 & 0.35 & 0.602 & 4 & S002 & S1 & 0.458 & Positive & 0 & $\\dots$ \\\\\n", + "\t5 & 0.517 & 1.951 & 5 & S002 & S1 & 0.47 & Negative & 1 & $\\dots$ \\\\\n", + "\t6 & 0.383 & 0.952 & 6 & S002 & S1 & 0.456 & Positive & 0 & $\\dots$ \\\\\n", + "\t7 & 0.383 & 0.875 & 7 & S002 & S1 & 0.445 & Positive & 1 & $\\dots$ \\\\\n", + "\t8 & 0.417 & 2.662 & 8 & S002 & S1 & 0.442 & Positive & 2 & $\\dots$ \\\\\n", + "\t9 & 0.367 & 2.8 & 9 & S002 & S1 & 0.433 & Positive & 3 & $\\dots$ \\\\\n", + "\t10 & 0.417 & 0.258 & 10 & S002 & S1 & 0.432 & Positive & 4 & $\\dots$ \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m10×9 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m RT \u001b[0m\u001b[1m ISI \u001b[0m\u001b[1m Trial \u001b[0m\u001b[1m Participant \u001b[0m\u001b[1m Session \u001b[0m\u001b[1m Threshold \u001b[0m\u001b[1m Feedback \u001b[0m\u001b[1m Fee\u001b[0m ⋯\n", + " │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String7 \u001b[0m\u001b[90m String3 \u001b[0m\u001b[90m String7 \u001b[0m\u001b[90m String15 \u001b[0m\u001b[90m Int\u001b[0m ⋯\n", + "─────┼──────────────────────────────────────────────────────────────────────────\n", + " 1 │ 0.517 1.638 1 S002 S1 NA Positive ⋯\n", + " 2 │ 0.433 2.282 2 S002 S1 0.475 Positive\n", + " 3 │ 0.533 0.739 3 S002 S1 0.494 Negative\n", + " 4 │ 0.35 0.602 4 S002 S1 0.458 Positive\n", + " 5 │ 0.517 1.951 5 S002 S1 0.47 Negative ⋯\n", + " 6 │ 0.383 0.952 6 S002 S1 0.456 Positive\n", + " 7 │ 0.383 0.875 7 S002 S1 0.445 Positive\n", + " 8 │ 0.417 2.662 8 S002 S1 0.442 Positive\n", + " 9 │ 0.367 2.8 9 S002 S1 0.433 Positive ⋯\n", + " 10 │ 0.417 0.258 10 S002 S1 0.432 Positive\n", + "\u001b[36m 2 columns omitted\u001b[0m" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using Downloads, CSV, DataFrames\n", + "using Turing, Distributions\n", + "using CairoMakie\n", + "\n", + "df = CSV.read(Downloads.download(\"https://raw.githubusercontent.com/RealityBending/DoggoNogo/main/study1/data/data_game.csv\"), DataFrame)\n", + "first(df, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cb030c10", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mFound initial step size\n", + "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m ϵ = 0.003125\n", + "\r", + "\u001b[32mSampling: 0%|█ | ETA: 0:00:54\u001b[39m" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "\u001b[32mSampling: 100%|█████████████████████████████████████████| Time: 0:00:01\u001b[39m\n" + ] + }, + { + "data": { + "text/plain": [ + "Quantiles\n", + " \u001b[1m parameters \u001b[0m \u001b[1m 2.5% \u001b[0m \u001b[1m 97.5% \u001b[0m\n", + " \u001b[90m Symbol \u001b[0m \u001b[90m Float64 \u001b[0m \u001b[90m Float64 \u001b[0m\n", + "\n", + " σ² 0.0662 0.0741\n", + " intercept 0.3432 0.3485\n", + " slope_isi -0.0215 -0.0188\n" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| code-fold: false\n", + "\n", + "@model function model_linear(rt; isi=nothing)\n", + "\n", + " # Set priors on variance, intercept and effect of ISI\n", + " σ² ~ truncated(Normal(0, 1); lower=0)\n", + " intercept ~ truncated(Normal(0, 1); lower=0)\n", + " slope_isi ~ Normal(0, 0.5)\n", + "\n", + " for i in 1:length(rt)\n", + " μ = intercept + slope_isi * isi[i]\n", + " rt[i] ~ Normal(μ, σ²)\n", + " end\n", + "end\n", + "\n", + "model = model_linear(df.RT, isi=df.ISI)\n", + "chain_linear = sample(model, NUTS(), 200)\n", + "\n", + "# Summary (95% CI)\n", + "quantile(chain_linear; q=[0.025, 0.975])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c0d701ef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "200×5894 Matrix{Float64}:\n", + " 0.343485 0.216215 0.417127 0.265295 … 0.270301 0.314704 0.362219\n", + " 0.285884 0.401988 0.313464 0.365707 0.413454 0.211854 0.295403\n", + " 0.272983 0.234762 0.42218 0.394312 0.285436 0.237363 0.256673\n", + " 0.376258 0.296507 0.400949 0.442421 0.387684 0.297768 0.416513\n", + " 0.233469 0.447568 0.196676 0.352262 0.271191 0.28259 0.263433\n", + " 0.33506 0.299781 0.446863 0.406212 … 0.363658 0.355659 0.250444\n", + " 0.361181 0.384573 0.259316 0.355663 0.387109 0.3969 0.260937\n", + " 0.199718 0.225176 0.323014 0.465536 0.340578 0.291982 0.276748\n", + " 0.344342 0.392598 0.41379 0.327856 0.313041 0.312774 0.427881\n", + " 0.367076 0.369818 0.279456 0.273186 0.166357 0.254137 0.296702\n", + " 0.352786 0.253908 0.326598 0.312641 … 0.354318 0.298042 0.335117\n", + " 0.348439 0.247241 0.326931 0.365456 0.271816 0.380503 0.304533\n", + " 0.403867 0.21232 0.305734 0.48954 0.447519 0.3935 0.34965\n", + " ⋮ ⋱ \n", + " 0.30927 0.341602 0.27437 0.358495 0.341485 0.299687 0.229771\n", + " 0.33373 0.341455 0.266786 0.335868 0.281408 0.247126 0.259789\n", + " 0.241149 0.295424 0.275409 0.333032 … 0.27089 0.258646 0.262858\n", + " 0.212142 0.331662 0.429286 0.198232 0.221235 0.288945 0.242003\n", + " 0.26651 0.318845 0.277398 0.33848 0.258332 0.352251 0.264074\n", + " 0.26791 0.351833 0.368078 0.296286 0.405033 0.324848 0.274246\n", + " 0.275976 0.407968 0.320193 0.523113 0.284379 0.217903 0.348659\n", + " 0.145726 0.34501 0.182133 0.332866 … 0.286436 0.241134 0.417765\n", + " 0.308746 0.272995 0.31599 0.362197 0.386962 0.350983 0.308101\n", + " 0.436478 0.361265 0.307198 0.296636 0.279867 0.32109 0.314042\n", + " 0.378674 0.240876 0.355034 0.3905 0.242 0.28695 0.282505\n", + " 0.38124 0.354282 0.370644 0.253414 0.3654 0.230301 0.274305" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| output: false\n", + "\n", + "pred = predict(model_linear([(missing) for i in 1:length(df.RT)]; isi=df.ISI), chain_linear)\n", + "pred = Array(pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5a9bdace", + "metadata": { + "fig-height": 7, + "fig-width": 10 + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mFound `resolution` in the theme when creating a `Scene`. The `resolution` keyword for `Scene`s and `Figure`s has been deprecated. Use `Figure(; size = ...` or `Scene(; size = ...)` instead, which better reflects that this is a unitless size and not a pixel resolution. The key could also come from `set_theme!` calls or related theming functions.\n", + "\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ Makie C:\\Users\\domma\\.julia\\packages\\Makie\\VRavR\\src\\scenes.jl:220\u001b[39m\n" + ] + }, + { + "data": { + "image/png": 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i]), color=\"orange\", alpha=0.1)\n", + "end\n", + "CairoMakie.ylims!(ax, (0, nothing))\n", + "f" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.10.2", + "language": "julia", + "name": "julia-1.10" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.10.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/content/.jupyter_cache/executed/acf8587a1fcb362872d5be8328ea7865/base.ipynb b/content/.jupyter_cache/executed/acf8587a1fcb362872d5be8328ea7865/base.ipynb new file mode 100644 index 0000000..4fee1d3 --- /dev/null +++ b/content/.jupyter_cache/executed/acf8587a1fcb362872d5be8328ea7865/base.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "786d096e", + "metadata": {}, + "outputs": [], + "source": [ + "import IJulia\n", + "\n", + "# The julia kernel has built in support for Revise.jl, so this is the \n", + "# recommended approach for long-running sessions:\n", + "# https://github.com/JuliaLang/IJulia.jl/blob/9b10fa9b879574bbf720f5285029e07758e50a5e/src/kernel.jl#L46-L51\n", + "\n", + "# Users should enable revise within .julia/config/startup_ijulia.jl:\n", + "# https://timholy.github.io/Revise.jl/stable/config/#Using-Revise-automatically-within-Jupyter/IJulia-1\n", + "\n", + "# clear console history\n", + "IJulia.clear_history()\n", + "\n", + "fig_width = 7\n", + "fig_height = 5\n", + "fig_format = :retina\n", + "fig_dpi = 96\n", + "\n", + "# no retina format type, use svg for high quality type/marks\n", + "if fig_format == :retina\n", + " fig_format = :svg\n", + "elseif fig_format == :pdf\n", + " fig_dpi = 96\n", + " # Enable PDF support for IJulia\n", + " IJulia.register_mime(MIME(\"application/pdf\"))\n", + "end\n", + "\n", + "# convert inches to pixels\n", + "fig_width = fig_width * fig_dpi\n", + "fig_height = fig_height * fig_dpi\n", + "\n", + "# Intialize Plots w/ default fig width/height\n", + "try\n", + " import Plots\n", + "\n", + " # Plots.jl doesn't support PDF output for versions < 1.28.1\n", + " # so use png (if the DPI remains the default of 300 then set to 96)\n", + " if (Plots._current_plots_version < v\"1.28.1\") & (fig_format == :pdf)\n", + " Plots.gr(size=(fig_width, fig_height), fmt = :png, dpi = fig_dpi)\n", + " else\n", + " Plots.gr(size=(fig_width, fig_height), fmt = fig_format, dpi = fig_dpi)\n", + " end\n", + "catch e\n", + " # @warn \"Plots init\" exception=(e, catch_backtrace())\n", + "end\n", + "\n", + "# Initialize CairoMakie with default fig width/height\n", + "try\n", + " import CairoMakie\n", + "\n", + " # CairoMakie's display() in PDF format opens an interactive window\n", + " # instead of saving to the ipynb file, so we don't do that.\n", + " # https://github.com/quarto-dev/quarto-cli/issues/7548\n", + " if fig_format == :pdf\n", + " CairoMakie.activate!(type = \"png\")\n", + " else\n", + " CairoMakie.activate!(type = string(fig_format))\n", + " end\n", + " CairoMakie.update_theme!(resolution=(fig_width, fig_height))\n", + "catch e\n", + " # @warn \"CairoMakie init\" exception=(e, catch_backtrace())\n", + "end\n", + " \n", + "# Set run_path if specified\n", + "try\n", + " run_path = raw\"C:\\Users\\domma\\Dropbox\\Software\\CognitiveModels\\content\"\n", + " if !isempty(run_path)\n", + " cd(run_path)\n", + " end\n", + "catch e\n", + " @warn \"Run path init:\" exception=(e, catch_backtrace())\n", + "end\n", + "\n", + "\n", + "# emulate old Pkg.installed beahvior, see\n", + "# https://discourse.julialang.org/t/how-to-use-pkg-dependencies-instead-of-pkg-installed/36416/9\n", + "import Pkg\n", + "function isinstalled(pkg::String)\n", + " any(x -> x.name == pkg && x.is_direct_dep, values(Pkg.dependencies()))\n", + "end\n", + "\n", + "# ojs_define\n", + "if isinstalled(\"JSON\") && isinstalled(\"DataFrames\")\n", + " import JSON, DataFrames\n", + " global function ojs_define(; kwargs...)\n", + " convert(x) = x\n", + " convert(x::DataFrames.AbstractDataFrame) = Tables.rows(x)\n", + " content = Dict(\"contents\" => [Dict(\"name\" => k, \"value\" => convert(v)) for (k, v) in kwargs])\n", + " tag = \"\"\n", + " IJulia.display(MIME(\"text/html\"), tag)\n", + " end\n", + "elseif isinstalled(\"JSON\")\n", + " import JSON\n", + " global function ojs_define(; kwargs...)\n", + " content = Dict(\"contents\" => [Dict(\"name\" => k, \"value\" => v) for (k, v) in kwargs])\n", + " tag = \"\"\n", + " IJulia.display(MIME(\"text/html\"), tag)\n", + " end\n", + "else\n", + " global function ojs_define(; kwargs...)\n", + " @warn \"JSON package not available. Please install the JSON.jl package to use ojs_define.\"\n", + " end\n", + "end\n", + "\n", + "\n", + "# don't return kernel dependencies (b/c Revise should take care of dependencies)\n", + "nothing\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1520bbe1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Very quick intro to Julia and Turing\n\n::: {#1520bbe1 .cell execution_count=1}\n``` {.julia .cell-code}\nusing Plots\n\nplot(1:10)\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n```\n:::\n:::\n\n\n2. Linear Regression: understand what the parameters mean (intercept, slopes, sigma)\n3. Boostrapping: Introduce concepts related to pseudo-posterior distribution description\n4. Hierarchical Models: Simpson's paradox, random effects, how to leverage them to model interindividual differences\n5. Bayesian estimation: introduce Bayesian estimation and priors over parameters\n6. Bayesian mixed linear regression: put everything together\n\n", + "supporting": [ + "1_introduction_files" + ], + "filters": [], + "includes": { + "include-in-header": [ + "\n\n\n" + ] + } + } +} \ No newline at end of file diff --git a/content/.quarto/_freeze/4_rt/execute-results/html.json b/content/.quarto/_freeze/4_rt/execute-results/html.json new file mode 100644 index 0000000..6969ef0 --- /dev/null +++ b/content/.quarto/_freeze/4_rt/execute-results/html.json @@ -0,0 +1,16 @@ +{ + "hash": "26c45ef47fe7e81fdb051963e24f2bf1", + "result": { + "engine": "jupyter", + "markdown": "# Reaction Times\n\n\nThis repository contain the following vignettes:\n\n- [**Drift Diffusion Model (DDM) in R: A Tutorial**](https://dominiquemakowski.github.io/easyRT/articles/ddm.html)\n- [**Ex-Gaussian models in R: A Tutorial**](https://dominiquemakowski.github.io/easyRT/articles/exgaussian.html)\n\n## Descriptive Models (ExGaussian, LogNormal, Wald)\n\n### Modelling RT with a Bayesian Linear Model\n\n#### The Data\n\n::: {#718393b9 .cell execution_count=1}\n``` {.julia .cell-code}\nusing Downloads, CSV, DataFrames\nusing Turing, Distributions\nusing CairoMakie\n\ndf = CSV.read(Downloads.download(\"https://raw.githubusercontent.com/RealityBending/DoggoNogo/main/study1/data/data_game.csv\"), DataFrame)\nfirst(df, 10)\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n
10×9 DataFrame
RowRTISITrialParticipantSessionThresholdFeedbackFeedback_N_PositiveFeedback_N_Negative
Float64Float64Int64String7String3String7String15Int64Int64
10.5171.6381S002S1NAPositive00
20.4332.2822S002S10.475Positive10
30.5330.7393S002S10.494Negative20
40.350.6024S002S10.458Positive01
50.5171.9515S002S10.47Negative10
60.3830.9526S002S10.456Positive01
70.3830.8757S002S10.445Positive10
80.4172.6628S002S10.442Positive20
90.3672.89S002S10.433Positive30
100.4170.25810S002S10.432Positive40
\n```\n:::\n:::\n\n\n#### The Model\n\n::: {#cb030c10 .cell execution_count=2}\n``` {.julia .cell-code code-fold=\"false\"}\n@model function model_linear(rt; isi=nothing)\n\n # Set priors on variance, intercept and effect of ISI\n σ² ~ truncated(Normal(0, 1); lower=0)\n intercept ~ truncated(Normal(0, 1); lower=0)\n slope_isi ~ Normal(0, 0.5)\n\n for i in 1:length(rt)\n μ = intercept + slope_isi * isi[i]\n rt[i] ~ Normal(μ, σ²)\n end\nend\n\nmodel = model_linear(df.RT, isi=df.ISI)\nchain_linear = sample(model, NUTS(), 200)\n\n# Summary (95% CI)\nquantile(chain_linear; q=[0.025, 0.975])\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n┌ Info: Found initial step size\n└ ϵ = 0.003125\n\rSampling: 0%|█ | ETA: 0:00:54\rSampling: 100%|█████████████████████████████████████████| Time: 0:00:01\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=3}\n\n::: {.ansi-escaped-output}\n```{=html}\n
Quantiles\n  parameters      2.5%     97.5% \n      Symbol   Float64   Float64 \n          σ²    0.0662    0.0741\n   intercept    0.3432    0.3485\n   slope_isi   -0.0215   -0.0188\n
\n```\n:::\n\n:::\n:::\n\n\n::: {.callout-tip title=\"Code Tip\"}\nWe first initialize the model by passing the `RT` and `ISI` columns.\n:::\n\n#### Posterior Predictive Check\n\n::: {#c0d701ef .cell execution_count=3}\n``` {.julia .cell-code}\npred = predict(model_linear([(missing) for i in 1:length(df.RT)]; isi=df.ISI), chain_linear)\npred = Array(pred)\n```\n:::\n\n\n::: {#5a9bdace .cell fig-height='7' fig-width='10' execution_count=4}\n``` {.julia .cell-code}\nf = Figure()\nax = Axis(f[1, 1], title=\"Predicted Data by Linear Model\",\n xlabel=\"RT (s)\",\n ylabel=\"Distribution\",\n yticksvisible=false,\n xticksvisible=false,\n yticklabelsvisible=false)\n\nCairoMakie.density!(df.RT, color=\"grey\")\nfor i in 1:length(chain_linear)\n lines!(ax, Makie.KernelDensity.kde(pred[:, i]), color=\"orange\", alpha=0.1)\nend\nCairoMakie.ylims!(ax, (0, nothing))\nf\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n┌ Warning: Found `resolution` in the theme when creating a `Scene`. The `resolution` keyword for `Scene`s and `Figure`s has been deprecated. Use `Figure(; size = ...` or `Scene(; size = ...)` instead, which better reflects that this is a unitless size and not a pixel resolution. The key could also come from `set_theme!` calls or related theming functions.\n└ @ Makie C:\\Users\\domma\\.julia\\packages\\Makie\\VRavR\\src\\scenes.jl:220\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=5}\n![](4_rt_files/figure-html/cell-5-output-2.svg){}\n:::\n:::\n\n\n### The Problem with Linear Models\n\nReaction time (RTs) have been traditionally modeled using traditional linear models and their derived statistical tests such as *t*-test and ANOVAs. Importantly, linear models - by definition - will try to predict the *mean* of the outcome variable by estimating the \"best fitting\" *Normal* distribution. In the context of reaction times (RTs), this is not ideal, as RTs typically exhibit a non-normal distribution, skewed towards the left with a long tail towards the right. This means that the parameters of a Normal distribution (mean $\\mu$ and standard deviation $\\sigma$) are not good descriptors of the data.\n\n![](media/rt_normal.gif)\n\n> Linear models try to find the best fitting Normal distribution for the data. However, for reaction times, even the best fitting Normal distribution (in red) does not capture well the actual data (in grey).\n\nA popular mitigation method to account for the non-normality of RTs is to transform the data, using for instance the popular *log-transform*. \nHowever, this practice should be avoided as it leads to various issues, including loss of power and distorted results interpretation [@lo2015transform; @schramm2019reaction].\nInstead, rather than applying arbitrary data transformation, it would be better to swap the Normal distribution used by the model for a more appropriate one that can better capture the characteristics of a RT distribution.\n\n\n### Shifted LogNormal Models\n\nOne of the obvious candidate alternative to the log-transformation would be to use a model with a Log-transformed Normal distribution.\n\n\n### Wald\n\nMoe from statistical models that *describe* to models that *generate* RT-like data.\n\n### Generative Models (DDM)\n\nUse DDM as a case study to introduce generative models\n\n### Other Models (LBA, LNR)\n\n\n## Additional Resources\n\n- [**Lindelov's overview of RT models**](https://lindeloev.github.io/shiny-rt/): An absolute must-read.\n- [**De Boeck & Jeon (2019)**](https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00102/full): A paper providing an overview of RT models.\n- [https://github.com/vasishth/bayescogsci](https://github.com/vasishth/bayescogsci)\n\n", + "supporting": [ + "4_rt_files" + ], + "filters": [], + "includes": { + "include-in-header": [ + "\n\n\n" + ] + } + } +} \ No newline at end of file diff --git a/content/.quarto/_freeze/4_rt/figure-html/cell-5-output-2.svg b/content/.quarto/_freeze/4_rt/figure-html/cell-5-output-2.svg new file mode 100644 index 0000000..68a1520 --- /dev/null +++ b/content/.quarto/_freeze/4_rt/figure-html/cell-5-output-2.svg @@ -0,0 +1,1561 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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Logistic models for binary data\n2. Beta models \n3. 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+{"title":"Reaction Times","markdown":{"headingText":"Reaction Times","containsRefs":false,"markdown":"\n\nThis repository contain the following vignettes:\n\n- [**Drift Diffusion Model (DDM) in R: A Tutorial**](https://dominiquemakowski.github.io/easyRT/articles/ddm.html)\n- [**Ex-Gaussian models in R: A Tutorial**](https://dominiquemakowski.github.io/easyRT/articles/exgaussian.html)\n\n## Descriptive Models (ExGaussian, LogNormal, Wald)\n\n### Modelling RT with a Bayesian Linear Model\n\n#### The Data\n\n```{julia}\nusing Downloads, CSV, DataFrames\nusing Turing, Distributions\nusing CairoMakie\n\ndf = CSV.read(Downloads.download(\"https://raw.githubusercontent.com/RealityBending/DoggoNogo/main/study1/data/data_game.csv\"), DataFrame)\nfirst(df, 10)\n```\n\n\n#### The Model\n\n```{julia}\n#| code-fold: false\n\n@model function model_linear(rt; isi=nothing)\n\n # Set priors on variance, intercept and effect of ISI\n σ² ~ truncated(Normal(0, 1); lower=0)\n intercept ~ truncated(Normal(0, 1); lower=0)\n slope_isi ~ Normal(0, 0.5)\n\n for i in 1:length(rt)\n μ = intercept + slope_isi * isi[i]\n rt[i] ~ Normal(μ, σ²)\n end\nend\n\nmodel = model_linear(df.RT, isi=df.ISI)\nchain_linear = sample(model, NUTS(), 200)\n\n# Summary (95% CI)\nquantile(chain_linear; q=[0.025, 0.975])\n```\n\n::: {.callout-tip title=\"Code Tip\"}\nWe first initialize the model by passing the `RT` and `ISI` columns.\n:::\n\n#### Posterior Predictive Check\n\n```{julia}\n#| output: false\n\npred = predict(model_linear([(missing) for i in 1:length(df.RT)]; isi=df.ISI), chain_linear)\npred = Array(pred)\n```\n\n```{julia}\n#| fig-width: 10\n#| fig-height: 7\n\nf = Figure()\nax = Axis(f[1, 1], title=\"Predicted Data by Linear Model\",\n xlabel=\"RT (s)\",\n ylabel=\"Distribution\",\n yticksvisible=false,\n xticksvisible=false,\n yticklabelsvisible=false)\n\nCairoMakie.density!(df.RT, color=\"grey\")\nfor i in 1:length(chain_linear)\n lines!(ax, Makie.KernelDensity.kde(pred[:, i]), color=\"orange\", alpha=0.1)\nend\nCairoMakie.ylims!(ax, (0, nothing))\nf\n```\n\n### The Problem with Linear Models\n\nReaction time (RTs) have been traditionally modeled using traditional linear models and their derived statistical tests such as *t*-test and ANOVAs. Importantly, linear models - by definition - will try to predict the *mean* of the outcome variable by estimating the \"best fitting\" *Normal* distribution. In the context of reaction times (RTs), this is not ideal, as RTs typically exhibit a non-normal distribution, skewed towards the left with a long tail towards the right. This means that the parameters of a Normal distribution (mean $\\mu$ and standard deviation $\\sigma$) are not good descriptors of the data.\n\n![](media/rt_normal.gif)\n\n> Linear models try to find the best fitting Normal distribution for the data. However, for reaction times, even the best fitting Normal distribution (in red) does not capture well the actual data (in grey).\n\nA popular mitigation method to account for the non-normality of RTs is to transform the data, using for instance the popular *log-transform*. \nHowever, this practice should be avoided as it leads to various issues, including loss of power and distorted results interpretation [@lo2015transform; @schramm2019reaction].\nInstead, rather than applying arbitrary data transformation, it would be better to swap the Normal distribution used by the model for a more appropriate one that can better capture the characteristics of a RT distribution.\n\n\n### Shifted LogNormal Models\n\nOne of the obvious candidate alternative to the log-transformation would be to use a model with a Log-transformed Normal distribution.\n\n\n### Wald\n\nMoe from statistical models that *describe* to models that *generate* RT-like data.\n\n### Generative Models (DDM)\n\nUse DDM as a case study to introduce generative models\n\n### Other Models (LBA, LNR)\n\n\n## Additional Resources\n\n- [**Lindelov's overview of RT models**](https://lindeloev.github.io/shiny-rt/): An absolute must-read.\n- [**De Boeck & Jeon (2019)**](https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00102/full): A paper providing an overview of RT models.\n- [https://github.com/vasishth/bayescogsci](https://github.com/vasishth/bayescogsci)\n","srcMarkdownNoYaml":""},"formats":{"html":{"identifier":{"display-name":"HTML","target-format":"html","base-format":"html"},"execute":{"fig-width":7,"fig-height":5,"fig-format":"retina","fig-dpi":96,"df-print":"default","error":false,"eval":true,"cache":true,"freeze":"auto","echo":true,"output":true,"warning":true,"include":true,"keep-md":false,"keep-ipynb":false,"ipynb":null,"enabled":null,"daemon":null,"daemon-restart":false,"debug":false,"ipynb-filters":[],"ipynb-shell-interactivity":null,"plotly-connected":true,"engine":"jupyter"},"render":{"keep-tex":false,"keep-typ":false,"keep-source":false,"keep-hidden":false,"prefer-html":false,"output-divs":true,"output-ext":"html","fig-align":"default","fig-pos":null,"fig-env":null,"code-fold":true,"code-overflow":"scroll","code-link":false,"code-line-numbers":false,"code-tools":false,"tbl-colwidths":"auto","merge-includes":true,"inline-includes":false,"preserve-yaml":false,"latex-auto-mk":true,"latex-auto-install":true,"latex-clean":true,"latex-min-runs":1,"latex-max-runs":10,"latex-makeindex":"makeindex","latex-makeindex-opts":[],"latex-tlmgr-opts":[],"latex-input-paths":[],"latex-output-dir":null,"link-external-icon":false,"link-external-newwindow":false,"self-contained-math":false,"format-resources":[],"notebook-links":true},"pandoc":{"standalone":true,"wrap":"none","default-image-extension":"png","to":"html","output-file":"4_rt.html"},"language":{"toc-title-document":"Table 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+{ + "hash": "25b2369fcc2baedaf8689c9668bca472", + "result": { + "engine": "jupyter", + "markdown": "# Pieces of Puzzle\n\n1. Very quick intro to Julia and Turing\n\n::: {#1520bbe1 .cell execution_count=1}\n``` {.julia .cell-code}\nusing Plots\n\nplot(1:10)\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n```\n:::\n:::\n\n\n2. Linear Regression: understand what the parameters mean (intercept, slopes, sigma)\n3. Boostrapping: Introduce concepts related to pseudo-posterior distribution description\n4. Hierarchical Models: Simpson's paradox, random effects, how to leverage them to model interindividual differences\n5. Bayesian estimation: introduce Bayesian estimation and priors over parameters\n6. Bayesian mixed linear regression: put everything together\n\n", + "supporting": [ + "1_introduction_files" + ], + "filters": [], + "includes": { + "include-in-header": [ + "\n\n\n" + ] + } + } +} \ No newline at end of file diff --git a/content/_freeze/4_rt/execute-results/html.json b/content/_freeze/4_rt/execute-results/html.json new file mode 100644 index 0000000..6969ef0 --- /dev/null +++ b/content/_freeze/4_rt/execute-results/html.json @@ -0,0 +1,16 @@ +{ + "hash": "26c45ef47fe7e81fdb051963e24f2bf1", + "result": { + "engine": "jupyter", + "markdown": "# Reaction Times\n\n\nThis repository contain the following vignettes:\n\n- [**Drift Diffusion Model (DDM) in R: A Tutorial**](https://dominiquemakowski.github.io/easyRT/articles/ddm.html)\n- [**Ex-Gaussian models in R: A Tutorial**](https://dominiquemakowski.github.io/easyRT/articles/exgaussian.html)\n\n## Descriptive Models (ExGaussian, LogNormal, Wald)\n\n### Modelling RT with a Bayesian Linear Model\n\n#### The Data\n\n::: {#718393b9 .cell execution_count=1}\n``` {.julia .cell-code}\nusing Downloads, CSV, DataFrames\nusing Turing, Distributions\nusing CairoMakie\n\ndf = CSV.read(Downloads.download(\"https://raw.githubusercontent.com/RealityBending/DoggoNogo/main/study1/data/data_game.csv\"), DataFrame)\nfirst(df, 10)\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```{=html}\n
10×9 DataFrame
RowRTISITrialParticipantSessionThresholdFeedbackFeedback_N_PositiveFeedback_N_Negative
Float64Float64Int64String7String3String7String15Int64Int64
10.5171.6381S002S1NAPositive00
20.4332.2822S002S10.475Positive10
30.5330.7393S002S10.494Negative20
40.350.6024S002S10.458Positive01
50.5171.9515S002S10.47Negative10
60.3830.9526S002S10.456Positive01
70.3830.8757S002S10.445Positive10
80.4172.6628S002S10.442Positive20
90.3672.89S002S10.433Positive30
100.4170.25810S002S10.432Positive40
\n```\n:::\n:::\n\n\n#### The Model\n\n::: {#cb030c10 .cell execution_count=2}\n``` {.julia .cell-code code-fold=\"false\"}\n@model function model_linear(rt; isi=nothing)\n\n # Set priors on variance, intercept and effect of ISI\n σ² ~ truncated(Normal(0, 1); lower=0)\n intercept ~ truncated(Normal(0, 1); lower=0)\n slope_isi ~ Normal(0, 0.5)\n\n for i in 1:length(rt)\n μ = intercept + slope_isi * isi[i]\n rt[i] ~ Normal(μ, σ²)\n end\nend\n\nmodel = model_linear(df.RT, isi=df.ISI)\nchain_linear = sample(model, NUTS(), 200)\n\n# Summary (95% CI)\nquantile(chain_linear; q=[0.025, 0.975])\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n┌ Info: Found initial step size\n└ ϵ = 0.003125\n\rSampling: 0%|█ | ETA: 0:00:54\rSampling: 100%|█████████████████████████████████████████| Time: 0:00:01\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=3}\n\n::: {.ansi-escaped-output}\n```{=html}\n
Quantiles\n  parameters      2.5%     97.5% \n      Symbol   Float64   Float64 \n          σ²    0.0662    0.0741\n   intercept    0.3432    0.3485\n   slope_isi   -0.0215   -0.0188\n
\n```\n:::\n\n:::\n:::\n\n\n::: {.callout-tip title=\"Code Tip\"}\nWe first initialize the model by passing the `RT` and `ISI` columns.\n:::\n\n#### Posterior Predictive Check\n\n::: {#c0d701ef .cell execution_count=3}\n``` {.julia .cell-code}\npred = predict(model_linear([(missing) for i in 1:length(df.RT)]; isi=df.ISI), chain_linear)\npred = Array(pred)\n```\n:::\n\n\n::: {#5a9bdace .cell fig-height='7' fig-width='10' execution_count=4}\n``` {.julia .cell-code}\nf = Figure()\nax = Axis(f[1, 1], title=\"Predicted Data by Linear Model\",\n xlabel=\"RT (s)\",\n ylabel=\"Distribution\",\n yticksvisible=false,\n xticksvisible=false,\n yticklabelsvisible=false)\n\nCairoMakie.density!(df.RT, color=\"grey\")\nfor i in 1:length(chain_linear)\n lines!(ax, Makie.KernelDensity.kde(pred[:, i]), color=\"orange\", alpha=0.1)\nend\nCairoMakie.ylims!(ax, (0, nothing))\nf\n```\n\n::: {.cell-output .cell-output-stderr}\n```\n┌ Warning: Found `resolution` in the theme when creating a `Scene`. The `resolution` keyword for `Scene`s and `Figure`s has been deprecated. Use `Figure(; size = ...` or `Scene(; size = ...)` instead, which better reflects that this is a unitless size and not a pixel resolution. The key could also come from `set_theme!` calls or related theming functions.\n└ @ Makie C:\\Users\\domma\\.julia\\packages\\Makie\\VRavR\\src\\scenes.jl:220\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=5}\n![](4_rt_files/figure-html/cell-5-output-2.svg){}\n:::\n:::\n\n\n### The Problem with Linear Models\n\nReaction time (RTs) have been traditionally modeled using traditional linear models and their derived statistical tests such as *t*-test and ANOVAs. Importantly, linear models - by definition - will try to predict the *mean* of the outcome variable by estimating the \"best fitting\" *Normal* distribution. In the context of reaction times (RTs), this is not ideal, as RTs typically exhibit a non-normal distribution, skewed towards the left with a long tail towards the right. This means that the parameters of a Normal distribution (mean $\\mu$ and standard deviation $\\sigma$) are not good descriptors of the data.\n\n![](media/rt_normal.gif)\n\n> Linear models try to find the best fitting Normal distribution for the data. However, for reaction times, even the best fitting Normal distribution (in red) does not capture well the actual data (in grey).\n\nA popular mitigation method to account for the non-normality of RTs is to transform the data, using for instance the popular *log-transform*. \nHowever, this practice should be avoided as it leads to various issues, including loss of power and distorted results interpretation [@lo2015transform; @schramm2019reaction].\nInstead, rather than applying arbitrary data transformation, it would be better to swap the Normal distribution used by the model for a more appropriate one that can better capture the characteristics of a RT distribution.\n\n\n### Shifted LogNormal Models\n\nOne of the obvious candidate alternative to the log-transformation would be to use a model with a Log-transformed Normal distribution.\n\n\n### Wald\n\nMoe from statistical models that *describe* to models that *generate* RT-like data.\n\n### Generative Models (DDM)\n\nUse DDM as a case study to introduce generative models\n\n### Other Models (LBA, LNR)\n\n\n## Additional Resources\n\n- [**Lindelov's overview of RT models**](https://lindeloev.github.io/shiny-rt/): An absolute must-read.\n- [**De Boeck & Jeon (2019)**](https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00102/full): A paper providing an overview of RT models.\n- [https://github.com/vasishth/bayescogsci](https://github.com/vasishth/bayescogsci)\n\n", + "supporting": [ + "4_rt_files" + ], + "filters": [], + "includes": { + "include-in-header": [ + "\n\n\n" + ] + } + } +} \ No newline at end of file diff --git a/content/_freeze/4_rt/figure-html/cell-5-output-2.svg b/content/_freeze/4_rt/figure-html/cell-5-output-2.svg new file mode 100644 index 0000000..68a1520 --- /dev/null +++ b/content/_freeze/4_rt/figure-html/cell-5-output-2.svg @@ -0,0 +1,1561 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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