1.2.response_accuracy_lmm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "# Copyright 2022 by the author(s) of CHI2023 submission \"Short-Form\n",
    "# Videos Degrade Our Capacity to Retain Intentions: Effect of Context\n",
    "# Switching On Prospective Memory\". All rights reserved.\n",
    "#\n",
    "# Use of this source code is governed by a GPLv3 license that\n",
    "# can be found in the LICENSE file.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: Matrix\n",
      "\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(lme4)\n",
    "library(dplyr)\n",
    "library(bayestestR)\n",
    "library(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "df <- read.csv(\"data/acc.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 120 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>folder_id</th><th scope=col>task</th><th scope=col>interrupt</th><th scope=col>measure</th><th scope=col>accuracy</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>101</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9685535</td></tr>\n",
       "\t<tr><td>102</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9272727</td></tr>\n",
       "\t<tr><td>103</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9875000</td></tr>\n",
       "\t<tr><td>104</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9937500</td></tr>\n",
       "\t<tr><td>105</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9875000</td></tr>\n",
       "\t<tr><td>106</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9750000</td></tr>\n",
       "\t<tr><td>107</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9937888</td></tr>\n",
       "\t<tr><td>108</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9875000</td></tr>\n",
       "\t<tr><td>109</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9627329</td></tr>\n",
       "\t<tr><td>110</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9556962</td></tr>\n",
       "\t<tr><td>111</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9936709</td></tr>\n",
       "\t<tr><td>112</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9875000</td></tr>\n",
       "\t<tr><td>113</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9937500</td></tr>\n",
       "\t<tr><td>114</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9689441</td></tr>\n",
       "\t<tr><td>115</td><td>LD</td><td>rest  </td><td>pre</td><td>0.9625000</td></tr>\n",
       "\t<tr><td>201</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9937500</td></tr>\n",
       "\t<tr><td>202</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9687500</td></tr>\n",
       "\t<tr><td>203</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9937500</td></tr>\n",
       "\t<tr><td>204</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9936306</td></tr>\n",
       "\t<tr><td>205</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9937500</td></tr>\n",
       "\t<tr><td>206</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9810127</td></tr>\n",
       "\t<tr><td>207</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9937500</td></tr>\n",
       "\t<tr><td>208</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9444444</td></tr>\n",
       "\t<tr><td>209</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9875000</td></tr>\n",
       "\t<tr><td>210</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9875000</td></tr>\n",
       "\t<tr><td>211</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9192547</td></tr>\n",
       "\t<tr><td>212</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9937500</td></tr>\n",
       "\t<tr><td>213</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9625000</td></tr>\n",
       "\t<tr><td>214</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9937107</td></tr>\n",
       "\t<tr><td>215</td><td>LD</td><td>tiktok</td><td>pre</td><td>0.9559748</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>301</td><td>LD</td><td>youtube</td><td>post</td><td>0.92857143</td></tr>\n",
       "\t<tr><td>302</td><td>LD</td><td>youtube</td><td>post</td><td>1.00000000</td></tr>\n",
       "\t<tr><td>303</td><td>LD</td><td>youtube</td><td>post</td><td>0.96250000</td></tr>\n",
       "\t<tr><td>304</td><td>LD</td><td>youtube</td><td>post</td><td>1.00000000</td></tr>\n",
       "\t<tr><td>305</td><td>LD</td><td>youtube</td><td>post</td><td>0.94375000</td></tr>\n",
       "\t<tr><td>306</td><td>LD</td><td>youtube</td><td>post</td><td>0.99375000</td></tr>\n",
       "\t<tr><td>307</td><td>LD</td><td>youtube</td><td>post</td><td>0.98750000</td></tr>\n",
       "\t<tr><td>308</td><td>LD</td><td>youtube</td><td>post</td><td>0.96875000</td></tr>\n",
       "\t<tr><td>309</td><td>LD</td><td>youtube</td><td>post</td><td>0.96875000</td></tr>\n",
       "\t<tr><td>310</td><td>LD</td><td>youtube</td><td>post</td><td>0.02484472</td></tr>\n",
       "\t<tr><td>311</td><td>LD</td><td>youtube</td><td>post</td><td>0.98750000</td></tr>\n",
       "\t<tr><td>312</td><td>LD</td><td>youtube</td><td>post</td><td>1.00000000</td></tr>\n",
       "\t<tr><td>313</td><td>LD</td><td>youtube</td><td>post</td><td>1.00000000</td></tr>\n",
       "\t<tr><td>314</td><td>LD</td><td>youtube</td><td>post</td><td>0.91025641</td></tr>\n",
       "\t<tr><td>315</td><td>LD</td><td>youtube</td><td>post</td><td>0.99375000</td></tr>\n",
       "\t<tr><td>401</td><td>LD</td><td>twitter</td><td>post</td><td>0.98742138</td></tr>\n",
       "\t<tr><td>402</td><td>LD</td><td>twitter</td><td>post</td><td>0.98750000</td></tr>\n",
       "\t<tr><td>403</td><td>LD</td><td>twitter</td><td>post</td><td>0.96875000</td></tr>\n",
       "\t<tr><td>404</td><td>LD</td><td>twitter</td><td>post</td><td>0.96250000</td></tr>\n",
       "\t<tr><td>405</td><td>LD</td><td>twitter</td><td>post</td><td>0.99375000</td></tr>\n",
       "\t<tr><td>406</td><td>LD</td><td>twitter</td><td>post</td><td>0.98125000</td></tr>\n",
       "\t<tr><td>407</td><td>LD</td><td>twitter</td><td>post</td><td>0.98125000</td></tr>\n",
       "\t<tr><td>408</td><td>LD</td><td>twitter</td><td>post</td><td>0.98750000</td></tr>\n",
       "\t<tr><td>409</td><td>LD</td><td>twitter</td><td>post</td><td>0.96875000</td></tr>\n",
       "\t<tr><td>410</td><td>LD</td><td>twitter</td><td>post</td><td>1.00000000</td></tr>\n",
       "\t<tr><td>411</td><td>LD</td><td>twitter</td><td>post</td><td>0.97500000</td></tr>\n",
       "\t<tr><td>412</td><td>LD</td><td>twitter</td><td>post</td><td>0.96875000</td></tr>\n",
       "\t<tr><td>413</td><td>LD</td><td>twitter</td><td>post</td><td>0.96875000</td></tr>\n",
       "\t<tr><td>414</td><td>LD</td><td>twitter</td><td>post</td><td>0.97500000</td></tr>\n",
       "\t<tr><td>415</td><td>LD</td><td>twitter</td><td>post</td><td>0.98750000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 120 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " folder\\_id & task & interrupt & measure & accuracy\\\\\n",
       " <int> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 101 & LD & rest   & pre & 0.9685535\\\\\n",
       "\t 102 & LD & rest   & pre & 0.9272727\\\\\n",
       "\t 103 & LD & rest   & pre & 0.9875000\\\\\n",
       "\t 104 & LD & rest   & pre & 0.9937500\\\\\n",
       "\t 105 & LD & rest   & pre & 0.9875000\\\\\n",
       "\t 106 & LD & rest   & pre & 0.9750000\\\\\n",
       "\t 107 & LD & rest   & pre & 0.9937888\\\\\n",
       "\t 108 & LD & rest   & pre & 0.9875000\\\\\n",
       "\t 109 & LD & rest   & pre & 0.9627329\\\\\n",
       "\t 110 & LD & rest   & pre & 0.9556962\\\\\n",
       "\t 111 & LD & rest   & pre & 0.9936709\\\\\n",
       "\t 112 & LD & rest   & pre & 0.9875000\\\\\n",
       "\t 113 & LD & rest   & pre & 0.9937500\\\\\n",
       "\t 114 & LD & rest   & pre & 0.9689441\\\\\n",
       "\t 115 & LD & rest   & pre & 0.9625000\\\\\n",
       "\t 201 & LD & tiktok & pre & 0.9937500\\\\\n",
       "\t 202 & LD & tiktok & pre & 0.9687500\\\\\n",
       "\t 203 & LD & tiktok & pre & 0.9937500\\\\\n",
       "\t 204 & LD & tiktok & pre & 0.9936306\\\\\n",
       "\t 205 & LD & tiktok & pre & 0.9937500\\\\\n",
       "\t 206 & LD & tiktok & pre & 0.9810127\\\\\n",
       "\t 207 & LD & tiktok & pre & 0.9937500\\\\\n",
       "\t 208 & LD & tiktok & pre & 0.9444444\\\\\n",
       "\t 209 & LD & tiktok & pre & 0.9875000\\\\\n",
       "\t 210 & LD & tiktok & pre & 0.9875000\\\\\n",
       "\t 211 & LD & tiktok & pre & 0.9192547\\\\\n",
       "\t 212 & LD & tiktok & pre & 0.9937500\\\\\n",
       "\t 213 & LD & tiktok & pre & 0.9625000\\\\\n",
       "\t 214 & LD & tiktok & pre & 0.9937107\\\\\n",
       "\t 215 & LD & tiktok & pre & 0.9559748\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 301 & LD & youtube & post & 0.92857143\\\\\n",
       "\t 302 & LD & youtube & post & 1.00000000\\\\\n",
       "\t 303 & LD & youtube & post & 0.96250000\\\\\n",
       "\t 304 & LD & youtube & post & 1.00000000\\\\\n",
       "\t 305 & LD & youtube & post & 0.94375000\\\\\n",
       "\t 306 & LD & youtube & post & 0.99375000\\\\\n",
       "\t 307 & LD & youtube & post & 0.98750000\\\\\n",
       "\t 308 & LD & youtube & post & 0.96875000\\\\\n",
       "\t 309 & LD & youtube & post & 0.96875000\\\\\n",
       "\t 310 & LD & youtube & post & 0.02484472\\\\\n",
       "\t 311 & LD & youtube & post & 0.98750000\\\\\n",
       "\t 312 & LD & youtube & post & 1.00000000\\\\\n",
       "\t 313 & LD & youtube & post & 1.00000000\\\\\n",
       "\t 314 & LD & youtube & post & 0.91025641\\\\\n",
       "\t 315 & LD & youtube & post & 0.99375000\\\\\n",
       "\t 401 & LD & twitter & post & 0.98742138\\\\\n",
       "\t 402 & LD & twitter & post & 0.98750000\\\\\n",
       "\t 403 & LD & twitter & post & 0.96875000\\\\\n",
       "\t 404 & LD & twitter & post & 0.96250000\\\\\n",
       "\t 405 & LD & twitter & post & 0.99375000\\\\\n",
       "\t 406 & LD & twitter & post & 0.98125000\\\\\n",
       "\t 407 & LD & twitter & post & 0.98125000\\\\\n",
       "\t 408 & LD & twitter & post & 0.98750000\\\\\n",
       "\t 409 & LD & twitter & post & 0.96875000\\\\\n",
       "\t 410 & LD & twitter & post & 1.00000000\\\\\n",
       "\t 411 & LD & twitter & post & 0.97500000\\\\\n",
       "\t 412 & LD & twitter & post & 0.96875000\\\\\n",
       "\t 413 & LD & twitter & post & 0.96875000\\\\\n",
       "\t 414 & LD & twitter & post & 0.97500000\\\\\n",
       "\t 415 & LD & twitter & post & 0.98750000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 120 × 5\n",
       "\n",
       "| folder_id &lt;int&gt; | task &lt;chr&gt; | interrupt &lt;chr&gt; | measure &lt;chr&gt; | accuracy &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 101 | LD | rest   | pre | 0.9685535 |\n",
       "| 102 | LD | rest   | pre | 0.9272727 |\n",
       "| 103 | LD | rest   | pre | 0.9875000 |\n",
       "| 104 | LD | rest   | pre | 0.9937500 |\n",
       "| 105 | LD | rest   | pre | 0.9875000 |\n",
       "| 106 | LD | rest   | pre | 0.9750000 |\n",
       "| 107 | LD | rest   | pre | 0.9937888 |\n",
       "| 108 | LD | rest   | pre | 0.9875000 |\n",
       "| 109 | LD | rest   | pre | 0.9627329 |\n",
       "| 110 | LD | rest   | pre | 0.9556962 |\n",
       "| 111 | LD | rest   | pre | 0.9936709 |\n",
       "| 112 | LD | rest   | pre | 0.9875000 |\n",
       "| 113 | LD | rest   | pre | 0.9937500 |\n",
       "| 114 | LD | rest   | pre | 0.9689441 |\n",
       "| 115 | LD | rest   | pre | 0.9625000 |\n",
       "| 201 | LD | tiktok | pre | 0.9937500 |\n",
       "| 202 | LD | tiktok | pre | 0.9687500 |\n",
       "| 203 | LD | tiktok | pre | 0.9937500 |\n",
       "| 204 | LD | tiktok | pre | 0.9936306 |\n",
       "| 205 | LD | tiktok | pre | 0.9937500 |\n",
       "| 206 | LD | tiktok | pre | 0.9810127 |\n",
       "| 207 | LD | tiktok | pre | 0.9937500 |\n",
       "| 208 | LD | tiktok | pre | 0.9444444 |\n",
       "| 209 | LD | tiktok | pre | 0.9875000 |\n",
       "| 210 | LD | tiktok | pre | 0.9875000 |\n",
       "| 211 | LD | tiktok | pre | 0.9192547 |\n",
       "| 212 | LD | tiktok | pre | 0.9937500 |\n",
       "| 213 | LD | tiktok | pre | 0.9625000 |\n",
       "| 214 | LD | tiktok | pre | 0.9937107 |\n",
       "| 215 | LD | tiktok | pre | 0.9559748 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 301 | LD | youtube | post | 0.92857143 |\n",
       "| 302 | LD | youtube | post | 1.00000000 |\n",
       "| 303 | LD | youtube | post | 0.96250000 |\n",
       "| 304 | LD | youtube | post | 1.00000000 |\n",
       "| 305 | LD | youtube | post | 0.94375000 |\n",
       "| 306 | LD | youtube | post | 0.99375000 |\n",
       "| 307 | LD | youtube | post | 0.98750000 |\n",
       "| 308 | LD | youtube | post | 0.96875000 |\n",
       "| 309 | LD | youtube | post | 0.96875000 |\n",
       "| 310 | LD | youtube | post | 0.02484472 |\n",
       "| 311 | LD | youtube | post | 0.98750000 |\n",
       "| 312 | LD | youtube | post | 1.00000000 |\n",
       "| 313 | LD | youtube | post | 1.00000000 |\n",
       "| 314 | LD | youtube | post | 0.91025641 |\n",
       "| 315 | LD | youtube | post | 0.99375000 |\n",
       "| 401 | LD | twitter | post | 0.98742138 |\n",
       "| 402 | LD | twitter | post | 0.98750000 |\n",
       "| 403 | LD | twitter | post | 0.96875000 |\n",
       "| 404 | LD | twitter | post | 0.96250000 |\n",
       "| 405 | LD | twitter | post | 0.99375000 |\n",
       "| 406 | LD | twitter | post | 0.98125000 |\n",
       "| 407 | LD | twitter | post | 0.98125000 |\n",
       "| 408 | LD | twitter | post | 0.98750000 |\n",
       "| 409 | LD | twitter | post | 0.96875000 |\n",
       "| 410 | LD | twitter | post | 1.00000000 |\n",
       "| 411 | LD | twitter | post | 0.97500000 |\n",
       "| 412 | LD | twitter | post | 0.96875000 |\n",
       "| 413 | LD | twitter | post | 0.96875000 |\n",
       "| 414 | LD | twitter | post | 0.97500000 |\n",
       "| 415 | LD | twitter | post | 0.98750000 |\n",
       "\n"
      ],
      "text/plain": [
       "    folder_id task interrupt measure accuracy  \n",
       "1   101       LD   rest      pre     0.9685535 \n",
       "2   102       LD   rest      pre     0.9272727 \n",
       "3   103       LD   rest      pre     0.9875000 \n",
       "4   104       LD   rest      pre     0.9937500 \n",
       "5   105       LD   rest      pre     0.9875000 \n",
       "6   106       LD   rest      pre     0.9750000 \n",
       "7   107       LD   rest      pre     0.9937888 \n",
       "8   108       LD   rest      pre     0.9875000 \n",
       "9   109       LD   rest      pre     0.9627329 \n",
       "10  110       LD   rest      pre     0.9556962 \n",
       "11  111       LD   rest      pre     0.9936709 \n",
       "12  112       LD   rest      pre     0.9875000 \n",
       "13  113       LD   rest      pre     0.9937500 \n",
       "14  114       LD   rest      pre     0.9689441 \n",
       "15  115       LD   rest      pre     0.9625000 \n",
       "16  201       LD   tiktok    pre     0.9937500 \n",
       "17  202       LD   tiktok    pre     0.9687500 \n",
       "18  203       LD   tiktok    pre     0.9937500 \n",
       "19  204       LD   tiktok    pre     0.9936306 \n",
       "20  205       LD   tiktok    pre     0.9937500 \n",
       "21  206       LD   tiktok    pre     0.9810127 \n",
       "22  207       LD   tiktok    pre     0.9937500 \n",
       "23  208       LD   tiktok    pre     0.9444444 \n",
       "24  209       LD   tiktok    pre     0.9875000 \n",
       "25  210       LD   tiktok    pre     0.9875000 \n",
       "26  211       LD   tiktok    pre     0.9192547 \n",
       "27  212       LD   tiktok    pre     0.9937500 \n",
       "28  213       LD   tiktok    pre     0.9625000 \n",
       "29  214       LD   tiktok    pre     0.9937107 \n",
       "30  215       LD   tiktok    pre     0.9559748 \n",
       "⋮   ⋮         ⋮    ⋮         ⋮       ⋮         \n",
       "91  301       LD   youtube   post    0.92857143\n",
       "92  302       LD   youtube   post    1.00000000\n",
       "93  303       LD   youtube   post    0.96250000\n",
       "94  304       LD   youtube   post    1.00000000\n",
       "95  305       LD   youtube   post    0.94375000\n",
       "96  306       LD   youtube   post    0.99375000\n",
       "97  307       LD   youtube   post    0.98750000\n",
       "98  308       LD   youtube   post    0.96875000\n",
       "99  309       LD   youtube   post    0.96875000\n",
       "100 310       LD   youtube   post    0.02484472\n",
       "101 311       LD   youtube   post    0.98750000\n",
       "102 312       LD   youtube   post    1.00000000\n",
       "103 313       LD   youtube   post    1.00000000\n",
       "104 314       LD   youtube   post    0.91025641\n",
       "105 315       LD   youtube   post    0.99375000\n",
       "106 401       LD   twitter   post    0.98742138\n",
       "107 402       LD   twitter   post    0.98750000\n",
       "108 403       LD   twitter   post    0.96875000\n",
       "109 404       LD   twitter   post    0.96250000\n",
       "110 405       LD   twitter   post    0.99375000\n",
       "111 406       LD   twitter   post    0.98125000\n",
       "112 407       LD   twitter   post    0.98125000\n",
       "113 408       LD   twitter   post    0.98750000\n",
       "114 409       LD   twitter   post    0.96875000\n",
       "115 410       LD   twitter   post    1.00000000\n",
       "116 411       LD   twitter   post    0.97500000\n",
       "117 412       LD   twitter   post    0.96875000\n",
       "118 413       LD   twitter   post    0.96875000\n",
       "119 414       LD   twitter   post    0.97500000\n",
       "120 415       LD   twitter   post    0.98750000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ld <- df %>% filter(task == \"LD\")\n",
    "df_ld"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 0.051736 (tol = 0.002, component 1)”\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model is nearly unidentifiable: very large eigenvalue\n",
      " - Rescale variables?”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in eval(family$initialize, rho):\n",
      "“non-integer #successes in a binomial glm!”\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“unable to evaluate scaled gradient”\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“ Hessian is numerically singular: parameters are not uniquely determined”\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 0.0533262 (tol = 0.002, component 1)”\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model is nearly unidentifiable: very large eigenvalue\n",
      " - Rescale variables?”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in eval(family$initialize, rho):\n",
      "“non-integer #successes in a binomial glm!”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 0.00300266 (tol = 0.002, component 1)”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in eval(family$initialize, rho):\n",
      "“non-integer #successes in a binomial glm!”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# LMM on the main effect `interrupt` w/wo link family\n",
    "m0 <-  lmer(accuracy  ~ interrupt + (1|folder_id), data = df_ld)\n",
    "m1 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df_ld, family = Gamma(link = \"log\"))\n",
    "m2 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df_ld, family = Gamma(link = \"inverse\"))\n",
    "m3 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df_ld, family = inverse.gaussian(link = \"log\"))\n",
    "m4 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df_ld, family = inverse.gaussian(link = \"inverse\"))\n",
    "m5 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df_ld, family = binomial(link = \"logit\")) # suggested by R2\n",
    "\n",
    "# LMM on the main effect `measure` w/wo link family\n",
    "m6  <-  lmer(accuracy  ~ measure + (1|folder_id), data = df_ld)\n",
    "m7  <- glmer(accuracy  ~ measure + (1|folder_id), data = df_ld, family = Gamma(link = \"log\"))\n",
    "m8  <- glmer(accuracy  ~ measure + (1|folder_id), data = df_ld, family = Gamma(link = \"inverse\"))\n",
    "m9  <- glmer(accuracy  ~ measure + (1|folder_id), data = df_ld, family = inverse.gaussian(link = \"log\"))\n",
    "m10 <- glmer(accuracy  ~ measure + (1|folder_id), data = df_ld, family = inverse.gaussian(link = \"inverse\"))\n",
    "m11 <- glmer(accuracy  ~ measure + (1|folder_id), data = df_ld, family = binomial(link = \"logit\")) # suggested by R2\n",
    "\n",
    "# LMM on the interaction `interrupt * measure` w/wo link family\n",
    "m12 <-  lmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_ld)\n",
    "m13 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_ld, family = Gamma(link = \"log\"))\n",
    "m14 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_ld, family = Gamma(link = \"inverse\"))\n",
    "m15 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_ld, family = inverse.gaussian(link = \"log\"))\n",
    "m16 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_ld, family = inverse.gaussian(link = \"inverse\"))\n",
    "m17 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_ld, family = binomial(link = \"logit\")) # suggested by R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "refitting model(s) with ML (instead of REML)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A anova: 18 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>npar</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>logLik</th><th scope=col>deviance</th><th scope=col>Chisq</th><th scope=col>Df</th><th scope=col>Pr(&gt;Chisq)</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>m11</th><td> 3</td><td>  26.34386</td><td>  34.70633</td><td> -10.171929</td><td>  20.34386</td><td>         NA</td><td>NA</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m6</th><td> 4</td><td>-386.59944</td><td>-375.44947</td><td> 197.299720</td><td>-394.59944</td><td>414.9432991</td><td> 1</td><td>3.076895e-92</td></tr>\n",
       "\t<tr><th scope=row>m7</th><td> 4</td><td>-200.30744</td><td>-189.15748</td><td> 104.153722</td><td>-208.30744</td><td>  0.0000000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m8</th><td> 4</td><td>  56.58561</td><td>  67.73558</td><td> -24.292804</td><td>  48.58561</td><td>  0.0000000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m9</th><td> 4</td><td>  56.54437</td><td>  67.69433</td><td> -24.272184</td><td>  48.54437</td><td>  0.0412409</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m10</th><td> 4</td><td> 314.49900</td><td> 325.64897</td><td>-153.249499</td><td> 306.49900</td><td>  0.0000000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m5</th><td> 5</td><td>  20.54052</td><td>  34.47798</td><td>  -5.270262</td><td>  10.54052</td><td>295.9584748</td><td> 1</td><td>2.502092e-66</td></tr>\n",
       "\t<tr><th scope=row>m0</th><td> 6</td><td>-385.85326</td><td>-369.12831</td><td> 198.926628</td><td>-397.85326</td><td>408.3937803</td><td> 1</td><td>8.199000e-91</td></tr>\n",
       "\t<tr><th scope=row>m1</th><td> 6</td><td>-197.24137</td><td>-180.51642</td><td> 104.620685</td><td>-209.24137</td><td>  0.0000000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m2</th><td> 6</td><td>  59.41062</td><td>  76.13557</td><td> -23.705312</td><td>  47.41062</td><td>  0.0000000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m3</th><td> 6</td><td> 134.92780</td><td> 151.65275</td><td> -61.463902</td><td> 122.92780</td><td>  0.0000000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m4</th><td> 6</td><td> 318.37205</td><td> 335.09700</td><td>-153.186027</td><td> 306.37205</td><td>  0.0000000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m17</th><td> 9</td><td>  32.69580</td><td>  57.78323</td><td>  -7.347901</td><td>  14.69580</td><td>291.6762523</td><td> 3</td><td>6.297650e-63</td></tr>\n",
       "\t<tr><th scope=row>m12</th><td>10</td><td>-383.09764</td><td>-355.22272</td><td> 201.548820</td><td>-403.09764</td><td>417.7934413</td><td> 1</td><td>7.374505e-93</td></tr>\n",
       "\t<tr><th scope=row>m13</th><td>10</td><td>-194.51885</td><td>-166.64394</td><td> 107.259426</td><td>-214.51885</td><td>  0.0000000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m14</th><td>10</td><td>  67.40238</td><td>  95.27730</td><td> -23.701191</td><td>  47.40238</td><td>  0.0000000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m15</th><td>10</td><td> -44.44534</td><td> -16.57042</td><td>  32.222671</td><td> -64.44534</td><td>111.8477246</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m16</th><td>10</td><td> 326.37119</td><td> 354.24611</td><td>-153.185597</td><td> 306.37119</td><td>  0.0000000</td><td> 0</td><td>          NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova: 18 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & npar & AIC & BIC & logLik & deviance & Chisq & Df & Pr(>Chisq)\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tm11 &  3 &   26.34386 &   34.70633 &  -10.171929 &   20.34386 &          NA & NA &           NA\\\\\n",
       "\tm6 &  4 & -386.59944 & -375.44947 &  197.299720 & -394.59944 & 414.9432991 &  1 & 3.076895e-92\\\\\n",
       "\tm7 &  4 & -200.30744 & -189.15748 &  104.153722 & -208.30744 &   0.0000000 &  0 &           NA\\\\\n",
       "\tm8 &  4 &   56.58561 &   67.73558 &  -24.292804 &   48.58561 &   0.0000000 &  0 &           NA\\\\\n",
       "\tm9 &  4 &   56.54437 &   67.69433 &  -24.272184 &   48.54437 &   0.0412409 &  0 &           NA\\\\\n",
       "\tm10 &  4 &  314.49900 &  325.64897 & -153.249499 &  306.49900 &   0.0000000 &  0 &           NA\\\\\n",
       "\tm5 &  5 &   20.54052 &   34.47798 &   -5.270262 &   10.54052 & 295.9584748 &  1 & 2.502092e-66\\\\\n",
       "\tm0 &  6 & -385.85326 & -369.12831 &  198.926628 & -397.85326 & 408.3937803 &  1 & 8.199000e-91\\\\\n",
       "\tm1 &  6 & -197.24137 & -180.51642 &  104.620685 & -209.24137 &   0.0000000 &  0 &           NA\\\\\n",
       "\tm2 &  6 &   59.41062 &   76.13557 &  -23.705312 &   47.41062 &   0.0000000 &  0 &           NA\\\\\n",
       "\tm3 &  6 &  134.92780 &  151.65275 &  -61.463902 &  122.92780 &   0.0000000 &  0 &           NA\\\\\n",
       "\tm4 &  6 &  318.37205 &  335.09700 & -153.186027 &  306.37205 &   0.0000000 &  0 &           NA\\\\\n",
       "\tm17 &  9 &   32.69580 &   57.78323 &   -7.347901 &   14.69580 & 291.6762523 &  3 & 6.297650e-63\\\\\n",
       "\tm12 & 10 & -383.09764 & -355.22272 &  201.548820 & -403.09764 & 417.7934413 &  1 & 7.374505e-93\\\\\n",
       "\tm13 & 10 & -194.51885 & -166.64394 &  107.259426 & -214.51885 &   0.0000000 &  0 &           NA\\\\\n",
       "\tm14 & 10 &   67.40238 &   95.27730 &  -23.701191 &   47.40238 &   0.0000000 &  0 &           NA\\\\\n",
       "\tm15 & 10 &  -44.44534 &  -16.57042 &   32.222671 &  -64.44534 & 111.8477246 &  0 &           NA\\\\\n",
       "\tm16 & 10 &  326.37119 &  354.24611 & -153.185597 &  306.37119 &   0.0000000 &  0 &           NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova: 18 × 8\n",
       "\n",
       "| <!--/--> | npar &lt;dbl&gt; | AIC &lt;dbl&gt; | BIC &lt;dbl&gt; | logLik &lt;dbl&gt; | deviance &lt;dbl&gt; | Chisq &lt;dbl&gt; | Df &lt;dbl&gt; | Pr(&gt;Chisq) &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| m11 |  3 |   26.34386 |   34.70633 |  -10.171929 |   20.34386 |          NA | NA |           NA |\n",
       "| m6 |  4 | -386.59944 | -375.44947 |  197.299720 | -394.59944 | 414.9432991 |  1 | 3.076895e-92 |\n",
       "| m7 |  4 | -200.30744 | -189.15748 |  104.153722 | -208.30744 |   0.0000000 |  0 |           NA |\n",
       "| m8 |  4 |   56.58561 |   67.73558 |  -24.292804 |   48.58561 |   0.0000000 |  0 |           NA |\n",
       "| m9 |  4 |   56.54437 |   67.69433 |  -24.272184 |   48.54437 |   0.0412409 |  0 |           NA |\n",
       "| m10 |  4 |  314.49900 |  325.64897 | -153.249499 |  306.49900 |   0.0000000 |  0 |           NA |\n",
       "| m5 |  5 |   20.54052 |   34.47798 |   -5.270262 |   10.54052 | 295.9584748 |  1 | 2.502092e-66 |\n",
       "| m0 |  6 | -385.85326 | -369.12831 |  198.926628 | -397.85326 | 408.3937803 |  1 | 8.199000e-91 |\n",
       "| m1 |  6 | -197.24137 | -180.51642 |  104.620685 | -209.24137 |   0.0000000 |  0 |           NA |\n",
       "| m2 |  6 |   59.41062 |   76.13557 |  -23.705312 |   47.41062 |   0.0000000 |  0 |           NA |\n",
       "| m3 |  6 |  134.92780 |  151.65275 |  -61.463902 |  122.92780 |   0.0000000 |  0 |           NA |\n",
       "| m4 |  6 |  318.37205 |  335.09700 | -153.186027 |  306.37205 |   0.0000000 |  0 |           NA |\n",
       "| m17 |  9 |   32.69580 |   57.78323 |   -7.347901 |   14.69580 | 291.6762523 |  3 | 6.297650e-63 |\n",
       "| m12 | 10 | -383.09764 | -355.22272 |  201.548820 | -403.09764 | 417.7934413 |  1 | 7.374505e-93 |\n",
       "| m13 | 10 | -194.51885 | -166.64394 |  107.259426 | -214.51885 |   0.0000000 |  0 |           NA |\n",
       "| m14 | 10 |   67.40238 |   95.27730 |  -23.701191 |   47.40238 |   0.0000000 |  0 |           NA |\n",
       "| m15 | 10 |  -44.44534 |  -16.57042 |   32.222671 |  -64.44534 | 111.8477246 |  0 |           NA |\n",
       "| m16 | 10 |  326.37119 |  354.24611 | -153.185597 |  306.37119 |   0.0000000 |  0 |           NA |\n",
       "\n"
      ],
      "text/plain": [
       "    npar AIC        BIC        logLik      deviance   Chisq       Df\n",
       "m11  3     26.34386   34.70633  -10.171929   20.34386          NA NA\n",
       "m6   4   -386.59944 -375.44947  197.299720 -394.59944 414.9432991  1\n",
       "m7   4   -200.30744 -189.15748  104.153722 -208.30744   0.0000000  0\n",
       "m8   4     56.58561   67.73558  -24.292804   48.58561   0.0000000  0\n",
       "m9   4     56.54437   67.69433  -24.272184   48.54437   0.0412409  0\n",
       "m10  4    314.49900  325.64897 -153.249499  306.49900   0.0000000  0\n",
       "m5   5     20.54052   34.47798   -5.270262   10.54052 295.9584748  1\n",
       "m0   6   -385.85326 -369.12831  198.926628 -397.85326 408.3937803  1\n",
       "m1   6   -197.24137 -180.51642  104.620685 -209.24137   0.0000000  0\n",
       "m2   6     59.41062   76.13557  -23.705312   47.41062   0.0000000  0\n",
       "m3   6    134.92780  151.65275  -61.463902  122.92780   0.0000000  0\n",
       "m4   6    318.37205  335.09700 -153.186027  306.37205   0.0000000  0\n",
       "m17  9     32.69580   57.78323   -7.347901   14.69580 291.6762523  3\n",
       "m12 10   -383.09764 -355.22272  201.548820 -403.09764 417.7934413  1\n",
       "m13 10   -194.51885 -166.64394  107.259426 -214.51885   0.0000000  0\n",
       "m14 10     67.40238   95.27730  -23.701191   47.40238   0.0000000  0\n",
       "m15 10    -44.44534  -16.57042   32.222671  -64.44534 111.8477246  0\n",
       "m16 10    326.37119  354.24611 -153.185597  306.37119   0.0000000  0\n",
       "    Pr(>Chisq)  \n",
       "m11           NA\n",
       "m6  3.076895e-92\n",
       "m7            NA\n",
       "m8            NA\n",
       "m9            NA\n",
       "m10           NA\n",
       "m5  2.502092e-66\n",
       "m0  8.199000e-91\n",
       "m1            NA\n",
       "m2            NA\n",
       "m3            NA\n",
       "m4            NA\n",
       "m17 6.297650e-63\n",
       "m12 7.374505e-93\n",
       "m13           NA\n",
       "m14           NA\n",
       "m15           NA\n",
       "m16           NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anova(m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14, m15, m16, m17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A bayesfactor_models: 18 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Model</th><th scope=col>log_BF</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>m1</th><td>interrupt + (1 | folder_id)                                </td><td> -94.270249</td></tr>\n",
       "\t<tr><th scope=row>m2</th><td>interrupt + (1 | folder_id)                                </td><td>-222.596246</td></tr>\n",
       "\t<tr><th scope=row>m3</th><td>interrupt + (1 | folder_id)                                </td><td>-260.354837</td></tr>\n",
       "\t<tr><th scope=row>m4</th><td>interrupt + (1 | folder_id)                                </td><td>-352.076962</td></tr>\n",
       "\t<tr><th scope=row>m5</th><td>interrupt + (1 | folder_id)                                </td><td>-201.767450</td></tr>\n",
       "\t<tr><th scope=row>m6</th><td>measure + (1 | folder_id)                                  </td><td>   3.196278</td></tr>\n",
       "\t<tr><th scope=row>m7</th><td>measure + (1 | folder_id)                                  </td><td> -89.949721</td></tr>\n",
       "\t<tr><th scope=row>m8</th><td>measure + (1 | folder_id)                                  </td><td>-218.396247</td></tr>\n",
       "\t<tr><th scope=row>m9</th><td>measure + (1 | folder_id)                                  </td><td>-218.375626</td></tr>\n",
       "\t<tr><th scope=row>m10</th><td>measure + (1 | folder_id)                                  </td><td>-347.352942</td></tr>\n",
       "\t<tr><th scope=row>m11</th><td>measure + (1 | folder_id)                                  </td><td>-201.881626</td></tr>\n",
       "\t<tr><th scope=row>m12</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td>  -6.917098</td></tr>\n",
       "\t<tr><th scope=row>m13</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td>-101.206492</td></tr>\n",
       "\t<tr><th scope=row>m14</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td>-232.167110</td></tr>\n",
       "\t<tr><th scope=row>m15</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td>-176.243247</td></tr>\n",
       "\t<tr><th scope=row>m16</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td>-361.651515</td></tr>\n",
       "\t<tr><th scope=row>m17</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td>-213.420073</td></tr>\n",
       "\t<tr><th scope=row>m0</th><td>interrupt + (1 | folder_id)                                </td><td>   0.000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A bayesfactor\\_models: 18 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Model & log\\_BF\\\\\n",
       "  & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\tm1 & interrupt + (1 \\textbar{} folder\\_id)                                 &  -94.270249\\\\\n",
       "\tm2 & interrupt + (1 \\textbar{} folder\\_id)                                 & -222.596246\\\\\n",
       "\tm3 & interrupt + (1 \\textbar{} folder\\_id)                                 & -260.354837\\\\\n",
       "\tm4 & interrupt + (1 \\textbar{} folder\\_id)                                 & -352.076962\\\\\n",
       "\tm5 & interrupt + (1 \\textbar{} folder\\_id)                                 & -201.767450\\\\\n",
       "\tm6 & measure + (1 \\textbar{} folder\\_id)                                   &    3.196278\\\\\n",
       "\tm7 & measure + (1 \\textbar{} folder\\_id)                                   &  -89.949721\\\\\n",
       "\tm8 & measure + (1 \\textbar{} folder\\_id)                                   & -218.396247\\\\\n",
       "\tm9 & measure + (1 \\textbar{} folder\\_id)                                   & -218.375626\\\\\n",
       "\tm10 & measure + (1 \\textbar{} folder\\_id)                                   & -347.352942\\\\\n",
       "\tm11 & measure + (1 \\textbar{} folder\\_id)                                   & -201.881626\\\\\n",
       "\tm12 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) &   -6.917098\\\\\n",
       "\tm13 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) & -101.206492\\\\\n",
       "\tm14 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) & -232.167110\\\\\n",
       "\tm15 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) & -176.243247\\\\\n",
       "\tm16 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) & -361.651515\\\\\n",
       "\tm17 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) & -213.420073\\\\\n",
       "\tm0 & interrupt + (1 \\textbar{} folder\\_id)                                 &    0.000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A bayesfactor_models: 18 × 2\n",
       "\n",
       "| <!--/--> | Model &lt;chr&gt; | log_BF &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| m1 | interrupt + (1 | folder_id)                                 |  -94.270249 |\n",
       "| m2 | interrupt + (1 | folder_id)                                 | -222.596246 |\n",
       "| m3 | interrupt + (1 | folder_id)                                 | -260.354837 |\n",
       "| m4 | interrupt + (1 | folder_id)                                 | -352.076962 |\n",
       "| m5 | interrupt + (1 | folder_id)                                 | -201.767450 |\n",
       "| m6 | measure + (1 | folder_id)                                   |    3.196278 |\n",
       "| m7 | measure + (1 | folder_id)                                   |  -89.949721 |\n",
       "| m8 | measure + (1 | folder_id)                                   | -218.396247 |\n",
       "| m9 | measure + (1 | folder_id)                                   | -218.375626 |\n",
       "| m10 | measure + (1 | folder_id)                                   | -347.352942 |\n",
       "| m11 | measure + (1 | folder_id)                                   | -201.881626 |\n",
       "| m12 | interrupt * measure + interrupt + measure + (1 | folder_id) |   -6.917098 |\n",
       "| m13 | interrupt * measure + interrupt + measure + (1 | folder_id) | -101.206492 |\n",
       "| m14 | interrupt * measure + interrupt + measure + (1 | folder_id) | -232.167110 |\n",
       "| m15 | interrupt * measure + interrupt + measure + (1 | folder_id) | -176.243247 |\n",
       "| m16 | interrupt * measure + interrupt + measure + (1 | folder_id) | -361.651515 |\n",
       "| m17 | interrupt * measure + interrupt + measure + (1 | folder_id) | -213.420073 |\n",
       "| m0 | interrupt + (1 | folder_id)                                 |    0.000000 |\n",
       "\n"
      ],
      "text/plain": [
       "    Model                                                       log_BF     \n",
       "m1  interrupt + (1 | folder_id)                                  -94.270249\n",
       "m2  interrupt + (1 | folder_id)                                 -222.596246\n",
       "m3  interrupt + (1 | folder_id)                                 -260.354837\n",
       "m4  interrupt + (1 | folder_id)                                 -352.076962\n",
       "m5  interrupt + (1 | folder_id)                                 -201.767450\n",
       "m6  measure + (1 | folder_id)                                      3.196278\n",
       "m7  measure + (1 | folder_id)                                    -89.949721\n",
       "m8  measure + (1 | folder_id)                                   -218.396247\n",
       "m9  measure + (1 | folder_id)                                   -218.375626\n",
       "m10 measure + (1 | folder_id)                                   -347.352942\n",
       "m11 measure + (1 | folder_id)                                   -201.881626\n",
       "m12 interrupt * measure + interrupt + measure + (1 | folder_id)   -6.917098\n",
       "m13 interrupt * measure + interrupt + measure + (1 | folder_id) -101.206492\n",
       "m14 interrupt * measure + interrupt + measure + (1 | folder_id) -232.167110\n",
       "m15 interrupt * measure + interrupt + measure + (1 | folder_id) -176.243247\n",
       "m16 interrupt * measure + interrupt + measure + (1 | folder_id) -361.651515\n",
       "m17 interrupt * measure + interrupt + measure + (1 | folder_id) -213.420073\n",
       "m0  interrupt + (1 | folder_id)                                    0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bayesfactor_models(m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14, m15, m16, m17, denominator = m0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fitted a linear mixed model (estimated using REML and nloptwrap optimizer)\n",
      "to predict accuracy with interrupt\n",
      " (formula: accuracy ~ interrupt). The model included folder_id\n",
      " as random effect (formula: ~1 | folder_id). The model's total explanatory power\n",
      "is substantial (conditional R2 = 0.99) and the part related to the fixed\n",
      "effects alone (marginal R2) is of 0.05. The model's intercept, corresponding to\n",
      "interrupt = tiktok\n",
      ", is at 0.98 (95% CI [0.92, 1.04], t(114) = 30.42, p < .001). Within this model:\n",
      "\n",
      "  - The effect of interrupt [rest] is statistically non-significant and negative\n",
      "(beta = -1.45e-03, 95% CI [-0.09, 0.09], t(114) = -0.03, p = 0.975; Std. beta =\n",
      "-0.01, 95% CI [-0.73, 0.71])\n",
      "  - The effect of interrupt [twitter] is statistically non-significant and\n",
      "positive (beta = 5.00e-03, 95% CI [-0.09, 0.10], t(114) = 0.11, p = 0.913; Std.\n",
      "beta = 0.04, 95% CI [-0.68, 0.76])\n",
      "  - The effect of interrupt [youtube] is statistically non-significant and\n",
      "negative (beta = -0.07, 95% CI [-0.16, 0.02], t(114) = -1.47, p = 0.144; Std.\n",
      "beta = -0.54, 95% CI [-1.26, 0.19])\n",
      "\n",
      "Standardized parameters were obtained by fitting the model on a standardized\n",
      "version of the dataset. 95% Confidence Intervals (CIs) and p-values were\n",
      "computed using a Wald t-distribution approximation.\n"
     ]
    }
   ],
   "source": [
    "df_ld$interrupt <- factor(df_ld$interrupt, levels = c(\"tiktok\", \"rest\", \"twitter\", \"youtube\"))\n",
    "df_ld$measure   <- factor(df_ld$measure, levels = c(\"post\", \"pre\"))\n",
    "m <- lmer(accuracy ~ interrupt + (1 | folder_id), data = df_ld)\n",
    "print(report(m), verbose = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fitted a linear mixed model (estimated using REML and nloptwrap optimizer)\n",
      "to predict accuracy with interrupt\n",
      " (formula: accuracy ~ interrupt). The model included folder_id\n",
      " as random effect (formula: ~1 | folder_id). The model's total explanatory power\n",
      "is substantial (conditional R2 = 0.99) and the part related to the fixed\n",
      "effects alone (marginal R2) is of 0.05. The model's intercept, corresponding to\n",
      "interrupt = rest\n",
      ", is at 0.98 (95% CI [0.91, 1.04], t(114) = 30.37, p < .001). Within this model:\n",
      "\n",
      "  - The effect of interrupt [twitter] is statistically non-significant and\n",
      "positive (beta = 6.45e-03, 95% CI [-0.08, 0.10], t(114) = 0.14, p = 0.888; Std.\n",
      "beta = 0.05, 95% CI [-0.67, 0.77])\n",
      "  - The effect of interrupt [youtube] is statistically non-significant and\n",
      "negative (beta = -0.07, 95% CI [-0.16, 0.02], t(114) = -1.44, p = 0.152; Std.\n",
      "beta = -0.53, 95% CI [-1.25, 0.20])\n",
      "  - The effect of interrupt [tiktok] is statistically non-significant and\n",
      "positive (beta = 1.45e-03, 95% CI [-0.09, 0.09], t(114) = 0.03, p = 0.975; Std.\n",
      "beta = 0.01, 95% CI [-0.71, 0.73])\n",
      "\n",
      "Standardized parameters were obtained by fitting the model on a standardized\n",
      "version of the dataset. 95% Confidence Intervals (CIs) and p-values were\n",
      "computed using a Wald t-distribution approximation.\n"
     ]
    }
   ],
   "source": [
    "df_ld$interrupt <- factor(df_ld$interrupt, levels = c(\"rest\", \"twitter\", \"youtube\", \"tiktok\"))\n",
    "df_ld$measure   <- factor(df_ld$measure, levels = c( \"pre\", \"post\"))\n",
    "m <- lmer(accuracy ~ interrupt + (1 | folder_id), data = df_ld)\n",
    "print(report(m), verbose = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fitted a linear mixed model (estimated using REML and nloptwrap optimizer)\n",
      "to predict accuracy with measure\n",
      " (formula: accuracy ~ measure). The model included folder_id\n",
      " as random effect (formula: ~1 | folder_id). The model's total explanatory power\n",
      "is substantial (conditional R2 = 0.99) and the part related to the fixed\n",
      "effects alone (marginal R2) is of 2.29e-05. The model's intercept,\n",
      "corresponding to measure = post\n",
      ", is at 0.96 (95% CI [0.93, 0.99], t(116) = 59.46, p < .001). Within this model:\n",
      "\n",
      "  - The effect of measure [pre] is statistically non-significant and positive\n",
      "(beta = 1.19e-03, 95% CI [-3.35e-03, 5.74e-03], t(116) = 0.52, p = 0.603; Std.\n",
      "beta = 9.56e-03, 95% CI [-0.03, 0.05])\n",
      "\n",
      "Standardized parameters were obtained by fitting the model on a standardized\n",
      "version of the dataset. 95% Confidence Intervals (CIs) and p-values were\n",
      "computed using a Wald t-distribution approximation.\n"
     ]
    }
   ],
   "source": [
    "df_ld$interrupt <- factor(df_ld$interrupt, levels = c(\"tiktok\", \"rest\", \"twitter\", \"youtube\"))\n",
    "df_ld$measure   <- factor(df_ld$measure, levels = c(\"post\", \"pre\"))\n",
    "m <- lmer(accuracy ~ measure + (1 | folder_id), data = df_ld)\n",
    "print(report(m), verbose = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fitted a linear mixed model (estimated using REML and nloptwrap optimizer)\n",
      "to predict accuracy with measure\n",
      " (formula: accuracy ~ measure). The model included folder_id\n",
      " as random effect (formula: ~1 | folder_id). The model's total explanatory power\n",
      "is substantial (conditional R2 = 0.99) and the part related to the fixed\n",
      "effects alone (marginal R2) is of 2.29e-05. The model's intercept,\n",
      "corresponding to measure = pre\n",
      ", is at 0.96 (95% CI [0.93, 1.00], t(116) = 59.53, p < .001). Within this model:\n",
      "\n",
      "  - The effect of measure [post] is statistically non-significant and negative\n",
      "(beta = -1.19e-03, 95% CI [-5.74e-03, 3.35e-03], t(116) = -0.52, p = 0.603;\n",
      "Std. beta = -9.56e-03, 95% CI [-0.05, 0.03])\n",
      "\n",
      "Standardized parameters were obtained by fitting the model on a standardized\n",
      "version of the dataset. 95% Confidence Intervals (CIs) and p-values were\n",
      "computed using a Wald t-distribution approximation.\n"
     ]
    }
   ],
   "source": [
    "df_ld$interrupt <- factor(df_ld$interrupt, levels = c(\"rest\", \"twitter\", \"youtube\", \"tiktok\"))\n",
    "df_ld$measure   <- factor(df_ld$measure, levels = c( \"pre\", \"post\"))\n",
    "m <- lmer(accuracy ~ measure + (1 | folder_id), data = df_ld)\n",
    "print(report(m), verbose = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 120 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>folder_id</th><th scope=col>task</th><th scope=col>interrupt</th><th scope=col>measure</th><th scope=col>accuracy</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>101</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>102</td><td>PM</td><td>rest  </td><td>pre</td><td>0.8750000</td></tr>\n",
       "\t<tr><td>103</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>104</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>105</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>106</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>107</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9166667</td></tr>\n",
       "\t<tr><td>108</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>109</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9230769</td></tr>\n",
       "\t<tr><td>110</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9090909</td></tr>\n",
       "\t<tr><td>111</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>112</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>113</td><td>PM</td><td>rest  </td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>114</td><td>PM</td><td>rest  </td><td>pre</td><td>0.4615385</td></tr>\n",
       "\t<tr><td>115</td><td>PM</td><td>rest  </td><td>pre</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>201</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.8571429</td></tr>\n",
       "\t<tr><td>202</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.7142857</td></tr>\n",
       "\t<tr><td>203</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>204</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>205</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>206</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.3571429</td></tr>\n",
       "\t<tr><td>207</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>208</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.9166667</td></tr>\n",
       "\t<tr><td>209</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>210</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>211</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.8333333</td></tr>\n",
       "\t<tr><td>212</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.8571429</td></tr>\n",
       "\t<tr><td>213</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.7692308</td></tr>\n",
       "\t<tr><td>214</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.3571429</td></tr>\n",
       "\t<tr><td>215</td><td>PM</td><td>tiktok</td><td>pre</td><td>0.7857143</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>301</td><td>PM</td><td>youtube</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>302</td><td>PM</td><td>youtube</td><td>post</td><td>0.6428571</td></tr>\n",
       "\t<tr><td>303</td><td>PM</td><td>youtube</td><td>post</td><td>0.2142857</td></tr>\n",
       "\t<tr><td>304</td><td>PM</td><td>youtube</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>305</td><td>PM</td><td>youtube</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>306</td><td>PM</td><td>youtube</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>307</td><td>PM</td><td>youtube</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>308</td><td>PM</td><td>youtube</td><td>post</td><td>0.3571429</td></tr>\n",
       "\t<tr><td>309</td><td>PM</td><td>youtube</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>310</td><td>PM</td><td>youtube</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>311</td><td>PM</td><td>youtube</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>312</td><td>PM</td><td>youtube</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>313</td><td>PM</td><td>youtube</td><td>post</td><td>0.3571429</td></tr>\n",
       "\t<tr><td>314</td><td>PM</td><td>youtube</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>315</td><td>PM</td><td>youtube</td><td>post</td><td>0.8571429</td></tr>\n",
       "\t<tr><td>401</td><td>PM</td><td>twitter</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>402</td><td>PM</td><td>twitter</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>403</td><td>PM</td><td>twitter</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>404</td><td>PM</td><td>twitter</td><td>post</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>405</td><td>PM</td><td>twitter</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>406</td><td>PM</td><td>twitter</td><td>post</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>407</td><td>PM</td><td>twitter</td><td>post</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>408</td><td>PM</td><td>twitter</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>409</td><td>PM</td><td>twitter</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>410</td><td>PM</td><td>twitter</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>411</td><td>PM</td><td>twitter</td><td>post</td><td>0.9285714</td></tr>\n",
       "\t<tr><td>412</td><td>PM</td><td>twitter</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>413</td><td>PM</td><td>twitter</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>414</td><td>PM</td><td>twitter</td><td>post</td><td>1.0000000</td></tr>\n",
       "\t<tr><td>415</td><td>PM</td><td>twitter</td><td>post</td><td>0.9285714</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 120 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " folder\\_id & task & interrupt & measure & accuracy\\\\\n",
       " <int> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 101 & PM & rest   & pre & 0.9285714\\\\\n",
       "\t 102 & PM & rest   & pre & 0.8750000\\\\\n",
       "\t 103 & PM & rest   & pre & 0.9285714\\\\\n",
       "\t 104 & PM & rest   & pre & 0.9285714\\\\\n",
       "\t 105 & PM & rest   & pre & 0.9285714\\\\\n",
       "\t 106 & PM & rest   & pre & 0.9285714\\\\\n",
       "\t 107 & PM & rest   & pre & 0.9166667\\\\\n",
       "\t 108 & PM & rest   & pre & 0.9285714\\\\\n",
       "\t 109 & PM & rest   & pre & 0.9230769\\\\\n",
       "\t 110 & PM & rest   & pre & 0.9090909\\\\\n",
       "\t 111 & PM & rest   & pre & 0.9285714\\\\\n",
       "\t 112 & PM & rest   & pre & 0.9285714\\\\\n",
       "\t 113 & PM & rest   & pre & 0.9285714\\\\\n",
       "\t 114 & PM & rest   & pre & 0.4615385\\\\\n",
       "\t 115 & PM & rest   & pre & 1.0000000\\\\\n",
       "\t 201 & PM & tiktok & pre & 0.8571429\\\\\n",
       "\t 202 & PM & tiktok & pre & 0.7142857\\\\\n",
       "\t 203 & PM & tiktok & pre & 0.9285714\\\\\n",
       "\t 204 & PM & tiktok & pre & 0.9285714\\\\\n",
       "\t 205 & PM & tiktok & pre & 0.9285714\\\\\n",
       "\t 206 & PM & tiktok & pre & 0.3571429\\\\\n",
       "\t 207 & PM & tiktok & pre & 0.9285714\\\\\n",
       "\t 208 & PM & tiktok & pre & 0.9166667\\\\\n",
       "\t 209 & PM & tiktok & pre & 0.9285714\\\\\n",
       "\t 210 & PM & tiktok & pre & 0.9285714\\\\\n",
       "\t 211 & PM & tiktok & pre & 0.8333333\\\\\n",
       "\t 212 & PM & tiktok & pre & 0.8571429\\\\\n",
       "\t 213 & PM & tiktok & pre & 0.7692308\\\\\n",
       "\t 214 & PM & tiktok & pre & 0.3571429\\\\\n",
       "\t 215 & PM & tiktok & pre & 0.7857143\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 301 & PM & youtube & post & 1.0000000\\\\\n",
       "\t 302 & PM & youtube & post & 0.6428571\\\\\n",
       "\t 303 & PM & youtube & post & 0.2142857\\\\\n",
       "\t 304 & PM & youtube & post & 1.0000000\\\\\n",
       "\t 305 & PM & youtube & post & 1.0000000\\\\\n",
       "\t 306 & PM & youtube & post & 1.0000000\\\\\n",
       "\t 307 & PM & youtube & post & 1.0000000\\\\\n",
       "\t 308 & PM & youtube & post & 0.3571429\\\\\n",
       "\t 309 & PM & youtube & post & 1.0000000\\\\\n",
       "\t 310 & PM & youtube & post & 1.0000000\\\\\n",
       "\t 311 & PM & youtube & post & 1.0000000\\\\\n",
       "\t 312 & PM & youtube & post & 1.0000000\\\\\n",
       "\t 313 & PM & youtube & post & 0.3571429\\\\\n",
       "\t 314 & PM & youtube & post & 1.0000000\\\\\n",
       "\t 315 & PM & youtube & post & 0.8571429\\\\\n",
       "\t 401 & PM & twitter & post & 1.0000000\\\\\n",
       "\t 402 & PM & twitter & post & 1.0000000\\\\\n",
       "\t 403 & PM & twitter & post & 1.0000000\\\\\n",
       "\t 404 & PM & twitter & post & 0.9285714\\\\\n",
       "\t 405 & PM & twitter & post & 1.0000000\\\\\n",
       "\t 406 & PM & twitter & post & 0.9285714\\\\\n",
       "\t 407 & PM & twitter & post & 0.9285714\\\\\n",
       "\t 408 & PM & twitter & post & 1.0000000\\\\\n",
       "\t 409 & PM & twitter & post & 1.0000000\\\\\n",
       "\t 410 & PM & twitter & post & 1.0000000\\\\\n",
       "\t 411 & PM & twitter & post & 0.9285714\\\\\n",
       "\t 412 & PM & twitter & post & 1.0000000\\\\\n",
       "\t 413 & PM & twitter & post & 1.0000000\\\\\n",
       "\t 414 & PM & twitter & post & 1.0000000\\\\\n",
       "\t 415 & PM & twitter & post & 0.9285714\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 120 × 5\n",
       "\n",
       "| folder_id &lt;int&gt; | task &lt;chr&gt; | interrupt &lt;chr&gt; | measure &lt;chr&gt; | accuracy &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 101 | PM | rest   | pre | 0.9285714 |\n",
       "| 102 | PM | rest   | pre | 0.8750000 |\n",
       "| 103 | PM | rest   | pre | 0.9285714 |\n",
       "| 104 | PM | rest   | pre | 0.9285714 |\n",
       "| 105 | PM | rest   | pre | 0.9285714 |\n",
       "| 106 | PM | rest   | pre | 0.9285714 |\n",
       "| 107 | PM | rest   | pre | 0.9166667 |\n",
       "| 108 | PM | rest   | pre | 0.9285714 |\n",
       "| 109 | PM | rest   | pre | 0.9230769 |\n",
       "| 110 | PM | rest   | pre | 0.9090909 |\n",
       "| 111 | PM | rest   | pre | 0.9285714 |\n",
       "| 112 | PM | rest   | pre | 0.9285714 |\n",
       "| 113 | PM | rest   | pre | 0.9285714 |\n",
       "| 114 | PM | rest   | pre | 0.4615385 |\n",
       "| 115 | PM | rest   | pre | 1.0000000 |\n",
       "| 201 | PM | tiktok | pre | 0.8571429 |\n",
       "| 202 | PM | tiktok | pre | 0.7142857 |\n",
       "| 203 | PM | tiktok | pre | 0.9285714 |\n",
       "| 204 | PM | tiktok | pre | 0.9285714 |\n",
       "| 205 | PM | tiktok | pre | 0.9285714 |\n",
       "| 206 | PM | tiktok | pre | 0.3571429 |\n",
       "| 207 | PM | tiktok | pre | 0.9285714 |\n",
       "| 208 | PM | tiktok | pre | 0.9166667 |\n",
       "| 209 | PM | tiktok | pre | 0.9285714 |\n",
       "| 210 | PM | tiktok | pre | 0.9285714 |\n",
       "| 211 | PM | tiktok | pre | 0.8333333 |\n",
       "| 212 | PM | tiktok | pre | 0.8571429 |\n",
       "| 213 | PM | tiktok | pre | 0.7692308 |\n",
       "| 214 | PM | tiktok | pre | 0.3571429 |\n",
       "| 215 | PM | tiktok | pre | 0.7857143 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 301 | PM | youtube | post | 1.0000000 |\n",
       "| 302 | PM | youtube | post | 0.6428571 |\n",
       "| 303 | PM | youtube | post | 0.2142857 |\n",
       "| 304 | PM | youtube | post | 1.0000000 |\n",
       "| 305 | PM | youtube | post | 1.0000000 |\n",
       "| 306 | PM | youtube | post | 1.0000000 |\n",
       "| 307 | PM | youtube | post | 1.0000000 |\n",
       "| 308 | PM | youtube | post | 0.3571429 |\n",
       "| 309 | PM | youtube | post | 1.0000000 |\n",
       "| 310 | PM | youtube | post | 1.0000000 |\n",
       "| 311 | PM | youtube | post | 1.0000000 |\n",
       "| 312 | PM | youtube | post | 1.0000000 |\n",
       "| 313 | PM | youtube | post | 0.3571429 |\n",
       "| 314 | PM | youtube | post | 1.0000000 |\n",
       "| 315 | PM | youtube | post | 0.8571429 |\n",
       "| 401 | PM | twitter | post | 1.0000000 |\n",
       "| 402 | PM | twitter | post | 1.0000000 |\n",
       "| 403 | PM | twitter | post | 1.0000000 |\n",
       "| 404 | PM | twitter | post | 0.9285714 |\n",
       "| 405 | PM | twitter | post | 1.0000000 |\n",
       "| 406 | PM | twitter | post | 0.9285714 |\n",
       "| 407 | PM | twitter | post | 0.9285714 |\n",
       "| 408 | PM | twitter | post | 1.0000000 |\n",
       "| 409 | PM | twitter | post | 1.0000000 |\n",
       "| 410 | PM | twitter | post | 1.0000000 |\n",
       "| 411 | PM | twitter | post | 0.9285714 |\n",
       "| 412 | PM | twitter | post | 1.0000000 |\n",
       "| 413 | PM | twitter | post | 1.0000000 |\n",
       "| 414 | PM | twitter | post | 1.0000000 |\n",
       "| 415 | PM | twitter | post | 0.9285714 |\n",
       "\n"
      ],
      "text/plain": [
       "    folder_id task interrupt measure accuracy \n",
       "1   101       PM   rest      pre     0.9285714\n",
       "2   102       PM   rest      pre     0.8750000\n",
       "3   103       PM   rest      pre     0.9285714\n",
       "4   104       PM   rest      pre     0.9285714\n",
       "5   105       PM   rest      pre     0.9285714\n",
       "6   106       PM   rest      pre     0.9285714\n",
       "7   107       PM   rest      pre     0.9166667\n",
       "8   108       PM   rest      pre     0.9285714\n",
       "9   109       PM   rest      pre     0.9230769\n",
       "10  110       PM   rest      pre     0.9090909\n",
       "11  111       PM   rest      pre     0.9285714\n",
       "12  112       PM   rest      pre     0.9285714\n",
       "13  113       PM   rest      pre     0.9285714\n",
       "14  114       PM   rest      pre     0.4615385\n",
       "15  115       PM   rest      pre     1.0000000\n",
       "16  201       PM   tiktok    pre     0.8571429\n",
       "17  202       PM   tiktok    pre     0.7142857\n",
       "18  203       PM   tiktok    pre     0.9285714\n",
       "19  204       PM   tiktok    pre     0.9285714\n",
       "20  205       PM   tiktok    pre     0.9285714\n",
       "21  206       PM   tiktok    pre     0.3571429\n",
       "22  207       PM   tiktok    pre     0.9285714\n",
       "23  208       PM   tiktok    pre     0.9166667\n",
       "24  209       PM   tiktok    pre     0.9285714\n",
       "25  210       PM   tiktok    pre     0.9285714\n",
       "26  211       PM   tiktok    pre     0.8333333\n",
       "27  212       PM   tiktok    pre     0.8571429\n",
       "28  213       PM   tiktok    pre     0.7692308\n",
       "29  214       PM   tiktok    pre     0.3571429\n",
       "30  215       PM   tiktok    pre     0.7857143\n",
       "⋮   ⋮         ⋮    ⋮         ⋮       ⋮        \n",
       "91  301       PM   youtube   post    1.0000000\n",
       "92  302       PM   youtube   post    0.6428571\n",
       "93  303       PM   youtube   post    0.2142857\n",
       "94  304       PM   youtube   post    1.0000000\n",
       "95  305       PM   youtube   post    1.0000000\n",
       "96  306       PM   youtube   post    1.0000000\n",
       "97  307       PM   youtube   post    1.0000000\n",
       "98  308       PM   youtube   post    0.3571429\n",
       "99  309       PM   youtube   post    1.0000000\n",
       "100 310       PM   youtube   post    1.0000000\n",
       "101 311       PM   youtube   post    1.0000000\n",
       "102 312       PM   youtube   post    1.0000000\n",
       "103 313       PM   youtube   post    0.3571429\n",
       "104 314       PM   youtube   post    1.0000000\n",
       "105 315       PM   youtube   post    0.8571429\n",
       "106 401       PM   twitter   post    1.0000000\n",
       "107 402       PM   twitter   post    1.0000000\n",
       "108 403       PM   twitter   post    1.0000000\n",
       "109 404       PM   twitter   post    0.9285714\n",
       "110 405       PM   twitter   post    1.0000000\n",
       "111 406       PM   twitter   post    0.9285714\n",
       "112 407       PM   twitter   post    0.9285714\n",
       "113 408       PM   twitter   post    1.0000000\n",
       "114 409       PM   twitter   post    1.0000000\n",
       "115 410       PM   twitter   post    1.0000000\n",
       "116 411       PM   twitter   post    0.9285714\n",
       "117 412       PM   twitter   post    1.0000000\n",
       "118 413       PM   twitter   post    1.0000000\n",
       "119 414       PM   twitter   post    1.0000000\n",
       "120 415       PM   twitter   post    0.9285714"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pm <- df %>% filter(task == \"PM\")\n",
    "df_pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in eval(family$initialize, rho):\n",
      "“non-integer #successes in a binomial glm!”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in eval(family$initialize, rho):\n",
      "“non-integer #successes in a binomial glm!”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 0.0306753 (tol = 0.002, component 1)”\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 0.00842509 (tol = 0.002, component 1)”\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 0.0346786 (tol = 0.002, component 1)”\n",
      "Warning message in eval(family$initialize, rho):\n",
      "“non-integer #successes in a binomial glm!”\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 0.00517157 (tol = 0.002, component 1)”\n"
     ]
    }
   ],
   "source": [
    "# LMM on the main effect `interrupt` w/wo link family\n",
    "m0 <-  lmer(accuracy  ~ interrupt + (1|folder_id), data = df_pm)\n",
    "m1 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df_pm, family = Gamma(link = \"log\"))\n",
    "m2 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df_pm, family = Gamma(link = \"inverse\"))\n",
    "m3 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df_pm, family = inverse.gaussian(link = \"log\"))\n",
    "m4 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df_pm, family = inverse.gaussian(link = \"inverse\"))\n",
    "m5 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df_pm, family = binomial(link = \"logit\")) # suggested by R2\n",
    "\n",
    "# LMM on the main effect `measure` w/wo link family\n",
    "m6  <-  lmer(accuracy  ~ measure + (1|folder_id), data = df_pm)\n",
    "m7  <- glmer(accuracy  ~ measure + (1|folder_id), data = df_pm, family = Gamma(link = \"log\"))\n",
    "m8  <- glmer(accuracy  ~ measure + (1|folder_id), data = df_pm, family = Gamma(link = \"inverse\"))\n",
    "m9  <- glmer(accuracy  ~ measure + (1|folder_id), data = df_pm, family = inverse.gaussian(link = \"log\"))\n",
    "m10 <- glmer(accuracy  ~ measure + (1|folder_id), data = df_pm, family = inverse.gaussian(link = \"inverse\"))\n",
    "m11 <- glmer(accuracy  ~ measure + (1|folder_id), data = df_pm, family = binomial(link = \"logit\")) # suggested by R2\n",
    "\n",
    "# LMM on the interaction `interrupt * measure` w/wo link family\n",
    "m12 <-  lmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_pm)\n",
    "m13 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_pm, family = Gamma(link = \"log\"))\n",
    "m14 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_pm, family = Gamma(link = \"inverse\"))\n",
    "m15 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_pm, family = inverse.gaussian(link = \"log\"))\n",
    "m16 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_pm, family = inverse.gaussian(link = \"inverse\"))\n",
    "m17 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df_pm, family = binomial(link = \"logit\")) # suggested by R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "refitting model(s) with ML (instead of REML)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A anova: 18 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>npar</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>logLik</th><th scope=col>deviance</th><th scope=col>Chisq</th><th scope=col>Df</th><th scope=col>Pr(&gt;Chisq)</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>m11</th><td> 3</td><td> 95.43989404</td><td>103.802369</td><td>-44.719947</td><td>  89.439894</td><td>      NA</td><td>NA</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m6</th><td> 4</td><td>-27.87878224</td><td>-16.728815</td><td> 17.939391</td><td> -35.878782</td><td>125.3187</td><td> 1</td><td>4.334394e-29</td></tr>\n",
       "\t<tr><th scope=row>m7</th><td> 4</td><td>  0.04511677</td><td> 11.195084</td><td>  3.977442</td><td>  -7.954883</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m8</th><td> 4</td><td> 14.39747182</td><td> 25.547439</td><td> -3.198736</td><td>   6.397472</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m9</th><td> 4</td><td> 36.10213265</td><td> 47.252100</td><td>-14.051066</td><td>  28.102133</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m10</th><td> 4</td><td> 54.78279328</td><td> 65.932760</td><td>-23.391397</td><td>  46.782793</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m5</th><td> 5</td><td> 86.44711974</td><td>100.384578</td><td>-38.223560</td><td>  76.447120</td><td>  0.0000</td><td> 1</td><td>1.000000e+00</td></tr>\n",
       "\t<tr><th scope=row>m0</th><td> 6</td><td>-51.75395812</td><td>-35.029008</td><td> 31.876979</td><td> -63.753958</td><td>140.2011</td><td> 1</td><td>2.405708e-32</td></tr>\n",
       "\t<tr><th scope=row>m1</th><td> 6</td><td> -4.21525150</td><td> 12.509699</td><td>  8.107626</td><td> -16.215251</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m2</th><td> 6</td><td>  4.37987864</td><td> 21.104829</td><td>  3.810061</td><td>  -7.620121</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m3</th><td> 6</td><td> 36.37704775</td><td> 53.101998</td><td>-12.188524</td><td>  24.377048</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m4</th><td> 6</td><td> 45.20548673</td><td> 61.930437</td><td>-16.602743</td><td>  33.205487</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m17</th><td> 9</td><td> 87.56375628</td><td>112.651182</td><td>-34.781878</td><td>  69.563756</td><td>  0.0000</td><td> 3</td><td>1.000000e+00</td></tr>\n",
       "\t<tr><th scope=row>m12</th><td>10</td><td>-82.88221423</td><td>-55.007297</td><td> 51.441107</td><td>-102.882214</td><td>172.4460</td><td> 1</td><td>2.162539e-39</td></tr>\n",
       "\t<tr><th scope=row>m13</th><td>10</td><td>-44.76918711</td><td>-16.894270</td><td> 32.384594</td><td> -64.769187</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m14</th><td>10</td><td>-20.66414208</td><td>  7.210775</td><td> 20.332071</td><td> -40.664142</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m15</th><td>10</td><td> -3.33386789</td><td> 24.541050</td><td> 11.666934</td><td> -23.333868</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>m16</th><td>10</td><td> 25.57837200</td><td> 53.453289</td><td> -2.789186</td><td>   5.578372</td><td>  0.0000</td><td> 0</td><td>          NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova: 18 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & npar & AIC & BIC & logLik & deviance & Chisq & Df & Pr(>Chisq)\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tm11 &  3 &  95.43989404 & 103.802369 & -44.719947 &   89.439894 &       NA & NA &           NA\\\\\n",
       "\tm6 &  4 & -27.87878224 & -16.728815 &  17.939391 &  -35.878782 & 125.3187 &  1 & 4.334394e-29\\\\\n",
       "\tm7 &  4 &   0.04511677 &  11.195084 &   3.977442 &   -7.954883 &   0.0000 &  0 &           NA\\\\\n",
       "\tm8 &  4 &  14.39747182 &  25.547439 &  -3.198736 &    6.397472 &   0.0000 &  0 &           NA\\\\\n",
       "\tm9 &  4 &  36.10213265 &  47.252100 & -14.051066 &   28.102133 &   0.0000 &  0 &           NA\\\\\n",
       "\tm10 &  4 &  54.78279328 &  65.932760 & -23.391397 &   46.782793 &   0.0000 &  0 &           NA\\\\\n",
       "\tm5 &  5 &  86.44711974 & 100.384578 & -38.223560 &   76.447120 &   0.0000 &  1 & 1.000000e+00\\\\\n",
       "\tm0 &  6 & -51.75395812 & -35.029008 &  31.876979 &  -63.753958 & 140.2011 &  1 & 2.405708e-32\\\\\n",
       "\tm1 &  6 &  -4.21525150 &  12.509699 &   8.107626 &  -16.215251 &   0.0000 &  0 &           NA\\\\\n",
       "\tm2 &  6 &   4.37987864 &  21.104829 &   3.810061 &   -7.620121 &   0.0000 &  0 &           NA\\\\\n",
       "\tm3 &  6 &  36.37704775 &  53.101998 & -12.188524 &   24.377048 &   0.0000 &  0 &           NA\\\\\n",
       "\tm4 &  6 &  45.20548673 &  61.930437 & -16.602743 &   33.205487 &   0.0000 &  0 &           NA\\\\\n",
       "\tm17 &  9 &  87.56375628 & 112.651182 & -34.781878 &   69.563756 &   0.0000 &  3 & 1.000000e+00\\\\\n",
       "\tm12 & 10 & -82.88221423 & -55.007297 &  51.441107 & -102.882214 & 172.4460 &  1 & 2.162539e-39\\\\\n",
       "\tm13 & 10 & -44.76918711 & -16.894270 &  32.384594 &  -64.769187 &   0.0000 &  0 &           NA\\\\\n",
       "\tm14 & 10 & -20.66414208 &   7.210775 &  20.332071 &  -40.664142 &   0.0000 &  0 &           NA\\\\\n",
       "\tm15 & 10 &  -3.33386789 &  24.541050 &  11.666934 &  -23.333868 &   0.0000 &  0 &           NA\\\\\n",
       "\tm16 & 10 &  25.57837200 &  53.453289 &  -2.789186 &    5.578372 &   0.0000 &  0 &           NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova: 18 × 8\n",
       "\n",
       "| <!--/--> | npar &lt;dbl&gt; | AIC &lt;dbl&gt; | BIC &lt;dbl&gt; | logLik &lt;dbl&gt; | deviance &lt;dbl&gt; | Chisq &lt;dbl&gt; | Df &lt;dbl&gt; | Pr(&gt;Chisq) &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| m11 |  3 |  95.43989404 | 103.802369 | -44.719947 |   89.439894 |       NA | NA |           NA |\n",
       "| m6 |  4 | -27.87878224 | -16.728815 |  17.939391 |  -35.878782 | 125.3187 |  1 | 4.334394e-29 |\n",
       "| m7 |  4 |   0.04511677 |  11.195084 |   3.977442 |   -7.954883 |   0.0000 |  0 |           NA |\n",
       "| m8 |  4 |  14.39747182 |  25.547439 |  -3.198736 |    6.397472 |   0.0000 |  0 |           NA |\n",
       "| m9 |  4 |  36.10213265 |  47.252100 | -14.051066 |   28.102133 |   0.0000 |  0 |           NA |\n",
       "| m10 |  4 |  54.78279328 |  65.932760 | -23.391397 |   46.782793 |   0.0000 |  0 |           NA |\n",
       "| m5 |  5 |  86.44711974 | 100.384578 | -38.223560 |   76.447120 |   0.0000 |  1 | 1.000000e+00 |\n",
       "| m0 |  6 | -51.75395812 | -35.029008 |  31.876979 |  -63.753958 | 140.2011 |  1 | 2.405708e-32 |\n",
       "| m1 |  6 |  -4.21525150 |  12.509699 |   8.107626 |  -16.215251 |   0.0000 |  0 |           NA |\n",
       "| m2 |  6 |   4.37987864 |  21.104829 |   3.810061 |   -7.620121 |   0.0000 |  0 |           NA |\n",
       "| m3 |  6 |  36.37704775 |  53.101998 | -12.188524 |   24.377048 |   0.0000 |  0 |           NA |\n",
       "| m4 |  6 |  45.20548673 |  61.930437 | -16.602743 |   33.205487 |   0.0000 |  0 |           NA |\n",
       "| m17 |  9 |  87.56375628 | 112.651182 | -34.781878 |   69.563756 |   0.0000 |  3 | 1.000000e+00 |\n",
       "| m12 | 10 | -82.88221423 | -55.007297 |  51.441107 | -102.882214 | 172.4460 |  1 | 2.162539e-39 |\n",
       "| m13 | 10 | -44.76918711 | -16.894270 |  32.384594 |  -64.769187 |   0.0000 |  0 |           NA |\n",
       "| m14 | 10 | -20.66414208 |   7.210775 |  20.332071 |  -40.664142 |   0.0000 |  0 |           NA |\n",
       "| m15 | 10 |  -3.33386789 |  24.541050 |  11.666934 |  -23.333868 |   0.0000 |  0 |           NA |\n",
       "| m16 | 10 |  25.57837200 |  53.453289 |  -2.789186 |    5.578372 |   0.0000 |  0 |           NA |\n",
       "\n"
      ],
      "text/plain": [
       "    npar AIC          BIC        logLik     deviance    Chisq    Df\n",
       "m11  3    95.43989404 103.802369 -44.719947   89.439894       NA NA\n",
       "m6   4   -27.87878224 -16.728815  17.939391  -35.878782 125.3187  1\n",
       "m7   4     0.04511677  11.195084   3.977442   -7.954883   0.0000  0\n",
       "m8   4    14.39747182  25.547439  -3.198736    6.397472   0.0000  0\n",
       "m9   4    36.10213265  47.252100 -14.051066   28.102133   0.0000  0\n",
       "m10  4    54.78279328  65.932760 -23.391397   46.782793   0.0000  0\n",
       "m5   5    86.44711974 100.384578 -38.223560   76.447120   0.0000  1\n",
       "m0   6   -51.75395812 -35.029008  31.876979  -63.753958 140.2011  1\n",
       "m1   6    -4.21525150  12.509699   8.107626  -16.215251   0.0000  0\n",
       "m2   6     4.37987864  21.104829   3.810061   -7.620121   0.0000  0\n",
       "m3   6    36.37704775  53.101998 -12.188524   24.377048   0.0000  0\n",
       "m4   6    45.20548673  61.930437 -16.602743   33.205487   0.0000  0\n",
       "m17  9    87.56375628 112.651182 -34.781878   69.563756   0.0000  3\n",
       "m12 10   -82.88221423 -55.007297  51.441107 -102.882214 172.4460  1\n",
       "m13 10   -44.76918711 -16.894270  32.384594  -64.769187   0.0000  0\n",
       "m14 10   -20.66414208   7.210775  20.332071  -40.664142   0.0000  0\n",
       "m15 10    -3.33386789  24.541050  11.666934  -23.333868   0.0000  0\n",
       "m16 10    25.57837200  53.453289  -2.789186    5.578372   0.0000  0\n",
       "    Pr(>Chisq)  \n",
       "m11           NA\n",
       "m6  4.334394e-29\n",
       "m7            NA\n",
       "m8            NA\n",
       "m9            NA\n",
       "m10           NA\n",
       "m5  1.000000e+00\n",
       "m0  2.405708e-32\n",
       "m1            NA\n",
       "m2            NA\n",
       "m3            NA\n",
       "m4            NA\n",
       "m17 1.000000e+00\n",
       "m12 2.162539e-39\n",
       "m13           NA\n",
       "m14           NA\n",
       "m15           NA\n",
       "m16           NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anova(m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14, m15, m16, m17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A bayesfactor_models: 18 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Model</th><th scope=col>log_BF</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>m1</th><td>interrupt + (1 | folder_id)                                </td><td>-23.733660</td></tr>\n",
       "\t<tr><th scope=row>m2</th><td>interrupt + (1 | folder_id)                                </td><td>-28.031225</td></tr>\n",
       "\t<tr><th scope=row>m3</th><td>interrupt + (1 | folder_id)                                </td><td>-44.029810</td></tr>\n",
       "\t<tr><th scope=row>m4</th><td>interrupt + (1 | folder_id)                                </td><td>-48.444029</td></tr>\n",
       "\t<tr><th scope=row>m5</th><td>interrupt + (1 | folder_id)                                </td><td>-67.671100</td></tr>\n",
       "\t<tr><th scope=row>m6</th><td>measure + (1 | folder_id)                                  </td><td> -9.114403</td></tr>\n",
       "\t<tr><th scope=row>m7</th><td>measure + (1 | folder_id)                                  </td><td>-23.076353</td></tr>\n",
       "\t<tr><th scope=row>m8</th><td>measure + (1 | folder_id)                                  </td><td>-30.252530</td></tr>\n",
       "\t<tr><th scope=row>m9</th><td>measure + (1 | folder_id)                                  </td><td>-41.104861</td></tr>\n",
       "\t<tr><th scope=row>m10</th><td>measure + (1 | folder_id)                                  </td><td>-50.445191</td></tr>\n",
       "\t<tr><th scope=row>m11</th><td>measure + (1 | folder_id)                                  </td><td>-69.379995</td></tr>\n",
       "\t<tr><th scope=row>m12</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td> 10.024838</td></tr>\n",
       "\t<tr><th scope=row>m13</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td> -9.031676</td></tr>\n",
       "\t<tr><th scope=row>m14</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td>-21.084198</td></tr>\n",
       "\t<tr><th scope=row>m15</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td>-29.749336</td></tr>\n",
       "\t<tr><th scope=row>m16</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td>-44.205456</td></tr>\n",
       "\t<tr><th scope=row>m17</th><td>interrupt * measure + interrupt + measure + (1 | folder_id)</td><td>-73.804402</td></tr>\n",
       "\t<tr><th scope=row>m0</th><td>interrupt + (1 | folder_id)                                </td><td>  0.000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A bayesfactor\\_models: 18 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Model & log\\_BF\\\\\n",
       "  & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\tm1 & interrupt + (1 \\textbar{} folder\\_id)                                 & -23.733660\\\\\n",
       "\tm2 & interrupt + (1 \\textbar{} folder\\_id)                                 & -28.031225\\\\\n",
       "\tm3 & interrupt + (1 \\textbar{} folder\\_id)                                 & -44.029810\\\\\n",
       "\tm4 & interrupt + (1 \\textbar{} folder\\_id)                                 & -48.444029\\\\\n",
       "\tm5 & interrupt + (1 \\textbar{} folder\\_id)                                 & -67.671100\\\\\n",
       "\tm6 & measure + (1 \\textbar{} folder\\_id)                                   &  -9.114403\\\\\n",
       "\tm7 & measure + (1 \\textbar{} folder\\_id)                                   & -23.076353\\\\\n",
       "\tm8 & measure + (1 \\textbar{} folder\\_id)                                   & -30.252530\\\\\n",
       "\tm9 & measure + (1 \\textbar{} folder\\_id)                                   & -41.104861\\\\\n",
       "\tm10 & measure + (1 \\textbar{} folder\\_id)                                   & -50.445191\\\\\n",
       "\tm11 & measure + (1 \\textbar{} folder\\_id)                                   & -69.379995\\\\\n",
       "\tm12 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) &  10.024838\\\\\n",
       "\tm13 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) &  -9.031676\\\\\n",
       "\tm14 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) & -21.084198\\\\\n",
       "\tm15 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) & -29.749336\\\\\n",
       "\tm16 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) & -44.205456\\\\\n",
       "\tm17 & interrupt * measure + interrupt + measure + (1 \\textbar{} folder\\_id) & -73.804402\\\\\n",
       "\tm0 & interrupt + (1 \\textbar{} folder\\_id)                                 &   0.000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A bayesfactor_models: 18 × 2\n",
       "\n",
       "| <!--/--> | Model &lt;chr&gt; | log_BF &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| m1 | interrupt + (1 | folder_id)                                 | -23.733660 |\n",
       "| m2 | interrupt + (1 | folder_id)                                 | -28.031225 |\n",
       "| m3 | interrupt + (1 | folder_id)                                 | -44.029810 |\n",
       "| m4 | interrupt + (1 | folder_id)                                 | -48.444029 |\n",
       "| m5 | interrupt + (1 | folder_id)                                 | -67.671100 |\n",
       "| m6 | measure + (1 | folder_id)                                   |  -9.114403 |\n",
       "| m7 | measure + (1 | folder_id)                                   | -23.076353 |\n",
       "| m8 | measure + (1 | folder_id)                                   | -30.252530 |\n",
       "| m9 | measure + (1 | folder_id)                                   | -41.104861 |\n",
       "| m10 | measure + (1 | folder_id)                                   | -50.445191 |\n",
       "| m11 | measure + (1 | folder_id)                                   | -69.379995 |\n",
       "| m12 | interrupt * measure + interrupt + measure + (1 | folder_id) |  10.024838 |\n",
       "| m13 | interrupt * measure + interrupt + measure + (1 | folder_id) |  -9.031676 |\n",
       "| m14 | interrupt * measure + interrupt + measure + (1 | folder_id) | -21.084198 |\n",
       "| m15 | interrupt * measure + interrupt + measure + (1 | folder_id) | -29.749336 |\n",
       "| m16 | interrupt * measure + interrupt + measure + (1 | folder_id) | -44.205456 |\n",
       "| m17 | interrupt * measure + interrupt + measure + (1 | folder_id) | -73.804402 |\n",
       "| m0 | interrupt + (1 | folder_id)                                 |   0.000000 |\n",
       "\n"
      ],
      "text/plain": [
       "    Model                                                       log_BF    \n",
       "m1  interrupt + (1 | folder_id)                                 -23.733660\n",
       "m2  interrupt + (1 | folder_id)                                 -28.031225\n",
       "m3  interrupt + (1 | folder_id)                                 -44.029810\n",
       "m4  interrupt + (1 | folder_id)                                 -48.444029\n",
       "m5  interrupt + (1 | folder_id)                                 -67.671100\n",
       "m6  measure + (1 | folder_id)                                    -9.114403\n",
       "m7  measure + (1 | folder_id)                                   -23.076353\n",
       "m8  measure + (1 | folder_id)                                   -30.252530\n",
       "m9  measure + (1 | folder_id)                                   -41.104861\n",
       "m10 measure + (1 | folder_id)                                   -50.445191\n",
       "m11 measure + (1 | folder_id)                                   -69.379995\n",
       "m12 interrupt * measure + interrupt + measure + (1 | folder_id)  10.024838\n",
       "m13 interrupt * measure + interrupt + measure + (1 | folder_id)  -9.031676\n",
       "m14 interrupt * measure + interrupt + measure + (1 | folder_id) -21.084198\n",
       "m15 interrupt * measure + interrupt + measure + (1 | folder_id) -29.749336\n",
       "m16 interrupt * measure + interrupt + measure + (1 | folder_id) -44.205456\n",
       "m17 interrupt * measure + interrupt + measure + (1 | folder_id) -73.804402\n",
       "m0  interrupt + (1 | folder_id)                                   0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bayesfactor_models(m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14, m15, m16, m17, denominator = m0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fitted a linear mixed model (estimated using REML and nloptwrap optimizer)\n",
      "to predict accuracy with interrupt and measure\n",
      " (formula: accuracy ~ interrupt * measure + interrupt + measure). The model\n",
      "included folder_id\n",
      " as random effect (formula: ~1 | folder_id). The model's total explanatory power\n",
      "is substantial (conditional R2 = 0.67) and the part related to the fixed\n",
      "effects alone (marginal R2) is of 0.40. The model's intercept, corresponding to\n",
      "interrupt = tiktok and measure = post\n",
      ", is at 0.49 (95% CI [0.40, 0.58], t(110) = 10.94, p < .001). Within this model:\n",
      "\n",
      "  - The effect of interrupt [rest] is statistically significant and positive\n",
      "(beta = 0.46, 95% CI [0.33, 0.58], t(110) = 7.25, p < .001; Std. beta = 2.08,\n",
      "95% CI [1.51, 2.65])\n",
      "  - The effect of interrupt [twitter] is statistically significant and positive\n",
      "(beta = 0.49, 95% CI [0.36, 0.61], t(110) = 7.77, p < .001; Std. beta = 2.23,\n",
      "95% CI [1.66, 2.80])\n",
      "  - The effect of interrupt [youtube] is statistically significant and positive\n",
      "(beta = 0.34, 95% CI [0.22, 0.47], t(110) = 5.42, p < .001; Std. beta = 1.56,\n",
      "95% CI [0.99, 2.13])\n",
      "  - The effect of measure [pre] is statistically significant and positive (beta =\n",
      "0.31, 95% CI [0.22, 0.41], t(110) = 6.70, p < .001; Std. beta = 1.44, 95% CI\n",
      "[1.01, 1.86])\n",
      "  - The interaction effect of measure [pre] on interrupt [rest] is statistically\n",
      "significant and negative (beta = -0.36, 95% CI [-0.49, -0.23], t(110) = -5.45,\n",
      "p < .001; Std. beta = -1.65, 95% CI [-2.25, -1.05])\n",
      "  - The interaction effect of measure [pre] on interrupt [twitter] is\n",
      "statistically significant and negative (beta = -0.42, 95% CI [-0.56, -0.29],\n",
      "t(110) = -6.41, p < .001; Std. beta = -1.94, 95% CI [-2.54, -1.34])\n",
      "  - The interaction effect of measure [pre] on interrupt [youtube] is\n",
      "statistically significant and negative (beta = -0.31, 95% CI [-0.45, -0.18],\n",
      "t(110) = -4.73, p < .001; Std. beta = -1.43, 95% CI [-2.03, -0.83])\n",
      "\n",
      "Standardized parameters were obtained by fitting the model on a standardized\n",
      "version of the dataset. 95% Confidence Intervals (CIs) and p-values were\n",
      "computed using a Wald t-distribution approximation.\n"
     ]
    }
   ],
   "source": [
    "df_pm$interrupt <- factor(df_pm$interrupt, levels = c(\"tiktok\", \"rest\", \"twitter\", \"youtube\"))\n",
    "df_pm$measure   <- factor(df_pm$measure, levels = c(\"post\", \"pre\"))\n",
    "m <- lmer(accuracy ~ interrupt * measure + interrupt + measure + (1 | folder_id), data = df_pm)\n",
    "print(report(m), verbose = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fitted a linear mixed model (estimated using REML and nloptwrap optimizer)\n",
      "to predict accuracy with interrupt and measure\n",
      " (formula: accuracy ~ interrupt * measure + interrupt + measure). The model\n",
      "included folder_id\n",
      " as random effect (formula: ~1 | folder_id). The model's total explanatory power\n",
      "is substantial (conditional R2 = 0.67) and the part related to the fixed\n",
      "effects alone (marginal R2) is of 0.40. The model's intercept, corresponding to\n",
      "interrupt = rest and measure = pre\n",
      ", is at 0.90 (95% CI [0.81, 0.98], t(110) = 20.13, p < .001). Within this model:\n",
      "\n",
      "  - The effect of interrupt [twitter] is statistically non-significant and\n",
      "negative (beta = -0.03, 95% CI [-0.16, 0.09], t(110) = -0.49, p = 0.628; Std.\n",
      "beta = -0.14, 95% CI [-0.71, 0.43])\n",
      "  - The effect of interrupt [youtube] is statistically non-significant and\n",
      "negative (beta = -0.07, 95% CI [-0.19, 0.06], t(110) = -1.06, p = 0.289; Std.\n",
      "beta = -0.31, 95% CI [-0.88, 0.26])\n",
      "  - The effect of interrupt [tiktok] is statistically non-significant and\n",
      "negative (beta = -0.09, 95% CI [-0.22, 0.03], t(110) = -1.51, p = 0.135; Std.\n",
      "beta = -0.43, 95% CI [-1.00, 0.14])\n",
      "  - The effect of measure [post] is statistically non-significant and positive\n",
      "(beta = 0.05, 95% CI [-0.05, 0.14], t(110) = 1.01, p = 0.316; Std. beta = 0.22,\n",
      "95% CI [-0.21, 0.64])\n",
      "  - The interaction effect of measure [post] on interrupt [twitter] is\n",
      "statistically non-significant and positive (beta = 0.06, 95% CI [-0.07, 0.19],\n",
      "t(110) = 0.96, p = 0.341; Std. beta = 0.29, 95% CI [-0.31, 0.89])\n",
      "  - The interaction effect of measure [post] on interrupt [youtube] is\n",
      "statistically non-significant and negative (beta = -0.05, 95% CI [-0.18, 0.08],\n",
      "t(110) = -0.72, p = 0.473; Std. beta = -0.22, 95% CI [-0.82, 0.38])\n",
      "  - The interaction effect of measure [post] on interrupt [tiktok] is\n",
      "statistically significant and negative (beta = -0.36, 95% CI [-0.49, -0.23],\n",
      "t(110) = -5.45, p < .001; Std. beta = -1.65, 95% CI [-2.25, -1.05])\n",
      "\n",
      "Standardized parameters were obtained by fitting the model on a standardized\n",
      "version of the dataset. 95% Confidence Intervals (CIs) and p-values were\n",
      "computed using a Wald t-distribution approximation.\n"
     ]
    }
   ],
   "source": [
    "df_pm$interrupt <- factor(df_pm$interrupt, levels = c(\"rest\", \"twitter\", \"youtube\", \"tiktok\"))\n",
    "df_pm$measure   <- factor(df_pm$measure, levels = c(\"pre\", \"post\"))\n",
    "m <- lmer(accuracy ~ interrupt * measure + interrupt + measure + (1 | folder_id), data = df_pm)\n",
    "print(report(m), verbose = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LD vs. PM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in eval(family$initialize, rho):\n",
      "“non-integer #successes in a binomial glm!”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in eval(family$initialize, rho):\n",
      "“non-integer #successes in a binomial glm!”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in eval(family$initialize, rho):\n",
      "“non-integer #successes in a binomial glm!”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in eval(family$initialize, rho):\n",
      "“non-integer #successes in a binomial glm!”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 0.00765475 (tol = 0.002, component 1)”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n",
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 0.0294662 (tol = 0.002, component 1)”\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## 1 way\n",
    "\n",
    "# LMM on the main effect `interrupt` w/wo link family\n",
    "m0 <-  lmer(accuracy  ~ interrupt + (1|folder_id), data = df)\n",
    "m1 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df, family = Gamma(link = \"log\"))\n",
    "m2 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df, family = Gamma(link = \"inverse\"))\n",
    "m3 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df, family = inverse.gaussian(link = \"log\"))\n",
    "m4 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df, family = inverse.gaussian(link = \"inverse\"))\n",
    "m5 <- glmer(accuracy  ~ interrupt + (1|folder_id), data = df, family = binomial(link = \"logit\"))\n",
    "\n",
    "# LMM on the main effect `measure` w/wo link family\n",
    "m6 <-  lmer(accuracy  ~ measure + (1|folder_id), data = df)\n",
    "m7 <- glmer(accuracy  ~ measure + (1|folder_id), data = df, family = Gamma(link = \"log\"))\n",
    "m8 <- glmer(accuracy  ~ measure + (1|folder_id), data = df, family = Gamma(link = \"inverse\"))\n",
    "m9 <- glmer(accuracy  ~ measure + (1|folder_id), data = df, family = inverse.gaussian(link = \"log\"))\n",
    "m10 <- glmer(accuracy  ~ measure + (1|folder_id), data = df, family = inverse.gaussian(link = \"inverse\"))\n",
    "m11 <- glmer(accuracy  ~ measure + (1|folder_id), data = df, family = binomial(link = \"logit\"))\n",
    "\n",
    "# LMM on the main effect `task` w/wo link family\n",
    "m12 <-  lmer(accuracy  ~ task + (1|folder_id), data = df)\n",
    "m13 <- glmer(accuracy  ~ task + (1|folder_id), data = df, family = Gamma(link = \"log\"))\n",
    "m14 <- glmer(accuracy  ~ task + (1|folder_id), data = df, family = Gamma(link = \"inverse\"))\n",
    "m15 <- glmer(accuracy  ~ task + (1|folder_id), data = df, family = inverse.gaussian(link = \"log\"))\n",
    "m16 <- glmer(accuracy  ~ task + (1|folder_id), data = df, family = inverse.gaussian(link = \"inverse\"))\n",
    "m17 <- glmer(accuracy  ~ task + (1|folder_id), data = df, family = binomial(link = \"logit\"))\n",
    "\n",
    "\n",
    "## 2 way\n",
    "\n",
    "# LMM on the interaction `interrupt * measure` w/wo link family\n",
    "m18 <-  lmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df)\n",
    "m19 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df, family = Gamma(link = \"log\"))\n",
    "m20 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df, family = Gamma(link = \"inverse\"))\n",
    "m21 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df, family = inverse.gaussian(link = \"log\"))\n",
    "m22 <- glmer(accuracy ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df, family = inverse.gaussian(link = \"inverse\"))\n",
    "m23 <- glmer(accuracy  ~ interrupt * measure + interrupt + measure + (1|folder_id), data = df, family = binomial(link = \"logit\"))\n",
    "\n",
    "\n",
    "# LMM on the interaction `interrupt * task` w/wo link family\n",
    "m24 <-  lmer(accuracy ~ interrupt * task + interrupt + task + (1|folder_id), data = df)\n",
    "m25 <- glmer(accuracy ~ interrupt * task + interrupt + task + (1|folder_id), data = df, family = Gamma(link = \"log\"))\n",
    "m26 <- glmer(accuracy ~ interrupt * task + interrupt + task + (1|folder_id), data = df, family = Gamma(link = \"inverse\"))\n",
    "m27 <- glmer(accuracy ~ interrupt * task + interrupt + task + (1|folder_id), data = df, family = inverse.gaussian(link = \"log\"))\n",
    "m28 <- glmer(accuracy ~ interrupt * task + interrupt + task + (1|folder_id), data = df, family = inverse.gaussian(link = \"inverse\"))\n",
    "# m29 <- glmer(accuracy ~ interrupt * task + interrupt + task + (1|folder_id), data = df, family = binomial(link = \"logit\")) # fail to converge\n",
    "\n",
    "# LMM on the interaction `measure * task` w/wo link family\n",
    "m30 <-  lmer(accuracy ~ measure * task + measure + task + (1|folder_id), data = df)\n",
    "m31 <- glmer(accuracy ~ measure * task + measure + task + (1|folder_id), data = df, family = Gamma(link = \"log\"))\n",
    "m32 <- glmer(accuracy ~ measure * task + measure + task + (1|folder_id), data = df, family = Gamma(link = \"inverse\"))\n",
    "m33 <- glmer(accuracy ~ measure * task + measure + task + (1|folder_id), data = df, family = inverse.gaussian(link = \"log\"))\n",
    "m34 <- glmer(accuracy ~ measure * task + measure + task + (1|folder_id), data = df, family = inverse.gaussian(link = \"inverse\"))\n",
    "# m35 <- glmer(accuracy  ~ measure * task + measure + task + (1|folder_id), data = df, family = binomial(link = \"logit\")) # fail to converge\n",
    "\n",
    "## 3 way\n",
    "\n",
    "# LMM on the interaction `measure * task` w/wo link family\n",
    "m36 <-  lmer(accuracy ~ interrupt * measure * task + interrupt * measure + measure * task + interrupt * task + interrupt + measure + task + (1|folder_id), data = df)\n",
    "m37 <- glmer(accuracy ~ interrupt * measure * task + interrupt * measure + measure * task + interrupt * task + interrupt + measure + task + (1|folder_id), data = df, family = Gamma(link = \"log\"))\n",
    "m38 <- glmer(accuracy ~ interrupt * measure * task + interrupt * measure + measure * task + interrupt * task + interrupt + measure + task + (1|folder_id), data = df, family = Gamma(link = \"inverse\"))\n",
    "m39 <- glmer(accuracy ~ interrupt * measure * task + interrupt * measure + measure * task + interrupt * task + interrupt + measure + task + (1|folder_id), data = df, family = inverse.gaussian(link = \"log\"))\n",
    "m40 <- glmer(accuracy ~ interrupt * measure * task + interrupt * measure + measure * task + interrupt * task + interrupt + measure + task + (1|folder_id), data = df, family = inverse.gaussian(link = \"inverse\"))\n",
    "# m41 <- glmer(accuracy  ~ interrupt * measure * task + interrupt * measure + measure * task + interrupt * task + interrupt + measure + task + (1|folder_id), data = df, family = binomial(link = \"logit\")) # fail to converge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "refitting model(s) with ML (instead of REML)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A anova: 39 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>npar</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>logLik</th><th scope=col>deviance</th><th scope=col>Chisq</th><th scope=col>Df</th><th scope=col>Pr(&gt;Chisq)</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>m11</th><td> 3</td><td> 129.89321</td><td> 140.33513</td><td> -61.946607</td><td> 123.89321</td><td>          NA</td><td>NA</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m17</th><td> 3</td><td> 120.58573</td><td> 131.02765</td><td> -57.292865</td><td> 114.58573</td><td>9.307484e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m6</th><td> 4</td><td>-111.53935</td><td> -97.61680</td><td>  59.769676</td><td>-119.53935</td><td>2.341251e+02</td><td> 1</td><td> 7.512164e-53</td></tr>\n",
       "\t<tr><th scope=row>m7</th><td> 4</td><td>  87.21490</td><td> 101.13746</td><td> -39.607451</td><td>  79.21490</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m8</th><td> 4</td><td>  87.21490</td><td> 101.13746</td><td> -39.607451</td><td>  79.21490</td><td>8.384404e-13</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m9</th><td> 4</td><td> 441.35245</td><td> 455.27501</td><td>-216.676226</td><td> 433.35245</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m10</th><td> 4</td><td> 441.35245</td><td> 455.27501</td><td>-216.676226</td><td> 433.35245</td><td>1.136868e-13</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m12</th><td> 4</td><td>-147.37571</td><td>-133.45315</td><td>  77.687853</td><td>-155.37571</td><td>5.887282e+02</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m13</th><td> 4</td><td>  74.23091</td><td>  88.15347</td><td> -33.115456</td><td>  66.23091</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m14</th><td> 4</td><td>  74.57626</td><td>  88.49881</td><td> -33.288128</td><td>  66.57626</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m15</th><td> 4</td><td> 438.76470</td><td> 452.68726</td><td>-215.382351</td><td> 430.76470</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m16</th><td> 4</td><td> 438.76470</td><td> 452.68726</td><td>-215.382351</td><td> 430.76470</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m5</th><td> 5</td><td> 120.61467</td><td> 138.01787</td><td> -55.307336</td><td> 110.61467</td><td>3.201500e+02</td><td> 1</td><td> 1.343450e-71</td></tr>\n",
       "\t<tr><th scope=row>m0</th><td> 6</td><td>-128.44719</td><td>-107.56336</td><td>  70.223595</td><td>-140.44719</td><td>2.510619e+02</td><td> 1</td><td> 1.523865e-56</td></tr>\n",
       "\t<tr><th scope=row>m1</th><td> 6</td><td>  81.65893</td><td> 102.54276</td><td> -34.829464</td><td>  69.65893</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m2</th><td> 6</td><td>  81.65893</td><td> 102.54276</td><td> -34.829464</td><td>  69.65893</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m3</th><td> 6</td><td> 443.36703</td><td> 464.25087</td><td>-215.683516</td><td> 431.36703</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m4</th><td> 6</td><td> 443.36703</td><td> 464.25087</td><td>-215.683516</td><td> 431.36703</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m30</th><td> 6</td><td>-145.10199</td><td>-124.21816</td><td>  78.550995</td><td>-157.10199</td><td>5.884690e+02</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m31</th><td> 6</td><td>  77.45750</td><td>  98.34133</td><td> -32.728749</td><td>  65.45750</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m32</th><td> 6</td><td>  77.91812</td><td>  98.80195</td><td> -32.959061</td><td>  65.91812</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m33</th><td> 6</td><td> 442.62290</td><td> 463.50673</td><td>-215.311449</td><td> 430.62290</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m34</th><td> 6</td><td> 442.62290</td><td> 463.50673</td><td>-215.311449</td><td> 430.62290</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m23</th><td> 9</td><td> 123.28435</td><td> 154.61010</td><td> -52.642176</td><td> 105.28435</td><td>3.253385e+02</td><td> 3</td><td> 3.258884e-70</td></tr>\n",
       "\t<tr><th scope=row>m18</th><td>10</td><td>-133.39685</td><td> -98.59046</td><td>  76.698426</td><td>-153.39685</td><td>2.586812e+02</td><td> 1</td><td> 3.326482e-58</td></tr>\n",
       "\t<tr><th scope=row>m19</th><td>10</td><td>  83.77877</td><td> 118.58516</td><td> -31.889386</td><td>  63.77877</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m20</th><td>10</td><td>  83.77877</td><td> 118.58516</td><td> -31.889386</td><td>  63.77877</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m21</th><td>10</td><td> 450.07664</td><td> 484.88303</td><td>-215.038322</td><td> 430.07664</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m22</th><td>10</td><td> 450.07664</td><td> 484.88303</td><td>-215.038322</td><td> 430.07664</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m24</th><td>10</td><td>-189.85593</td><td>-155.04954</td><td> 104.927963</td><td>-209.85593</td><td>6.399326e+02</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m25</th><td>10</td><td>  60.47771</td><td>  95.28410</td><td> -20.238857</td><td>  40.47771</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m26</th><td>10</td><td>  60.47771</td><td>  95.28410</td><td> -20.238857</td><td>  40.47771</td><td>1.627143e-12</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m27</th><td>10</td><td> 358.59708</td><td> 393.40347</td><td>-169.298539</td><td> 338.59708</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m28</th><td>10</td><td> 445.06933</td><td> 479.87572</td><td>-212.534666</td><td> 425.06933</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m36</th><td>18</td><td>-216.40936</td><td>-153.75786</td><td> 126.204679</td><td>-252.40936</td><td>6.774787e+02</td><td> 8</td><td>5.042771e-141</td></tr>\n",
       "\t<tr><th scope=row>m37</th><td>18</td><td>  54.02924</td><td> 116.68074</td><td>  -9.014619</td><td>  18.02924</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m38</th><td>18</td><td>  54.26916</td><td> 116.92066</td><td>  -9.134580</td><td>  18.26916</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m39</th><td>18</td><td> 317.55041</td><td> 380.20191</td><td>-140.775206</td><td> 281.55041</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "\t<tr><th scope=row>m40</th><td>18</td><td> 455.49617</td><td> 518.14767</td><td>-209.748083</td><td> 419.49617</td><td>0.000000e+00</td><td> 0</td><td>           NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova: 39 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & npar & AIC & BIC & logLik & deviance & Chisq & Df & Pr(>Chisq)\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tm11 &  3 &  129.89321 &  140.33513 &  -61.946607 &  123.89321 &           NA & NA &            NA\\\\\n",
       "\tm17 &  3 &  120.58573 &  131.02765 &  -57.292865 &  114.58573 & 9.307484e+00 &  0 &            NA\\\\\n",
       "\tm6 &  4 & -111.53935 &  -97.61680 &   59.769676 & -119.53935 & 2.341251e+02 &  1 &  7.512164e-53\\\\\n",
       "\tm7 &  4 &   87.21490 &  101.13746 &  -39.607451 &   79.21490 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm8 &  4 &   87.21490 &  101.13746 &  -39.607451 &   79.21490 & 8.384404e-13 &  0 &            NA\\\\\n",
       "\tm9 &  4 &  441.35245 &  455.27501 & -216.676226 &  433.35245 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm10 &  4 &  441.35245 &  455.27501 & -216.676226 &  433.35245 & 1.136868e-13 &  0 &            NA\\\\\n",
       "\tm12 &  4 & -147.37571 & -133.45315 &   77.687853 & -155.37571 & 5.887282e+02 &  0 &            NA\\\\\n",
       "\tm13 &  4 &   74.23091 &   88.15347 &  -33.115456 &   66.23091 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm14 &  4 &   74.57626 &   88.49881 &  -33.288128 &   66.57626 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm15 &  4 &  438.76470 &  452.68726 & -215.382351 &  430.76470 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm16 &  4 &  438.76470 &  452.68726 & -215.382351 &  430.76470 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm5 &  5 &  120.61467 &  138.01787 &  -55.307336 &  110.61467 & 3.201500e+02 &  1 &  1.343450e-71\\\\\n",
       "\tm0 &  6 & -128.44719 & -107.56336 &   70.223595 & -140.44719 & 2.510619e+02 &  1 &  1.523865e-56\\\\\n",
       "\tm1 &  6 &   81.65893 &  102.54276 &  -34.829464 &   69.65893 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm2 &  6 &   81.65893 &  102.54276 &  -34.829464 &   69.65893 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm3 &  6 &  443.36703 &  464.25087 & -215.683516 &  431.36703 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm4 &  6 &  443.36703 &  464.25087 & -215.683516 &  431.36703 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm30 &  6 & -145.10199 & -124.21816 &   78.550995 & -157.10199 & 5.884690e+02 &  0 &            NA\\\\\n",
       "\tm31 &  6 &   77.45750 &   98.34133 &  -32.728749 &   65.45750 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm32 &  6 &   77.91812 &   98.80195 &  -32.959061 &   65.91812 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm33 &  6 &  442.62290 &  463.50673 & -215.311449 &  430.62290 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm34 &  6 &  442.62290 &  463.50673 & -215.311449 &  430.62290 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm23 &  9 &  123.28435 &  154.61010 &  -52.642176 &  105.28435 & 3.253385e+02 &  3 &  3.258884e-70\\\\\n",
       "\tm18 & 10 & -133.39685 &  -98.59046 &   76.698426 & -153.39685 & 2.586812e+02 &  1 &  3.326482e-58\\\\\n",
       "\tm19 & 10 &   83.77877 &  118.58516 &  -31.889386 &   63.77877 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm20 & 10 &   83.77877 &  118.58516 &  -31.889386 &   63.77877 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm21 & 10 &  450.07664 &  484.88303 & -215.038322 &  430.07664 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm22 & 10 &  450.07664 &  484.88303 & -215.038322 &  430.07664 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm24 & 10 & -189.85593 & -155.04954 &  104.927963 & -209.85593 & 6.399326e+02 &  0 &            NA\\\\\n",
       "\tm25 & 10 &   60.47771 &   95.28410 &  -20.238857 &   40.47771 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm26 & 10 &   60.47771 &   95.28410 &  -20.238857 &   40.47771 & 1.627143e-12 &  0 &            NA\\\\\n",
       "\tm27 & 10 &  358.59708 &  393.40347 & -169.298539 &  338.59708 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm28 & 10 &  445.06933 &  479.87572 & -212.534666 &  425.06933 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm36 & 18 & -216.40936 & -153.75786 &  126.204679 & -252.40936 & 6.774787e+02 &  8 & 5.042771e-141\\\\\n",
       "\tm37 & 18 &   54.02924 &  116.68074 &   -9.014619 &   18.02924 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm38 & 18 &   54.26916 &  116.92066 &   -9.134580 &   18.26916 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm39 & 18 &  317.55041 &  380.20191 & -140.775206 &  281.55041 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\tm40 & 18 &  455.49617 &  518.14767 & -209.748083 &  419.49617 & 0.000000e+00 &  0 &            NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova: 39 × 8\n",
       "\n",
       "| <!--/--> | npar &lt;dbl&gt; | AIC &lt;dbl&gt; | BIC &lt;dbl&gt; | logLik &lt;dbl&gt; | deviance &lt;dbl&gt; | Chisq &lt;dbl&gt; | Df &lt;dbl&gt; | Pr(&gt;Chisq) &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| m11 |  3 |  129.89321 |  140.33513 |  -61.946607 |  123.89321 |           NA | NA |            NA |\n",
       "| m17 |  3 |  120.58573 |  131.02765 |  -57.292865 |  114.58573 | 9.307484e+00 |  0 |            NA |\n",
       "| m6 |  4 | -111.53935 |  -97.61680 |   59.769676 | -119.53935 | 2.341251e+02 |  1 |  7.512164e-53 |\n",
       "| m7 |  4 |   87.21490 |  101.13746 |  -39.607451 |   79.21490 | 0.000000e+00 |  0 |            NA |\n",
       "| m8 |  4 |   87.21490 |  101.13746 |  -39.607451 |   79.21490 | 8.384404e-13 |  0 |            NA |\n",
       "| m9 |  4 |  441.35245 |  455.27501 | -216.676226 |  433.35245 | 0.000000e+00 |  0 |            NA |\n",
       "| m10 |  4 |  441.35245 |  455.27501 | -216.676226 |  433.35245 | 1.136868e-13 |  0 |            NA |\n",
       "| m12 |  4 | -147.37571 | -133.45315 |   77.687853 | -155.37571 | 5.887282e+02 |  0 |            NA |\n",
       "| m13 |  4 |   74.23091 |   88.15347 |  -33.115456 |   66.23091 | 0.000000e+00 |  0 |            NA |\n",
       "| m14 |  4 |   74.57626 |   88.49881 |  -33.288128 |   66.57626 | 0.000000e+00 |  0 |            NA |\n",
       "| m15 |  4 |  438.76470 |  452.68726 | -215.382351 |  430.76470 | 0.000000e+00 |  0 |            NA |\n",
       "| m16 |  4 |  438.76470 |  452.68726 | -215.382351 |  430.76470 | 0.000000e+00 |  0 |            NA |\n",
       "| m5 |  5 |  120.61467 |  138.01787 |  -55.307336 |  110.61467 | 3.201500e+02 |  1 |  1.343450e-71 |\n",
       "| m0 |  6 | -128.44719 | -107.56336 |   70.223595 | -140.44719 | 2.510619e+02 |  1 |  1.523865e-56 |\n",
       "| m1 |  6 |   81.65893 |  102.54276 |  -34.829464 |   69.65893 | 0.000000e+00 |  0 |            NA |\n",
       "| m2 |  6 |   81.65893 |  102.54276 |  -34.829464 |   69.65893 | 0.000000e+00 |  0 |            NA |\n",
       "| m3 |  6 |  443.36703 |  464.25087 | -215.683516 |  431.36703 | 0.000000e+00 |  0 |            NA |\n",
       "| m4 |  6 |  443.36703 |  464.25087 | -215.683516 |  431.36703 | 0.000000e+00 |  0 |            NA |\n",
       "| m30 |  6 | -145.10199 | -124.21816 |   78.550995 | -157.10199 | 5.884690e+02 |  0 |            NA |\n",
       "| m31 |  6 |   77.45750 |   98.34133 |  -32.728749 |   65.45750 | 0.000000e+00 |  0 |            NA |\n",
       "| m32 |  6 |   77.91812 |   98.80195 |  -32.959061 |   65.91812 | 0.000000e+00 |  0 |            NA |\n",
       "| m33 |  6 |  442.62290 |  463.50673 | -215.311449 |  430.62290 | 0.000000e+00 |  0 |            NA |\n",
       "| m34 |  6 |  442.62290 |  463.50673 | -215.311449 |  430.62290 | 0.000000e+00 |  0 |            NA |\n",
       "| m23 |  9 |  123.28435 |  154.61010 |  -52.642176 |  105.28435 | 3.253385e+02 |  3 |  3.258884e-70 |\n",
       "| m18 | 10 | -133.39685 |  -98.59046 |   76.698426 | -153.39685 | 2.586812e+02 |  1 |  3.326482e-58 |\n",
       "| m19 | 10 |   83.77877 |  118.58516 |  -31.889386 |   63.77877 | 0.000000e+00 |  0 |            NA |\n",
       "| m20 | 10 |   83.77877 |  118.58516 |  -31.889386 |   63.77877 | 0.000000e+00 |  0 |            NA |\n",
       "| m21 | 10 |  450.07664 |  484.88303 | -215.038322 |  430.07664 | 0.000000e+00 |  0 |            NA |\n",
       "| m22 | 10 |  450.07664 |  484.88303 | -215.038322 |  430.07664 | 0.000000e+00 |  0 |            NA |\n",
       "| m24 | 10 | -189.85593 | -155.04954 |  104.927963 | -209.85593 | 6.399326e+02 |  0 |            NA |\n",
       "| m25 | 10 |   60.47771 |   95.28410 |  -20.238857 |   40.47771 | 0.000000e+00 |  0 |            NA |\n",
       "| m26 | 10 |   60.47771 |   95.28410 |  -20.238857 |   40.47771 | 1.627143e-12 |  0 |            NA |\n",
       "| m27 | 10 |  358.59708 |  393.40347 | -169.298539 |  338.59708 | 0.000000e+00 |  0 |            NA |\n",
       "| m28 | 10 |  445.06933 |  479.87572 | -212.534666 |  425.06933 | 0.000000e+00 |  0 |            NA |\n",
       "| m36 | 18 | -216.40936 | -153.75786 |  126.204679 | -252.40936 | 6.774787e+02 |  8 | 5.042771e-141 |\n",
       "| m37 | 18 |   54.02924 |  116.68074 |   -9.014619 |   18.02924 | 0.000000e+00 |  0 |            NA |\n",
       "| m38 | 18 |   54.26916 |  116.92066 |   -9.134580 |   18.26916 | 0.000000e+00 |  0 |            NA |\n",
       "| m39 | 18 |  317.55041 |  380.20191 | -140.775206 |  281.55041 | 0.000000e+00 |  0 |            NA |\n",
       "| m40 | 18 |  455.49617 |  518.14767 | -209.748083 |  419.49617 | 0.000000e+00 |  0 |            NA |\n",
       "\n"
      ],
      "text/plain": [
       "    npar AIC        BIC        logLik      deviance   Chisq        Df\n",
       "m11  3    129.89321  140.33513  -61.946607  123.89321           NA NA\n",
       "m17  3    120.58573  131.02765  -57.292865  114.58573 9.307484e+00  0\n",
       "m6   4   -111.53935  -97.61680   59.769676 -119.53935 2.341251e+02  1\n",
       "m7   4     87.21490  101.13746  -39.607451   79.21490 0.000000e+00  0\n",
       "m8   4     87.21490  101.13746  -39.607451   79.21490 8.384404e-13  0\n",
       "m9   4    441.35245  455.27501 -216.676226  433.35245 0.000000e+00  0\n",
       "m10  4    441.35245  455.27501 -216.676226  433.35245 1.136868e-13  0\n",
       "m12  4   -147.37571 -133.45315   77.687853 -155.37571 5.887282e+02  0\n",
       "m13  4     74.23091   88.15347  -33.115456   66.23091 0.000000e+00  0\n",
       "m14  4     74.57626   88.49881  -33.288128   66.57626 0.000000e+00  0\n",
       "m15  4    438.76470  452.68726 -215.382351  430.76470 0.000000e+00  0\n",
       "m16  4    438.76470  452.68726 -215.382351  430.76470 0.000000e+00  0\n",
       "m5   5    120.61467  138.01787  -55.307336  110.61467 3.201500e+02  1\n",
       "m0   6   -128.44719 -107.56336   70.223595 -140.44719 2.510619e+02  1\n",
       "m1   6     81.65893  102.54276  -34.829464   69.65893 0.000000e+00  0\n",
       "m2   6     81.65893  102.54276  -34.829464   69.65893 0.000000e+00  0\n",
       "m3   6    443.36703  464.25087 -215.683516  431.36703 0.000000e+00  0\n",
       "m4   6    443.36703  464.25087 -215.683516  431.36703 0.000000e+00  0\n",
       "m30  6   -145.10199 -124.21816   78.550995 -157.10199 5.884690e+02  0\n",
       "m31  6     77.45750   98.34133  -32.728749   65.45750 0.000000e+00  0\n",
       "m32  6     77.91812   98.80195  -32.959061   65.91812 0.000000e+00  0\n",
       "m33  6    442.62290  463.50673 -215.311449  430.62290 0.000000e+00  0\n",
       "m34  6    442.62290  463.50673 -215.311449  430.62290 0.000000e+00  0\n",
       "m23  9    123.28435  154.61010  -52.642176  105.28435 3.253385e+02  3\n",
       "m18 10   -133.39685  -98.59046   76.698426 -153.39685 2.586812e+02  1\n",
       "m19 10     83.77877  118.58516  -31.889386   63.77877 0.000000e+00  0\n",
       "m20 10     83.77877  118.58516  -31.889386   63.77877 0.000000e+00  0\n",
       "m21 10    450.07664  484.88303 -215.038322  430.07664 0.000000e+00  0\n",
       "m22 10    450.07664  484.88303 -215.038322  430.07664 0.000000e+00  0\n",
       "m24 10   -189.85593 -155.04954  104.927963 -209.85593 6.399326e+02  0\n",
       "m25 10     60.47771   95.28410  -20.238857   40.47771 0.000000e+00  0\n",
       "m26 10     60.47771   95.28410  -20.238857   40.47771 1.627143e-12  0\n",
       "m27 10    358.59708  393.40347 -169.298539  338.59708 0.000000e+00  0\n",
       "m28 10    445.06933  479.87572 -212.534666  425.06933 0.000000e+00  0\n",
       "m36 18   -216.40936 -153.75786  126.204679 -252.40936 6.774787e+02  8\n",
       "m37 18     54.02924  116.68074   -9.014619   18.02924 0.000000e+00  0\n",
       "m38 18     54.26916  116.92066   -9.134580   18.26916 0.000000e+00  0\n",
       "m39 18    317.55041  380.20191 -140.775206  281.55041 0.000000e+00  0\n",
       "m40 18    455.49617  518.14767 -209.748083  419.49617 0.000000e+00  0\n",
       "    Pr(>Chisq)   \n",
       "m11            NA\n",
       "m17            NA\n",
       "m6   7.512164e-53\n",
       "m7             NA\n",
       "m8             NA\n",
       "m9             NA\n",
       "m10            NA\n",
       "m12            NA\n",
       "m13            NA\n",
       "m14            NA\n",
       "m15            NA\n",
       "m16            NA\n",
       "m5   1.343450e-71\n",
       "m0   1.523865e-56\n",
       "m1             NA\n",
       "m2             NA\n",
       "m3             NA\n",
       "m4             NA\n",
       "m30            NA\n",
       "m31            NA\n",
       "m32            NA\n",
       "m33            NA\n",
       "m34            NA\n",
       "m23  3.258884e-70\n",
       "m18  3.326482e-58\n",
       "m19            NA\n",
       "m20            NA\n",
       "m21            NA\n",
       "m22            NA\n",
       "m24            NA\n",
       "m25            NA\n",
       "m26            NA\n",
       "m27            NA\n",
       "m28            NA\n",
       "m36 5.042771e-141\n",
       "m37            NA\n",
       "m38            NA\n",
       "m39            NA\n",
       "m40            NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anova(m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13, m14, m15, m16, m17, m18, m19, m20, m21, m22, m23, m24, m25, m26, m27, m28, m30, m31, m32, m33, m34, m36, m37, m38, m39, m40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fitted a linear mixed model (estimated using REML and nloptwrap optimizer)\n",
      "to predict accuracy with interrupt and task\n",
      " (formula: accuracy ~ interrupt * task + interrupt + task). The model included\n",
      "folder_id\n",
      " as random effect (formula: ~1 | folder_id). The model's total explanatory power\n",
      "is substantial (conditional R2 = 0.38) and the part related to the fixed\n",
      "effects alone (marginal R2) is of 0.31. The model's intercept, corresponding to\n",
      "interrupt = rest and task = LD\n",
      ", is at 0.98 (95% CI [0.92, 1.04], t(230) = 31.80, p < .001). Within this model:\n",
      "\n",
      "  - The effect of interrupt [tiktok] is statistically non-significant and\n",
      "positive (beta = 1.45e-03, 95% CI [-0.08, 0.09], t(230) = 0.03, p = 0.973; Std.\n",
      "beta = 7.61e-03, 95% CI [-0.44, 0.46])\n",
      "  - The effect of interrupt [twitter] is statistically non-significant and\n",
      "positive (beta = 6.45e-03, 95% CI [-0.08, 0.09], t(230) = 0.15, p = 0.882; Std.\n",
      "beta = 0.03, 95% CI [-0.42, 0.48])\n",
      "  - The effect of interrupt [youtube] is statistically non-significant and\n",
      "negative (beta = -0.07, 95% CI [-0.15, 0.02], t(230) = -1.51, p = 0.133; Std.\n",
      "beta = -0.34, 95% CI [-0.80, 0.11])\n",
      "  - The effect of task [PM] is statistically non-significant and negative (beta =\n",
      "-0.06, 95% CI [-0.13, 0.02], t(230) = -1.48, p = 0.140; Std. beta = -0.30, 95%\n",
      "CI [-0.71, 0.10])\n",
      "  - The interaction effect of task [PM] on interrupt [tiktok] is statistically\n",
      "significant and negative (beta = -0.28, 95% CI [-0.39, -0.17], t(230) = -5.01,\n",
      "p < .001; Std. beta = -1.46, 95% CI [-2.03, -0.88])\n",
      "  - The interaction effect of task [PM] on interrupt [twitter] is statistically\n",
      "non-significant and negative (beta = -5.35e-03, 95% CI [-0.11, 0.10], t(230) =\n",
      "-0.10, p = 0.923; Std. beta = -0.03, 95% CI [-0.60, 0.54])\n",
      "  - The interaction effect of task [PM] on interrupt [youtube] is statistically\n",
      "non-significant and negative (beta = -0.03, 95% CI [-0.13, 0.08], t(230) =\n",
      "-0.46, p = 0.648; Std. beta = -0.13, 95% CI [-0.71, 0.44])\n",
      "\n",
      "Standardized parameters were obtained by fitting the model on a standardized\n",
      "version of the dataset. 95% Confidence Intervals (CIs) and p-values were\n",
      "computed using a Wald t-distribution approximation.\n"
     ]
    }
   ],
   "source": [
    "print(report(m24), verbose=TRUE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Raw Accuracy - LD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>folder_id</th><th scope=col>task</th><th scope=col>interrupt</th><th scope=col>measure</th><th scope=col>stimulus</th><th scope=col>success</th><th scope=col>correct</th><th scope=col>rt</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>101</td><td>LD</td><td>rest</td><td>pre</td><td>OÜRB   </td><td>True</td><td>1</td><td>0.4386205</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>101</td><td>LD</td><td>rest</td><td>pre</td><td>KLINGEN</td><td>True</td><td>1</td><td>0.5688469</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>101</td><td>LD</td><td>rest</td><td>pre</td><td>LIEBEN </td><td>True</td><td>1</td><td>0.4892312</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>101</td><td>LD</td><td>rest</td><td>pre</td><td>LAHW   </td><td>True</td><td>1</td><td>0.4658707</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>101</td><td>LD</td><td>rest</td><td>pre</td><td>GUSTIL </td><td>True</td><td>1</td><td>0.5583337</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>101</td><td>LD</td><td>rest</td><td>pre</td><td>HUND   </td><td>True</td><td>1</td><td>0.5797601</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & folder\\_id & task & interrupt & measure & stimulus & success & correct & rt\\\\\n",
       "  & <int> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 101 & LD & rest & pre & OÜRB    & True & 1 & 0.4386205\\\\\n",
       "\t2 & 101 & LD & rest & pre & KLINGEN & True & 1 & 0.5688469\\\\\n",
       "\t3 & 101 & LD & rest & pre & LIEBEN  & True & 1 & 0.4892312\\\\\n",
       "\t4 & 101 & LD & rest & pre & LAHW    & True & 1 & 0.4658707\\\\\n",
       "\t5 & 101 & LD & rest & pre & GUSTIL  & True & 1 & 0.5583337\\\\\n",
       "\t6 & 101 & LD & rest & pre & HUND    & True & 1 & 0.5797601\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 8\n",
       "\n",
       "| <!--/--> | folder_id &lt;int&gt; | task &lt;chr&gt; | interrupt &lt;chr&gt; | measure &lt;chr&gt; | stimulus &lt;chr&gt; | success &lt;chr&gt; | correct &lt;dbl&gt; | rt &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 101 | LD | rest | pre | OÜRB    | True | 1 | 0.4386205 |\n",
       "| 2 | 101 | LD | rest | pre | KLINGEN | True | 1 | 0.5688469 |\n",
       "| 3 | 101 | LD | rest | pre | LIEBEN  | True | 1 | 0.4892312 |\n",
       "| 4 | 101 | LD | rest | pre | LAHW    | True | 1 | 0.4658707 |\n",
       "| 5 | 101 | LD | rest | pre | GUSTIL  | True | 1 | 0.5583337 |\n",
       "| 6 | 101 | LD | rest | pre | HUND    | True | 1 | 0.5797601 |\n",
       "\n"
      ],
      "text/plain": [
       "  folder_id task interrupt measure stimulus success correct rt       \n",
       "1 101       LD   rest      pre     OÜRB     True    1       0.4386205\n",
       "2 101       LD   rest      pre     KLINGEN  True    1       0.5688469\n",
       "3 101       LD   rest      pre     LIEBEN   True    1       0.4892312\n",
       "4 101       LD   rest      pre     LAHW     True    1       0.4658707\n",
       "5 101       LD   rest      pre     GUSTIL   True    1       0.5583337\n",
       "6 101       LD   rest      pre     HUND     True    1       0.5797601"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df <- read.csv(\"data/rt.csv\")\n",
    "df_ld <- df %>% filter(task == \"LD\") %>% filter(measure != \"train\")\n",
    "df_ld$correct <- ifelse(df_ld$correct == 'True', 1, 0)\n",
    "head(df_ld)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 0.0440729 (tol = 0.002, component 1)”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fitted a logistic mixed model (estimated using ML and Nelder-Mead optimizer)\n",
      "to predict correct with interrupt and measure\n",
      " (formula: correct ~ interrupt * measure + interrupt + measure). The model\n",
      "included folder_id and stimulus\n",
      " as random effects (formula: list(~1 | folder_id, ~1 | stimulus)). The model's\n",
      "total explanatory power is substantial (conditional R2 = 0.57) and the part\n",
      "related to the fixed effects alone (marginal R2) is of 0.02. The model's\n",
      "intercept, corresponding to interrupt = tiktok and measure = post\n",
      ", is at 5.15 (95% CI [4.30, 5.99], p < .001). Within this model:\n",
      "\n",
      "  - The effect of interrupt [rest] is statistically non-significant and negative\n",
      "(beta = -0.11, 95% CI [-1.22, 1.00], p = 0.841; Std. beta = -0.11, 95% CI\n",
      "[-1.22, 1.00])\n",
      "  - The effect of interrupt [twitter] is statistically non-significant and\n",
      "negative (beta = -0.24, 95% CI [-1.34, 0.87], p = 0.673; Std. beta = -0.24, 95%\n",
      "CI [-1.34, 0.87])\n",
      "  - The effect of interrupt [youtube] is statistically non-significant and\n",
      "negative (beta = -0.88, 95% CI [-1.98, 0.22], p = 0.118; Std. beta = -0.88, 95%\n",
      "CI [-1.98, 0.22])\n",
      "  - The effect of measure [pre] is statistically non-significant and negative\n",
      "(beta = -0.02, 95% CI [-0.59, 0.55], p = 0.952; Std. beta = -0.02, 95% CI\n",
      "[-0.59, 0.55])\n",
      "  - The interaction effect of measure [pre] on interrupt [rest] is statistically\n",
      "non-significant and positive (beta = 0.10, 95% CI [-0.50, 0.70], p = 0.743;\n",
      "Std. beta = 0.10, 95% CI [-0.50, 0.70])\n",
      "  - The interaction effect of measure [pre] on interrupt [twitter] is\n",
      "statistically significant and positive (beta = 0.80, 95% CI [0.15, 1.46], p =\n",
      "0.016; Std. beta = 0.80, 95% CI [0.15, 1.46])\n",
      "  - The interaction effect of measure [pre] on interrupt [youtube] is\n",
      "statistically non-significant and positive (beta = 0.18, 95% CI [-0.40, 0.75],\n",
      "p = 0.547; Std. beta = 0.18, 95% CI [-0.40, 0.75])\n",
      "\n",
      "Standardized parameters were obtained by fitting the model on a standardized\n",
      "version of the dataset. 95% Confidence Intervals (CIs) and p-values were\n",
      "computed using a Wald z-distribution approximation.\n"
     ]
    }
   ],
   "source": [
    "df_ld$interrupt <- factor(df_ld$interrupt, levels = c(\"tiktok\", \"rest\", \"twitter\", \"youtube\"))\n",
    "df_ld$measure   <- factor(df_ld$measure, levels = c(\"post\", \"pre\"))\n",
    "m <- glmer(correct ~ interrupt * measure + interrupt + measure + (1|folder_id) + (1|stimulus), data = df_ld, family = binomial(link = \"logit\"))\n",
    "print(report(m), verbose = TRUE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Raw Accuracy - PM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>folder_id</th><th scope=col>task</th><th scope=col>interrupt</th><th scope=col>measure</th><th scope=col>stimulus</th><th scope=col>success</th><th scope=col>correct</th><th scope=col>rt</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>101</td><td>PM</td><td>rest</td><td>pre</td><td>GRÜN  </td><td>True </td><td>1</td><td>1.7350967</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>101</td><td>PM</td><td>rest</td><td>pre</td><td>LILA  </td><td>True </td><td>1</td><td>1.0381220</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>101</td><td>PM</td><td>rest</td><td>pre</td><td>BLAU  </td><td>True </td><td>1</td><td>0.9347805</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>101</td><td>PM</td><td>rest</td><td>pre</td><td>GRÜN  </td><td>True </td><td>1</td><td>1.0928914</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>101</td><td>PM</td><td>rest</td><td>pre</td><td>LILA  </td><td>True </td><td>1</td><td>1.8238099</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>101</td><td>PM</td><td>rest</td><td>pre</td><td>GEFÜHL</td><td>False</td><td>0</td><td>       NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & folder\\_id & task & interrupt & measure & stimulus & success & correct & rt\\\\\n",
       "  & <int> & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 101 & PM & rest & pre & GRÜN   & True  & 1 & 1.7350967\\\\\n",
       "\t2 & 101 & PM & rest & pre & LILA   & True  & 1 & 1.0381220\\\\\n",
       "\t3 & 101 & PM & rest & pre & BLAU   & True  & 1 & 0.9347805\\\\\n",
       "\t4 & 101 & PM & rest & pre & GRÜN   & True  & 1 & 1.0928914\\\\\n",
       "\t5 & 101 & PM & rest & pre & LILA   & True  & 1 & 1.8238099\\\\\n",
       "\t6 & 101 & PM & rest & pre & GEFÜHL & False & 0 &        NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 8\n",
       "\n",
       "| <!--/--> | folder_id &lt;int&gt; | task &lt;chr&gt; | interrupt &lt;chr&gt; | measure &lt;chr&gt; | stimulus &lt;chr&gt; | success &lt;chr&gt; | correct &lt;dbl&gt; | rt &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 101 | PM | rest | pre | GRÜN   | True  | 1 | 1.7350967 |\n",
       "| 2 | 101 | PM | rest | pre | LILA   | True  | 1 | 1.0381220 |\n",
       "| 3 | 101 | PM | rest | pre | BLAU   | True  | 1 | 0.9347805 |\n",
       "| 4 | 101 | PM | rest | pre | GRÜN   | True  | 1 | 1.0928914 |\n",
       "| 5 | 101 | PM | rest | pre | LILA   | True  | 1 | 1.8238099 |\n",
       "| 6 | 101 | PM | rest | pre | GEFÜHL | False | 0 |        NA |\n",
       "\n"
      ],
      "text/plain": [
       "  folder_id task interrupt measure stimulus success correct rt       \n",
       "1 101       PM   rest      pre     GRÜN     True    1       1.7350967\n",
       "2 101       PM   rest      pre     LILA     True    1       1.0381220\n",
       "3 101       PM   rest      pre     BLAU     True    1       0.9347805\n",
       "4 101       PM   rest      pre     GRÜN     True    1       1.0928914\n",
       "5 101       PM   rest      pre     LILA     True    1       1.8238099\n",
       "6 101       PM   rest      pre     GEFÜHL   False   0              NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df <- read.csv(\"data/rt.csv\")\n",
    "df_pm <- df %>% filter(task == \"PM\") %>% filter(measure != \"train\")\n",
    "df_pm$correct <- ifelse(df_pm$correct == 'True', 1, 0)\n",
    "head(df_pm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 1.07139 (tol = 0.002, component 1)”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fitted a logistic mixed model (estimated using ML and Nelder-Mead optimizer)\n",
      "to predict correct with interrupt and measure\n",
      " (formula: correct ~ interrupt * measure + interrupt + measure). The model\n",
      "included folder_id and stimulus\n",
      " as random effects (formula: list(~1 | folder_id, ~1 | stimulus)). The model's\n",
      "total explanatory power is substantial (conditional R2 = 0.91) and the part\n",
      "related to the fixed effects alone (marginal R2) is of 0.04. The model's\n",
      "intercept, corresponding to interrupt = tiktok and measure = post\n",
      ", is at -9.41 (95% CI [-12.60, -6.22], p < .001). Within this model:\n",
      "\n",
      "  - The effect of interrupt [rest] is statistically significant and positive\n",
      "(beta = 3.54, 95% CI [2.52, 4.57], p < .001; Std. beta = 3.54, 95% CI [2.52,\n",
      "4.57])\n",
      "  - The effect of interrupt [twitter] is statistically significant and positive\n",
      "(beta = 4.23, 95% CI [3.04, 5.42], p < .001; Std. beta = 4.23, 95% CI [3.04,\n",
      "5.42])\n",
      "  - The effect of interrupt [youtube] is statistically significant and positive\n",
      "(beta = 2.33, 95% CI [1.43, 3.24], p < .001; Std. beta = 2.33, 95% CI [1.43,\n",
      "3.24])\n",
      "  - The effect of measure [pre] is statistically significant and positive (beta =\n",
      "1.87, 95% CI [1.38, 2.36], p < .001; Std. beta = 1.87, 95% CI [1.38, 2.36])\n",
      "  - The interaction effect of measure [pre] on interrupt [rest] is statistically\n",
      "significant and negative (beta = -2.63, 95% CI [-3.56, -1.70], p < .001; Std.\n",
      "beta = -2.63, 95% CI [-3.56, -1.70])\n",
      "  - The interaction effect of measure [pre] on interrupt [twitter] is\n",
      "statistically significant and negative (beta = -3.72, 95% CI [-4.82, -2.63], p\n",
      "< .001; Std. beta = -3.72, 95% CI [-4.82, -2.63])\n",
      "  - The interaction effect of measure [pre] on interrupt [youtube] is\n",
      "statistically significant and negative (beta = -1.90, 95% CI [-2.66, -1.14], p\n",
      "< .001; Std. beta = -1.90, 95% CI [-2.66, -1.14])\n",
      "\n",
      "Standardized parameters were obtained by fitting the model on a standardized\n",
      "version of the dataset. 95% Confidence Intervals (CIs) and p-values were\n",
      "computed using a Wald z-distribution approximation.\n"
     ]
    }
   ],
   "source": [
    "df_pm$interrupt <- factor(df_pm$interrupt, levels = c(\"tiktok\", \"rest\", \"twitter\", \"youtube\"))\n",
    "df_pm$measure   <- factor(df_pm$measure, levels = c(\"post\", \"pre\"))\n",
    "m <- glmer(correct ~ interrupt * measure + interrupt + measure + (1|folder_id) + (1|stimulus), data = df_pm, family = binomial(link = \"logit\"))\n",
    "print(report(m), verbose = TRUE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Raw Accuracy - LD vs. PM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, :\n",
      "“Model failed to converge with max|grad| = 0.0417051 (tol = 0.002, component 1)”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fitted a logistic mixed model (estimated using ML and Nelder-Mead optimizer)\n",
      "to predict correct with interrupt, measure and task\n",
      " (formula: correct ~ interrupt * measure * task + interrupt * measure + measure\n",
      "* task + interrupt * task + interrupt + measure + task). The model included\n",
      "folder_id and stimulus\n",
      " as random effects (formula: list(~1 | folder_id, ~1 | stimulus)). The model's\n",
      "total explanatory power is substantial (conditional R2 = 0.48) and the part\n",
      "related to the fixed effects alone (marginal R2) is of 0.14. The model's\n",
      "intercept, corresponding to interrupt = rest, measure = post and task = LD\n",
      ", is at 4.66 (95% CI [4.02, 5.31], p < .001). Within this model:\n",
      "\n",
      "  - The effect of interrupt [tiktok] is statistically non-significant and\n",
      "negative (beta = -0.08, 95% CI [-0.96, 0.79], p = 0.851; Std. beta = -0.08, 95%\n",
      "CI [-0.96, 0.79])\n",
      "  - The effect of interrupt [twitter] is statistically non-significant and\n",
      "negative (beta = -0.15, 95% CI [-1.02, 0.72], p = 0.738; Std. beta = -0.15, 95%\n",
      "CI [-1.02, 0.72])\n",
      "  - The effect of interrupt [youtube] is statistically significant and negative\n",
      "(beta = -0.87, 95% CI [-1.72, -7.28e-03], p = 0.048; Std. beta = -0.87, 95% CI\n",
      "[-1.72, -7.28e-03])\n",
      "  - The effect of measure [pre] is statistically non-significant and negative\n",
      "(beta = -0.17, 95% CI [-0.60, 0.27], p = 0.454; Std. beta = -0.17, 95% CI\n",
      "[-0.60, 0.27])\n",
      "  - The effect of task [PM] is statistically significant and negative (beta =\n",
      "-2.37, 95% CI [-3.13, -1.60], p < .001; Std. beta = -2.37, 95% CI [-3.13,\n",
      "-1.60])\n",
      "  - The interaction effect of measure [pre] on interrupt [tiktok] is\n",
      "statistically non-significant and positive (beta = 0.01, 95% CI [-0.51, 0.54],\n",
      "p = 0.958; Std. beta = 0.01, 95% CI [-0.51, 0.54])\n",
      "  - The interaction effect of measure [pre] on interrupt [twitter] is\n",
      "statistically significant and positive (beta = 0.80, 95% CI [0.22, 1.39], p =\n",
      "0.007; Std. beta = 0.80, 95% CI [0.22, 1.39])\n",
      "  - The interaction effect of measure [pre] on interrupt [youtube] is\n",
      "statistically non-significant and positive (beta = 0.16, 95% CI [-0.32, 0.64],\n",
      "p = 0.511; Std. beta = 0.16, 95% CI [-0.32, 0.64])\n",
      "  - The interaction effect of task [PM] on interrupt [tiktok] is statistically\n",
      "significant and negative (beta = -2.75, 95% CI [-3.43, -2.08], p < .001; Std.\n",
      "beta = -2.75, 95% CI [-3.43, -2.08])\n",
      "  - The interaction effect of task [PM] on interrupt [twitter] is statistically\n",
      "significant and positive (beta = 1.26, 95% CI [0.26, 2.25], p = 0.013; Std.\n",
      "beta = 1.26, 95% CI [0.26, 2.25])\n",
      "  - The interaction effect of task [PM] on interrupt [youtube] is statistically\n",
      "non-significant and negative (beta = -0.22, 95% CI [-0.92, 0.48], p = 0.541;\n",
      "Std. beta = -0.22, 95% CI [-0.92, 0.48])\n",
      "  - The interaction effect of task [PM] on measure [pre] is statistically\n",
      "non-significant and negative (beta = -0.37, 95% CI [-1.12, 0.38], p = 0.337;\n",
      "Std. beta = -0.37, 95% CI [-1.12, 0.38])\n",
      "  - The interaction effect of task [PM] on (interrupt [tiktok] * measure [pre])\n",
      "is statistically significant and positive (beta = 2.17, 95% CI [1.24, 3.10], p\n",
      "< .001; Std. beta = 2.17, 95% CI [1.24, 3.10])\n",
      "  - The interaction effect of task [PM] on (interrupt [twitter] * measure [pre])\n",
      "is statistically significant and negative (beta = -1.96, 95% CI [-3.19, -0.74],\n",
      "p = 0.002; Std. beta = -1.96, 95% CI [-3.19, -0.74])\n",
      "  - The interaction effect of task [PM] on (interrupt [youtube] * measure [pre])\n",
      "is statistically non-significant and positive (beta = 0.57, 95% CI [-0.40,\n",
      "1.53], p = 0.249; Std. beta = 0.57, 95% CI [-0.40, 1.53])\n",
      "\n",
      "Standardized parameters were obtained by fitting the model on a standardized\n",
      "version of the dataset. 95% Confidence Intervals (CIs) and p-values were\n",
      "computed using a Wald z-distribution approximation.\n"
     ]
    }
   ],
   "source": [
    "df <- df %>% filter(measure != \"train\")\n",
    "df$correct <- ifelse(df$correct == 'True', 1, 0)\n",
    "m <- glmer(correct ~ interrupt * measure * task + interrupt * measure + measure * task + interrupt * task + interrupt + measure + task + (1|folder_id) + (1|stimulus), data = df, family = binomial(link = \"logit\"))\n",
    "\n",
    "print(report(m), verbose=TRUE)"
   ]
  }
 ],
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