3.ddm_anova.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": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "library(dplyr)\n",
    "library(report)\n",
    "source('stats.R')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>interrupt</th><th scope=col>task</th><th scope=col>measure</th><th scope=col>drift</th><th scope=col>noise</th><th scope=col>bound</th><th scope=col>nondectime</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;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>1</th><td>115</td><td>rest</td><td>LD</td><td>pre </td><td>2.178330</td><td>1.571794</td><td>1.799961</td><td>0.4359061</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>115</td><td>rest</td><td>PM</td><td>pre </td><td>1.931195</td><td>1.027810</td><td>1.342180</td><td>0.8411700</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>115</td><td>rest</td><td>LD</td><td>post</td><td>2.271652</td><td>1.758814</td><td>1.912728</td><td>0.4668220</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>115</td><td>rest</td><td>PM</td><td>post</td><td>3.352346</td><td>1.418835</td><td>1.967540</td><td>0.7719479</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>114</td><td>rest</td><td>LD</td><td>pre </td><td>3.643791</td><td>1.615253</td><td>1.766066</td><td>0.3017197</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>114</td><td>rest</td><td>PM</td><td>pre </td><td>0.000000</td><td>1.771786</td><td>1.229336</td><td>0.6659692</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & folder\\_id & interrupt & task & measure & drift & noise & bound & nondectime\\\\\n",
       "  & <int> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 115 & rest & LD & pre  & 2.178330 & 1.571794 & 1.799961 & 0.4359061\\\\\n",
       "\t2 & 115 & rest & PM & pre  & 1.931195 & 1.027810 & 1.342180 & 0.8411700\\\\\n",
       "\t3 & 115 & rest & LD & post & 2.271652 & 1.758814 & 1.912728 & 0.4668220\\\\\n",
       "\t4 & 115 & rest & PM & post & 3.352346 & 1.418835 & 1.967540 & 0.7719479\\\\\n",
       "\t5 & 114 & rest & LD & pre  & 3.643791 & 1.615253 & 1.766066 & 0.3017197\\\\\n",
       "\t6 & 114 & rest & PM & pre  & 0.000000 & 1.771786 & 1.229336 & 0.6659692\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 8\n",
       "\n",
       "| <!--/--> | folder_id &lt;int&gt; | interrupt &lt;chr&gt; | task &lt;chr&gt; | measure &lt;chr&gt; | drift &lt;dbl&gt; | noise &lt;dbl&gt; | bound &lt;dbl&gt; | nondectime &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 115 | rest | LD | pre  | 2.178330 | 1.571794 | 1.799961 | 0.4359061 |\n",
       "| 2 | 115 | rest | PM | pre  | 1.931195 | 1.027810 | 1.342180 | 0.8411700 |\n",
       "| 3 | 115 | rest | LD | post | 2.271652 | 1.758814 | 1.912728 | 0.4668220 |\n",
       "| 4 | 115 | rest | PM | post | 3.352346 | 1.418835 | 1.967540 | 0.7719479 |\n",
       "| 5 | 114 | rest | LD | pre  | 3.643791 | 1.615253 | 1.766066 | 0.3017197 |\n",
       "| 6 | 114 | rest | PM | pre  | 0.000000 | 1.771786 | 1.229336 | 0.6659692 |\n",
       "\n"
      ],
      "text/plain": [
       "  folder_id interrupt task measure drift    noise    bound    nondectime\n",
       "1 115       rest      LD   pre     2.178330 1.571794 1.799961 0.4359061 \n",
       "2 115       rest      PM   pre     1.931195 1.027810 1.342180 0.8411700 \n",
       "3 115       rest      LD   post    2.271652 1.758814 1.912728 0.4668220 \n",
       "4 115       rest      PM   post    3.352346 1.418835 1.967540 0.7719479 \n",
       "5 114       rest      LD   pre     3.643791 1.615253 1.766066 0.3017197 \n",
       "6 114       rest      PM   pre     0.000000 1.771786 1.229336 0.6659692 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>interrupt</th><th scope=col>task</th><th scope=col>measure</th><th scope=col>drift</th><th scope=col>noise</th><th scope=col>bound</th><th scope=col>nondectime</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;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>1</th><td>115</td><td>rest</td><td>PM</td><td>pre </td><td>1.9311947</td><td>1.027810</td><td>1.3421803</td><td>0.8411700</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>115</td><td>rest</td><td>PM</td><td>post</td><td>3.3523463</td><td>1.418835</td><td>1.9675401</td><td>0.7719479</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>114</td><td>rest</td><td>PM</td><td>pre </td><td>0.0000000</td><td>1.771786</td><td>1.2293357</td><td>0.6659692</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>114</td><td>rest</td><td>PM</td><td>post</td><td>0.0000000</td><td>3.068772</td><td>1.8800980</td><td>0.7148080</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>113</td><td>rest</td><td>PM</td><td>pre </td><td>0.7426351</td><td>0.540584</td><td>0.6090474</td><td>0.5770751</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>113</td><td>rest</td><td>PM</td><td>post</td><td>2.2487210</td><td>1.070278</td><td>1.9631977</td><td>0.4651087</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & folder\\_id & interrupt & task & measure & drift & noise & bound & nondectime\\\\\n",
       "  & <int> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 115 & rest & PM & pre  & 1.9311947 & 1.027810 & 1.3421803 & 0.8411700\\\\\n",
       "\t2 & 115 & rest & PM & post & 3.3523463 & 1.418835 & 1.9675401 & 0.7719479\\\\\n",
       "\t3 & 114 & rest & PM & pre  & 0.0000000 & 1.771786 & 1.2293357 & 0.6659692\\\\\n",
       "\t4 & 114 & rest & PM & post & 0.0000000 & 3.068772 & 1.8800980 & 0.7148080\\\\\n",
       "\t5 & 113 & rest & PM & pre  & 0.7426351 & 0.540584 & 0.6090474 & 0.5770751\\\\\n",
       "\t6 & 113 & rest & PM & post & 2.2487210 & 1.070278 & 1.9631977 & 0.4651087\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 8\n",
       "\n",
       "| <!--/--> | folder_id &lt;int&gt; | interrupt &lt;chr&gt; | task &lt;chr&gt; | measure &lt;chr&gt; | drift &lt;dbl&gt; | noise &lt;dbl&gt; | bound &lt;dbl&gt; | nondectime &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 115 | rest | PM | pre  | 1.9311947 | 1.027810 | 1.3421803 | 0.8411700 |\n",
       "| 2 | 115 | rest | PM | post | 3.3523463 | 1.418835 | 1.9675401 | 0.7719479 |\n",
       "| 3 | 114 | rest | PM | pre  | 0.0000000 | 1.771786 | 1.2293357 | 0.6659692 |\n",
       "| 4 | 114 | rest | PM | post | 0.0000000 | 3.068772 | 1.8800980 | 0.7148080 |\n",
       "| 5 | 113 | rest | PM | pre  | 0.7426351 | 0.540584 | 0.6090474 | 0.5770751 |\n",
       "| 6 | 113 | rest | PM | post | 2.2487210 | 1.070278 | 1.9631977 | 0.4651087 |\n",
       "\n"
      ],
      "text/plain": [
       "  folder_id interrupt task measure drift     noise    bound     nondectime\n",
       "1 115       rest      PM   pre     1.9311947 1.027810 1.3421803 0.8411700 \n",
       "2 115       rest      PM   post    3.3523463 1.418835 1.9675401 0.7719479 \n",
       "3 114       rest      PM   pre     0.0000000 1.771786 1.2293357 0.6659692 \n",
       "4 114       rest      PM   post    0.0000000 3.068772 1.8800980 0.7148080 \n",
       "5 113       rest      PM   pre     0.7426351 0.540584 0.6090474 0.5770751 \n",
       "6 113       rest      PM   post    2.2487210 1.070278 1.9631977 0.4651087 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>interrupt</th><th scope=col>task</th><th scope=col>measure</th><th scope=col>drift</th><th scope=col>noise</th><th scope=col>bound</th><th scope=col>nondectime</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;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>1</th><td>115</td><td>rest</td><td>LD</td><td>pre </td><td>2.178330</td><td>1.571794</td><td>1.799961</td><td>0.4359061</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>115</td><td>rest</td><td>LD</td><td>post</td><td>2.271652</td><td>1.758814</td><td>1.912728</td><td>0.4668220</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>114</td><td>rest</td><td>LD</td><td>pre </td><td>3.643791</td><td>1.615253</td><td>1.766066</td><td>0.3017197</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>114</td><td>rest</td><td>LD</td><td>post</td><td>4.947397</td><td>2.096200</td><td>1.673768</td><td>0.3561984</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>113</td><td>rest</td><td>LD</td><td>pre </td><td>5.990405</td><td>1.840045</td><td>1.645242</td><td>0.4466186</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>113</td><td>rest</td><td>LD</td><td>post</td><td>5.661109</td><td>2.430699</td><td>1.825276</td><td>0.4619769</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & folder\\_id & interrupt & task & measure & drift & noise & bound & nondectime\\\\\n",
       "  & <int> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 115 & rest & LD & pre  & 2.178330 & 1.571794 & 1.799961 & 0.4359061\\\\\n",
       "\t2 & 115 & rest & LD & post & 2.271652 & 1.758814 & 1.912728 & 0.4668220\\\\\n",
       "\t3 & 114 & rest & LD & pre  & 3.643791 & 1.615253 & 1.766066 & 0.3017197\\\\\n",
       "\t4 & 114 & rest & LD & post & 4.947397 & 2.096200 & 1.673768 & 0.3561984\\\\\n",
       "\t5 & 113 & rest & LD & pre  & 5.990405 & 1.840045 & 1.645242 & 0.4466186\\\\\n",
       "\t6 & 113 & rest & LD & post & 5.661109 & 2.430699 & 1.825276 & 0.4619769\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 8\n",
       "\n",
       "| <!--/--> | folder_id &lt;int&gt; | interrupt &lt;chr&gt; | task &lt;chr&gt; | measure &lt;chr&gt; | drift &lt;dbl&gt; | noise &lt;dbl&gt; | bound &lt;dbl&gt; | nondectime &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 115 | rest | LD | pre  | 2.178330 | 1.571794 | 1.799961 | 0.4359061 |\n",
       "| 2 | 115 | rest | LD | post | 2.271652 | 1.758814 | 1.912728 | 0.4668220 |\n",
       "| 3 | 114 | rest | LD | pre  | 3.643791 | 1.615253 | 1.766066 | 0.3017197 |\n",
       "| 4 | 114 | rest | LD | post | 4.947397 | 2.096200 | 1.673768 | 0.3561984 |\n",
       "| 5 | 113 | rest | LD | pre  | 5.990405 | 1.840045 | 1.645242 | 0.4466186 |\n",
       "| 6 | 113 | rest | LD | post | 5.661109 | 2.430699 | 1.825276 | 0.4619769 |\n",
       "\n"
      ],
      "text/plain": [
       "  folder_id interrupt task measure drift    noise    bound    nondectime\n",
       "1 115       rest      LD   pre     2.178330 1.571794 1.799961 0.4359061 \n",
       "2 115       rest      LD   post    2.271652 1.758814 1.912728 0.4668220 \n",
       "3 114       rest      LD   pre     3.643791 1.615253 1.766066 0.3017197 \n",
       "4 114       rest      LD   post    4.947397 2.096200 1.673768 0.3561984 \n",
       "5 113       rest      LD   pre     5.990405 1.840045 1.645242 0.4466186 \n",
       "6 113       rest      LD   post    5.661109 2.430699 1.825276 0.4619769 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ddm <- read.csv('data/ddm.csv')\n",
    "head(df_ddm)\n",
    "\n",
    "df_ddm_pm = df_ddm %>% filter((task %in% c('PM')))\n",
    "head(df_ddm_pm)\n",
    "\n",
    "df_ddm_ld = df_ddm %>% filter((task %in% c('LD')))\n",
    "head(df_ddm_ld)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "df_ddm_pm$interrupt <- factor(df_ddm_pm$interrupt, levels=c('rest', 'twitter', 'youtube', 'tiktok'))\n",
    "df_ddm_pm$measure   <- factor(df_ddm_pm$measure, levels=c('pre', 'post'))\n",
    "\n",
    "df_ddm_ld$interrupt <- factor(df_ddm_ld$interrupt, levels=c('rest', 'twitter', 'youtube', 'tiktok'))\n",
    "df_ddm_ld$measure   <- factor(df_ddm_ld$measure, levels=c('pre', 'post'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"not normally distributed:\"\n",
      "\n",
      "\tShapiro-Wilk normality test\n",
      "\n",
      "data:  df[[feature]]\n",
      "W = 0.94801, p-value = 0.0001549\n",
      "\n",
      "drift ~ interrupt * measure + (1 | folder_id)\n",
      "<environment: 0x12e914898>\n",
      "     Term                 F               Df            Df.res  \n",
      " Length:3           Min.   :2.420   Min.   :1.000   Min.   :56  \n",
      " Class :character   1st Qu.:3.249   1st Qu.:2.000   1st Qu.:56  \n",
      " Mode  :character   Median :4.078   Median :3.000   Median :56  \n",
      "                    Mean   :4.321   Mean   :2.333   Mean   :56  \n",
      "                    3rd Qu.:5.272   3rd Qu.:3.000   3rd Qu.:56  \n",
      "                    Max.   :6.466   Max.   :3.000   Max.   :56  \n",
      "     Pr(>F)         \n",
      " Min.   :0.0007767  \n",
      " 1st Qu.:0.0058278  \n",
      " Median :0.0108789  \n",
      " Mean   :0.0457021  \n",
      " 3rd Qu.:0.0681648  \n",
      " Max.   :0.1254508  \n",
      "Analysis of Variance of Aligned Rank Transformed Data\n",
      "\n",
      "Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) \n",
      "Model: Mixed Effects (lmer)\n",
      "Response: art(drift)\n",
      "\n",
      "                         F Df Df.res     Pr(>F)    \n",
      "1 interrupt         4.0784  3     56 0.01087889   *\n",
      "2 measure           2.4197  1     56 0.12545082    \n",
      "3 interrupt:measure 6.4663  3     56 0.00077668 ***\n",
      "---\n",
      "Signif. codes:   0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 \n",
      "\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Eta2 (partial) |       95% CI\n",
      "-------------------------------------------------\n",
      "interrupt         |           0.18 | [0.03, 1.00]\n",
      "measure           |           0.04 | [0.00, 1.00]\n",
      "interrupt:measure |           0.26 | [0.08, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Omega2 (partial) |       95% CI\n",
      "---------------------------------------------------\n",
      "interrupt         |             0.13 | [0.00, 1.00]\n",
      "measure           |             0.02 | [0.00, 1.00]\n",
      "interrupt:measure |             0.21 | [0.05, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m                      contrast    estimate        SE       df     t.ratio\n",
      "1         rest,post - rest,pre   3.2666667  8.456349 56.00000  0.38629753\n",
      "2      rest,post - tiktok,post  48.7666667 11.660322 91.44924  4.18227455\n",
      "3       rest,post - tiktok,pre   9.1666667 11.660322 91.44924  0.78614183\n",
      "4     rest,post - twitter,post  -5.7000000 11.660322 91.44924 -0.48883729\n",
      "5      rest,post - twitter,pre   2.4333333 11.660322 91.44924  0.20868492\n",
      "6     rest,post - youtube,post   7.5333333 11.660322 91.44924  0.64606565\n",
      "7      rest,post - youtube,pre   6.5333333 11.660322 91.44924  0.56030472\n",
      "8       rest,pre - tiktok,post  45.5000000 11.660322 91.44924  3.90212219\n",
      "9        rest,pre - tiktok,pre   5.9000000 11.660322 91.44924  0.50598947\n",
      "10     rest,pre - twitter,post  -8.9666667 11.660322 91.44924 -0.76898965\n",
      "11      rest,pre - twitter,pre  -0.8333333 11.660322 91.44924 -0.07146744\n",
      "12     rest,pre - youtube,post   4.2666667 11.660322 91.44924  0.36591329\n",
      "13      rest,pre - youtube,pre   3.2666667 11.660322 91.44924  0.28015236\n",
      "14    tiktok,post - tiktok,pre -39.6000000  8.456349 56.00000 -4.68287215\n",
      "15  tiktok,post - twitter,post -54.4666667 11.660322 91.44924 -4.67111184\n",
      "16   tiktok,post - twitter,pre -46.3333333 11.660322 91.44924 -3.97358963\n",
      "17  tiktok,post - youtube,post -41.2333333 11.660322 91.44924 -3.53620890\n",
      "18   tiktok,post - youtube,pre -42.2333333 11.660322 91.44924 -3.62196983\n",
      "19   tiktok,pre - twitter,post -14.8666667 11.660322 91.44924 -1.27497912\n",
      "20    tiktok,pre - twitter,pre  -6.7333333 11.660322 91.44924 -0.57745691\n",
      "21   tiktok,pre - youtube,post  -1.6333333 11.660322 91.44924 -0.14007618\n",
      "22    tiktok,pre - youtube,pre  -2.6333333 11.660322 91.44924 -0.22583711\n",
      "23  twitter,post - twitter,pre   8.1333333  8.456349 56.00000  0.96180202\n",
      "24 twitter,post - youtube,post  13.2333333 11.660322 91.44924  1.13490294\n",
      "25  twitter,post - youtube,pre  12.2333333 11.660322 91.44924  1.04914201\n",
      "26  twitter,pre - youtube,post   5.1000000 11.660322 91.44924  0.43738073\n",
      "27   twitter,pre - youtube,pre   4.1000000 11.660322 91.44924  0.35161980\n",
      "28  youtube,post - youtube,pre  -1.0000000  8.456349 56.00000 -0.11825435\n",
      "        p.value sig.\n",
      "1  1.0000000000     \n",
      "2  0.0017185539   **\n",
      "3  1.0000000000     \n",
      "4  1.0000000000     \n",
      "5  1.0000000000     \n",
      "6  1.0000000000     \n",
      "7  1.0000000000     \n",
      "8  0.0043680980   **\n",
      "9  1.0000000000     \n",
      "10 1.0000000000     \n",
      "11 1.0000000000     \n",
      "12 1.0000000000     \n",
      "13 1.0000000000     \n",
      "14 0.0004975439  ***\n",
      "15 0.0002871479  ***\n",
      "16 0.0035282411   **\n",
      "17 0.0140690162    *\n",
      "18 0.0110334095    *\n",
      "19 1.0000000000     \n",
      "20 1.0000000000     \n",
      "21 1.0000000000     \n",
      "22 1.0000000000     \n",
      "23 1.0000000000     \n",
      "24 1.0000000000     \n",
      "25 1.0000000000     \n",
      "26 1.0000000000     \n",
      "27 1.0000000000     \n",
      "28 1.0000000000     \n",
      "3 & 56 & 4.078 & \\textbf{0.011} & 0.133 & 1 & 56 & 2.42 & 0.125 & 0.024 & 3 & 56 & 6.466 & \\textbf{0.001} & 0.215"
     ]
    }
   ],
   "source": [
    "twoway_anova_test(df_ddm_pm, 'drift')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"not normally distributed:\"\n",
      "\n",
      "\tShapiro-Wilk normality test\n",
      "\n",
      "data:  df[[feature]]\n",
      "W = 0.97059, p-value = 0.009887\n",
      "\n",
      "noise ~ interrupt * measure + (1 | folder_id)\n",
      "<environment: 0x16f7ca300>\n",
      "     Term                 F                Df            Df.res  \n",
      " Length:3           Min.   : 4.233   Min.   :1.000   Min.   :56  \n",
      " Class :character   1st Qu.: 4.473   1st Qu.:2.000   1st Qu.:56  \n",
      " Mode  :character   Median : 4.712   Median :3.000   Median :56  \n",
      "                    Mean   : 7.179   Mean   :2.333   Mean   :56  \n",
      "                    3rd Qu.: 8.653   3rd Qu.:3.000   3rd Qu.:56  \n",
      "                    Max.   :12.593   Max.   :3.000   Max.   :56  \n",
      "     Pr(>F)         \n",
      " Min.   :0.0007922  \n",
      " 1st Qu.:0.0030432  \n",
      " Median :0.0052941  \n",
      " Mean   :0.0050683  \n",
      " 3rd Qu.:0.0072064  \n",
      " Max.   :0.0091186  \n",
      "Analysis of Variance of Aligned Rank Transformed Data\n",
      "\n",
      "Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) \n",
      "Model: Mixed Effects (lmer)\n",
      "Response: art(noise)\n",
      "\n",
      "                          F Df Df.res     Pr(>F)    \n",
      "1 interrupt          4.2327  3     56 0.00911864  **\n",
      "2 measure           12.5927  1     56 0.00079218 ***\n",
      "3 interrupt:measure  4.7125  3     56 0.00529413  **\n",
      "---\n",
      "Signif. codes:   0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 \n",
      "\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Eta2 (partial) |       95% CI\n",
      "-------------------------------------------------\n",
      "interrupt         |           0.18 | [0.03, 1.00]\n",
      "measure           |           0.18 | [0.05, 1.00]\n",
      "interrupt:measure |           0.20 | [0.04, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Omega2 (partial) |       95% CI\n",
      "---------------------------------------------------\n",
      "interrupt         |             0.14 | [0.00, 1.00]\n",
      "measure           |             0.17 | [0.04, 1.00]\n",
      "interrupt:measure |             0.16 | [0.01, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m                      contrast    estimate       SE       df     t.ratio\n",
      "1         rest,post - rest,pre -20.8666667 10.18447  56.0000 -2.04887209\n",
      "2      rest,post - tiktok,post -42.8666667 11.51567 106.9261 -3.72246426\n",
      "3       rest,post - tiktok,pre -30.8666667 11.51567 106.9261 -2.68040584\n",
      "4     rest,post - twitter,post   4.1666667 11.51567 106.9261  0.36182584\n",
      "5      rest,post - twitter,pre -29.2666667 11.51567 106.9261 -2.54146471\n",
      "6     rest,post - youtube,post  -1.6666667 11.51567 106.9261 -0.14473034\n",
      "7      rest,post - youtube,pre -31.4333333 11.51567 106.9261 -2.72961415\n",
      "8       rest,pre - tiktok,post -22.0000000 11.51567 106.9261 -1.91044044\n",
      "9        rest,pre - tiktok,pre -10.0000000 11.51567 106.9261 -0.86838202\n",
      "10     rest,pre - twitter,post  25.0333333 11.51567 106.9261  2.17384966\n",
      "11      rest,pre - twitter,pre  -8.4000000 11.51567 106.9261 -0.72944090\n",
      "12     rest,pre - youtube,post  19.2000000 11.51567 106.9261  1.66729348\n",
      "13      rest,pre - youtube,pre -10.5666667 11.51567 106.9261 -0.91759033\n",
      "14    tiktok,post - tiktok,pre  12.0000000 10.18447  56.0000  1.17826510\n",
      "15  tiktok,post - twitter,post  47.0333333 11.51567 106.9261  4.08429010\n",
      "16   tiktok,post - twitter,pre  13.6000000 11.51567 106.9261  1.18099955\n",
      "17  tiktok,post - youtube,post  41.2000000 11.51567 106.9261  3.57773392\n",
      "18   tiktok,post - youtube,pre  11.4333333 11.51567 106.9261  0.99285011\n",
      "19   tiktok,pre - twitter,post  35.0333333 11.51567 106.9261  3.04223168\n",
      "20    tiktok,pre - twitter,pre   1.6000000 11.51567 106.9261  0.13894112\n",
      "21   tiktok,pre - youtube,post  29.2000000 11.51567 106.9261  2.53567550\n",
      "22    tiktok,pre - youtube,pre  -0.5666667 11.51567 106.9261 -0.04920831\n",
      "23  twitter,post - twitter,pre -33.4333333 10.18447  56.0000 -3.28277748\n",
      "24 twitter,post - youtube,post  -5.8333333 11.51567 106.9261 -0.50655618\n",
      "25  twitter,post - youtube,pre -35.6000000 11.51567 106.9261 -3.09143999\n",
      "26  twitter,pre - youtube,post  27.6000000 11.51567 106.9261  2.39673438\n",
      "27   twitter,pre - youtube,pre  -2.1666667 11.51567 106.9261 -0.18814944\n",
      "28  youtube,post - youtube,pre -29.7666667 10.18447  56.0000 -2.92275203\n",
      "       p.value sig.\n",
      "1  0.677574649     \n",
      "2  0.008548603   **\n",
      "3  0.170356282     \n",
      "4  1.000000000     \n",
      "5  0.236987146     \n",
      "6  1.000000000     \n",
      "7  0.155743823     \n",
      "8  0.822572368     \n",
      "9  1.000000000     \n",
      "10 0.510751960     \n",
      "11 1.000000000     \n",
      "12 1.000000000     \n",
      "13 1.000000000     \n",
      "14 1.000000000     \n",
      "15 0.002397299   **\n",
      "16 1.000000000     \n",
      "17 0.013580022    *\n",
      "18 1.000000000     \n",
      "19 0.067958844    .\n",
      "20 1.000000000     \n",
      "21 0.236987146     \n",
      "22 1.000000000     \n",
      "23 0.044351243    *\n",
      "24 1.000000000     \n",
      "25 0.060966469    .\n",
      "26 0.310716583     \n",
      "27 1.000000000     \n",
      "28 0.109929361     \n",
      "3 & 56 & 4.233 & \\textbf{0.009} & 0.139 & 1 & 56 & 12.593 & \\textbf{0.001} & 0.167 & 3 & 56 & 4.712 & \\textbf{0.005} & 0.157"
     ]
    }
   ],
   "source": [
    "twoway_anova_test(df_ddm_pm, 'noise')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"not normally distributed:\"\n",
      "\n",
      "\tShapiro-Wilk normality test\n",
      "\n",
      "data:  df[[feature]]\n",
      "W = 0.89726, p-value = 1.425e-07\n",
      "\n",
      "bound ~ interrupt * measure + (1 | folder_id)\n",
      "<environment: 0x160112988>\n"
     ]
    },
    {
     "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Term                 F                 Df            Df.res  \n",
      " Length:3           Min.   :0.02519   Min.   :1.000   Min.   :56  \n",
      " Class :character   1st Qu.:1.20500   1st Qu.:2.000   1st Qu.:56  \n",
      " Mode  :character   Median :2.38481   Median :3.000   Median :56  \n",
      "                    Mean   :1.80990   Mean   :2.333   Mean   :56  \n",
      "                    3rd Qu.:2.70225   3rd Qu.:3.000   3rd Qu.:56  \n",
      "                    Max.   :3.01970   Max.   :3.000   Max.   :56  \n",
      "     Pr(>F)       \n",
      " Min.   :0.03723  \n",
      " 1st Qu.:0.05803  \n",
      " Median :0.07883  \n",
      " Mean   :0.33017  \n",
      " 3rd Qu.:0.47664  \n",
      " Max.   :0.87446  \n",
      "Analysis of Variance of Aligned Rank Transformed Data\n",
      "\n",
      "Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) \n",
      "Model: Mixed Effects (lmer)\n",
      "Response: art(bound)\n",
      "\n",
      "                           F Df Df.res   Pr(>F)  \n",
      "1 interrupt         3.019695  3     56 0.037228 *\n",
      "2 measure           0.025193  1     56 0.874458  \n",
      "3 interrupt:measure 2.384814  3     56 0.078830 .\n",
      "---\n",
      "Signif. codes:   0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 \n"
     ]
    },
    {
     "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Eta2 (partial) |       95% CI\n",
      "-------------------------------------------------\n",
      "interrupt         |           0.14 | [0.00, 1.00]\n",
      "measure           |       4.50e-04 | [0.00, 1.00]\n",
      "interrupt:measure |           0.11 | [0.00, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m"
     ]
    },
    {
     "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Omega2 (partial) |       95% CI\n",
      "---------------------------------------------------\n",
      "interrupt         |             0.09 | [0.00, 1.00]\n",
      "measure           |            -0.02 | [0.00, 1.00]\n",
      "interrupt:measure |             0.06 | [0.00, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "boundary (singular) fit: see help('isSingular')\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      contrast   estimate       SE  df     t.ratio     p.value\n",
      "1         rest,post - rest,pre  33.466667 12.03941  56  2.77975905 0.177444912\n",
      "2      rest,post - tiktok,post  49.800000 12.03941 112  4.13641436 0.001917398\n",
      "3       rest,post - tiktok,pre  37.466667 12.03941 112  3.11200117 0.063673095\n",
      "4     rest,post - twitter,post  34.600000 12.03941 112  2.87389432 0.121310232\n",
      "5      rest,post - twitter,pre  37.266667 12.03941 112  3.09538906 0.064564469\n",
      "6     rest,post - youtube,post  29.600000 12.03941 112  2.45859167 0.356021773\n",
      "7      rest,post - youtube,pre  22.866667 12.03941 112  1.89931744 1.000000000\n",
      "8       rest,pre - tiktok,post  16.333333 12.03941 112  1.35665531 1.000000000\n",
      "9        rest,pre - tiktok,pre   4.000000 12.03941 112  0.33224212 1.000000000\n",
      "10     rest,pre - twitter,post   1.133333 12.03941 112  0.09413527 1.000000000\n",
      "11      rest,pre - twitter,pre   3.800000 12.03941 112  0.31563001 1.000000000\n",
      "12     rest,pre - youtube,post  -3.866667 12.03941 112 -0.32116738 1.000000000\n",
      "13      rest,pre - youtube,pre -10.600000 12.03941 112 -0.88044161 1.000000000\n",
      "14    tiktok,post - tiktok,pre -12.333333 12.03941  56 -1.02441320 1.000000000\n",
      "15  tiktok,post - twitter,post -15.200000 12.03941 112 -1.26252005 1.000000000\n",
      "16   tiktok,post - twitter,pre -12.533333 12.03941 112 -1.04102530 1.000000000\n",
      "17  tiktok,post - youtube,post -20.200000 12.03941 112 -1.67782269 1.000000000\n",
      "18   tiktok,post - youtube,pre -26.933333 12.03941 112 -2.23709693 0.599714307\n",
      "19   tiktok,pre - twitter,post  -2.866667 12.03941 112 -0.23810685 1.000000000\n",
      "20    tiktok,pre - twitter,pre  -0.200000 12.03941 112 -0.01661211 1.000000000\n",
      "21   tiktok,pre - youtube,post  -7.866667 12.03941 112 -0.65340950 1.000000000\n",
      "22    tiktok,pre - youtube,pre -14.600000 12.03941 112 -1.21268373 1.000000000\n",
      "23  twitter,post - twitter,pre   2.666667 12.03941  56  0.22149475 1.000000000\n",
      "24 twitter,post - youtube,post  -5.000000 12.03941 112 -0.41530265 1.000000000\n",
      "25  twitter,post - youtube,pre -11.733333 12.03941 112 -0.97457688 1.000000000\n",
      "26  twitter,pre - youtube,post  -7.666667 12.03941 112 -0.63679739 1.000000000\n",
      "27   twitter,pre - youtube,pre -14.400000 12.03941 112 -1.19607162 1.000000000\n",
      "28  youtube,post - youtube,pre  -6.733333 12.03941  56 -0.55927423 1.000000000\n",
      "   sig.\n",
      "1      \n",
      "2    **\n",
      "3     .\n",
      "4      \n",
      "5     .\n",
      "6      \n",
      "7      \n",
      "8      \n",
      "9      \n",
      "10     \n",
      "11     \n",
      "12     \n",
      "13     \n",
      "14     \n",
      "15     \n",
      "16     \n",
      "17     \n",
      "18     \n",
      "19     \n",
      "20     \n",
      "21     \n",
      "22     \n",
      "23     \n",
      "24     \n",
      "25     \n",
      "26     \n",
      "27     \n",
      "28     \n",
      "3 & 56 & 3.02 & \\textbf{0.037} & 0.092 & 1 & 56 & \\textbf{0.025} & 0.874 & -0.017 & 3 & 56 & 2.385 & 0.079 & 0.065"
     ]
    }
   ],
   "source": [
    "twoway_anova_test(df_ddm_pm, 'bound')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"not normally distributed:\"\n",
      "\n",
      "\tShapiro-Wilk normality test\n",
      "\n",
      "data:  df[[feature]]\n",
      "W = 0.94655, p-value = 0.0001218\n",
      "\n",
      "nondectime ~ interrupt * measure + (1 | folder_id)\n",
      "<environment: 0x16833cd40>\n",
      "     Term                 F                Df            Df.res  \n",
      " Length:3           Min.   : 1.695   Min.   :1.000   Min.   :56  \n",
      " Class :character   1st Qu.: 2.194   1st Qu.:2.000   1st Qu.:56  \n",
      " Mode  :character   Median : 2.692   Median :3.000   Median :56  \n",
      "                    Mean   : 6.746   Mean   :2.333   Mean   :56  \n",
      "                    3rd Qu.: 9.272   3rd Qu.:3.000   3rd Qu.:56  \n",
      "                    Max.   :15.851   Max.   :3.000   Max.   :56  \n",
      "     Pr(>F)         \n",
      " Min.   :0.0001994  \n",
      " 1st Qu.:0.0274825  \n",
      " Median :0.0547656  \n",
      " Mean   :0.0778430  \n",
      " 3rd Qu.:0.1166648  \n",
      " Max.   :0.1785639  \n",
      "Analysis of Variance of Aligned Rank Transformed Data\n",
      "\n",
      "Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) \n",
      "Model: Mixed Effects (lmer)\n",
      "Response: art(nondectime)\n",
      "\n",
      "                          F Df Df.res     Pr(>F)    \n",
      "1 interrupt          1.6947  3     56 0.17856395    \n",
      "2 measure           15.8513  1     56 0.00019944 ***\n",
      "3 interrupt:measure  2.6924  3     56 0.05476557   .\n",
      "---\n",
      "Signif. codes:   0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 \n",
      "\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Eta2 (partial) |       95% CI\n",
      "-------------------------------------------------\n",
      "interrupt         |           0.08 | [0.00, 1.00]\n",
      "measure           |           0.22 | [0.08, 1.00]\n",
      "interrupt:measure |           0.13 | [0.00, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Omega2 (partial) |       95% CI\n",
      "---------------------------------------------------\n",
      "interrupt         |             0.03 | [0.00, 1.00]\n",
      "measure           |             0.20 | [0.07, 1.00]\n",
      "interrupt:measure |             0.08 | [0.00, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m                      contrast    estimate        SE       df     t.ratio\n",
      "1         rest,post - rest,pre -22.4666667  8.469984 56.00000 -2.65250394\n",
      "2      rest,post - tiktok,post -34.6666667 12.133968 88.68451 -2.85699345\n",
      "3       rest,post - tiktok,pre -28.1333333 12.133968 88.68451 -2.31856007\n",
      "4     rest,post - twitter,post  -6.4000000 12.133968 88.68451 -0.52744494\n",
      "5      rest,post - twitter,pre -22.6666667 12.133968 88.68451 -1.86803418\n",
      "6     rest,post - youtube,post   1.1333333 12.133968 88.68451  0.09340171\n",
      "7      rest,post - youtube,pre -28.4000000 12.133968 88.68451 -2.34053694\n",
      "8       rest,pre - tiktok,post -12.2000000 12.133968 88.68451 -1.00544192\n",
      "9        rest,pre - tiktok,pre  -5.6666667 12.133968 88.68451 -0.46700854\n",
      "10     rest,pre - twitter,post  16.0666667 12.133968 88.68451  1.32410658\n",
      "11      rest,pre - twitter,pre  -0.2000000 12.133968 88.68451 -0.01648265\n",
      "12     rest,pre - youtube,post  23.6000000 12.133968 88.68451  1.94495323\n",
      "13      rest,pre - youtube,pre  -5.9333333 12.133968 88.68451 -0.48898542\n",
      "14    tiktok,post - tiktok,pre   6.5333333  8.469984 56.00000  0.77135129\n",
      "15  tiktok,post - twitter,post  28.2666667 12.133968 88.68451  2.32954850\n",
      "16   tiktok,post - twitter,pre  12.0000000 12.133968 88.68451  0.98895927\n",
      "17  tiktok,post - youtube,post  35.8000000 12.133968 88.68451  2.95039516\n",
      "18   tiktok,post - youtube,pre   6.2666667 12.133968 88.68451  0.51645651\n",
      "19   tiktok,pre - twitter,post  21.7333333 12.133968 88.68451  1.79111512\n",
      "20    tiktok,pre - twitter,pre   5.4666667 12.133968 88.68451  0.45052589\n",
      "21   tiktok,pre - youtube,post  29.2666667 12.133968 88.68451  2.41196178\n",
      "22    tiktok,pre - youtube,pre  -0.2666667 12.133968 88.68451 -0.02197687\n",
      "23  twitter,post - twitter,pre -16.2666667  8.469984 56.00000 -1.92050731\n",
      "24 twitter,post - youtube,post   7.5333333 12.133968 88.68451  0.62084665\n",
      "25  twitter,post - youtube,pre -22.0000000 12.133968 88.68451 -1.81309200\n",
      "26  twitter,pre - youtube,post  23.8000000 12.133968 88.68451  1.96143589\n",
      "27   twitter,pre - youtube,pre  -5.7333333 12.133968 88.68451 -0.47250276\n",
      "28  youtube,post - youtube,pre -29.5333333  8.469984 56.00000 -3.48682269\n",
      "      p.value sig.\n",
      "1  0.25937934     \n",
      "2  0.13851497     \n",
      "3  0.49448753     \n",
      "4  1.00000000     \n",
      "5  1.00000000     \n",
      "6  1.00000000     \n",
      "7  0.49448753     \n",
      "8  1.00000000     \n",
      "9  1.00000000     \n",
      "10 1.00000000     \n",
      "11 1.00000000     \n",
      "12 1.00000000     \n",
      "13 1.00000000     \n",
      "14 1.00000000     \n",
      "15 0.49448753     \n",
      "16 1.00000000     \n",
      "17 0.10958586     \n",
      "18 1.00000000     \n",
      "19 1.00000000     \n",
      "20 1.00000000     \n",
      "21 0.43026663     \n",
      "22 1.00000000     \n",
      "23 1.00000000     \n",
      "24 1.00000000     \n",
      "25 1.00000000     \n",
      "26 1.00000000     \n",
      "27 1.00000000     \n",
      "28 0.02683226    *\n",
      "3 & 56 & 1.695 & 0.179 & 0.034 & 1 & 56 & 15.851 & \\textbf{\\textbf{< .001}} & 0.204 & 3 & 56 & 2.692 & 0.055 & 0.078"
     ]
    }
   ],
   "source": [
    "twoway_anova_test(df_ddm_pm, 'nondectime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   Df Sum Sq Mean Sq F value  Pr(>F)   \n",
       "interrupt           3   44.6  14.859   4.327 0.00632 **\n",
       "measure             1    1.0   1.004   0.292 0.58982   \n",
       "interrupt:measure   3    2.3   0.762   0.222 0.88094   \n",
       "Residuals         112  384.6   3.434                   \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# twoway_anova_test(df_ddm_ld, 'drift')\n",
    "m <- aov(drift ~ interrupt*measure, data=df_ddm_ld)\n",
    "summary(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A effectsize_anova: 3 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Parameter</th><th scope=col>Eta2_partial</th><th scope=col>CI</th><th scope=col>CI_low</th><th scope=col>CI_high</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&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><td>interrupt        </td><td>0.103862106</td><td>0.95</td><td>0.01875161</td><td>1</td></tr>\n",
       "\t<tr><td>measure          </td><td>0.002603152</td><td>0.95</td><td>0.00000000</td><td>1</td></tr>\n",
       "\t<tr><td>interrupt:measure</td><td>0.005911266</td><td>0.95</td><td>0.00000000</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A effectsize\\_anova: 3 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " Parameter & Eta2\\_partial & CI & CI\\_low & CI\\_high\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t interrupt         & 0.103862106 & 0.95 & 0.01875161 & 1\\\\\n",
       "\t measure           & 0.002603152 & 0.95 & 0.00000000 & 1\\\\\n",
       "\t interrupt:measure & 0.005911266 & 0.95 & 0.00000000 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A effectsize_anova: 3 × 5\n",
       "\n",
       "| Parameter &lt;chr&gt; | Eta2_partial &lt;dbl&gt; | CI &lt;dbl&gt; | CI_low &lt;dbl&gt; | CI_high &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| interrupt         | 0.103862106 | 0.95 | 0.01875161 | 1 |\n",
       "| measure           | 0.002603152 | 0.95 | 0.00000000 | 1 |\n",
       "| interrupt:measure | 0.005911266 | 0.95 | 0.00000000 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  Parameter         Eta2_partial CI   CI_low     CI_high\n",
       "1 interrupt         0.103862106  0.95 0.01875161 1      \n",
       "2 measure           0.002603152  0.95 0.00000000 1      \n",
       "3 interrupt:measure 0.005911266  0.95 0.00000000 1      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eta_squared(m, partial = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"not normally distributed:\"\n",
      "\n",
      "\tShapiro-Wilk normality test\n",
      "\n",
      "data:  df[[feature]]\n",
      "W = 0.97498, p-value = 0.0245\n",
      "\n",
      "noise ~ interrupt * measure + (1 | folder_id)\n",
      "<environment: 0x171e5c180>\n",
      "     Term                 F                Df            Df.res  \n",
      " Length:3           Min.   :0.6971   Min.   :1.000   Min.   :56  \n",
      " Class :character   1st Qu.:1.1440   1st Qu.:2.000   1st Qu.:56  \n",
      " Mode  :character   Median :1.5909   Median :3.000   Median :56  \n",
      "                    Mean   :1.3009   Mean   :2.333   Mean   :56  \n",
      "                    3rd Qu.:1.6028   3rd Qu.:3.000   3rd Qu.:56  \n",
      "                    Max.   :1.6146   Max.   :3.000   Max.   :56  \n",
      "     Pr(>F)      \n",
      " Min.   :0.1962  \n",
      " 1st Qu.:0.2043  \n",
      " Median :0.2124  \n",
      " Mean   :0.3221  \n",
      " 3rd Qu.:0.3851  \n",
      " Max.   :0.5577  \n",
      "Analysis of Variance of Aligned Rank Transformed Data\n",
      "\n",
      "Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) \n",
      "Model: Mixed Effects (lmer)\n",
      "Response: art(noise)\n",
      "\n",
      "                         F Df Df.res  Pr(>F)  \n",
      "1 interrupt         1.6146  3     56 0.19624  \n",
      "2 measure           1.5909  1     56 0.21242  \n",
      "3 interrupt:measure 0.6971  3     56 0.55773  \n",
      "---\n",
      "Signif. codes:   0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 \n",
      "\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Eta2 (partial) |       95% CI\n",
      "-------------------------------------------------\n",
      "interrupt         |           0.08 | [0.00, 1.00]\n",
      "measure           |           0.03 | [0.00, 1.00]\n",
      "interrupt:measure |           0.04 | [0.00, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Omega2 (partial) |       95% CI\n",
      "---------------------------------------------------\n",
      "interrupt         |             0.03 | [0.00, 1.00]\n",
      "measure           |             0.01 | [0.00, 1.00]\n",
      "interrupt:measure |            -0.02 | [0.00, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m                      contrast    estimate       SE       df     t.ratio\n",
      "1         rest,post - rest,pre   7.1333333 11.29796  56.0000  0.63138224\n",
      "2      rest,post - tiktok,post  24.0666667 12.63123 107.6938  1.90533062\n",
      "3       rest,post - tiktok,pre  16.6666667 12.63123 107.6938  1.31948104\n",
      "4     rest,post - twitter,post  -4.4666667 12.63123 107.6938 -0.35362092\n",
      "5      rest,post - twitter,pre   8.5333333 12.63123 107.6938  0.67557429\n",
      "6     rest,post - youtube,post  -2.7333333 12.63123 107.6938 -0.21639489\n",
      "7      rest,post - youtube,pre   8.6666667 12.63123 107.6938  0.68613014\n",
      "8       rest,pre - tiktok,post  16.9333333 12.63123 107.6938  1.34059274\n",
      "9        rest,pre - tiktok,pre   9.5333333 12.63123 107.6938  0.75474316\n",
      "10     rest,pre - twitter,post -11.6000000 12.63123 107.6938 -0.91835881\n",
      "11      rest,pre - twitter,pre   1.4000000 12.63123 107.6938  0.11083641\n",
      "12     rest,pre - youtube,post  -9.8666667 12.63123 107.6938 -0.78113278\n",
      "13      rest,pre - youtube,pre   1.5333333 12.63123 107.6938  0.12139226\n",
      "14    tiktok,post - tiktok,pre  -7.4000000 11.29796  56.0000 -0.65498532\n",
      "15  tiktok,post - twitter,post -28.5333333 12.63123 107.6938 -2.25895154\n",
      "16   tiktok,post - twitter,pre -15.5333333 12.63123 107.6938 -1.22975633\n",
      "17  tiktok,post - youtube,post -26.8000000 12.63123 107.6938 -2.12172552\n",
      "18   tiktok,post - youtube,pre -15.4000000 12.63123 107.6938 -1.21920048\n",
      "19   tiktok,pre - twitter,post -21.1333333 12.63123 107.6938 -1.67310196\n",
      "20    tiktok,pre - twitter,pre  -8.1333333 12.63123 107.6938 -0.64390675\n",
      "21   tiktok,pre - youtube,post -19.4000000 12.63123 107.6938 -1.53587593\n",
      "22    tiktok,pre - youtube,pre  -8.0000000 12.63123 107.6938 -0.63335090\n",
      "23  twitter,post - twitter,pre  13.0000000 11.29796  56.0000  1.15064989\n",
      "24 twitter,post - youtube,post   1.7333333 12.63123 107.6938  0.13722603\n",
      "25  twitter,post - youtube,pre  13.1333333 12.63123 107.6938  1.03975106\n",
      "26  twitter,pre - youtube,post -11.2666667 12.63123 107.6938 -0.89196918\n",
      "27   twitter,pre - youtube,pre   0.1333333 12.63123 107.6938  0.01055585\n",
      "28  youtube,post - youtube,pre  11.4000000 11.29796  56.0000  1.00903144\n",
      "     p.value sig.\n",
      "1  1.0000000     \n",
      "2  1.0000000     \n",
      "3  1.0000000     \n",
      "4  1.0000000     \n",
      "5  1.0000000     \n",
      "6  1.0000000     \n",
      "7  1.0000000     \n",
      "8  1.0000000     \n",
      "9  1.0000000     \n",
      "10 1.0000000     \n",
      "11 1.0000000     \n",
      "12 1.0000000     \n",
      "13 1.0000000     \n",
      "14 1.0000000     \n",
      "15 0.7252130     \n",
      "16 1.0000000     \n",
      "17 0.9761642     \n",
      "18 1.0000000     \n",
      "19 1.0000000     \n",
      "20 1.0000000     \n",
      "21 1.0000000     \n",
      "22 1.0000000     \n",
      "23 1.0000000     \n",
      "24 1.0000000     \n",
      "25 1.0000000     \n",
      "26 1.0000000     \n",
      "27 1.0000000     \n",
      "28 1.0000000     \n",
      "3 & 56 & 1.615 & 0.196 & 0.03 & 1 & 56 & 1.591 & 0.212 & 0.01 & 3 & 56 & 0.697 & 0.558 & -0.015"
     ]
    }
   ],
   "source": [
    "twoway_anova_test(df_ddm_ld, 'noise')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"not normally distributed:\"\n",
      "\n",
      "\tShapiro-Wilk normality test\n",
      "\n",
      "data:  df[[feature]]\n",
      "W = 0.90123, p-value = 2.268e-07\n",
      "\n",
      "bound ~ interrupt * measure + (1 | folder_id)\n",
      "<environment: 0x11e73c768>\n",
      "     Term                 F                 Df            Df.res  \n",
      " Length:3           Min.   :0.06894   Min.   :1.000   Min.   :56  \n",
      " Class :character   1st Qu.:0.23371   1st Qu.:2.000   1st Qu.:56  \n",
      " Mode  :character   Median :0.39849   Median :3.000   Median :56  \n",
      "                    Mean   :0.75977   Mean   :2.333   Mean   :56  \n",
      "                    3rd Qu.:1.10518   3rd Qu.:3.000   3rd Qu.:56  \n",
      "                    Max.   :1.81187   Max.   :3.000   Max.   :56  \n",
      "     Pr(>F)      \n",
      " Min.   :0.1555  \n",
      " 1st Qu.:0.4550  \n",
      " Median :0.7546  \n",
      " Mean   :0.5680  \n",
      " 3rd Qu.:0.7742  \n",
      " Max.   :0.7939  \n",
      "Analysis of Variance of Aligned Rank Transformed Data\n",
      "\n",
      "Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) \n",
      "Model: Mixed Effects (lmer)\n",
      "Response: art(bound)\n",
      "\n",
      "                           F Df Df.res  Pr(>F)  \n",
      "1 interrupt         1.811874  3     56 0.15549  \n",
      "2 measure           0.068936  1     56 0.79386  \n",
      "3 interrupt:measure 0.398489  3     56 0.75460  \n",
      "---\n",
      "Signif. codes:   0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 \n",
      "\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Eta2 (partial) |       95% CI\n",
      "-------------------------------------------------\n",
      "interrupt         |           0.09 | [0.00, 1.00]\n",
      "measure           |       1.23e-03 | [0.00, 1.00]\n",
      "interrupt:measure |           0.02 | [0.00, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Omega2 (partial) |       95% CI\n",
      "---------------------------------------------------\n",
      "interrupt         |             0.04 | [0.00, 1.00]\n",
      "measure           |            -0.02 | [0.00, 1.00]\n",
      "interrupt:measure |            -0.03 | [0.00, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m                      contrast      estimate       SE       df       t.ratio\n",
      "1         rest,post - rest,pre  6.666667e+00 12.14845  56.0000  5.487670e-01\n",
      "2      rest,post - tiktok,post  5.533333e+00 12.69435 111.2123  4.358895e-01\n",
      "3       rest,post - tiktok,pre -4.333333e+00 12.69435 111.2123 -3.413592e-01\n",
      "4     rest,post - twitter,post  8.533333e+00 12.69435 111.2123  6.722151e-01\n",
      "5      rest,post - twitter,pre  6.666667e+00 12.69435 111.2123  5.251680e-01\n",
      "6     rest,post - youtube,post -1.586667e+01 12.69435 111.2123 -1.249900e+00\n",
      "7      rest,post - youtube,pre -1.066667e+01 12.69435 111.2123 -8.402689e-01\n",
      "8       rest,pre - tiktok,post -1.133333e+00 12.69435 111.2123 -8.927857e-02\n",
      "9        rest,pre - tiktok,pre -1.100000e+01 12.69435 111.2123 -8.665273e-01\n",
      "10     rest,pre - twitter,post  1.866667e+00 12.69435 111.2123  1.470471e-01\n",
      "11      rest,pre - twitter,pre  6.217249e-15 12.69435 111.2123  4.897651e-16\n",
      "12     rest,pre - youtube,post -2.253333e+01 12.69435 111.2123 -1.775068e+00\n",
      "13      rest,pre - youtube,pre -1.733333e+01 12.69435 111.2123 -1.365437e+00\n",
      "14    tiktok,post - tiktok,pre -9.866667e+00 12.14845  56.0000 -8.121751e-01\n",
      "15  tiktok,post - twitter,post  3.000000e+00 12.69435 111.2123  2.363256e-01\n",
      "16   tiktok,post - twitter,pre  1.133333e+00 12.69435 111.2123  8.927857e-02\n",
      "17  tiktok,post - youtube,post -2.140000e+01 12.69435 111.2123 -1.685789e+00\n",
      "18   tiktok,post - youtube,pre -1.620000e+01 12.69435 111.2123 -1.276158e+00\n",
      "19   tiktok,pre - twitter,post  1.286667e+01 12.69435 111.2123  1.013574e+00\n",
      "20    tiktok,pre - twitter,pre  1.100000e+01 12.69435 111.2123  8.665273e-01\n",
      "21   tiktok,pre - youtube,post -1.153333e+01 12.69435 111.2123 -9.085407e-01\n",
      "22    tiktok,pre - youtube,pre -6.333333e+00 12.69435 111.2123 -4.989096e-01\n",
      "23  twitter,post - twitter,pre -1.866667e+00 12.14845  56.0000 -1.536548e-01\n",
      "24 twitter,post - youtube,post -2.440000e+01 12.69435 111.2123 -1.922115e+00\n",
      "25  twitter,post - youtube,pre -1.920000e+01 12.69435 111.2123 -1.512484e+00\n",
      "26  twitter,pre - youtube,post -2.253333e+01 12.69435 111.2123 -1.775068e+00\n",
      "27   twitter,pre - youtube,pre -1.733333e+01 12.69435 111.2123 -1.365437e+00\n",
      "28  youtube,post - youtube,pre  5.200000e+00 12.14845  56.0000  4.280383e-01\n",
      "   p.value sig.\n",
      "1        1     \n",
      "2        1     \n",
      "3        1     \n",
      "4        1     \n",
      "5        1     \n",
      "6        1     \n",
      "7        1     \n",
      "8        1     \n",
      "9        1     \n",
      "10       1     \n",
      "11       1     \n",
      "12       1     \n",
      "13       1     \n",
      "14       1     \n",
      "15       1     \n",
      "16       1     \n",
      "17       1     \n",
      "18       1     \n",
      "19       1     \n",
      "20       1     \n",
      "21       1     \n",
      "22       1     \n",
      "23       1     \n",
      "24       1     \n",
      "25       1     \n",
      "26       1     \n",
      "27       1     \n",
      "28       1     \n",
      "3 & 56 & 1.812 & 0.155 & 0.039 & 1 & 56 & 0.069 & 0.794 & -0.016 & 3 & 56 & 0.398 & 0.755 & -0.031"
     ]
    }
   ],
   "source": [
    "twoway_anova_test(df_ddm_ld, 'bound')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"not normally distributed:\"\n",
      "\n",
      "\tShapiro-Wilk normality test\n",
      "\n",
      "data:  df[[feature]]\n",
      "W = 0.86981, p-value = 7.412e-09\n",
      "\n",
      "nondectime ~ interrupt * measure + (1 | folder_id)\n",
      "<environment: 0x12ed400e8>\n",
      "     Term                 F                Df            Df.res  \n",
      " Length:3           Min.   :0.6074   Min.   :1.000   Min.   :56  \n",
      " Class :character   1st Qu.:0.6218   1st Qu.:2.000   1st Qu.:56  \n",
      " Mode  :character   Median :0.6361   Median :3.000   Median :56  \n",
      "                    Mean   :0.9201   Mean   :2.333   Mean   :56  \n",
      "                    3rd Qu.:1.0765   3rd Qu.:3.000   3rd Qu.:56  \n",
      "                    Max.   :1.5168   Max.   :3.000   Max.   :56  \n",
      "     Pr(>F)      \n",
      " Min.   :0.2201  \n",
      " 1st Qu.:0.3296  \n",
      " Median :0.4390  \n",
      " Mean   :0.4180  \n",
      " 3rd Qu.:0.5170  \n",
      " Max.   :0.5949  \n",
      "Analysis of Variance of Aligned Rank Transformed Data\n",
      "\n",
      "Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) \n",
      "Model: Mixed Effects (lmer)\n",
      "Response: art(nondectime)\n",
      "\n",
      "                          F Df Df.res  Pr(>F)  \n",
      "1 interrupt         1.51680  3     56 0.22014  \n",
      "2 measure           0.60745  1     56 0.43903  \n",
      "3 interrupt:measure 0.63610  3     56 0.59489  \n",
      "---\n",
      "Signif. codes:   0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 \n",
      "\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Eta2 (partial) |       95% CI\n",
      "-------------------------------------------------\n",
      "interrupt         |           0.08 | [0.00, 1.00]\n",
      "measure           |           0.01 | [0.00, 1.00]\n",
      "interrupt:measure |           0.03 | [0.00, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m\u001b[34m# Effect Size for ANOVA (Type I)\u001b[39m\n",
      "\n",
      "Parameter         | Omega2 (partial) |       95% CI\n",
      "---------------------------------------------------\n",
      "interrupt         |             0.03 | [0.00, 1.00]\n",
      "measure           |        -6.81e-03 | [0.00, 1.00]\n",
      "interrupt:measure |            -0.02 | [0.00, 1.00]\n",
      "\u001b[36m\n",
      "- One-sided CIs: upper bound fixed at [1.00].\u001b[39m                      contrast   estimate       SE       df     t.ratio\n",
      "1         rest,post - rest,pre   6.333333  8.89653 56.00000  0.71188801\n",
      "2      rest,post - tiktok,post  31.800000 12.65369 89.19231  2.51310090\n",
      "3       rest,post - tiktok,pre  16.866667 12.65369 89.19231  1.33294450\n",
      "4     rest,post - twitter,post   8.600000 12.65369 89.19231  0.67964364\n",
      "5      rest,post - twitter,pre   6.933333 12.65369 89.19231  0.54792976\n",
      "6     rest,post - youtube,post  11.866667 12.65369 89.19231  0.93780285\n",
      "7      rest,post - youtube,pre   9.600000 12.65369 89.19231  0.75867197\n",
      "8       rest,pre - tiktok,post  25.466667 12.65369 89.19231  2.01258814\n",
      "9        rest,pre - tiktok,pre  10.533333 12.65369 89.19231  0.83243174\n",
      "10     rest,pre - twitter,post   2.266667 12.65369 89.19231  0.17913088\n",
      "11      rest,pre - twitter,pre   0.600000 12.65369 89.19231  0.04741700\n",
      "12     rest,pre - youtube,post   5.533333 12.65369 89.19231  0.43729009\n",
      "13      rest,pre - youtube,pre   3.266667 12.65369 89.19231  0.25815921\n",
      "14    tiktok,post - tiktok,pre -14.933333  8.89653 56.00000 -1.67855699\n",
      "15  tiktok,post - twitter,post -23.200000 12.65369 89.19231 -1.83345726\n",
      "16   tiktok,post - twitter,pre -24.866667 12.65369 89.19231 -1.96517114\n",
      "17  tiktok,post - youtube,post -19.933333 12.65369 89.19231 -1.57529805\n",
      "18   tiktok,post - youtube,pre -22.200000 12.65369 89.19231 -1.75442893\n",
      "19   tiktok,pre - twitter,post  -8.266667 12.65369 89.19231 -0.65330086\n",
      "20    tiktok,pre - twitter,pre  -9.933333 12.65369 89.19231 -0.78501475\n",
      "21   tiktok,pre - youtube,post  -5.000000 12.65369 89.19231 -0.39514165\n",
      "22    tiktok,pre - youtube,pre  -7.266667 12.65369 89.19231 -0.57427253\n",
      "23  twitter,post - twitter,pre  -1.666667  8.89653 56.00000 -0.18733895\n",
      "24 twitter,post - youtube,post   3.266667 12.65369 89.19231  0.25815921\n",
      "25  twitter,post - youtube,pre   1.000000 12.65369 89.19231  0.07902833\n",
      "26  twitter,pre - youtube,post   4.933333 12.65369 89.19231  0.38987310\n",
      "27   twitter,pre - youtube,pre   2.666667 12.65369 89.19231  0.21074221\n",
      "28  youtube,post - youtube,pre  -2.266667  8.89653 56.00000 -0.25478097\n",
      "     p.value sig.\n",
      "1  1.0000000     \n",
      "2  0.3853885     \n",
      "3  1.0000000     \n",
      "4  1.0000000     \n",
      "5  1.0000000     \n",
      "6  1.0000000     \n",
      "7  1.0000000     \n",
      "8  1.0000000     \n",
      "9  1.0000000     \n",
      "10 1.0000000     \n",
      "11 1.0000000     \n",
      "12 1.0000000     \n",
      "13 1.0000000     \n",
      "14 1.0000000     \n",
      "15 1.0000000     \n",
      "16 1.0000000     \n",
      "17 1.0000000     \n",
      "18 1.0000000     \n",
      "19 1.0000000     \n",
      "20 1.0000000     \n",
      "21 1.0000000     \n",
      "22 1.0000000     \n",
      "23 1.0000000     \n",
      "24 1.0000000     \n",
      "25 1.0000000     \n",
      "26 1.0000000     \n",
      "27 1.0000000     \n",
      "28 1.0000000     \n",
      "3 & 56 & 1.517 & 0.22 & 0.025 & 1 & 56 & 0.607 & 0.439 & -0.007 & 3 & 56 & 0.636 & 0.595 & -0.019"
     ]
    }
   ],
   "source": [
    "twoway_anova_test(df_ddm_ld, 'nondectime')"
   ]
  }
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