09-Task-Evaluate_Data/03a2_EVAL_BEH_Confusion_LMEM Duration.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7c9a9ba0-1fa4-467c-be6d-de2c92c50a30",
   "metadata": {},
   "source": [
    "# EVALUATION: Behavioral Duration with LMEM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "774c436e-1ab4-477b-ba08-dcf2008f1774",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\maure\\AppData\\Roaming\\Python\\Python311\\site-packages\\rpy2\\robjects\\packages.py:367: UserWarning: The symbol 'quartz' is not in this R namespace/package.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Lade nötiges Paket: Matrix\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Attache Paket: 'lmerTest'\n",
       "\n",
       "Das folgende Objekt ist maskiert 'package:lme4':\n",
       "\n",
       "    lmer\n",
       "\n",
       "Das folgende Objekt ist maskiert 'package:stats':\n",
       "\n",
       "    step\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "ciTools version 0.6.1 (C) Institute for Defense Analyses\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Attache Paket: 'nlme'\n",
       "\n",
       "Das folgende Objekt ist maskiert 'package:lme4':\n",
       "\n",
       "    lmList\n",
       "\n",
       "In addition: Warning message:\n",
       "Paket 'nlme' wurde unter R Version 4.3.3 erstellt \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Lade nötiges Paket: mgcv\n",
       "This is mgcv 1.9-0. For overview type 'help(\"mgcv-package\")'.\n",
       "Lade nötiges Paket: plotfunctions\n",
       "Loaded package itsadug 2.4 (see 'help(\"itsadug\")' ).\n",
       "In addition: Warning messages:\n",
       "1: Paket 'itsadug' wurde unter R Version 4.3.3 erstellt \n",
       "2: Paket 'plotfunctions' wurde unter R Version 4.3.3 erstellt \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from utils.utils import *\n",
    "\n",
    "%run utils/LMEM_helpers.ipynb"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e75c7be5-217d-4fcd-9a80-f6b83409a9a4",
   "metadata": {},
   "source": [
    "### Load behavioral data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "365c1ce4-2d53-4f4b-9ab7-7159c28446c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Snippet</th>\n",
       "      <th>Fixation_Start</th>\n",
       "      <th>End</th>\n",
       "      <th>Start</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Answer</th>\n",
       "      <th>Correctness</th>\n",
       "      <th>Rating</th>\n",
       "      <th>right_answer</th>\n",
       "      <th>possible_wrong_answers</th>\n",
       "      <th>...</th>\n",
       "      <th>HDF_index</th>\n",
       "      <th>Participant</th>\n",
       "      <th>start_event</th>\n",
       "      <th>Block</th>\n",
       "      <th>Block_Index</th>\n",
       "      <th>Stimulus_id</th>\n",
       "      <th>Trial_No</th>\n",
       "      <th>Block_No</th>\n",
       "      <th>In_Block_No</th>\n",
       "      <th>Duration_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16-obf-v0</td>\n",
       "      <td>130.144131</td>\n",
       "      <td>165.194542</td>\n",
       "      <td>135.137310</td>\n",
       "      <td>30.057232</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5;3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>P003</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stimulus/S 12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.403103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5-clean-v0</td>\n",
       "      <td>174.369258</td>\n",
       "      <td>203.895033</td>\n",
       "      <td>179.370753</td>\n",
       "      <td>24.524280</td>\n",
       "      <td>false</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>true</td>\n",
       "      <td>false</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>P003</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Stimulus/S 11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.199664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61-clean-v0</td>\n",
       "      <td>211.250803</td>\n",
       "      <td>221.093158</td>\n",
       "      <td>216.250725</td>\n",
       "      <td>4.842433</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2;0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>P003</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Stimulus/S 11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.577417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13-clean-v0</td>\n",
       "      <td>228.432896</td>\n",
       "      <td>237.992304</td>\n",
       "      <td>233.416647</td>\n",
       "      <td>4.575657</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6;3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>P003</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Stimulus/S 11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.520750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10-obf-v0</td>\n",
       "      <td>245.032454</td>\n",
       "      <td>264.358911</td>\n",
       "      <td>250.001624</td>\n",
       "      <td>14.357288</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0;2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>P003</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Stimulus/S 12</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2.664258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1722</th>\n",
       "      <td>18-clean-v2</td>\n",
       "      <td>650.097955</td>\n",
       "      <td>671.641398</td>\n",
       "      <td>655.097237</td>\n",
       "      <td>16.544161</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4;5</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>P106</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>Stimulus/S 11</td>\n",
       "      <td>68</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>2.806033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>54-obf-v2</td>\n",
       "      <td>678.104435</td>\n",
       "      <td>687.740039</td>\n",
       "      <td>683.096211</td>\n",
       "      <td>4.643827</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6;2</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>P106</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>Stimulus/S 12</td>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>1.535539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1724</th>\n",
       "      <td>9-clean-v2</td>\n",
       "      <td>694.153446</td>\n",
       "      <td>707.572308</td>\n",
       "      <td>699.128912</td>\n",
       "      <td>8.443396</td>\n",
       "      <td>false</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>false</td>\n",
       "      <td>true</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>P106</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>Stimulus/S 11</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>2.133385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>12-obf-v2</td>\n",
       "      <td>713.678701</td>\n",
       "      <td>726.539764</td>\n",
       "      <td>718.661568</td>\n",
       "      <td>7.878196</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3;2</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>P106</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>Stimulus/S 12</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>2.064099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>15-obf-v2</td>\n",
       "      <td>733.845005</td>\n",
       "      <td>755.571496</td>\n",
       "      <td>738.827485</td>\n",
       "      <td>16.744011</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>312.0;63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>P106</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>Stimulus/S 12</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>2.818041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1727 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Snippet  Fixation_Start         End       Start   Duration Answer   \n",
       "0       16-obf-v0      130.144131  165.194542  135.137310  30.057232      3  \\\n",
       "1      5-clean-v0      174.369258  203.895033  179.370753  24.524280  false   \n",
       "2     61-clean-v0      211.250803  221.093158  216.250725   4.842433      4   \n",
       "3     13-clean-v0      228.432896  237.992304  233.416647   4.575657      5   \n",
       "4       10-obf-v0      245.032454  264.358911  250.001624  14.357288      1   \n",
       "...           ...             ...         ...         ...        ...    ...   \n",
       "1722  18-clean-v2      650.097955  671.641398  655.097237  16.544161      6   \n",
       "1723    54-obf-v2      678.104435  687.740039  683.096211   4.643827      6   \n",
       "1724   9-clean-v2      694.153446  707.572308  699.128912   8.443396  false   \n",
       "1725    12-obf-v2      713.678701  726.539764  718.661568   7.878196      4   \n",
       "1726    15-obf-v2      733.845005  755.571496  738.827485  16.744011   62.0   \n",
       "\n",
       "      Correctness  Rating right_answer possible_wrong_answers  ... HDF_index   \n",
       "0               0       1            7                    5;3  ...       0.0  \\\n",
       "1               0       1         true                  false  ...       0.0   \n",
       "2               1       1            4                    2;0  ...       0.0   \n",
       "3               1       1            5                    6;3  ...       0.0   \n",
       "4               1       0            1                    0;2  ...       0.0   \n",
       "...           ...     ...          ...                    ...  ...       ...   \n",
       "1722            1       1            6                    4;5  ...       7.0   \n",
       "1723            0       1            0                    6;2  ...       7.0   \n",
       "1724            1       1        false                   true  ...       7.0   \n",
       "1725            1       1            4                    3;2  ...       7.0   \n",
       "1726            1       0         62.0             312.0;63.0  ...       7.0   \n",
       "\n",
       "     Participant start_event Block Block_Index    Stimulus_id  Trial_No   \n",
       "0           P003          12     1           1  Stimulus/S 12         1  \\\n",
       "1           P003          11     1           2  Stimulus/S 11         2   \n",
       "2           P003          11     1           3  Stimulus/S 11         3   \n",
       "3           P003          11     1           4  Stimulus/S 11         4   \n",
       "4           P003          12     1           5  Stimulus/S 12         5   \n",
       "...          ...         ...   ...         ...            ...       ...   \n",
       "1722        P106          11     3          20  Stimulus/S 11        68   \n",
       "1723        P106          12     3          21  Stimulus/S 12        69   \n",
       "1724        P106          11     3          22  Stimulus/S 11        70   \n",
       "1725        P106          12     3          23  Stimulus/S 12        71   \n",
       "1726        P106          12     3          24  Stimulus/S 12        72   \n",
       "\n",
       "     Block_No  In_Block_No  Duration_log  \n",
       "0           1            1      3.403103  \n",
       "1           1            2      3.199664  \n",
       "2           1            3      1.577417  \n",
       "3           1            4      1.520750  \n",
       "4           1            5      2.664258  \n",
       "...       ...          ...           ...  \n",
       "1722        3           20      2.806033  \n",
       "1723        3           21      1.535539  \n",
       "1724        3           22      2.133385  \n",
       "1725        3           23      2.064099  \n",
       "1726        3           24      2.818041  \n",
       "\n",
       "[1727 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Snippet                    object\n",
      "Fixation_Start            float64\n",
      "End                       float64\n",
      "Start                     float64\n",
      "Duration                  float64\n",
      "Answer                     object\n",
      "Correctness                 int64\n",
      "Rating                      int64\n",
      "right_answer               object\n",
      "possible_wrong_answers     object\n",
      "number                     object\n",
      "variant                    object\n",
      "version                    object\n",
      "Langhout AoC_category      object\n",
      "AoC_category               object\n",
      "Condition                  object\n",
      "HDF_index                 float64\n",
      "Participant                object\n",
      "start_event                 int64\n",
      "Block                       int64\n",
      "Block_Index                 int64\n",
      "Stimulus_id                object\n",
      "Trial_No                    int64\n",
      "Block_No                    int64\n",
      "In_Block_No                 int64\n",
      "Duration_log              float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Snippet data\n",
    "behavioral_data = pd.read_csv(get_behavioral_data_path(\n",
    "    final_data_exclusion=True, analysis_prep=True), sep=SEPARATOR, index_col=False, dtype={PARTICIPANT: str})\n",
    "display(behavioral_data)\n",
    "print(behavioral_data.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d179a8eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "sns.violinplot(behavioral_data, y=BEHAVIORAL_COLUMN_DURATION, ax=ax1,)\n",
    "sns.violinplot(behavioral_data, y=BEHAVIORAL_COLUMN_DURATION_LOG, log_scale=True, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e843ec2",
   "metadata": {},
   "source": [
    "### Create models for confusion influence level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8acddd",
   "metadata": {},
   "source": [
    "#### Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d8fc18c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duration_log ~ 1 + Condition + (1 + Condition | Participant) + (1 + Condition | number)\n",
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: formula\n",
      "   Data: data\n",
      "\n",
      "REML criterion at convergence: 2136.7\n",
      "\n",
      "Scaled residuals: \n",
      "    Min      1Q  Median      3Q     Max \n",
      "-3.5490 -0.6500 -0.0627  0.5981  3.3002 \n",
      "\n",
      "Random effects:\n",
      " Groups      Name               Variance Std.Dev. Corr \n",
      " Participant (Intercept)        0.079005 0.28108       \n",
      "             Conditionconfusing 0.002131 0.04616  0.08 \n",
      " number      (Intercept)        0.123842 0.35191       \n",
      "             Conditionconfusing 0.005583 0.07472  -0.27\n",
      " Residual                       0.180261 0.42457       \n",
      "Number of obs: 1727, groups:  Participant, 24; number, 24\n",
      "\n",
      "Fixed effects:\n",
      "                   Estimate Std. Error       df t value Pr(>|t|)    \n",
      "(Intercept)         2.23142    0.09306 42.03500  23.978   <2e-16 ***\n",
      "Conditionconfusing  0.07711    0.02718 17.18848   2.837   0.0113 *  \n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr)\n",
      "Cndtncnfsng -0.185\n",
      "Type III Analysis of Variance Table with Satterthwaite's method\n",
      "          Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)  \n",
      "Condition 1.4507  1.4507     1 17.189  8.0476 0.0113 *\n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "AIC [2154.70700436] BIC [2203.79427407]\n"
     ]
    }
   ],
   "source": [
    "# Duration: Gaussian on log values\n",
    "res2 = create_glmer_in_R(\n",
    "    f'{BEHAVIORAL_COLUMN_DURATION_LOG} ~ 1 + {CONDITION} + ' +\n",
    "    f'(1 + {CONDITION} | {PARTICIPANT}) + ' +\n",
    "    f'(1 + {CONDITION} | {SNIPPET_COLUMN_NUMBER})',\n",
    "    behavioral_data, description='duration confusion base model log gaussian')\n",
    "# --> significant effect of confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d190aca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duration_log ~ 1 + Condition * Block_No * In_Block_No + (1 + Condition | Participant) + (1 + Condition | number)\n",
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: formula\n",
      "   Data: data\n",
      "\n",
      "REML criterion at convergence: 1829.3\n",
      "\n",
      "Scaled residuals: \n",
      "    Min      1Q  Median      3Q     Max \n",
      "-3.3093 -0.6759 -0.0274  0.5920  4.0442 \n",
      "\n",
      "Random effects:\n",
      " Groups      Name               Variance Std.Dev. Corr \n",
      " Participant (Intercept)        0.079826 0.28253       \n",
      "             Conditionconfusing 0.003885 0.06233  0.00 \n",
      " number      (Intercept)        0.125095 0.35369       \n",
      "             Conditionconfusing 0.008262 0.09090  -0.28\n",
      " Residual                       0.144913 0.38067       \n",
      "Number of obs: 1727, groups:  Participant, 24; number, 24\n",
      "\n",
      "Fixed effects:\n",
      "                                          Estimate Std. Error         df\n",
      "(Intercept)                              2.744e+00  1.195e-01  1.128e+02\n",
      "Conditionconfusing                       2.530e-01  1.041e-01  1.270e+03\n",
      "Block_No                                -2.286e-01  3.471e-02  1.648e+03\n",
      "In_Block_No                             -1.054e-02  5.167e-03  1.649e+03\n",
      "Conditionconfusing:Block_No             -7.420e-02  4.696e-02  1.644e+03\n",
      "Conditionconfusing:In_Block_No          -6.194e-03  7.132e-03  1.647e+03\n",
      "Block_No:In_Block_No                     3.144e-03  2.389e-03  1.652e+03\n",
      "Conditionconfusing:Block_No:In_Block_No  1.728e-03  3.295e-03  1.647e+03\n",
      "                                        t value Pr(>|t|)    \n",
      "(Intercept)                              22.962  < 2e-16 ***\n",
      "Conditionconfusing                        2.429   0.0153 *  \n",
      "Block_No                                 -6.584 6.12e-11 ***\n",
      "In_Block_No                              -2.040   0.0415 *  \n",
      "Conditionconfusing:Block_No              -1.580   0.1143    \n",
      "Conditionconfusing:In_Block_No           -0.868   0.3853    \n",
      "Block_No:In_Block_No                      1.316   0.1884    \n",
      "Conditionconfusing:Block_No:In_Block_No   0.524   0.6001    \n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr) Cndtnc Blck_N In_B_N Cn:B_N C:I_B_ B_N:I_\n",
      "Cndtncnfsng -0.492                                          \n",
      "Block_No    -0.587  0.674                                   \n",
      "In_Block_No -0.566  0.650  0.827                            \n",
      "Cndtncn:B_N  0.434 -0.904 -0.740 -0.613                     \n",
      "Cndtn:I_B_N  0.410 -0.857 -0.600 -0.725  0.813              \n",
      "Blc_N:I_B_N  0.520 -0.597 -0.889 -0.924  0.658  0.670       \n",
      "C:B_N:I_B_N -0.377  0.793  0.646  0.671 -0.878 -0.926 -0.726\n",
      "Type III Analysis of Variance Table with Satterthwaite's method\n",
      "                                Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)\n",
      "Condition                       0.8549  0.8549     1 1270.1   5.8997 0.0152812\n",
      "Block_No                       18.5820 18.5820     1 1648.4 128.2288 < 2.2e-16\n",
      "In_Block_No                     2.1233  2.1233     1 1652.2  14.6521 0.0001341\n",
      "Condition:Block_No              0.3618  0.3618     1 1643.6   2.4964 0.1142970\n",
      "Condition:In_Block_No           0.1093  0.1093     1 1646.7   0.7542 0.3852623\n",
      "Block_No:In_Block_No            0.8595  0.8595     1 1652.7   5.9309 0.0149826\n",
      "Condition:Block_No:In_Block_No  0.0398  0.0398     1 1647.1   0.2750 0.6000902\n",
      "                                  \n",
      "Condition                      *  \n",
      "Block_No                       ***\n",
      "In_Block_No                    ***\n",
      "Condition:Block_No                \n",
      "Condition:In_Block_No             \n",
      "Block_No:In_Block_No           *  \n",
      "Condition:Block_No:In_Block_No    \n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "AIC [1859.27734988] BIC [1941.08946606]\n"
     ]
    }
   ],
   "source": [
    "# Duration: Gaussian on log values with trial number and confusion in random\n",
    "res2 = create_glmer_in_R(\n",
    "    f'{BEHAVIORAL_COLUMN_DURATION_LOG} ~ 1 + {CONDITION} * Block_No * In_Block_No + ' +\n",
    "    f'(1 + {CONDITION} | {PARTICIPANT}) + ' +\n",
    "    f'(1 + {CONDITION} | {SNIPPET_COLUMN_NUMBER})',\n",
    "    behavioral_data, description='duration confusion block_no in_block_no 3wise log gaussian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57639bea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duration_log ~ 1 + Condition * Block_No + Condition * In_Block_No + Block_No * In_Block_No + (1 + Condition | Participant) + (1 + Condition | number)\n",
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: formula\n",
      "   Data: data\n",
      "\n",
      "REML criterion at convergence: 1820\n",
      "\n",
      "Scaled residuals: \n",
      "    Min      1Q  Median      3Q     Max \n",
      "-3.2990 -0.6772 -0.0349  0.5986  4.0185 \n",
      "\n",
      "Random effects:\n",
      " Groups      Name               Variance Std.Dev. Corr \n",
      " Participant (Intercept)        0.079848 0.28257       \n",
      "             Conditionconfusing 0.003891 0.06238  0.00 \n",
      " number      (Intercept)        0.125088 0.35368       \n",
      "             Conditionconfusing 0.008236 0.09075  -0.28\n",
      " Residual                       0.144850 0.38059       \n",
      "Number of obs: 1727, groups:  Participant, 24; number, 24\n",
      "\n",
      "Fixed effects:\n",
      "                                 Estimate Std. Error         df t value\n",
      "(Intercept)                     2.768e+00  1.107e-01  8.352e+01  25.011\n",
      "Conditionconfusing              2.096e-01  6.339e-02  4.227e+02   3.307\n",
      "Block_No                       -2.403e-01  2.650e-02  1.646e+03  -9.068\n",
      "In_Block_No                    -1.236e-02  3.830e-03  1.653e+03  -3.226\n",
      "Conditionconfusing:Block_No    -5.259e-02  2.249e-02  1.629e+03  -2.338\n",
      "Conditionconfusing:In_Block_No -2.732e-03  2.696e-03  1.652e+03  -1.013\n",
      "Block_No:In_Block_No            4.053e-03  1.643e-03  1.653e+03   2.467\n",
      "                               Pr(>|t|)    \n",
      "(Intercept)                     < 2e-16 ***\n",
      "Conditionconfusing              0.00102 ** \n",
      "Block_No                        < 2e-16 ***\n",
      "In_Block_No                     0.00128 ** \n",
      "Conditionconfusing:Block_No     0.01952 *  \n",
      "Conditionconfusing:In_Block_No  0.31106    \n",
      "Block_No:In_Block_No            0.01372 *  \n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr) Cndtnc Blck_N In_B_N Cn:B_N C:I_B_\n",
      "Cndtncnfsng -0.342                                   \n",
      "Block_No    -0.485  0.348                            \n",
      "In_Block_No -0.455  0.260  0.696                     \n",
      "Cndtncn:B_N  0.232 -0.712 -0.473 -0.066              \n",
      "Cndtn:I_B_N  0.174 -0.533 -0.008 -0.371  0.001       \n",
      "Blc_N:I_B_N  0.386 -0.050 -0.801 -0.857  0.063 -0.009\n",
      "Type III Analysis of Variance Table with Satterthwaite's method\n",
      "                       Sum Sq Mean Sq NumDF   DenDF  F value    Pr(>F)    \n",
      "Condition              1.5844  1.5844     1  422.68  10.9382 0.0010225 ** \n",
      "Block_No              18.8252 18.8252     1 1649.09 129.9634 < 2.2e-16 ***\n",
      "In_Block_No            2.1549  2.1549     1 1653.23  14.8767 0.0001192 ***\n",
      "Condition:Block_No     0.7916  0.7916     1 1628.74   5.4652 0.0195192 *  \n",
      "Condition:In_Block_No  0.1487  0.1487     1 1651.84   1.0268 0.3110600    \n",
      "Block_No:In_Block_No   0.8816  0.8816     1 1653.48   6.0863 0.0137240 *  \n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "AIC [1847.95908082] BIC [1924.31705592]\n"
     ]
    }
   ],
   "source": [
    "# simplification Runde 1: 3wise interaction rauswerfen\n",
    "res2 = create_glmer_in_R(\n",
    "    f'{BEHAVIORAL_COLUMN_DURATION_LOG} ~ 1 + {CONDITION} * Block_No + {CONDITION} * In_Block_No + Block_No * In_Block_No + ' +\n",
    "    f'(1 + {CONDITION} | {PARTICIPANT}) + ' +\n",
    "    f'(1 + {CONDITION} | {SNIPPET_COLUMN_NUMBER})',\n",
    "    behavioral_data, description='duration confusion block_no in_block_no all 2wise log gaussian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c2f7663",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duration_log ~ 1 + Condition * Block_No + Block_No * In_Block_No + (1 + Condition | Participant) + (1 + Condition | number)\n",
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: formula\n",
      "   Data: data\n",
      "\n",
      "REML criterion at convergence: 1811\n",
      "\n",
      "Scaled residuals: \n",
      "    Min      1Q  Median      3Q     Max \n",
      "-3.3096 -0.6632 -0.0264  0.5986  4.0630 \n",
      "\n",
      "Random effects:\n",
      " Groups      Name               Variance Std.Dev. Corr \n",
      " Participant (Intercept)        0.079794 0.28248       \n",
      "             Conditionconfusing 0.003895 0.06241  0.00 \n",
      " number      (Intercept)        0.125167 0.35379       \n",
      "             Conditionconfusing 0.008269 0.09094  -0.28\n",
      " Residual                       0.144848 0.38059       \n",
      "Number of obs: 1727, groups:  Participant, 24; number, 24\n",
      "\n",
      "Fixed effects:\n",
      "                              Estimate Std. Error         df t value Pr(>|t|)\n",
      "(Intercept)                  2.787e+00  1.090e-01  7.861e+01  25.574  < 2e-16\n",
      "Conditionconfusing           1.754e-01  5.364e-02  2.405e+02   3.270 0.001234\n",
      "Block_No                    -2.405e-01  2.650e-02  1.647e+03  -9.076  < 2e-16\n",
      "In_Block_No                 -1.380e-02  3.557e-03  1.654e+03  -3.879 0.000109\n",
      "Conditionconfusing:Block_No -5.257e-02  2.249e-02  1.630e+03  -2.337 0.019548\n",
      "Block_No:In_Block_No         4.039e-03  1.643e-03  1.654e+03   2.459 0.014052\n",
      "                               \n",
      "(Intercept)                 ***\n",
      "Conditionconfusing          ** \n",
      "Block_No                    ***\n",
      "In_Block_No                 ***\n",
      "Conditionconfusing:Block_No *  \n",
      "Block_No:In_Block_No        *  \n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr) Cndtnc Blck_N In_B_N Cn:B_N\n",
      "Cndtncnfsng -0.299                            \n",
      "Block_No    -0.491  0.406                     \n",
      "In_Block_No -0.427  0.080  0.747              \n",
      "Cndtncn:B_N  0.236 -0.841 -0.473 -0.071       \n",
      "Blc_N:I_B_N  0.393 -0.064 -0.801 -0.926  0.063\n",
      "Type III Analysis of Variance Table with Satterthwaite's method\n",
      "                      Sum Sq Mean Sq NumDF   DenDF  F value    Pr(>F)    \n",
      "Condition             1.5487  1.5487     1  240.48  10.6920 0.0012335 ** \n",
      "Block_No             18.8548 18.8548     1 1650.09 130.1690 < 2.2e-16 ***\n",
      "In_Block_No           2.1790  2.1790     1 1654.24  15.0437 0.0001092 ***\n",
      "Condition:Block_No    0.7913  0.7913     1 1629.71   5.4626 0.0195479 *  \n",
      "Block_No:In_Block_No  0.8755  0.8755     1 1654.42   6.0445 0.0140518 *  \n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "AIC [1836.99181645] BIC [1907.89565046]\n"
     ]
    }
   ],
   "source": [
    "# simplification Runde 2: 2wise interaction {CONDITION} * In_Block_No rauswerfen\n",
    "res2 = create_glmer_in_R(\n",
    "    f'{BEHAVIORAL_COLUMN_DURATION_LOG} ~ 1 + {CONDITION} * Block_No + Block_No * In_Block_No + ' +\n",
    "    f'(1 + {CONDITION} | {PARTICIPANT}) + ' +\n",
    "    f'(1 + {CONDITION} | {SNIPPET_COLUMN_NUMBER})',\n",
    "    behavioral_data, description='duration confusion block_no in_block_no 2 2wise with b gaussian final')\n",
    "# with aic = -1033.7478601 best model in round 2, no more rounds due to all factors being significant"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}