statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'out/result/results_dyadic.csv'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mFileNotFoundError\u001B[0m                         Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_9844/1540294113.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mpandas\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0mpd\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      2\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 3\u001B[1;33m \u001B[0mdf_dyadic\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mread_csv\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"out/result/results_dyadic.csv\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mindex_col\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"Subject\"\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      4\u001B[0m \u001B[0mdf_dyadic\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"Subject\"\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdf_dyadic\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mindex\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      5\u001B[0m \u001B[0mdf_dyadic\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001B[0m in \u001B[0;36mwrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    309\u001B[0m                     \u001B[0mstacklevel\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mstacklevel\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    310\u001B[0m                 )\n\u001B[1;32m--> 311\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mfunc\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    312\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    313\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mwrapper\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001B[0m in \u001B[0;36mread_csv\u001B[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001B[0m\n\u001B[0;32m    584\u001B[0m     \u001B[0mkwds\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mupdate\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkwds_defaults\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    585\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 586\u001B[1;33m     \u001B[1;32mreturn\u001B[0m \u001B[0m_read\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfilepath_or_buffer\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mkwds\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    587\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    588\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001B[0m in \u001B[0;36m_read\u001B[1;34m(filepath_or_buffer, kwds)\u001B[0m\n\u001B[0;32m    480\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    481\u001B[0m     \u001B[1;31m# Create the parser.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 482\u001B[1;33m     \u001B[0mparser\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mTextFileReader\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfilepath_or_buffer\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwds\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    483\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    484\u001B[0m     \u001B[1;32mif\u001B[0m \u001B[0mchunksize\u001B[0m \u001B[1;32mor\u001B[0m \u001B[0miterator\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, f, engine, **kwds)\u001B[0m\n\u001B[0;32m    809\u001B[0m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0moptions\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"has_index_names\"\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mkwds\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"has_index_names\"\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    810\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 811\u001B[1;33m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_engine\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_make_engine\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mengine\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    812\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    813\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mclose\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001B[0m in \u001B[0;36m_make_engine\u001B[1;34m(self, engine)\u001B[0m\n\u001B[0;32m   1038\u001B[0m             )\n\u001B[0;32m   1039\u001B[0m         \u001B[1;31m# error: Too many arguments for \"ParserBase\"\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1040\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mmapping\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mengine\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mf\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0moptions\u001B[0m\u001B[1;33m)\u001B[0m  \u001B[1;31m# type: ignore[call-arg]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1041\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1042\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0m_failover_to_python\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, src, **kwds)\u001B[0m\n\u001B[0;32m     49\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     50\u001B[0m         \u001B[1;31m# open handles\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 51\u001B[1;33m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_open_handles\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0msrc\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mkwds\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     52\u001B[0m         \u001B[1;32massert\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mhandles\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     53\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\base_parser.py\u001B[0m in \u001B[0;36m_open_handles\u001B[1;34m(self, src, kwds)\u001B[0m\n\u001B[0;32m    220\u001B[0m         \u001B[0mLet\u001B[0m \u001B[0mthe\u001B[0m \u001B[0mreaders\u001B[0m \u001B[0mopen\u001B[0m \u001B[0mIOHandles\u001B[0m \u001B[0mafter\u001B[0m \u001B[0mthey\u001B[0m \u001B[0mare\u001B[0m \u001B[0mdone\u001B[0m \u001B[1;32mwith\u001B[0m \u001B[0mtheir\u001B[0m \u001B[0mpotential\u001B[0m \u001B[0mraises\u001B[0m\u001B[1;33m.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    221\u001B[0m         \"\"\"\n\u001B[1;32m--> 222\u001B[1;33m         self.handles = get_handle(\n\u001B[0m\u001B[0;32m    223\u001B[0m             \u001B[0msrc\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    224\u001B[0m             \u001B[1;34m\"r\"\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001B[0m in \u001B[0;36mget_handle\u001B[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001B[0m\n\u001B[0;32m    700\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mioargs\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mencoding\u001B[0m \u001B[1;32mand\u001B[0m \u001B[1;34m\"b\"\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mioargs\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmode\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    701\u001B[0m             \u001B[1;31m# Encoding\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 702\u001B[1;33m             handle = open(\n\u001B[0m\u001B[0;32m    703\u001B[0m                 \u001B[0mhandle\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    704\u001B[0m                 \u001B[0mioargs\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmode\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: 'out/result/results_dyadic.csv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_dyadic = pd.read_csv(\"out/result/results_dyadic.csv\", index_col=[\"Subject\"])\n",
    "df_dyadic[\"Subject\"] = df_dyadic.index\n",
    "df_dyadic"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# CREATE IDENTIFIERS FROM INDEX\n",
    "\n",
    "df_dyadic[\"Dyad\"] = df_dyadic.index.map(lambda x: x.split('_')[0])\n",
    "df_dyadic[\"Partner\"] = df_dyadic.index.map(lambda x: x.split('_')[1])\n",
    "df_dyadic"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# CREATE GROUPS\n",
    "\n",
    "conditions = {\n",
    "    \"LipSync\": ['6', '7', '8', '9', '11'],\n",
    "    \"FaceTracking\": ['1', '2', '3', '4', '10'],\n",
    "}\n",
    "\n",
    "df_dyadic.loc[df_dyadic.apply(lambda r: r[\"Dyad\"] in conditions[\"LipSync\"], axis=1), \"Group\"] = \"LipSync\"\n",
    "df_dyadic.loc[df_dyadic.apply(lambda r: r[\"Dyad\"] in conditions[\"FaceTracking\"], axis=1), \"Group\"] = \"FaceTracking\"\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "group_ls = df_dyadic[df_dyadic[\"Group\"] == \"LipSync\"]\n",
    "group_ft = df_dyadic[df_dyadic[\"Group\"] == \"FaceTracking\"]\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n",
    "print(\"Prior knowing Facial Tracking:\", group_ft[\"Prior_Knowing\"].std())\n",
    "print(\"Prior knowing Facial Simulation:\",group_ls[\"Prior_Knowing\"].std())\n",
    "print(\"Prior knowing Total:\",df_dyadic[\"Prior_Knowing\"].std())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from scipy.stats import shapiro, skew, kurtosis, levene\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import math\n",
    "\n",
    "from pandas.api.types import is_numeric_dtype\n",
    "\n",
    "transformation = True\n",
    "\n",
    "for col in [\"IA\", \"Trust\", \"Similarity\", \"Smoothness\", \"Rapport\", \"Overlap\"]:\n",
    "    if not is_numeric_dtype(df_dyadic[col]):\n",
    "        continue\n",
    "    data = df_dyadic[col]\n",
    "\n",
    "    print(col)\n",
    "    data_nona = data[~np.isnan(data)]\n",
    "    print(\"Outliers\", data_nona[(np.abs(stats.zscore(data_nona)) > 3)])\n",
    "    print(\"shapiro\", shapiro(data_nona).pvalue)\n",
    "    print(\"Skew\", skew(data_nona))\n",
    "    print(\"kurtosis\", kurtosis(data_nona))\n",
    "    print(\"levene\", levene(df_dyadic[df_dyadic[\"Group\"] == \"LipSync\"][col], df_dyadic[df_dyadic[\"Group\"] == \"FaceTracking\"][col]))\n",
    "\n",
    "    if transformation:\n",
    "        _min = min(data)\n",
    "        if _min < 0:\n",
    "            data += abs(_min)\n",
    "        # df_dyadic[col] = data.apply(lambda x: np.exp2(x) if not np.isnan(x) else x)\n",
    "        df_dyadic[col] = np.exp2(data)\n",
    "\n",
    "\n",
    "        print(\"\\nTransformed:\")\n",
    "        data_nona = data[~np.isnan(data)]\n",
    "        print(\"Outliers\", data_nona[(np.abs(stats.zscore(data_nona)) > 3)])\n",
    "        print(\"shapiro\", shapiro(data_nona).pvalue)\n",
    "        print(\"Skew\", skew(data_nona))\n",
    "        print(\"kurtosis\", kurtosis(data_nona))\n",
    "        print(\"levene\", levene(df_dyadic[df_dyadic[\"Group\"] == \"LipSync\"][col], df_dyadic[df_dyadic[\"Group\"] == \"FaceTracking\"][col]))\n",
    "        print(\"\\n==================\\n\")\n",
    "        df_dyadic[col] = data\n",
    "\n",
    "\n",
    "\n"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import pingouin as pg\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "dv =\"IA\"\n",
    "\n",
    "def add_effect_size(aov):\n",
    "    aov['mean_sq'] = aov[:]['sum_sq'] / aov[:]['df']\n",
    "    aov['eta_sq'] = aov[:-1]['sum_sq'] / sum(aov['sum_sq'])\n",
    "    aov['omega_sq'] = ((aov[:-1]['sum_sq'] - (aov[:-1]['df'] * aov['mean_sq'][-1]))\n",
    "                        / (sum(aov['sum_sq'])+aov['mean_sq'][-1]))\n",
    "    cols = ['sum_sq', 'df', 'mean_sq', 'F', 'PR(>F)', 'eta_sq', 'omega_sq']\n",
    "    aov = aov[cols]\n",
    "    return aov\n",
    "\n",
    "\n",
    "model = ols(f\"{dv} ~ Group\", data=df_dyadic).fit()\n",
    "aov_table = sm.stats.anova_lm(model, typ=2)\n",
    "print(add_effect_size(aov_table))\n",
    "\n",
    "print(\"\\nFacetracking Mean & Std\", group_ft[dv].mean(), group_ft[dv].std())\n",
    "print(\"Simulated Mean & Std\", group_ls[dv].mean(), group_ls[dv].std())\n",
    "\n",
    "print(\"\\nShapiro of residuals: \", shapiro(model.resid))\n",
    "\n",
    "posthocs = pg.pairwise_ttests(dv=dv, between='Group', data=df_dyadic, effsize=\"cohen\")\n",
    "pg.print_table(posthocs)\n",
    "\n",
    "sns.histplot(model.resid, bins=7)"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "aov = pg.anova(data=df_dyadic, dv=\"IA\", between=\"Group\")\n",
    "pg.print_table(aov)"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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