pingouin_repeated_measures_anova_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RM ANOVA in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Sub_id</th>\n",
       "      <th>rt</th>\n",
       "      <th>iv1</th>\n",
       "      <th>iv2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1082.986553</td>\n",
       "      <td>noise</td>\n",
       "      <td>down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>938.799689</td>\n",
       "      <td>noise</td>\n",
       "      <td>down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1101.470970</td>\n",
       "      <td>noise</td>\n",
       "      <td>down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1123.030275</td>\n",
       "      <td>noise</td>\n",
       "      <td>down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>938.051589</td>\n",
       "      <td>noise</td>\n",
       "      <td>down</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sub_id           rt    iv1   iv2\n",
       "0       1  1082.986553  noise  down\n",
       "1       2   938.799689  noise  down\n",
       "2       3  1101.470970  noise  down\n",
       "3       4  1123.030275  noise  down\n",
       "4       5   938.051589  noise  down"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pingouin as pg\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/marsja/jupyter/master/Python_ANOVA/rmAOV2way.csv'\n",
    "df = pd.read_csv(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Way ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Source          SS  DF          MS        F        p-unc    np2 eps\n",
      "0    iv1  8061895.94   1  8061895.94  2207.02  1.90585e-48  0.974   1\n",
      "1  Error   215518.06  59     3652.85        -            -      -   -\n"
     ]
    }
   ],
   "source": [
    "aov = pg.rm_anova(dv='rt', within='iv1',\n",
    "                   subject='Sub_id', data=df, detailed=True)\n",
    "print(aov.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two-Way ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Source           SS  ddof1  ddof2           MS        F  p-unc  \\\n",
      "0        iv1  24185687.82      1     59  24185687.82  2207.02   0.00   \n",
      "1        iv2   4836438.74      2    118   2418219.37   275.41   0.00   \n",
      "2  iv1 * iv2     39246.50      2    118     19623.25     1.86   0.16   \n",
      "\n",
      "   p-GG-corr   np2   eps  \n",
      "0       0.00  0.97  1.00  \n",
      "1       0.00  0.82  0.99  \n",
      "2       0.16  0.03  0.99  \n"
     ]
    }
   ],
   "source": [
    " aov = pg.rm_anova(dv='rt',\n",
    "                   within=['iv1', 'iv2'],\n",
    "                   subject='Sub_id', data=df)\n",
    "print(aov.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interaction Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "ax = sns.pointplot(x=\"iv1\", y=\"rt\", hue=\"iv2\",\n",
    "                    data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}