{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  单因素方差分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10150375939849626\n",
      "0.9038208903685354\n"
     ]
    }
   ],
   "source": [
    "# 呷哺呷哺3个城市不同用户评分\n",
    "from scipy.stats import f_oneway\n",
    "a = [10,9,9,8,8,7,7,8,8,9]  # 3个城市每个城市10个人评价\n",
    "b = [10,8,9,8,7,7,7,8,9,9]\n",
    "c = [9,9,8,8,8,7,6,9,8,9]\n",
    "\n",
    "f,p = f_oneway(a,b,c)\n",
    "print(f)  # 统计量\n",
    "print(p)  # 概率值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不能认为所检验的因素对观测值有显著影响"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多因素方差分析"
   ]
  },
  {
   "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>E</th>\n",
       "      <th>I</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   E  I  S\n",
       "0  5  5  5\n",
       "1  5  4  5\n",
       "2  5  3  4\n",
       "3  5  2  3\n",
       "4  5  1  2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 呷哺呷哺2因素：环境等级，食材等级\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "\n",
    "\n",
    "environmental = [5,5,5,5,5,4,4,4,4,4,3,3,3,3,3,2,2,2,2,2,1,1,1,1,1]\n",
    "ingredients =   [5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1]\n",
    "score =         [5,5,4,3,2,5,4,4,3,2,4,4,3,3,2,4,3,2,2,2,3,3,3,2,1]\n",
    "\n",
    "data = {'E':environmental, 'I':ingredients, 'S':score}\n",
    "df = pd.DataFrame(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "符号意义：\n",
    "\n",
    "(~)隔离因变量和自变量(左边因变量,右边自变量)\n",
    "<br>(+)分隔各个自变量\n",
    "<br>(:)表示两个自变量交互影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            df  sum_sq    mean_sq           F        PR(>F)\n",
      "E          1.0    7.22   7.220000   54.539568  2.896351e-07\n",
      "I          1.0   18.00  18.000000  135.971223  1.233581e-10\n",
      "E:I        1.0    0.64   0.640000    4.834532  3.924030e-02\n",
      "Residual  21.0    2.78   0.132381         NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "formula = 'S~E+I+E:I'  #指定公式\n",
    "\n",
    "model = ols(formula, df).fit()\n",
    "results = anova_lm(model)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P值很小，拒绝原假设，F值越大。\n",
    "\n",
    "表示该因素对结果影响越大，分别是E和I\n",
    "\n",
    "E:I行的P值表示交互情况，小于0.05，之间并无交互"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}