korelacja.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mlt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.stats.api as sms\n",
    "import statsmodels.stats.power as smp\n",
    "import scipy.stats\n",
    "from scipy.stats import t, norm, ttest_ind, ttest_rel, ttest_1samp, pearsonr\n",
    "import decimal\n",
    "import random\n",
    "decimal.getcontext().prec = 4\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def przedziały(x1,x2):\n",
    "    r,p = pearsonr(x1,x2)\n",
    "    n = len(x1)\n",
    "    z = 1/2*np.log((1+r)/(1-r))\n",
    "    a,b = z-t.ppf(1-0.1/2,df=n-2)*np.sqrt(1/(n-3)),z+t.ppf(1-0.05/2,df=n-2)*np.sqrt(1/(n-3))\n",
    "    a = (np.exp(2*a)-1)/(np.exp(2*a)+1)\n",
    "    b = (np.exp(2*b)-1)/(np.exp(2*b)+1)\n",
    "    return a,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def corr_test(sample1,sample2,delta,alternative = 'two-tailed'):\n",
    "    corr,p = pearsonr(sample1,sample2)\n",
    "    n = np.size(sample1)\n",
    "    ts = (corr - delta)/np.sqrt((1- corr**2)/(np.size(sample1)-2))\n",
    "    df = n-2\n",
    "    if alternative == 'two-tailed':\n",
    "        p_val = 2*t.sf(np.abs(ts),df)\n",
    "    if alternative == 'greater':\n",
    "        p_val = t.sf(ts,df)\n",
    "    if alternative == 'less':\n",
    "        p_val = t.cdf(ts, df)\n",
    "    return p_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample1,sample2 = generate(100,0.5,10,10,2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_normal_sample(n,mean,sd):\n",
    "    return np.random.normal(loc=mean,scale=sd,size=n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate(n,r,mean1,mean2,sd1,sd2):\n",
    "    #r = 0.9\n",
    "    s1 = np.array(get_normal_sample(n,mean1,sd1))\n",
    "    s2 = np.array(get_normal_sample(n,mean2,sd2))\n",
    "    s1 = (s1-np.mean(s1))/np.std(s1)\n",
    "    s2 = (s2-np.mean(s2))/np.std(s2)\n",
    "    a = r/np.sqrt(1-r**2)\n",
    "    Z = a*s1 + s2\n",
    "    X = s1*2+4\n",
    "    return Z,X\n",
    "Z,X = generate(40,0.3,40,40,2,2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.29959908524624235, 0.7254508371291767)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "przedziały(Z,X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def podejście2_przedziały(cor,n,delta):\n",
    "    lst = []\n",
    "    for i in range(10000):\n",
    "        Z,X = generate(n,cor)\n",
    "        a,b = przedziały(Z,X)\n",
    "        if a >= 0:\n",
    "            return 'd'\n",
    "        elif a <0 and b >0:\n",
    "            return 'nwd'\n",
    "        else:\n",
    "            return 'm'\n",
    "    #return lst\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def podejście3_przedziały(cor,n,delta):\n",
    "    lst = []\n",
    "    for i in range(10000):\n",
    "        Z,X = generate(n,cor)\n",
    "        a,b = przedziały(Z,X)\n",
    "        if a >= delta:\n",
    "            return 'd'\n",
    "        elif a < delta and b > delta:\n",
    "            return 'nwd'\n",
    "        else:\n",
    "            return 'm'\n",
    "    #return lst\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def podejście2(sample1,sample2,delta,alfa):\n",
    "    cor,p = pearsonr(sample1,sample2)  \n",
    "    #result = corr_test(sample1,sample2,0)\n",
    "    #result1 = corr_test(sample1,sample2,delta,'greater')\n",
    "    #result2 = corr_test(sample1,sample2,delta,'less')\n",
    "    if p < alfa and cor > delta:\n",
    "        return 'd'\n",
    "    if p > alfa and cor < delta:\n",
    "        return 'm'\n",
    "    else:\n",
    "        return 'nwd'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def podejście3(sample1,sample2,delta,alfa):\n",
    "    cor,p = pearsonr(sample1,sample2)\n",
    "    result1 = corr_test(sample1,sample2,delta,alternative='greater')\n",
    "    result2 = corr_test(sample1,sample2,delta,alternative = 'less')\n",
    "    \n",
    "    if result1 < alfa and cor > delta:\n",
    "        return 'd'\n",
    "    if result2 < alfa and cor < delta:\n",
    "        return 'm'\n",
    "    else:\n",
    "        return 'nwd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dobrze_duże2 =[]\n",
    "dobrze_duże3 =[]\n",
    "\n",
    "źle_duże2 = []\n",
    "źle_duże3 = []\n",
    "\n",
    "niewiadomo_duże2 = []\n",
    "niewiadomo_duże3 = []\n",
    "\n",
    "dobrze_małe2 =[]\n",
    "dobrze_małe3 =[]\n",
    "\n",
    "źle_małe2 = []\n",
    "źle_małe3 = []\n",
    "\n",
    "niewiadomo_małe2 = []\n",
    "niewiadomo_małe3 = []\n",
    "\n",
    "\n",
    "\n",
    "delta = 0.5\n",
    "n = 50\n",
    "alfa = 0.05\n",
    "rhos = []\n",
    "sds = []\n",
    "ns = []\n",
    "for i in range(50):\n",
    "    ns.append(40+5*i)\n",
    "for sd in range(20,60):\n",
    "    sd = sd*0.1\n",
    "    for n in ns:\n",
    "        sds.append(sd)\n",
    "        app2 = []\n",
    "        app3 = []\n",
    "        for rho in range(1,100):\n",
    "            rho = rho*0.01\n",
    "            for i in range(1):\n",
    "                z,x = generate(n,rho,10,10,sd,sd)\n",
    "                pod2 = podejście2(z,x,delta,alfa)\n",
    "                pod3 = podejście3(z,x,delta,alfa)\n",
    "                app2.append([pod2,rho])\n",
    "                app3.append([pod3,rho])\n",
    "        \n",
    "        # ile procent efektów które faktycznie były duże zostały wskazane przez test jako duże\n",
    "        duże_dobrze2 = [i[1] for i in app2 if i[0] == 'd' and i[1] > delta]\n",
    "        duże_dobrze3 = [i[1] for i in app3 if i[0] == 'd' and i[1] > delta]\n",
    "\n",
    "        # ile procent efektów które faktycznie były małe zostały wskazane przez test jako duże\n",
    "        duże_źle2 = [i[1] for i in app2 if i[0] == 'd' and i[1] <= delta]\n",
    "        duże_źle3 = [i[1] for i in app3 if i[0] == 'd' and i[1] <= delta]\n",
    "\n",
    "        # ile procent efektów które faktycznie były duże zostały wskazane przez test jako 'nie wiadomo'\n",
    "        duże_niewiadomo2 = [i[1] for i in app2 if i[0] == 'nwd'and i[1] > delta]\n",
    "        duże_niewiadomo3 = [i[1] for i in app3 if i[0] == 'nwd'and i[1] > delta]\n",
    "\n",
    "        # ile procent efektów które faktycznie były małe zostały wskazane przez test jako małe\n",
    "        małe_dobrze2 = [i[1] for i in app2 if i[0] == 'm' and i[1] <= delta]\n",
    "        małe_dobrze3 = [i[1] for i in app3 if i[0] == 'm' and i[1] <= delta]\n",
    "\n",
    "        # ile procent efektów które faktycznie były duże zostały wskazane przez test jako małe\n",
    "        małe_źle2 = [i[1] for i in app2 if i[0] == 'm' and i[1] > delta]\n",
    "        małe_źle3 = [i[1] for i in app3 if i[0] == 'm' and i[1] > delta]\n",
    "\n",
    "        # ile procent efektów które faktycznie były małe zostały wskazane przez test jako 'nie wiadomo'\n",
    "        małe_niewiadomo2 = [i[1] for i in app2 if i[0] == 'nwd'and i[1] <= delta]\n",
    "        małe_niewiadomo3 = [i[1] for i in app3 if i[0] == 'nwd'and i[1] <= delta]\n",
    "\n",
    "        #duże na teście i duże w rzeczywistości\n",
    "        dobrze_duże2.append(len(duże_dobrze2)/len([i for i in app2 if i[1] > delta]))\n",
    "        dobrze_duże3.append(len(duże_dobrze3)/len([i for i in app3 if i[1] > delta]))\n",
    "        # duże na teście gdy w rzeczywistości jest małe\n",
    "        źle_duże2.append((len(duże_źle2)/len([i for i in app2 if i[1] <= delta])))\n",
    "        źle_duże3.append(len(duże_źle3)/len([i for i in app3 if i[1] <= delta]))\n",
    "        # nie wiadomo gdy w rzeczywistości jest duże\n",
    "        niewiadomo_duże2.append(len(duże_niewiadomo2)/len([i for i in app2 if i[1] > delta]))\n",
    "        niewiadomo_duże3.append(len(duże_niewiadomo3)/len([i for i in app3 if i[1] > delta]))\n",
    "        # małe na teście i w rzeczywistości\n",
    "        dobrze_małe2.append(len(małe_dobrze2)/len([i for i in app2 if i[1] <= delta]))\n",
    "        dobrze_małe3.append(len(małe_dobrze3)/len([i for i in app3 if i[1] <= delta]))\n",
    "        # małe na teście, duże w rzeczywistości\n",
    "        źle_małe2.append(len(małe_źle2)/len([i for i in app2 if i[1] > delta]))\n",
    "        źle_małe3.append(len(małe_źle3)/len([i for i in app3 if i[1] > delta]))\n",
    "        # nie wiadomo na teście, małe w rzeczywistości\n",
    "        niewiadomo_małe2.append(len(małe_niewiadomo2)/len([i for i in app2 if i[1] <= delta]))\n",
    "        niewiadomo_małe3.append(len(małe_niewiadomo3)/len([i for i in app3 if i[1] <= delta]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "nss = ns*40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>n</th>\n",
       "      <th>sd</th>\n",
       "      <th>efekt duży, podejście2</th>\n",
       "      <th>efekt duży, podejście3</th>\n",
       "      <th>źle_duże2</th>\n",
       "      <th>źle_duże3</th>\n",
       "      <th>nie wiadomo duże2</th>\n",
       "      <th>nie wiadomo duże3</th>\n",
       "      <th>mały_podejście2</th>\n",
       "      <th>mały_podejście3</th>\n",
       "      <th>źle_małe2</th>\n",
       "      <th>źle_małe3</th>\n",
       "      <th>niewiadomo_małe2</th>\n",
       "      <th>niewiadomo_małe3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.959184</td>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.918367</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.081633</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.959184</td>\n",
       "      <td>0.632653</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>0.367347</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.938776</td>\n",
       "      <td>0.693878</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.061224</td>\n",
       "      <td>0.306122</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.979592</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.44</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>265</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.979592</td>\n",
       "      <td>0.836735</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.163265</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>270</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.979592</td>\n",
       "      <td>0.877551</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>275</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>280</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.938776</td>\n",
       "      <td>0.877551</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.061224</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>285</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.979592</td>\n",
       "      <td>0.836735</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.163265</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        n   sd  efekt duży, podejście2  efekt duży, podejście3  źle_duże2  \\\n",
       "0      40  2.0                0.959184                0.673469       0.06   \n",
       "1      45  2.0                0.918367                0.714286       0.10   \n",
       "2      50  2.0                0.959184                0.632653       0.06   \n",
       "3      55  2.0                0.938776                0.693878       0.00   \n",
       "4      60  2.0                0.979592                0.714286       0.08   \n",
       "...   ...  ...                     ...                     ...        ...   \n",
       "1995  265  5.9                0.979592                0.836735       0.02   \n",
       "1996  270  5.9                0.979592                0.877551       0.04   \n",
       "1997  275  5.9                1.000000                0.897959       0.00   \n",
       "1998  280  5.9                0.938776                0.877551       0.06   \n",
       "1999  285  5.9                0.979592                0.836735       0.02   \n",
       "\n",
       "      źle_duże3  nie wiadomo duże2  nie wiadomo duże3  mały_podejście2  \\\n",
       "0           0.0           0.040816           0.326531             0.64   \n",
       "1           0.0           0.081633           0.285714             0.50   \n",
       "2           0.0           0.040816           0.367347             0.52   \n",
       "3           0.0           0.061224           0.306122             0.52   \n",
       "4           0.0           0.020408           0.285714             0.52   \n",
       "...         ...                ...                ...              ...   \n",
       "1995        0.0           0.020408           0.163265             0.24   \n",
       "1996        0.0           0.020408           0.122449             0.28   \n",
       "1997        0.0           0.000000           0.102041             0.28   \n",
       "1998        0.0           0.061224           0.122449             0.16   \n",
       "1999        0.0           0.020408           0.163265             0.26   \n",
       "\n",
       "      mały_podejście3  źle_małe2  źle_małe3  niewiadomo_małe2  \\\n",
       "0                0.46        0.0        0.0              0.30   \n",
       "1                0.42        0.0        0.0              0.40   \n",
       "2                0.48        0.0        0.0              0.42   \n",
       "3                0.54        0.0        0.0              0.48   \n",
       "4                0.56        0.0        0.0              0.40   \n",
       "...               ...        ...        ...               ...   \n",
       "1995             0.78        0.0        0.0              0.74   \n",
       "1996             0.80        0.0        0.0              0.68   \n",
       "1997             0.78        0.0        0.0              0.72   \n",
       "1998             0.84        0.0        0.0              0.78   \n",
       "1999             0.84        0.0        0.0              0.72   \n",
       "\n",
       "      niewiadomo_małe3  \n",
       "0                 0.54  \n",
       "1                 0.58  \n",
       "2                 0.52  \n",
       "3                 0.46  \n",
       "4                 0.44  \n",
       "...                ...  \n",
       "1995              0.22  \n",
       "1996              0.20  \n",
       "1997              0.22  \n",
       "1998              0.16  \n",
       "1999              0.16  \n",
       "\n",
       "[2000 rows x 14 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dwa_podejścia = pd.DataFrame(zip(nss,sds,dobrze_duże2,dobrze_duże3,źle_duże2,źle_duże3,niewiadomo_duże2,niewiadomo_duże3,dobrze_małe2,dobrze_małe3,źle_małe2,źle_małe3,niewiadomo_małe2,niewiadomo_małe3),\n",
    "                             columns = ['n','sd','efekt duży, podejście2','efekt duży, podejście3','źle_duże2','źle_duże3','nie wiadomo duże2','nie wiadomo duże3','mały_podejście2','mały_podejście3','źle_małe2','źle_małe3',\n",
    "                                                                                 'niewiadomo_małe2','niewiadomo_małe3'])\n",
    "dwa_podejścia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c95aa4b580>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 796.5x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "duży = ['efekt duży, podejście2','efekt duży, podejście3']\n",
    "\n",
    "dt = dwa_podejścia[['n','sd','efekt duży, podejście2','efekt duży, podejście3']]\n",
    "dtt = dt[dt['n'].isin([40,80,120,160,240,280])]\n",
    "nowe = dtt.melt(value_vars=['efekt duży, podejście2','efekt duży, podejście3'],value_name='procent efekt duży',id_vars = ['n','sd'],var_name='podejście')\n",
    "g = sns.FacetGrid(nowe,col='n',hue='podejście',col_wrap=3)\n",
    "#nowe\n",
    "g.map(sns.scatterplot,'sd','procent efekt duży')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c95aa4b730>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 733.625x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "duży_źle = ['źle_duże2','źle_duże3']\n",
    "mały_dobrze = ['mały_podejście2','mały_podejście3']\n",
    "\n",
    "dt = dwa_podejścia[['n','sd','źle_duże2','źle_duże3']]\n",
    "dtt = dt[dt['n'].isin([40,80,120,160,240,280])]\n",
    "nowe = dtt.melt(value_vars=['źle_duże2','źle_duże3'],value_name='procent źle wskazanych',id_vars = ['n','sd'],var_name='podejście')\n",
    "g = sns.FacetGrid(nowe,col='n',hue='podejście',col_wrap=3)\n",
    "#nowe\n",
    "g.map(sns.scatterplot,'sd','procent źle wskazanych')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c95aa4b340>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 781.25x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "duży_niewiadomo = ['nie wiadomo duże2','nie wiadomo duże3']\n",
    "\n",
    "dt = dwa_podejścia[['n','sd','nie wiadomo duże2','nie wiadomo duże3']]\n",
    "dtt = dt[dt['n'].isin([40,80,120,160,240,280])]\n",
    "nowe = dtt.melt(value_vars=['nie wiadomo duże2','nie wiadomo duże3'],value_name='procent nie wiadomo',id_vars = ['n','sd'],var_name='podejście')\n",
    "g = sns.FacetGrid(nowe,col='n',hue='podejście',col_wrap=3)\n",
    "#nowe\n",
    "g.map(sns.scatterplot,'sd','procent nie wiadomo')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c95b2c6160>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 767.25x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "mały_dobrze = ['mały_podejście2','mały_podejście3']\n",
    "\n",
    "dt = dwa_podejścia[['n','sd','mały_podejście2','mały_podejście3']]\n",
    "dtt = dt[dt['n'].isin([40,80,120,160,240,280])]\n",
    "nowe = dtt.melt(value_vars=['mały_podejście2','mały_podejście3'],value_name='procent dobrze wskazanych',id_vars = ['n','sd'],var_name='podejście')\n",
    "g = sns.FacetGrid(nowe,col='n',hue='podejście',col_wrap=3)\n",
    "#nowe\n",
    "g.map(sns.scatterplot,'sd','procent dobrze wskazanych')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c95b056550>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 780.625x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "mały_niewiadomo = ['niewiadomo_małe2','niewiadomo_małe3']\n",
    "\n",
    "dt = dwa_podejścia[['n','sd','niewiadomo_małe2','niewiadomo_małe3']]\n",
    "dtt = dt[dt['n'].isin([40,80,120,160,240,280])]\n",
    "nowe = dtt.melt(value_vars=['niewiadomo_małe2','niewiadomo_małe3'],value_name='procent nie wiadomo',id_vars = ['n','sd'],var_name='podejście')\n",
    "g = sns.FacetGrid(nowe,col='n',hue='podejście',col_wrap=3)\n",
    "#nowe\n",
    "g.map(sns.scatterplot,'sd','procent nie wiadomo')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c9587f8f40>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 734.25x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ogólnie sd w zależności od wielkości próby\n",
    "#trzy_podejścia[trzy_podejścia['wielkość próby'] == 65]\n",
    "źle_małe = ['źle_małe2','źle_małe3']\n",
    "\n",
    "dt = dwa_podejścia[['n','sd','źle_małe2','źle_małe3']]\n",
    "dtt = dt[dt['n'].isin([40,80,120,160,240,280])]\n",
    "nowe = dtt.melt(value_vars=['źle_małe2','źle_małe3'],value_name='procent źle wskazanych',id_vars = ['n','sd'],var_name='podejście')\n",
    "g = sns.FacetGrid(nowe,col='n',hue='podejście',col_wrap=3)\n",
    "#nowe\n",
    "g.map(sns.scatterplot,'sd','procent źle wskazanych')\n",
    "g.add_legend()\n",
    "#g.set(xlabel = 'asd')"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}