agreement plots

bland-altman.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xlrd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('lows.xlsx')\n",
    "df1 = pd.read_excel('mediums.xlsx')\n",
    "df2 = pd.read_excel('highs.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, df1, df2], sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('cherrie_reviews.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['f/ml.y','Cherrie f/ml']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>f/ml.y</th>\n",
       "      <th>Cherrie f/ml</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.003682</td>\n",
       "      <td>0.003682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001176</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.045000</td>\n",
       "      <td>0.007500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.303750</td>\n",
       "      <td>0.101250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.278438</td>\n",
       "      <td>0.278438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.101250</td>\n",
       "      <td>0.101250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.030909</td>\n",
       "      <td>0.073636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.761364</td>\n",
       "      <td>0.552273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>833.004000</td>\n",
       "      <td>48.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        f/ml.y  Cherrie f/ml\n",
       "0     0.003682      0.003682\n",
       "1     0.001176      0.000051\n",
       "2     1.400000      1.400000\n",
       "3     0.800000      0.200000\n",
       "4     0.000100      0.000050\n",
       "5     0.045000      0.007500\n",
       "6     0.303750      0.101250\n",
       "7     0.278438      0.278438\n",
       "8     1.250000      1.250000\n",
       "9     0.101250      0.101250\n",
       "10    1.030909      0.073636\n",
       "11    0.960000      0.960000\n",
       "12    2.761364      0.552273\n",
       "13    5.400000      1.080000\n",
       "14  833.004000     48.600000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['f/ml.y','Cherrie f/ml']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>f/ml.y</th>\n",
       "      <th>Cherrie f/ml</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.489311</td>\n",
       "      <td>3.640542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>214.821046</td>\n",
       "      <td>12.447650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.073125</td>\n",
       "      <td>0.040568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.325000</td>\n",
       "      <td>1.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>833.004000</td>\n",
       "      <td>48.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           f/ml.y  Cherrie f/ml\n",
       "count   15.000000     15.000000\n",
       "mean    56.489311      3.640542\n",
       "std    214.821046     12.447650\n",
       "min      0.000100      0.000050\n",
       "25%      0.073125      0.040568\n",
       "50%      0.800000      0.200000\n",
       "75%      1.325000      1.020000\n",
       "max    833.004000     48.600000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe33c847c18>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AkhpnEEhS4w6Lq4aSbAe+c4izHwt8bwbLmYvso6nZR1Ozj6Y27D76+aqa8he5h0UQvBhJNvZz+VTL7KOp2UdTs4+mNlv7yENDktQ4g0CSGtdCEKwbdQGHAftoavbR1Oyjqc3KPprz5wgkSZNrYY9AkjSJOR0ESc5P8nCSLUmuGXU9o5DkhCR3JnkgydeTXNG1H5PkjiSPdO9LuvYk+fOuz+5Ncvpov8HwJJmf5GtJbuuGT0pyd9cXN3a3TSfJom54Szd+1SjrHqYkRye5KclDSR5M8jq3pX0l+Z3u79r9ST6RZPFs35bmbBAkmQ98CPhVYA1wSZI1o61qJPYA76mqNcCZwDu7frgG2FBVq4EN3TD0+mt191oLfHj4JY/MFcCDE4b/BLi2qk4GdgKXd+2XAzu79mu76VrxQeCzVfUq4BR6/eW21ElyPPAuYKyqXk3vVvtvZ7ZvS1U1J1/A64DPTRh+L/DeUdc16hdwK3Ae8DCwomtbATzcff5vwCUTpn9+urn8ovekvA3AOcBtQOj98GfB/tsTvWdpvK77vKCbLqP+DkPoo5cD39r/u7ot7dMX489kP6bbNm4DfmW2b0tzdo+AF/5Axm3t2prV7XaeBtwNLK+qx7tRTwDLu8+t9tsHgKuA8QdeLwWerqo93fDEfni+j7rx3++mn+tOArYDf9MdQvtIkpfgtvS8qtoG/CnwXeBxetvGJmb5tjSXg0ATJDkKuBl4d1X9YOK46v13pNnLx5K8GXiqqjaNupZZbgFwOvDhqjoN+BEvHAYC3Ja68yMX0gvN44CXAOePtKg+zOUg2AZMfD7lyq6tOUkW0guBj1fVLV3zk0lWdONXAE917S3221nABUm+DdxA7/DQB4Gjk4w/znViPzzfR934lwM7hlnwiGwFtlbV3d3wTfSCwW3pBecC36qq7VX1LHALve1rVm9LczkIvgKs7s7WH0HvhM36Edc0dEkCXAc8WFV/NmHUeuCy7vNl9M4djLe/o7vi40zg+xN2++ekqnpvVa2sqlX0tpPPV9WlwJ3Axd1k+/fReN9d3E0/5/8XXFVPAI8leWXX9EbgAdyWJvoucGaSn+v+7o330ezelkZ9cmXAJ27eBHwD+Cbw+6OuZ0R98AZ6u+r3Apu715voHYfcADwC/G/gmG760Lva6pvAffSufhj59xhif/0ScFv3+ReAvwe2AJ8CFnXti7vhLd34Xxh13UPsn1OBjd329BlgidvSz/TRHwAPAfcDHwMWzfZtyV8WS1Lj5vKhIUlSHwwCSWqcQSBJjTMIJKlxBoEkNc4gkDpJ3tXdUfPjSRYm+eo05v2NJH85yPqkQVkw9SRSM/4dcG5VbU1yNnDXqAuShsE9AglI8lf0fvRze5LfoXd/mNuTrOruvf/RJN/o9hbOTXJXd//9M6ZY7heTnDph+EtJThnst5GmxyCQgKr6beD/AWdX1bXA2cAXutEnA/8FeFX3+jV6v9j+XeD3plj0dcBvACT5x8DiqrpnhsuXXhSDQNpP93CRf6iqH3dN36qq+6pqL/B1eg9hKXq3TVg1xeI+Bby5u/HfvwE+OpiqpUPnOQLpZ51P74Eh43ZP+Lx3wvBepvg7VFU/TnIHvVsTvw34JzNYpzQj3COQftb5wO0zuLyPAH8OfKWqds7gcqUZYRBI+5oPnFxVDx3qApJckOQPx4er98CbHwB/MwP1STPOu49KEyR5A/Cvu5PHM7XM4+ideH5Vd55BmlUMAmmAkrwD+CPgyqr61KjrkQ7EIJCkxnmOQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDXu/wOErdOjQ0vq0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='f/ml.y', y='Cherrie f/ml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9993357844140116, 4.341512183105722e-20)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(df['f/ml.y'],df['Cherrie f/ml'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['log_cherrie'] = np.log(df['Cherrie f/ml'])\n",
    "df['log_carl'] = np.log(df['f/ml.y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe33a513e80>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='log_carl', y='log_cherrie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9552325739249552, 3.004824088347171e-08)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(df['log_carl'],df['log_cherrie'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score is 0.04760861473831357\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score \n",
    "r2 = r2_score(df['f/ml.y'],df['Cherrie f/ml'])\n",
    "print('r2 score is', r2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score is 0.8185407463800839\n"
     ]
    }
   ],
   "source": [
    "r2 = r2_score(df['log_carl'],df['log_cherrie'])\n",
    "print('r2 score is', r2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, figsize = (8,5))\n",
    "sm.graphics.mean_diff_plot(df['f/ml.y'],df['Cherrie f/ml'], ax = ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "f, ax = plt.subplots(1, figsize = (8,5))\n",
    "sm.graphics.mean_diff_plot(df['log_carl'], df['log_cherrie'], ax = ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pingouin as pg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pg.plot_blandaltman(df['log_carl'], df['log_cherrie'])"
   ]
  }
 ],
 "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}