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b/Clinical Deterioration Prediction Model - Inferential Statistics.ipynb |
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"cells": [ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"import pandas as pd\n", |
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"# ^^^ pyforest auto-imports - don't write above this line\n", |
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"# Clinical Deterioration Prediction Model: Inferential Statistics\n", |
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"\n", |
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"`This notebook includes a Statistical Data Analysis based on the final dataset prepared for a Clinical Deterioration (cd) Prediction Model.`\n", |
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"\n", |
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"`To learn more about the data source, descriptive statistics and additional exploratory data analysis please visit https://github.com/abebual/Exploratory-Data-Analysis-Clinical-Deterioration`\n", |
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"\n", |
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"`Abebual Demilew | github.com/abebual | @evidence2policy`\n", |
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"____" |
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] |
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}, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import os\n", |
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"from scipy.stats import norm\n", |
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"from scipy.stats import t\n", |
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"import numpy as np\n", |
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"import pandas as pd\n", |
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"from numpy.random import seed\n", |
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"import matplotlib.pyplot as plt\n" |
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] |
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}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"'C:\\\\Users\\\\abebu\\\\Dropbox\\\\Data Science\\\\Projects\\\\Capstone Project 1\\\\Potential Projects\\\\9. MIMIC\\\\Statistical_Data_Analysis\\\\Clinical-Deterioration-Prediction-Model--Inferential-Statistics'" |
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] |
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}, |
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"os.getcwd()" |
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" <th></th>\n", |
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" <th>SUBJECT_ID</th>\n", |
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" <th>HADM_ID</th>\n", |
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" <th>ICUSTAY_ID</th>\n", |
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" <th>los</th>\n", |
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], |
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"text/plain": [ |
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" SUBJECT_ID HADM_ID ICUSTAY_ID los hdeath death admission ud \\\n", |
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"0 268 110404 280836 3.2490 1 1 8 0.0 \n", |
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"1 269 106296 206613 3.2788 0 0 8 17.0 \n", |
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"2 270 188028 220345 2.8939 0 0 0 0.0 \n", |
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"3 271 173727 249196 2.0600 0 0 8 0.0 \n", |
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"4 272 164716 210407 1.6202 0 0 8 0.0 \n", |
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"\n", |
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" bun Bicarbonate ... WBC_3.0 hr_0.0 hr_2.0 hr_4.0 hr_7.0 hr_11.0 \\\n", |
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"0 6.0 0.0 ... 0 0 0 0 0 1 \n", |
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"1 0.0 0.0 ... 0 1 0 0 0 0 \n", |
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"2 0.0 0.0 ... 0 0 0 0 0 1 \n", |
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"3 0.0 0.0 ... 0 1 0 0 0 0 \n", |
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"4 0.0 0.0 ... 0 1 0 0 0 0 \n", |
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"\n", |
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" bp_0.0 bp_2.0 bp_5.0 bp_13.0 \n", |
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"0 0 0 0 1 \n", |
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"1 0 0 1 0 \n", |
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"2 0 0 0 1 \n", |
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"3 1 0 0 0 \n", |
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"4 0 0 1 0 \n", |
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"\n", |
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"[5 rows x 33 columns]" |
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}, |
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"execution_count": 5, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"os.chdir(\"C://Users/abebu/Google Drive/mimic-iii-clinical-database-1.4\")\n", |
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"saps = pd.read_csv(\"saps_ts.csv\", header=0, index_col=0)\n", |
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"saps.head()" |
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] |
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}, |
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"cell_type": "code", |
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"execution_count": 6, |
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"metadata": {}, |
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"outputs": [ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"<class 'pandas.core.frame.DataFrame'>\n", |
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"Int64Index: 61117 entries, 0 to 61116\n", |
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"Data columns (total 33 columns):\n", |
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"SUBJECT_ID 61117 non-null int64\n", |
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"HADM_ID 61117 non-null int64\n", |
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"ICUSTAY_ID 61117 non-null int64\n", |
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"los 61117 non-null float64\n", |
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"hdeath 61117 non-null int64\n", |
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"death 61117 non-null int64\n", |
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"admission 61117 non-null int64\n", |
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"ud 61117 non-null float64\n", |
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"bun 61117 non-null float64\n", |
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"Bicarbonate 61117 non-null float64\n", |
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"ventilation 61117 non-null float64\n", |
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"Temp 61117 non-null float64\n", |
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"Bilirubin 61117 non-null float64\n", |
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"gcs 61117 non-null float64\n", |
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"AGE 61117 non-null float64\n", |
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"UO 61117 non-null float64\n", |
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"saps2 61117 non-null float64\n", |
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"Potassium_0.0 61117 non-null int64\n", |
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"Potassium_3.0 61117 non-null int64\n", |
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"Sodium_0.0 61117 non-null int64\n", |
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"Sodium_1.0 61117 non-null int64\n", |
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"Sodium_5.0 61117 non-null int64\n", |
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"WBC_0.0 61117 non-null int64\n", |
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"WBC_3.0 61117 non-null int64\n", |
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"hr_0.0 61117 non-null int64\n", |
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"hr_2.0 61117 non-null int64\n", |
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"hr_4.0 61117 non-null int64\n", |
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"hr_7.0 61117 non-null int64\n", |
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"hr_11.0 61117 non-null int64\n", |
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"bp_0.0 61117 non-null int64\n", |
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"bp_2.0 61117 non-null int64\n", |
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"bp_5.0 61117 non-null int64\n", |
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"bp_13.0 61117 non-null int64\n", |
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"dtypes: float64(11), int64(22)\n", |
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"memory usage: 15.9 MB\n" |
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] |
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} |
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], |
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"source": [ |
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"saps.info()" |
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] |
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}, |
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+Gimtb9ZUlp6XhoIf/oV2D8bN6qZHj0QkTQROAg6PiPUls24AjpG0taTRwBjgPuB+YEw+E2srUuf7DTn5/BE4Mi8/Gbi+p16HWW+yYvoKVkz3TamsGPU8xfe3wN3AWEmtko4HfgoMAGZImiXpZwAR8SgwHXgM+D1wQkS8mo8yvgjcAswFpue6kJLRVyUtIPWRXFSv12JmZpXVrTkrIo6tUNzuF31EnAWcVaH8JuCmCuULSWdvmZlZQXzFupmZ1cxJxMzMauah4M0a3F537FV0CNbEfCRiZmY1cxIxa3CLf7iYxT9cXHQY1qScRMwa3NM3Ps3TNz5ddBjWpNwnYmabRN+uNApRdWJqu6MVWYPwkYiZmdXMScTMzGrm5iyzBtdnG4/g2yx6Y9Ohk4hZg9vzZt9PxIrj5iwzM6uZk4hZg1v0nUUs+s6iosOwJuUkYtbg1ty+hjW3ryk6DGtSTiJmZlYzJxEzM6uZk4iZmdXMp/iaNbgtd9iy6BCsiTmJmDW4Pa7eo+gQrInVrTlL0jRJKyTNKSkbLGmGpPn576BcLknnSlogabak8SXLTM7150uaXFL+95IeycucK6n2SznNzKwm9ewTuRiYWFZ2MnB7RIwBbs/PAQ4BxuTHFOB8SEkHmArsA+wNTG1LPLnOlJLlyrdl1hQWnrKQhacsLDoMa1J1SyIRcRewuqx4EnBJnr4EOKKk/NJI7gG2lzQMOBiYERGrI2INMAOYmOdtFxF3R0QAl5asy6yprL17LWvvXlt0GNakevrsrLdGxHKA/HdoLh8OLCmp15rLOipvrVBekaQpkmZKmrly5cpNfhFmZpb0llN8K/VnRA3lFUXEBRExISImtLS01BiimZmV6+kk8lRuiiL/XZHLW4GRJfVGAMs6KR9RodzMzHpQTyeRG4C2M6wmA9eXlH8qn6W1L7A2N3fdAhwkaVDuUD8IuCXPWydp33xW1qdK1mXWVLYesTVbj9i66DCsSdXtOhFJvwX2B4ZIaiWdZfV9YLqk44HFwFG5+k3AocACYD1wHEBErJb0HeD+XO+MiGjrrP886QywbYCb88Os6Yz71biiQ7AmVrckEhHHtjPrgAp1AzihnfVMA6ZVKJ8J+CorM7MCVdWcJclf1ma91PwT5zP/xPlFh2FNqtojkZ9J2orUfPSbiHimfiGZWVc8N+u5okOwJlbVkUhEvA/4BOlMqZmSfiPpw3WNzMzMer2qz86KiPnAacBJwAeBcyX9RdI/1Ss4MzPr3artE9lT0jnAXOBDwEciYrc8fU4d4zMzs16s2j6RnwK/AE6NiBfaCiNimaTT6hKZmVWl/zv6Fx2CNbFqk8ihwAsR8SqApC2AfhGxPiIuq1t0ZtapsReMLToEa2LV9oncRrqor03/XGZmZk2s2iTSLyJeP48wT/sY2qwXmDdlHvOmzCs6DGtS1TZnPS9pfEQ8COmugsALnSxjZj1g/V/XFx2CNbFqk8iJwJWS2kbKHQZ8vD4hmZlZo6gqiUTE/ZLeCYwl3cvjLxHxSl0jMzOzXq8rAzC+GxiVl9lLEhFxaV2iMjOzhlBVEpF0GfB2YBbwai5uu7e5mRVo23dtW3QI1sSqPRKZAIzLQ7abWS8y5sdjig7Bmli1p/jOAd5Wz0DMzKzxVHskMgR4TNJ9wEtthRFxeF2iMrOqPfbPjwG+w6EVo9okcno9gzCz2r3U+lLnlczqpNr7idwJLAK2zNP3Aw/WulFJX5H0qKQ5kn4rqZ+k0ZLulTRf0hX5JlhI2jo/X5DnjypZzym5fJ6kg2uNx8zMalPtUPD/ClwF/DwXDQeuq2WDkoYDXwYmRMQeQB/gGOAHwDkRMQZYAxyfFzkeWBMRu5KGnf9BXs+4vNzuwETgPEl9aonJzMxqU23H+gnAfsCz8PoNqoZuwnb7AttI6ksag2s56d4kV+X5lwBH5OlJ+Tl5/gGSlMsvj4iXIuJxYAGw9ybEZGZmXVRtn8hLEfFy+u6G/OVf0+m+EbFU0g+BxaTxt24FHgCeiYgNuVor6WiH/HdJXnaDpLXADrn8npJVly5j1jQGvmdg0SFYE6s2idwp6VTS0cOHgS8Av6tlg5IGkY4iRgPPAFcCh1So2pak1M689sorbXMKMAVgp5126mLEZr3bLt/bpegQrIlV25x1MrASeAT4LHAT6X7rtTgQeDwiVubxt64B3gtsn49wAEYAbYM9tgIj4fUjoIHA6tLyCsu8SURcEBETImJCS0tLjWGbmVm5as/Oei0ifhERR0XEkXm61qvXFwP7Suqf+zYOAB4D/ggcmetMBq7P0zfk5+T5f8jbvgE4Jp+9NRoYA9xXY0xmDWvOx+Yw52Nzig7DmlS1Y2c9ToWmoojo8nF0RNwr6SrSKcIbgIeAC4D/Bi6XdGYuuygvchFwmaQFpCOQY/J6HpU0nZSANgAntN2+16yZvPK0B9S24nRl7Kw2/YCjgMG1bjQipgJTy4oXUuHsqoh4MW+v0nrOAs6qNQ4zM9s01TZnPV3yWBoRPyadkmtmZk2s2uas8SVPtyAdmQyoS0RmZtYwqm3OOrtkegNpCJSjuz0aM+uyQQcMKjoEa2LV3h73H+odiJnVZtQ3RxUdgjWxapuzvtrR/Ij4UfeEY2ZmjaQrZ2e9m3RtBsBHgLvIw5GYWXFmHzIbgD1v3rPgSKwZdeWmVOMjYh2ApNOBKyPiM/UKzMyq8+oLvjzKilPtsCc7AS+XPH8ZGNXt0ZiZWUOp9kjkMuA+SdeSrlz/KHBp3aIyM7OGUO3ZWWdJuhl4fy46LiIeql9YZmbWCKo9EoF086hnI+KXklokjc43gzKzAu1w2A5Fh2BNrNpTfKeSztAaC/wS2BL4Feluh2ZWoJ2+7nvkWHGq7Vj/KHA48DxARCzDw56YmTW9apPIy/keHgEg6S31C8nMuuKh/R/iof3dRWnFqDaJTJf0c9LdB/8VuA34Rf3CMjOzRlDt2Vk/zPdWf5bUL/KtiJhR18jMzKzX6zSJSOoD3BIRBwJOHGZm9rpOm7PyLWfXSxrYA/GYmVkDqfY6kReBRyTNIJ+hBRARX65LVGZWtaFHDy06BGti1SaR/86PbiFpe+BCYA/SGV+fBuYBV5DG5FoEHB0RayQJ+AlwKLAe+JeIeDCvZzJwWl7tmRFxSXfFaNYohn9heNEhWBPrMIlI2ikiFtfhy/knwO8j4khJW5Guhj8VuD0ivi/pZOBk4CTgEGBMfuwDnA/sI2kw0HYRZAAPSLohItZ0c6xmvdqr69Movn369yk4EmtGnfWJXNc2Ienq7tigpO2ADwAXAUTEyxHxDDAJaEtWlwBH5OlJwKWR3EM6zXgYcDAwIyJW58QxA5jYHTGaNZLZh85m9qGziw7DmlRnSUQl07t00zZ3AVYCv5T0kKQL88WLb42I5QD5b1tD73DefPOr1lzWXvlGJE2RNFPSzJUrV3bTyzAzs86SSLQzvSn6AuOB8yNiL1JH/ckd1FeFsuigfOPCiAsiYkJETGhpaelqvGZm1o7OksjfSXpW0jpgzzz9rKR1kp6tcZutQGtE3JufX0VKKk/lZiry3xUl9UeWLD8CWNZBuZmZ9ZAOk0hE9ImI7SJiQET0zdNtz7erZYMR8SSwRNLYXHQA8Bjp/u2Tc9lk4Po8fQPwKSX7Amtzc9ctwEGSBkkaBByUy8zMrId05X4i3elLwK/zmVkLgeNICW26pOOBxcBRue5NpNN7F5BO8T0OICJWS/oOcH+ud0ZErO65l2DWO7ztX95WdAhNR9+u1JpenZjaXT0DvUMhSSQiZpFOzS13QIW6AZzQznqmAdO6NzqzxjLsX4YVHYI1sWpH8TWzXurlVS/z8qqXiw7DmlRRzVlm1k0ePfJRAPa6Y6+CI7Fm5CMRMzOrmZOImZnVzEnEzMxq5j4RM2s6m3KKrr2Zk4hZgxv+eQ8Fb8VxEjFrcEM/7ptSWXHcJ2LW4F5c8iIvLnmx6DCsSflIxKzBzf3kXMDXiVgxfCRiZmY1cxIxM7OaOYmYmVnNnETMzKxm7lg3a3Ajvzay80pmdeIkYtbghnxkSNEhWBNzc5ZZg1s/bz3r560vOgxrUj4SMWtw8z47D/B1IlYMH4mYmVnNCksikvpIekjSjfn5aEn3Spov6QpJW+XyrfPzBXn+qJJ1nJLL50k6uJhXYmbWvIo8Evk3YG7J8x8A50TEGGANcHwuPx5YExG7AufkekgaBxwD7A5MBM6T1KeHYjczMwpKIpJGAP8IXJifC/gQcFWucglwRJ6elJ+T5x+Q608CLo+IlyLicWABsHfPvAIzM4PiOtZ/DPw7MCA/3wF4JiI25OetQNtNEoYDSwAiYoOktbn+cOCeknWWLvMmkqYAUwB22mmn7nsVZr3AzqftXHQI1sR6/EhE0mHAioh4oLS4QtXoZF5Hy7y5MOKCiJgQERNaWlq6FK9Zbzf4wMEMPnBw0WFYkyriSGQ/4HBJhwL9gO1IRybbS+qbj0ZGAMty/VZgJNAqqS8wEFhdUt6mdBmzprFu1joABrxrQCc1zbpfjx+JRMQpETEiIkaROsb/EBGfAP4IHJmrTQauz9M35Ofk+X+IiMjlx+Szt0YDY4D7euhlmPUaC05cwIITFxQdhjWp3nSx4UnA5ZLOBB4CLsrlFwGXSVpAOgI5BiAiHpU0HXgM2ACcEBGv9nzYZmbNq9AkEhF3AHfk6YVUOLsqIl4Ejmpn+bOAs+oXoZmZdcRXrJuZWc2cRMzMrGa9qU/EzGqwy3d3KToEa2JOImYNbuB7BxYdgjUxN2eZNbi1f17L2j+vLToMa1I+EjFrcAtPXQj4fiJWDB+JmJlZzZxEzMysZk4iZmZWMycRMzOrmTvWzRrcrj/etegQrIk5iZg1OA8Bb0Vyc5ZZg1t922pW37a66DCsSflIxKzBPXHmEwC+u6EVwkciZmZWMycRMzOrmZOImZnVzEnEzMxq5o51swY39udjiw7BmliPH4lIGinpj5LmSnpU0r/l8sGSZkian/8OyuWSdK6kBZJmSxpfsq7Juf58SZN7+rWY9Qb9x/an/9j+RYdhTaqI5qwNwNciYjdgX+AESeOAk4HbI2IMcHt+DnAIMCY/pgDnQ0o6wFRgH2BvYGpb4jFrJqt+t4pVv1tVdBjWpHo8iUTE8oh4ME+vA+YCw4FJwCW52iXAEXl6EnBpJPcA20saBhwMzIiI1RGxBpgBTOzBl2LWKyw5ewlLzl5SdBjWpArtWJc0CtgLuBd4a0Qsh5RogKG52nCg9D+kNZe1V15pO1MkzZQ0c+XKld35EszMmlphSUTStsDVwIkR8WxHVSuURQflGxdGXBAREyJiQktLS9eDNTOzigpJIpK2JCWQX0fENbn4qdxMRf67Ipe3AiNLFh8BLOug3MzMekgRZ2cJuAiYGxE/Kpl1A9B2htVk4PqS8k/ls7T2Bdbm5q5bgIMkDcod6gflMjMz6yFFXCeyH/BJ4BFJs3LZqcD3gemSjgcWA0fleTcBhwILgPXAcQARsVrSd4D7c70zIsJDmVrT2e2y3YoOwZpYjyeRiPgfKvdnABxQoX4AJ7SzrmnAtO6Lzqzx9BvZr+gQrIl52BOzBrfiihWsuGJF5xXN6sDDnpg1uKXnLwVg6MeHdlLTrPv5SMTMzGrmJGJmZjVzEjEzs5o5iZiZWc3csW7W4Ha/aveiQ7Am5iRi1uC2GrJV0SFYE3NzllmDW37xcpZfvLzoMKxJOYmYNbgnL36SJy9+sugwrEk5iZiZWc2cRMzMrGZOImZmVjMnETMzq5lP8TVrcHvetGfRIVgTcxIxa3B9+vcpOgRrYm7OMmtwS89bytLzlhYdhjUpJxGzBrdi+gpWTPdNqawYTiJmZlazhk8ikiZKmidpgaSTi47HzKyZNHQSkdQH+C/gEGAccKykccVGZWbWPBo6iQB7AwsiYmFEvAxcDkwqOCYzs6ahiCg6hppJOhKYGBGfyc8/CewTEV8sqzcFmJKfjgXm1bjJIcCqGpftSY6z+zVKrI6z+zVKrPWOc+eIaCkvbPTrRFShbKOsGBEXABds8sakmRExYVPXU2+Os/s1SqyOs/s1SqxFxdnozVmtwMiS5yOAZQXFYmbWdBo9idwPjJE0WtJWwDHADe6WBWcAAAlASURBVAXHZGbWNBq6OSsiNkj6InAL0AeYFhGP1nGTm9wk1kMcZ/drlFgdZ/drlFgLibOhO9bNzKxYjd6cZWZmBXISMTOzmjmJVNDZUCqStpZ0RZ5/r6RRBcQ4UtIfJc2V9Kikf6tQZ39JayXNyo9v9XScOY5Fkh7JMcysMF+Szs37c7ak8QXFObZkX82S9KykE8vqFLJPJU2TtELSnJKywZJmSJqf/w5qZ9nJuc58SZMLiPM/JP0lv7fXStq+nWU7/Jz0UKynS1pa8v4e2s6yPTbcUjtxXlES4yJJs9pZtv77NCL8KHmQOuj/BuwCbAU8DIwrq/MF4Gd5+hjgigLiHAaMz9MDgL9WiHN/4MZesE8XAUM6mH8ocDPpup99gXt7Qcx9gCdJF1gVvk+BDwDjgTklZf8PODlPnwz8oMJyg4GF+e+gPD2oh+M8COibp39QKc5qPic9FOvpwNer+Gx0+B1R7zjL5p8NfKuofeojkY1VM5TKJOCSPH0VcICkShc+1k1ELI+IB/P0OmAuMLwnY+hGk4BLI7kH2F7SsIJjOgD4W0Q8UXAcAETEXcDqsuLSz+ElwBEVFj0YmBERqyNiDTADmNiTcUbErRGxIT+9h3Q9V+Ha2afV6NHhljqKM3/vHA38tl7b74yTyMaGA0tKnrey8Zfz63XyP8daYIceia6C3Jy2F3BvhdnvkfSwpJsl7d6jgb0hgFslPZCHoClXzT7vacfQ/j9mb9inAG+NiOWQflQAQyvU6W379tOko85KOvuc9JQv5qa3ae00Efamffp+4KmImN/O/LrvUyeRjVUzlEpVw630BEnbAlcDJ0bEs2WzHyQ1x/wd8J/AdT0dX7ZfRIwnjbZ8gqQPlM3vNfsTIF+4ejhwZYXZvWWfVqvX7FtJ3wA2AL9up0pnn5OecD7wduBdwHJSU1G5XrNPgWPp+Cik7vvUSWRj1Qyl8nodSX2BgdR2WLxJJG1JSiC/johryudHxLMR8VyevgnYUtKQHg6TiFiW/64AriU1B5TqbcPXHAI8GBFPlc/oLfs0e6qt2S//rXR7w16xb3OH/mHAJyI31per4nNSdxHxVES8GhGvAb9oJ4besk/7Av8EXNFenZ7Yp04iG6tmKJUbgLazXI4E/tDeP0a95LbQi4C5EfGjduq8ra2vRtLepPf76Z6LEiS9RdKAtmlSJ+ucsmo3AJ/KZ2ntC6xta6YpSLu/7nrDPi1R+jmcDFxfoc4twEGSBuWmmYNyWY+RNBE4CTg8Ita3U6eaz0ndlfXFfbSdGHrLcEsHAn+JiNZKM3tsn9az175RH6Szhf5KOgPjG7nsDNI/AUA/UlPHAuA+YJcCYnwf6RB6NjArPw4FPgd8Ltf5IvAo6eyRe4D3FhDnLnn7D+dY2vZnaZwi3Vzsb8AjwIQC3/v+pKQwsKSs8H1KSmrLgVdIv4SPJ/XD3Q7Mz38H57oTgAtLlv10/qwuAI4rIM4FpD6Ets9p25mNOwI3dfQ5KSDWy/JncDYpMQwrjzU/3+g7oifjzOUXt30uS+r2+D71sCdmZlYzN2eZmVnNnETMzKxmTiJmZlYzJxEzM6uZk4iZmdXMScSsCyTtJenCPP1WSTfmIVAek3RTWd2vSHpR0sCSsrZRgB9SGoF5ai7vL+nXecTVOZL+J49GgKTn8t9RpSO5lqxzC6VRkOfk5e+XNLqO+6BF0u/rtX5rLA19e1yzApwKnJmnzyANbvgTAEl7ltU9lnRh2kdJ5/S3+VNEHJYvAJsl6UbShWBPRcT/yesaS7ouoBofJ10fsGdEvCZpBPB8l19ZCUl9441BE98kIlZKWi5pv4j4303ZjjU+H4nYZknSdXnQuUdLB56TdLykv0q6Q9IvJP00l7dIujr/ir9f0n4V1jmA9EX9cC4aRrr4C4CImF1S9+3AtsBppGSykYh4HniANFbTMGBpybx5EfFSlS93GLA80lAdRERrpBF72+578WA+Wro9lw3O+2e2pHvakp/SvTQukHQrcKmkPkr3Ark/1/1syTavAz5RZXy2Oav3VaF++FHEgzeu3t6GNNTDDqRf64tI99bYEvgT8NNc7zfA+/L0TqThZMrX+Q/A1SXPDwaeAf4IfAPYsWTeacA3ST/UFgFDc/n+5PuR5JgWAbuTBvxbAdxNOtIZU7Ku5/LfUVS4pwRp7KZFpKvBzwb2yuUtpCvFR5ftk/8EpubpDwGz8vTppKS2TX4+BTgtT28NzCxZ13DgkaLfZz+Kf7g5yzZXX5b00Tw9EhgDvA24MyJWA0i6EnhHrnMgME5v3BZmO0kDIt2rpc0wYGXbk4i4RdIupPtzHAI8JGmPiFhJGk/po5Gal64BjiIN7QLwfkkPAa8B34+IR3M8u5CatQ4E7pf0noiY29kLjYjW3Pz1ofy4XdJRpCFc7oqIx3O9tkFC3wd8LJf9QdIOJf02N0TEC3n6IGBPSUfm5wPzfnyclPB27Cw22/w5idhmR9L+pC/i90TEekl3kMY76+jGYVvk+i90UOeFvJ7X5S/m3wC/yX0bH5A0n/RlOyMnpa1IdxRsSyJ/iojDylceaXTga4BrJL1GGp+p0ySSl32JdJ+OmyU9RbpB1QwqD1He0VDmz5fV+1JEVBqwsR9pf1iTc5+IbY4GAmtyAnkn6Za7kAbL/GAe0bYv+dd4ditpcEUAJL2rwnrnAruW1PmQpP55egCpb2MxqQ/k9IgYlR87AsMl7dxewJL2y6Pstt3PZBxQ1V0VJY2XtGOe3gLYMy97d369o/O8wXmRu8j9GTnhroqN70UDabTfzyvdcgBJ78gnA0A6guvxUXat9/GRiG2Ofg98TtJsYB5ptF0iYqmk75LuALkMeIx0V0qALwP/lZfpS/qi/VzpSiPiL5IGljRz/T3wU0kbSD/ILoyI+yVNJzVvlbqW1MRV6e6TkBLQ+UqHLlsA/026V0w1hgK/kLR1fn4fqa/nxXxSwTU5uawAPkzq+/hlfq3reWM4+XIXkvphHsxxreSNW/D+Q47RmpxH8bWmImnbiHguH4lcC0yLiGu7sPxXgHURcWHdgmwAku4CJkU+C8yal5uzrNmcLmkWqSnmcbp+e9vzgWpPvd0sSWoBfuQEYuAjETMz2wQ+EjEzs5o5iZiZWc2cRMzMrGZOImZmVjMnETMzq9n/B20odDH78YdEAAAAAElFTkSuQmCC\n", |
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327 |
"text/plain": [ |
|
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328 |
"<Figure size 432x288 with 1 Axes>" |
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329 |
] |
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}, |
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"metadata": { |
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"needs_background": "light" |
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}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
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338 |
"#Inferential Statistics - Frequentism\n", |
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339 |
"#1.AGE Score\n", |
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|
340 |
"mean_age=np.mean(saps.AGE)\n", |
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"std_age=np.std(saps.AGE)\n", |
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"\n", |
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|
343 |
"_ = plt.hist(saps.AGE, bins=20, color='g')\n", |
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"_ = plt.xlabel('age (SAPSII Score)')\n", |
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345 |
"_ = plt.ylabel('Frequency')\n", |
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|
346 |
"_ = plt.title('Distribution of ICU pateint age score')\n", |
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347 |
"_ = plt.axvline(mean_age, color='m', linestyle='--')" |
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348 |
] |
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|
349 |
}, |
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350 |
{ |
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351 |
"cell_type": "code", |
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352 |
"execution_count": 8, |
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353 |
"metadata": {}, |
|
|
354 |
"outputs": [], |
|
|
355 |
"source": [ |
|
|
356 |
"#saps['saps2']= saps.iloc[:, -15:].sum(axis=1)" |
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357 |
] |
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|
358 |
}, |
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359 |
{ |
|
|
360 |
"cell_type": "code", |
|
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361 |
"execution_count": 9, |
|
|
362 |
"metadata": {}, |
|
|
363 |
"outputs": [ |
|
|
364 |
{ |
|
|
365 |
"data": { |
|
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366 |
"text/plain": [ |
|
|
367 |
"[Text(0.5, 1, 'Correlations: ICU Patients SAPSII Score')]" |
|
|
368 |
] |
|
|
369 |
}, |
|
|
370 |
"execution_count": 9, |
|
|
371 |
"metadata": {}, |
|
|
372 |
"output_type": "execute_result" |
|
|
373 |
}, |
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|
374 |
{ |
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|
375 |
"data": { |
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|
376 |
"image/png": 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\n", |
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|
377 |
"text/plain": [ |
|
|
378 |
"<Figure size 1296x1296 with 2 Axes>" |
|
|
379 |
] |
|
|
380 |
}, |
|
|
381 |
"metadata": {}, |
|
|
382 |
"output_type": "display_data" |
|
|
383 |
} |
|
|
384 |
], |
|
|
385 |
"source": [ |
|
|
386 |
"import seaborn as sns\n", |
|
|
387 |
"# Compute correlations\n", |
|
|
388 |
"corr = saps.corr()\n", |
|
|
389 |
"\n", |
|
|
390 |
"# Exclude duplicate correlations by masking uper right values\n", |
|
|
391 |
"mask = np.zeros_like(corr, dtype=np.bool)\n", |
|
|
392 |
"mask[np.triu_indices_from(mask)] = True\n", |
|
|
393 |
"\n", |
|
|
394 |
"# Set background color / chart style\n", |
|
|
395 |
"sns.set_style(style = 'white')\n", |
|
|
396 |
"\n", |
|
|
397 |
"# Set up matplotlib figure\n", |
|
|
398 |
"f, ax = plt.subplots(figsize=(18, 18))\n", |
|
|
399 |
"\n", |
|
|
400 |
"# Add diverging colormap\n", |
|
|
401 |
"#cmap =sns.diverging_palette(150, 275, s=80, l=55, n=12)\n", |
|
|
402 |
"cmap = sns.diverging_palette(220, 20, sep=20, as_cmap=True)\n", |
|
|
403 |
"\n", |
|
|
404 |
"# Draw correlation plot\n", |
|
|
405 |
"sns.heatmap(corr, mask=mask, cmap=cmap, \n", |
|
|
406 |
" square=True,\n", |
|
|
407 |
" linewidths=.5, cbar_kws={\"shrink\": .5}, ax=ax)\n", |
|
|
408 |
"ax.set (title='Correlations: ICU Patients SAPSII Score')" |
|
|
409 |
] |
|
|
410 |
}, |
|
|
411 |
{ |
|
|
412 |
"cell_type": "markdown", |
|
|
413 |
"metadata": {}, |
|
|
414 |
"source": [ |
|
|
415 |
"## Inferential Statistics - Frequentist" |
|
|
416 |
] |
|
|
417 |
}, |
|
|
418 |
{ |
|
|
419 |
"cell_type": "code", |
|
|
420 |
"execution_count": 10, |
|
|
421 |
"metadata": {}, |
|
|
422 |
"outputs": [], |
|
|
423 |
"source": [ |
|
|
424 |
"survived=saps.AGE.loc[saps.hdeath==0]\n", |
|
|
425 |
"deceased=saps.AGE.loc[saps.hdeath==1]" |
|
|
426 |
] |
|
|
427 |
}, |
|
|
428 |
{ |
|
|
429 |
"cell_type": "code", |
|
|
430 |
"execution_count": 11, |
|
|
431 |
"metadata": {}, |
|
|
432 |
"outputs": [ |
|
|
433 |
{ |
|
|
434 |
"data": { |
|
|
435 |
"image/png": 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\n", |
|
|
436 |
"text/plain": [ |
|
|
437 |
"<Figure size 432x288 with 1 Axes>" |
|
|
438 |
] |
|
|
439 |
}, |
|
|
440 |
"metadata": {}, |
|
|
441 |
"output_type": "display_data" |
|
|
442 |
} |
|
|
443 |
], |
|
|
444 |
"source": [ |
|
|
445 |
"_ = plt.hist(survived, bins=30, alpha=0.5, label='ICU Patients Survived')\n", |
|
|
446 |
"_ = plt.hist(deceased, bins=30, alpha=0.5, label='ICU Patients Deceased')\n", |
|
|
447 |
"_ = plt.xlabel('SAPSII Score')\n", |
|
|
448 |
"_ = plt.ylabel('Frequency')\n", |
|
|
449 |
"_ = plt.legend()" |
|
|
450 |
] |
|
|
451 |
}, |
|
|
452 |
{ |
|
|
453 |
"cell_type": "code", |
|
|
454 |
"execution_count": 16, |
|
|
455 |
"metadata": {}, |
|
|
456 |
"outputs": [ |
|
|
457 |
{ |
|
|
458 |
"data": { |
|
|
459 |
"text/plain": [ |
|
|
460 |
"Ttest_indResult(statistic=-113.38421526216234, pvalue=0.0)" |
|
|
461 |
] |
|
|
462 |
}, |
|
|
463 |
"execution_count": 16, |
|
|
464 |
"metadata": {}, |
|
|
465 |
"output_type": "execute_result" |
|
|
466 |
} |
|
|
467 |
], |
|
|
468 |
"source": [ |
|
|
469 |
"# perform hypothesis test of spas2 score between those passed away at hospital and survived\n", |
|
|
470 |
"\n", |
|
|
471 |
"from scipy.stats import ttest_ind\n", |
|
|
472 |
"survived=saps.saps2.loc[saps.hdeath==0]\n", |
|
|
473 |
"deceased=saps.saps2.loc[saps.hdeath==1]\n", |
|
|
474 |
"ttest_ind(survived, deceased)\n" |
|
|
475 |
] |
|
|
476 |
}, |
|
|
477 |
{ |
|
|
478 |
"cell_type": "code", |
|
|
479 |
"execution_count": 13, |
|
|
480 |
"metadata": {}, |
|
|
481 |
"outputs": [ |
|
|
482 |
{ |
|
|
483 |
"data": { |
|
|
484 |
"text/plain": [ |
|
|
485 |
"['SUBJECT_ID',\n", |
|
|
486 |
" 'HADM_ID',\n", |
|
|
487 |
" 'ICUSTAY_ID',\n", |
|
|
488 |
" 'los',\n", |
|
|
489 |
" 'hdeath',\n", |
|
|
490 |
" 'death',\n", |
|
|
491 |
" 'admission',\n", |
|
|
492 |
" 'ud',\n", |
|
|
493 |
" 'bun',\n", |
|
|
494 |
" 'Bicarbonate',\n", |
|
|
495 |
" 'ventilation',\n", |
|
|
496 |
" 'Temp',\n", |
|
|
497 |
" 'Bilirubin',\n", |
|
|
498 |
" 'gcs',\n", |
|
|
499 |
" 'AGE',\n", |
|
|
500 |
" 'UO',\n", |
|
|
501 |
" 'saps2',\n", |
|
|
502 |
" 'Potassium_0.0',\n", |
|
|
503 |
" 'Potassium_3.0',\n", |
|
|
504 |
" 'Sodium_0.0',\n", |
|
|
505 |
" 'Sodium_1.0',\n", |
|
|
506 |
" 'Sodium_5.0',\n", |
|
|
507 |
" 'WBC_0.0',\n", |
|
|
508 |
" 'WBC_3.0',\n", |
|
|
509 |
" 'hr_0.0',\n", |
|
|
510 |
" 'hr_2.0',\n", |
|
|
511 |
" 'hr_4.0',\n", |
|
|
512 |
" 'hr_7.0',\n", |
|
|
513 |
" 'hr_11.0',\n", |
|
|
514 |
" 'bp_0.0',\n", |
|
|
515 |
" 'bp_2.0',\n", |
|
|
516 |
" 'bp_5.0',\n", |
|
|
517 |
" 'bp_13.0']" |
|
|
518 |
] |
|
|
519 |
}, |
|
|
520 |
"execution_count": 13, |
|
|
521 |
"metadata": {}, |
|
|
522 |
"output_type": "execute_result" |
|
|
523 |
} |
|
|
524 |
], |
|
|
525 |
"source": [ |
|
|
526 |
"list(saps.columns)" |
|
|
527 |
] |
|
|
528 |
}, |
|
|
529 |
{ |
|
|
530 |
"cell_type": "code", |
|
|
531 |
"execution_count": 57, |
|
|
532 |
"metadata": {}, |
|
|
533 |
"outputs": [ |
|
|
534 |
{ |
|
|
535 |
"name": "stdout", |
|
|
536 |
"output_type": "stream", |
|
|
537 |
"text": [ |
|
|
538 |
"admission 422.0928311045037 0.0\n", |
|
|
539 |
"ud 79.48553209547315 0.0\n", |
|
|
540 |
"bun 103.82321908155082 0.0\n", |
|
|
541 |
"Bicarbonate 50.20290726333376 0.0\n", |
|
|
542 |
"ventilation 269.5616326641353 0.0\n", |
|
|
543 |
"Temp 428.78581356046413 0.0\n", |
|
|
544 |
"Bilirubin 68.31563939515497 0.0\n", |
|
|
545 |
"gcs 185.50528236030465 0.0\n", |
|
|
546 |
"AGE 374.9833756789964 0.0\n", |
|
|
547 |
"UO -86.57830736617781 0.0\n", |
|
|
548 |
"saps2 528.4388303796287 0.0\n", |
|
|
549 |
"Potassium_0.0 613.9450781425617 0.0\n", |
|
|
550 |
"Potassium_3.0 -59.82832139042311 0.0\n", |
|
|
551 |
"Sodium_0.0 575.893954295901 0.0\n", |
|
|
552 |
"Sodium_1.0 -64.01776903987019 0.0\n", |
|
|
553 |
"Sodium_5.0 -66.93879118526705 0.0\n", |
|
|
554 |
"WBC_0.0 572.7364948958383 0.0\n", |
|
|
555 |
"WBC_3.0 -47.051164834922716 0.0\n", |
|
|
556 |
"hr_0.0 137.92767245017222 0.0\n", |
|
|
557 |
"hr_2.0 136.97522688863378 0.0\n", |
|
|
558 |
"hr_4.0 -27.759615489236438 4.484733069363873e-169\n", |
|
|
559 |
"hr_7.0 -79.36376090302323 0.0\n", |
|
|
560 |
"hr_11.0 -31.431895017712954 5.427222076346614e-216\n", |
|
|
561 |
"bp_0.0 33.35969234249016 6.566038544175584e-243\n", |
|
|
562 |
"bp_2.0 -70.68488389053853 0.0\n", |
|
|
563 |
"bp_5.0 162.14385989281462 0.0\n", |
|
|
564 |
"bp_13.0 90.75917162078173 0.0\n" |
|
|
565 |
] |
|
|
566 |
} |
|
|
567 |
], |
|
|
568 |
"source": [ |
|
|
569 |
"ttest_name_list = ['admission', 'ud', 'bun', 'Bicarbonate', 'ventilation', 'Temp', 'Bilirubin', 'gcs', 'AGE', 'UO',\n", |
|
|
570 |
" 'saps2', 'Potassium_0.0', 'Potassium_3.0', 'Sodium_0.0', 'Sodium_1.0', 'Sodium_5.0', 'WBC_0.0',\n", |
|
|
571 |
" 'WBC_3.0', 'hr_0.0', 'hr_2.0', 'hr_4.0', 'hr_7.0', 'hr_11.0', 'bp_0.0', 'bp_2.0', 'bp_5.0', 'bp_13.0']\n", |
|
|
572 |
"for i in (ttest_name_list):\n", |
|
|
573 |
" stat, pvalue=ttest_ind(saps[i], saps['hdeath']) \n", |
|
|
574 |
" print(i, stat, pvalue)" |
|
|
575 |
] |
|
|
576 |
}, |
|
|
577 |
{ |
|
|
578 |
"cell_type": "markdown", |
|
|
579 |
"metadata": {}, |
|
|
580 |
"source": [ |
|
|
581 |
"`Indipendent ttest result shows:`\n", |
|
|
582 |
"* There is a statistically significant diffrence in admission type between deceased and survived ICU patients `(tstat=422, pvalue=0.0)`. On average we have higher number of unscheduled surgical admissions among deceased ICU patients. \n", |
|
|
583 |
"* There is a statistically significant diffrence in the diagnosis of chronic disease between deceased and survived ICU patients `(tstat=79.5, pvalue=0.0)`. On average we have higher number of ICU patients with chronic dieases such as metastatic cancer,hematologic malignancy, and AIDS when compared between decieased and survived ICU stay. \n", |
|
|
584 |
"* There is a statistically significant diffrence in blood urea nitrogin levels between deceased and survived ICU patients `(tstat=103.8, pvalue=0.0)`. On average we have higher number of ICU patients with high levels of blood urea nitrogen among deceased ICU patients. \n", |
|
|
585 |
"* There is a statistically significant diffrence in blood bicarbonate levels between deceased and survived ICU patients `(tstat=50, pvalue=0.0)`. On average we have higher number of ICU patients with low levels of blood bicarbonate levels among deceased ICU patients. \n", |
|
|
586 |
"* There is a statistically significant diffrence in ventilation use between deceased and survived ICU patients `(tstat=269.6, pvalue=0.0)`. On average we have higher number of ICU patients on ventilation among deceased ICU patients. \n", |
|
|
587 |
"* There is a statistically significant diffrence in level of body temprature between deceased and survived ICU patients `(tstat=428.8, pvalue=0.0)`. On average we have patients with high body temprature among deceased ICU patients. \n", |
|
|
588 |
"* There is a statistically significant diffrence in Bilirubin level between deceased and survived ICU patients `(tstat=68.3, pvalue=0.0)`. On average we have higher number of patients with high Bilirubin levels among deceased ICU patients. \n", |
|
|
589 |
"* There is a statistically significant diffrence total Glasgow Coma Score between deceased and survived ICU patients `(tstat=185.5, pvalue=0.0)`. On average we have higher number of ICU patients with higher Glsgow Coma Score among deceased ICU patients. The total Glasgow Coma Score is based on best eye response, best verbal response, and best motor response scores. \n", |
|
|
590 |
"* There is a statistically significant diffrence in patient age between deceased and survived ICU patients `(tstat=375, pvalue=0.0)`. On average we have higher proportion of deceased ICU patients who are older. \n", |
|
|
591 |
"* There is a statistically significant diffrence in urine output between deceased and survived ICU patients `(tstat=-86.6, pvalue=0.0)`. On average ICU patients who are deceased have lower level of urine output compared to survived ICU patients. \n", |
|
|
592 |
"\n", |
|
|
593 |
"* There is a statistically significant diffrence in blood potassium level between deceased and survived ICU patients `(tstat=-59.8, pvalue=0.0)`. On average we have higher number of ICU patients with low levels of blood potassium among deceased ICU patients. \n", |
|
|
594 |
"* There is a statistically significant diffrence in blood sodium levels (sodium sapsii score=1 and 5) between deceased and survived ICU patients `(tstat=-64,-67, pvalue=0.0, 0.0)`. On average we have higher number of patients with low level of blood sodium among deceased ICU patients. \n", |
|
|
595 |
"* There is a statistically significant diffrence in white blood cell counts between deceased and survived ICU patients `(tstat=-47, pvalue=0.0)`. On average we have higher proportion of ICU patients with above normal white blood cell counts among deceased ICU patients. \n", |
|
|
596 |
"* There is no statistical diffrence in the proportion of patients with cardiac arrest (`hr_11.0`), however we observed statistically significant diffrence in the proportion of patients with extreme tachycardia (`hr_7.0, -79.4, 0.0`), and heart rate below normal (`hr_2.0, 137, 0.0). \n", |
|
|
597 |
"\n", |
|
|
598 |
"* There is a statistically significant diffrence in the patient systolic blood pressure between deceased and survived ICU patients. On average we have higher number of ICU patients with systeolic blood pressure less than 70mmHg `(tstat=90.8, pvalue=0.0)`, below normal (between 70 -99mmHg), `(tstat=162, pvalue=0.0)`, above normal (more than and equal to 200mmHg) `(tstat=-70.7, pvalue=0.0)`among deceased ICU patients.\n", |
|
|
599 |
"\n", |
|
|
600 |
"\n" |
|
|
601 |
] |
|
|
602 |
}, |
|
|
603 |
{ |
|
|
604 |
"cell_type": "markdown", |
|
|
605 |
"metadata": {}, |
|
|
606 |
"source": [ |
|
|
607 |
"## Inferential Statistics - Bootstrapping" |
|
|
608 |
] |
|
|
609 |
}, |
|
|
610 |
{ |
|
|
611 |
"cell_type": "code", |
|
|
612 |
"execution_count": 19, |
|
|
613 |
"metadata": {}, |
|
|
614 |
"outputs": [ |
|
|
615 |
{ |
|
|
616 |
"data": { |
|
|
617 |
"text/plain": [ |
|
|
618 |
"42.88408855744203" |
|
|
619 |
] |
|
|
620 |
}, |
|
|
621 |
"execution_count": 19, |
|
|
622 |
"metadata": {}, |
|
|
623 |
"output_type": "execute_result" |
|
|
624 |
} |
|
|
625 |
], |
|
|
626 |
"source": [ |
|
|
627 |
"np.random.seed(47)\n", |
|
|
628 |
"N_rep = 10000\n", |
|
|
629 |
"bs_means=np.empty(N_rep)\n", |
|
|
630 |
"\n", |
|
|
631 |
"for i in range (N_rep):\n", |
|
|
632 |
" bs_sample = np.random.choice(saps.saps2, size=len(saps.saps2))\n", |
|
|
633 |
" bs_means[i] = np.mean(bs_sample)\n", |
|
|
634 |
"\n", |
|
|
635 |
"bs_mean, bs_std=np.mean(bs_means), np.std(bs_means)\n", |
|
|
636 |
"lower_limit=bs_mean-(1.64*bs_std)\n", |
|
|
637 |
"lower_limit" |
|
|
638 |
] |
|
|
639 |
}, |
|
|
640 |
{ |
|
|
641 |
"cell_type": "code", |
|
|
642 |
"execution_count": 20, |
|
|
643 |
"metadata": {}, |
|
|
644 |
"outputs": [ |
|
|
645 |
{ |
|
|
646 |
"name": "stdout", |
|
|
647 |
"output_type": "stream", |
|
|
648 |
"text": [ |
|
|
649 |
"The 95% confidence interval for the difference between the standard deviations of survived and deceased SAPSII Score is: [0.28484245733704616, 0.6520856816890048]\n" |
|
|
650 |
] |
|
|
651 |
} |
|
|
652 |
], |
|
|
653 |
"source": [ |
|
|
654 |
"\n", |
|
|
655 |
"survived=saps.saps2.loc[saps.death==0]\n", |
|
|
656 |
"deceased=saps.saps2.loc[saps.death==1]\n", |
|
|
657 |
"\n", |
|
|
658 |
"np.random.seed(47)\n", |
|
|
659 |
"bs_std_diff=np.empty(N_rep)\n", |
|
|
660 |
"\n", |
|
|
661 |
"for i in range (N_rep):\n", |
|
|
662 |
" bs_survived = np.random.choice(survived, size=len(survived))\n", |
|
|
663 |
" bs_deceased = np.random.choice(deceased, size=len(deceased))\n", |
|
|
664 |
" bs_std_diff[i]=np.std(bs_deceased) - np.std(bs_survived)\n", |
|
|
665 |
" \n", |
|
|
666 |
"bs_std_diff_mean, bs_std_diff_std=np.mean(bs_std_diff), np.std(bs_std_diff)\n", |
|
|
667 |
"ci_std_diff=[bs_std_diff_mean - 1.96*bs_std_diff_std, bs_std_diff_mean + 1.96*bs_std_diff_std]\n", |
|
|
668 |
"\n", |
|
|
669 |
"print('The 95% confidence interval for the difference between the standard deviations\\\n", |
|
|
670 |
" of survived and deceased SAPSII Score is: ', ci_std_diff)" |
|
|
671 |
] |
|
|
672 |
}, |
|
|
673 |
{ |
|
|
674 |
"cell_type": "code", |
|
|
675 |
"execution_count": 21, |
|
|
676 |
"metadata": {}, |
|
|
677 |
"outputs": [ |
|
|
678 |
{ |
|
|
679 |
"data": { |
|
|
680 |
"image/png": 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\n", |
|
|
681 |
"text/plain": [ |
|
|
682 |
"<Figure size 432x288 with 1 Axes>" |
|
|
683 |
] |
|
|
684 |
}, |
|
|
685 |
"metadata": {}, |
|
|
686 |
"output_type": "display_data" |
|
|
687 |
} |
|
|
688 |
], |
|
|
689 |
"source": [ |
|
|
690 |
"# Plot the histogram of values and mark the locations of the percentiles\n", |
|
|
691 |
"_ = plt.hist(bs_std_diff, bins=30, linewidth=0.5, color='turquoise')\n", |
|
|
692 |
"_ = plt.xlabel('Mean of standard devations of SAPSII Score')\n", |
|
|
693 |
"_ = plt.ylabel('Frequency')\n", |
|
|
694 |
"_ = plt.title('Distribution of mean of SDs of all SAPSII Score')\n", |
|
|
695 |
"_ = plt.axvline(bs_std_diff_mean, color='m')\n", |
|
|
696 |
"_ = plt.axvline(ci_std_diff[0], color='m', linestyle='--')\n", |
|
|
697 |
"_ = plt.axvline(ci_std_diff[1], color='m', linestyle='--')" |
|
|
698 |
] |
|
|
699 |
}, |
|
|
700 |
{ |
|
|
701 |
"cell_type": "code", |
|
|
702 |
"execution_count": 22, |
|
|
703 |
"metadata": {}, |
|
|
704 |
"outputs": [ |
|
|
705 |
{ |
|
|
706 |
"data": { |
|
|
707 |
"text/plain": [ |
|
|
708 |
"14.74874822658952" |
|
|
709 |
] |
|
|
710 |
}, |
|
|
711 |
"execution_count": 22, |
|
|
712 |
"metadata": {}, |
|
|
713 |
"output_type": "execute_result" |
|
|
714 |
} |
|
|
715 |
], |
|
|
716 |
"source": [ |
|
|
717 |
"#Perform a bootstrapped hypothesis test at the 5% significance level to calculate the p-value of the observed difference between survived and deceased icu patients.\n", |
|
|
718 |
"\n", |
|
|
719 |
"# Compute the difference in mean charges: diff_means\n", |
|
|
720 |
"diff_means=np.mean(deceased) - np.mean(survived)\n", |
|
|
721 |
"diff_means" |
|
|
722 |
] |
|
|
723 |
}, |
|
|
724 |
{ |
|
|
725 |
"cell_type": "code", |
|
|
726 |
"execution_count": 23, |
|
|
727 |
"metadata": {}, |
|
|
728 |
"outputs": [], |
|
|
729 |
"source": [ |
|
|
730 |
"#Define a function to generate a permutation sample from two data sets (insured and uninsured)\n", |
|
|
731 |
"def permutation_sample(data1, data2):\n", |
|
|
732 |
"\n", |
|
|
733 |
" # Concatenate the data sets: data\n", |
|
|
734 |
" data = np.concatenate((data1, data2))\n", |
|
|
735 |
"\n", |
|
|
736 |
" # Permute the concatenated array: permuted_data\n", |
|
|
737 |
" permuted_data = np.random.permutation(data)\n", |
|
|
738 |
"\n", |
|
|
739 |
" # Split the permuted array into two: perm_sample_1, perm_sample_2\n", |
|
|
740 |
" perm_sample_1 = permuted_data[:len(data1)]\n", |
|
|
741 |
" perm_sample_2 = permuted_data[len(data1):]\n", |
|
|
742 |
"\n", |
|
|
743 |
" return perm_sample_1, perm_sample_2" |
|
|
744 |
] |
|
|
745 |
}, |
|
|
746 |
{ |
|
|
747 |
"cell_type": "code", |
|
|
748 |
"execution_count": 24, |
|
|
749 |
"metadata": {}, |
|
|
750 |
"outputs": [ |
|
|
751 |
{ |
|
|
752 |
"name": "stdout", |
|
|
753 |
"output_type": "stream", |
|
|
754 |
"text": [ |
|
|
755 |
"Permuation Pvalue: 0.0\n", |
|
|
756 |
"Pvalue: 0.0\n" |
|
|
757 |
] |
|
|
758 |
} |
|
|
759 |
], |
|
|
760 |
"source": [ |
|
|
761 |
"#A bootstrap hypothesis test for difference of means\n", |
|
|
762 |
"np.random.seed(47)\n", |
|
|
763 |
"deceased_shifted=deceased - np.mean(deceased) + np.mean(survived)\n", |
|
|
764 |
"perm_mean_replicates=np.empty(N_rep)\n", |
|
|
765 |
"for i in range(N_rep):\n", |
|
|
766 |
" perm_survived, perm_deceased=permutation_sample(survived, deceased_shifted)\n", |
|
|
767 |
" perm_mean_replicates[i]=np.mean(perm_survived) - np.mean(perm_deceased)\n", |
|
|
768 |
" \n", |
|
|
769 |
"bs_mean_diff=np.empty(N_rep)\n", |
|
|
770 |
"for i in range(N_rep):\n", |
|
|
771 |
" bs_mean_diff[i]=np.mean(bs_survived) - np.mean(np.random.choice(deceased_shifted, len(deceased_shifted)))\n", |
|
|
772 |
"\n", |
|
|
773 |
"# Compute p-value: perm_p, p\n", |
|
|
774 |
"perm_p = np.sum(perm_mean_replicates>=diff_means)/len(perm_mean_replicates)\n", |
|
|
775 |
"print('Permuation Pvalue: ', perm_p)\n", |
|
|
776 |
"p=np.sum(bs_mean_diff>=diff_means)/len(bs_mean_diff)\n", |
|
|
777 |
"print('Pvalue: ', p)\n" |
|
|
778 |
] |
|
|
779 |
}, |
|
|
780 |
{ |
|
|
781 |
"cell_type": "code", |
|
|
782 |
"execution_count": 25, |
|
|
783 |
"metadata": { |
|
|
784 |
"scrolled": true |
|
|
785 |
}, |
|
|
786 |
"outputs": [ |
|
|
787 |
{ |
|
|
788 |
"data": { |
|
|
789 |
"image/png": 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\n", |
|
|
790 |
"text/plain": [ |
|
|
791 |
"<Figure size 432x288 with 1 Axes>" |
|
|
792 |
] |
|
|
793 |
}, |
|
|
794 |
"metadata": {}, |
|
|
795 |
"output_type": "display_data" |
|
|
796 |
} |
|
|
797 |
], |
|
|
798 |
"source": [ |
|
|
799 |
"_=plt.hist(bs_mean_diff, alpha=0.5, bins=5, color='red')\n", |
|
|
800 |
"_=plt.hist(perm_mean_replicates, linewidth=0.5, alpha=0.25, bins=30, color='turquoise')\n", |
|
|
801 |
"_=plt.axvline(np.mean(perm_mean_replicates), color='g')\n", |
|
|
802 |
"_=plt.axvline(np.mean(bs_mean_diff), color='r', linestyle='--')\n", |
|
|
803 |
"_=plt.axvline(np.mean(diff_means), color='b', linestyle=':')\n" |
|
|
804 |
] |
|
|
805 |
}, |
|
|
806 |
{ |
|
|
807 |
"cell_type": "markdown", |
|
|
808 |
"metadata": {}, |
|
|
809 |
"source": [ |
|
|
810 |
"## Inferential Statistics - Bayesian" |
|
|
811 |
] |
|
|
812 |
}, |
|
|
813 |
{ |
|
|
814 |
"cell_type": "code", |
|
|
815 |
"execution_count": 26, |
|
|
816 |
"metadata": {}, |
|
|
817 |
"outputs": [], |
|
|
818 |
"source": [ |
|
|
819 |
"import pymc3 as pm\n", |
|
|
820 |
"import pandas as pd\n", |
|
|
821 |
"import numpy as np\n", |
|
|
822 |
"from numpy.random import seed\n", |
|
|
823 |
"import matplotlib.pyplot as plt\n", |
|
|
824 |
"from scipy.stats import gamma" |
|
|
825 |
] |
|
|
826 |
}, |
|
|
827 |
{ |
|
|
828 |
"cell_type": "code", |
|
|
829 |
"execution_count": 27, |
|
|
830 |
"metadata": {}, |
|
|
831 |
"outputs": [ |
|
|
832 |
{ |
|
|
833 |
"data": { |
|
|
834 |
"text/plain": [ |
|
|
835 |
"(7.702206830403064, 6.742807762839468)" |
|
|
836 |
] |
|
|
837 |
}, |
|
|
838 |
"execution_count": 27, |
|
|
839 |
"metadata": {}, |
|
|
840 |
"output_type": "execute_result" |
|
|
841 |
} |
|
|
842 |
], |
|
|
843 |
"source": [ |
|
|
844 |
"#Initial parameter estimation for the gamma distribution's 𝛼 and 𝛽 \n", |
|
|
845 |
"alpha_est = np.mean(deceased)**2 / np.var(deceased)\n", |
|
|
846 |
"beta_est = np.var(deceased) / np.mean(deceased)\n", |
|
|
847 |
"alpha_est, beta_est\n" |
|
|
848 |
] |
|
|
849 |
}, |
|
|
850 |
{ |
|
|
851 |
"cell_type": "code", |
|
|
852 |
"execution_count": 28, |
|
|
853 |
"metadata": {}, |
|
|
854 |
"outputs": [], |
|
|
855 |
"source": [ |
|
|
856 |
"#Initial simulation - \n", |
|
|
857 |
"seed(47)\n", |
|
|
858 |
"n_survived = len(survived)\n", |
|
|
859 |
"n_deceased = len(deceased)\n", |
|
|
860 |
"deceased_model_rvs = gamma(alpha_est, scale=beta_est).rvs(n_deceased)" |
|
|
861 |
] |
|
|
862 |
}, |
|
|
863 |
{ |
|
|
864 |
"cell_type": "code", |
|
|
865 |
"execution_count": 29, |
|
|
866 |
"metadata": {}, |
|
|
867 |
"outputs": [ |
|
|
868 |
{ |
|
|
869 |
"data": { |
|
|
870 |
"image/png": 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\n", |
|
|
871 |
"text/plain": [ |
|
|
872 |
"<Figure size 432x288 with 1 Axes>" |
|
|
873 |
] |
|
|
874 |
}, |
|
|
875 |
"metadata": {}, |
|
|
876 |
"output_type": "display_data" |
|
|
877 |
} |
|
|
878 |
], |
|
|
879 |
"source": [ |
|
|
880 |
"_ = plt.hist(deceased_model_rvs, bins=30, alpha=0.5, label='simulated')\n", |
|
|
881 |
"_ = plt.hist(deceased, bins=30, alpha=0.5, label='observed')\n", |
|
|
882 |
"_ = plt.xlabel('SAPSII Score')\n", |
|
|
883 |
"_ = plt.ylabel('Frequency')\n", |
|
|
884 |
"_ = plt.legend()" |
|
|
885 |
] |
|
|
886 |
}, |
|
|
887 |
{ |
|
|
888 |
"cell_type": "code", |
|
|
889 |
"execution_count": 32, |
|
|
890 |
"metadata": {}, |
|
|
891 |
"outputs": [], |
|
|
892 |
"source": [ |
|
|
893 |
"#creating a PyMC3 model\n", |
|
|
894 |
"# PyMC3 Gamma seems to use rate = 1/beta\n", |
|
|
895 |
"rate_est = 1/beta_est\n", |
|
|
896 |
"# Initial parameter estimates we'll use below\n", |
|
|
897 |
"alpha_est, rate_est\n", |
|
|
898 |
"mean=np.mean(deceased)\n", |
|
|
899 |
"sd=np.std(deceased)" |
|
|
900 |
] |
|
|
901 |
}, |
|
|
902 |
{ |
|
|
903 |
"cell_type": "code", |
|
|
904 |
"execution_count": 44, |
|
|
905 |
"metadata": {}, |
|
|
906 |
"outputs": [ |
|
|
907 |
{ |
|
|
908 |
"name": "stderr", |
|
|
909 |
"output_type": "stream", |
|
|
910 |
"text": [ |
|
|
911 |
"Auto-assigning NUTS sampler...\n", |
|
|
912 |
"Initializing NUTS using jitter+adapt_diag...\n", |
|
|
913 |
"Multiprocess sampling (4 chains in 4 jobs)\n", |
|
|
914 |
"NUTS: [beta, alpha]\n", |
|
|
915 |
"Sampling 4 chains: 100%|██████████| 48000/48000 [01:22<00:00, 580.59draws/s]\n", |
|
|
916 |
"The number of effective samples is smaller than 25% for some parameters.\n" |
|
|
917 |
] |
|
|
918 |
} |
|
|
919 |
], |
|
|
920 |
"source": [ |
|
|
921 |
"with pm.Model() as a_model:\n", |
|
|
922 |
" alpha_ = pm.Exponential('alpha', 1/alpha_est)\n", |
|
|
923 |
" rate_ = pm.Exponential('beta', 1/rate_est)\n", |
|
|
924 |
" saps_deceased =pm.Gamma('Saps2_score_deceased', alpha=alpha_, beta=rate_, observed=deceased)\n", |
|
|
925 |
" trace = pm.sample(10000, tune=2000, cores=4)" |
|
|
926 |
] |
|
|
927 |
}, |
|
|
928 |
{ |
|
|
929 |
"cell_type": "code", |
|
|
930 |
"execution_count": 45, |
|
|
931 |
"metadata": {}, |
|
|
932 |
"outputs": [ |
|
|
933 |
{ |
|
|
934 |
"name": "stdout", |
|
|
935 |
"output_type": "stream", |
|
|
936 |
"text": [ |
|
|
937 |
"95% confidence interval for alpha: [7.33467864 7.59837212]\n", |
|
|
938 |
"95% confidence interval for beta: [0.14113107 0.14638464]\n" |
|
|
939 |
] |
|
|
940 |
} |
|
|
941 |
], |
|
|
942 |
"source": [ |
|
|
943 |
"alpha_samples = trace['alpha']\n", |
|
|
944 |
"beta_samples = trace['beta']\n", |
|
|
945 |
"alpha_ci = np.percentile(alpha_samples, [2.5, 97.5])\n", |
|
|
946 |
"beta_ci = np.percentile(beta_samples, [2.5, 97.5])\n", |
|
|
947 |
"print('95% confidence interval for alpha: ', alpha_ci)\n", |
|
|
948 |
"print('95% confidence interval for beta: ', beta_ci)" |
|
|
949 |
] |
|
|
950 |
}, |
|
|
951 |
{ |
|
|
952 |
"cell_type": "code", |
|
|
953 |
"execution_count": 46, |
|
|
954 |
"metadata": {}, |
|
|
955 |
"outputs": [ |
|
|
956 |
{ |
|
|
957 |
"data": { |
|
|
958 |
"image/png": 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\n", |
|
|
959 |
"text/plain": [ |
|
|
960 |
"<Figure size 1440x432 with 4 Axes>" |
|
|
961 |
] |
|
|
962 |
}, |
|
|
963 |
"metadata": {}, |
|
|
964 |
"output_type": "display_data" |
|
|
965 |
} |
|
|
966 |
], |
|
|
967 |
"source": [ |
|
|
968 |
"plt.figure(figsize=(20,6))\n", |
|
|
969 |
"plt.subplot(2, 2, 1)\n", |
|
|
970 |
"_ = plt.plot(alpha_samples, color='turquoise')\n", |
|
|
971 |
"_ = plt.title(r'$\\alpha$ line plot')\n", |
|
|
972 |
"_ = plt.xlabel('index')\n", |
|
|
973 |
"_ = plt.ylabel(r'$alpha$')\n", |
|
|
974 |
"plt.subplot(2, 2, 2)\n", |
|
|
975 |
"_ = plt.hist(alpha_samples, bins=30, color='turquoise')\n", |
|
|
976 |
"_ = plt.xlabel(r'$\\alpha$')\n", |
|
|
977 |
"_ = plt.ylabel('Frequency')\n", |
|
|
978 |
"_ = plt.title(r'$alpha$ histogram with 95% CI')\n", |
|
|
979 |
"_ = plt.axvline(x=alpha_ci[0], c='m')\n", |
|
|
980 |
"_ = plt.axvline(x=alpha_ci[1], c='m')\n", |
|
|
981 |
"plt.subplot(2, 2, 3)\n", |
|
|
982 |
"_ = plt.plot(beta_samples, color='turquoise')\n", |
|
|
983 |
"_ = plt.title(r'$\\beta$ line plot')\n", |
|
|
984 |
"_ = plt.xlabel('index')\n", |
|
|
985 |
"_ = plt.ylabel(r'$beta$')\n", |
|
|
986 |
"plt.subplot(2, 2, 4)\n", |
|
|
987 |
"_ = plt.hist(beta_samples, bins=30, color='turquoise')\n", |
|
|
988 |
"_ = plt.xlabel(r'$\\beta$')\n", |
|
|
989 |
"_ = plt.ylabel('Frequency')\n", |
|
|
990 |
"_ = plt.title(r'$beta$ histogram with 95% CI')\n", |
|
|
991 |
"_ = plt.axvline(x=beta_ci[0], c='m')\n", |
|
|
992 |
"_ = plt.axvline(x=beta_ci[1], c='m')\n", |
|
|
993 |
"plt.tight_layout()" |
|
|
994 |
] |
|
|
995 |
}, |
|
|
996 |
{ |
|
|
997 |
"cell_type": "code", |
|
|
998 |
"execution_count": 47, |
|
|
999 |
"metadata": {}, |
|
|
1000 |
"outputs": [ |
|
|
1001 |
{ |
|
|
1002 |
"data": { |
|
|
1003 |
"image/png": 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\n", |
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|
1004 |
"text/plain": [ |
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|
1005 |
"<Figure size 432x288 with 1 Axes>" |
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1006 |
] |
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|
1007 |
}, |
|
|
1008 |
"metadata": {}, |
|
|
1009 |
"output_type": "display_data" |
|
|
1010 |
}, |
|
|
1011 |
{ |
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|
1012 |
"data": { |
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1013 |
"image/png": 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\n", |
|
|
1014 |
"text/plain": [ |
|
|
1015 |
"<Figure size 432x288 with 1 Axes>" |
|
|
1016 |
] |
|
|
1017 |
}, |
|
|
1018 |
"metadata": {}, |
|
|
1019 |
"output_type": "display_data" |
|
|
1020 |
} |
|
|
1021 |
], |
|
|
1022 |
"source": [ |
|
|
1023 |
"_ = pm.plots.plot_posterior(data=trace['alpha'])\n", |
|
|
1024 |
"_ = pm.plots.plot_posterior(data=trace[\"beta\"])" |
|
|
1025 |
] |
|
|
1026 |
}, |
|
|
1027 |
{ |
|
|
1028 |
"cell_type": "code", |
|
|
1029 |
"execution_count": 48, |
|
|
1030 |
"metadata": {}, |
|
|
1031 |
"outputs": [ |
|
|
1032 |
{ |
|
|
1033 |
"data": { |
|
|
1034 |
"text/plain": [ |
|
|
1035 |
"(7.465256701387036, 6.956907617912886)" |
|
|
1036 |
] |
|
|
1037 |
}, |
|
|
1038 |
"execution_count": 48, |
|
|
1039 |
"metadata": {}, |
|
|
1040 |
"output_type": "execute_result" |
|
|
1041 |
} |
|
|
1042 |
], |
|
|
1043 |
"source": [ |
|
|
1044 |
"alpha_best = np.mean(alpha_samples)\n", |
|
|
1045 |
"beta_best = np.mean(beta_samples)\n", |
|
|
1046 |
"alpha_best, 1/beta_best\n" |
|
|
1047 |
] |
|
|
1048 |
}, |
|
|
1049 |
{ |
|
|
1050 |
"cell_type": "code", |
|
|
1051 |
"execution_count": 50, |
|
|
1052 |
"metadata": {}, |
|
|
1053 |
"outputs": [ |
|
|
1054 |
{ |
|
|
1055 |
"data": { |
|
|
1056 |
"image/png": 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\n", |
|
|
1057 |
"text/plain": [ |
|
|
1058 |
"<Figure size 432x288 with 1 Axes>" |
|
|
1059 |
] |
|
|
1060 |
}, |
|
|
1061 |
"metadata": {}, |
|
|
1062 |
"output_type": "display_data" |
|
|
1063 |
} |
|
|
1064 |
], |
|
|
1065 |
"source": [ |
|
|
1066 |
"seed(47)\n", |
|
|
1067 |
"best_shot_simulated = gamma(alpha_best, scale=1/beta_best).rvs(n_deceased)\n", |
|
|
1068 |
"_ = plt.hist(best_shot_simulated, bins=35, alpha=0.5, color='m', label='simulated_data')\n", |
|
|
1069 |
"_ = plt.hist(deceased, bins=35, alpha=0.5, color='turquoise', label='observed_data')\n", |
|
|
1070 |
"_ = plt.xlabel('SAPSII Total Score')\n", |
|
|
1071 |
"_ = plt.ylabel('Frequency')\n", |
|
|
1072 |
"_ = plt.legend()" |
|
|
1073 |
] |
|
|
1074 |
} |
|
|
1075 |
], |
|
|
1076 |
"metadata": { |
|
|
1077 |
"kernelspec": { |
|
|
1078 |
"display_name": "Python 3", |
|
|
1079 |
"language": "python", |
|
|
1080 |
"name": "python3" |
|
|
1081 |
}, |
|
|
1082 |
"language_info": { |
|
|
1083 |
"codemirror_mode": { |
|
|
1084 |
"name": "ipython", |
|
|
1085 |
"version": 3 |
|
|
1086 |
}, |
|
|
1087 |
"file_extension": ".py", |
|
|
1088 |
"mimetype": "text/x-python", |
|
|
1089 |
"name": "python", |
|
|
1090 |
"nbconvert_exporter": "python", |
|
|
1091 |
"pygments_lexer": "ipython3", |
|
|
1092 |
"version": "3.7.4" |
|
|
1093 |
} |
|
|
1094 |
}, |
|
|
1095 |
"nbformat": 4, |
|
|
1096 |
"nbformat_minor": 2 |
|
|
1097 |
} |