492 lines (491 with data), 118.1 kB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here are the simple examples for plotting nomogram, ROC curves, Calibration curves, and Decision curves in training and test dataset by using R language."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Library and data\n",
    "library(rms)\n",
    "library(pROC)\n",
    "library(rmda)\n",
    "train <-read.csv(\"E:/Experiments/YinjunDong/nomogram/EGFR-nomogram.csv\")\n",
    "test <-read.csv(\"E:/Experiments/YinjunDong/nomogram/EGFR-nomogram-test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAMFBMVEUAAABNTU1oaGh8fHyM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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nomogram\n",
    "dd=datadist(train)\n",
    "options(datadist=\"dd\")\n",
    "f1 <- lrm(EGFR~ Rad\n",
    "          +Smoking\n",
    "          +Type\n",
    "          ,data = train,x = TRUE,y = TRUE)\n",
    "\n",
    "nom <- nomogram(f1, fun=plogis,fun.at=c(.001, .01, seq(.1,.9, by=.4)), lp=F, funlabel=\"EGFR Mutations\")\n",
    "plot(nom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting levels: control = 0, case = 1\n",
      "\n",
      "Setting direction: controls < cases\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAYFBMVEUAAAAAAP9NTU1NTf9o\naGhoaP98fHx8fP+MjIyMjP+ampqamv+np6enp/+pqamysrKysv+9vb29vf/Hx8fHx//Q0NDQ\n0P/Z2dnZ2f/h4eHh4f/p6enp6f/w8PDw8P////+2ELMfAAAACXBIWXMAABJ0AAASdAHeZh94\nAAAgAElEQVR4nO3d24KayBpA4ZIQYgyzjeMYxzDE93/LDQUqeOTwU8f1XSS23U2Znl4DFAjq\nDGA2ZfsFACEgJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBAS\nIICQAAGEBAggJKvUxXp3fe6wSZRKN4fbVx03qVLJen//3a+eh3mEZJW6yZpniuz6RNl+0fry\nTHLsffOr52EBIVnVCUnpdVKZ3J5ImpKyztecOt/76nnYQEhWVQ3ov8u82pqrH9RrmW1RrZh2\n9fZe+0yyK7vPNF49DysIyapLSJdHx+qvdjutaFY0p6qXonnmlG6L63c+Pn9ZVvt39VeRqrxa\n4kY/vWmWXOaJSvLbciCDkKy6D6laMeWXz23147zd5rv3+PxjSGm955Vcn06qP4t205G9KmGE\nZNXll7/YNJtnWWd356QnIKpnnq4+Hp9/DKmyr4urJwAPTaKXXbBkgX9M1AjJqu5kw/HcXUNd\nPug9c7775NNnbiHpeb9Ts23XbNnt9JPlpi4MkgjJqk5Hh/bj3ifnhdQciqo28Kp2msmMdf1Y\nf5LpCVmEZNU1o215+bj3yXkhNcvc1quffTVEdzy27YQRklX6l74+BtvOwKUP+0jpi4NEj88/\nhtQ8X9TLaXepOmtA8X9L3Ph5WtX+QmeXExu6s3b57Fm79hP1DEZ7mCohoIXwc7Xq8tuetHv/\nx+ueTT3NVq9zjrfjRcfucaTH59ttueNDSNVm3brNbn1dPGQRklWX3/bjZaelPu+nPlxa5A9n\nNmxfnNlweT7Ra7Bj8hBSqTfl9A7Tvjkxb389tQ9CCMmq62/7ZZVRPJxr1zv7rnPo6OH5TX//\np7MbVH+mDef6TRyRlUVIVnVnBJp1Rufs77aa8vpM2jsEe/980XyQP4ZUbyXubw/1Vy39L4sN\nIVl1+23Pr9tt+v1ISff9SI/vUHr6/Kla82T7x8mGzlR4fa5dWo3EjpI0QgIEEBIggJAAAYQE\nCCAkQAAhAQIICRBASIAAQgIEEBIggJAAAYQECCAkQAAhAQIICRBgICQF+OV/E37L5cOxMAQg\n6H8TfmUJCej735RfWUICev436VeWkICuev+IkIB59DwDIQGzNPN1hATM0c57ExIww+X4ESEB\n012PwxoN6bhd66PA6/zDZaQJCV64nc9gMKQy7ZxR8f7OBoQEH3TOCzIYUq6SfXPHuOKQvL8k\nOyHBA93z6wyGlHRuvHh6f0NSQoL7euepGgypdwvF9/dTJCQ4r3++N2skYIq7902Y3Uc6NLe9\nYh8Jvrt//5HJ6e+sM2uXlu++kpDgtof38Zk9jpTr40jJestxJPjs8f2wnNkAjPXkfeWEBIz0\n7PoMnCIEjPP0OiecIgSM8vx6QZwiBIzx4rpbHJCN0wpTvOzIoVOEepfbmzgEBrL9C+mtl9eB\nZI0Upfr/rRjv9fVUOUUoSoQ0yZvrEnOKUJQIaYp31/fmFKEoEdIEb6+Tz5kNUSKk8d7fb4KQ\nokRIo324bwshRYmQxvp0/yNCihIhjfTxPmKEFCVCGufz/fiMntkw+OQFQloYIY0y4L6WBkPa\nEZIzCGmMIfeHNblpd0rev3lCYAhoA04as/0SPTLoPstG95FO708MkhgC52GnpNp+jf4Ydr9y\ns5MNu855qwsNATbcRA3riFm7EBGSnIEdEVKICEnM0I4IKUSEJGVwR4QUIkISMrwjQgoRIckY\n0REhhYiQRIzpiJBCREgSRnVESCEiJAHjOiKkEBHSfCM7IqQQEdJsYzsipBAR0lyjOyKkEBHS\nTOM7IqQQEdI8EzoipBAR0ixTOiKkEBHSHJM6IqQQEdIM0zoipBAR0nQTOyKkEBHSZFM7IqQQ\nEdJUkzsipBAR0kTTOyKkEBHSNDM6IqQQEdIkczoipBAR0hSzOiIku2beY5vLP8qZ1xEhWbVU\nR4Q02syOCMkqfuNdMbcjQrKKkBwxuyNCsoqQ3DC/I0KyipCcINARIVlFSC6Q6IiQrCIkB4h0\nREhWEZJ9Mh0RklWEZJ1QR4RkFSHZJtURIVlFSJaJdURIVhGSXXIdEZJVhGSVYEeEZBUh2STZ\nESFZRUgWiXZESFYRkj2yHRGSVYRkjXBHhGQVIdki3REhWUVIloh3REhWEZId8h0RklWEZMUC\nHRGSVYRkwxIdEZJVhGTBIh0RklWEZN4yHRGSVYRk3EIdRRaS6llokBEIybSlOoosJDOLH46Q\nDFusI0KyipDMWq4jQrKKkIxasCNCsoqQTFqyI0KyipAMWrQjQrKKkMxZtiNCsoqQjFm4I0Ky\nipBMWbojQrKKkAxZvCNCsoqQzFi+I0KyipCMMNARIRnFXZNtMNERIZnE7cdtMNIRIZlENxaY\n6YiQTCIk8wx1REgmEZJxpjoiJJMIyTRjHRGSSYRkmLmOCMkkQjLLYEeEZBIhGWWyI0IyiZBM\nMtoRIZlESAaZ7YiQTCIkcwx3REgmEZIxpjsiJDlPzqTj1DpLjHdESGKGdERIZpjviJDEUIkz\nLHRESGIIyRU2OiIkMYTkCCsdEZIYQnKDnY4ISQwhOcFSR4QkhpBcYKsjQhJDSA6w1hEhiSEk\n++x1REhiCMk6ix0RkhhCss1mR4QkhpAss9oRIYkhJLvsdkRIYgjJKssdEZIYQrLJdkeEJIaQ\nLLLeESGJISR77HdESGIIyRoHOiIkMYRkiwsdEZIYQrLEiY4ISQwh2eFGR4QkhpCscKQjQhJD\nSDa40hEhiSEkC5zpiJDEEJJ57nRkNqTjdq1q6/y41BCDiCyeqz9a51BHJkMqU3WTLTLEQBKL\n5zKq1rnUkcmQcpXsT/pRcUhUvsQQA8mEJLAQzOBURyZDStTp+vikkiWGGIiQAuBWRyZDUurV\nB2JDDH0lAssgJLsc64g10lSEZJVrHRneRzoU+hH7SJjHuY6MTn9nnVm7tFxkiGEIyXPudWT4\nOFKujyMl663/x5EIyR4HO+LMhqkIyRoXOyKkqQjJFic7MhpSsVHJ9nzepSp5O9VASHjNzY6M\nniKU1DtIu62DpwgNupEyZwS5wNGOzE5/V+uhPFGb8lzmj9PfqmviEAPdL35SR4Rkg6sdmT0g\nq79b6Ylvpw7IEoUvnO3I/ClC7drGqVOECMkT7nZkY41U/1myRsJoDndkYx8pL9vH8kMMREhe\ncrkjZu1qhOQDpzviOFKNkDzgdkf+n9kw5dWIhGTgx4AbxzsipBohOc/1jgipRkiuc76jKEN6\nOIZFSI5zv6MYQ3o8BYmQ3OZBRxGG1JzMN/+8OUIyxYeOAgjJmuX++ejyoqMAQhq/cJkICMkM\nPzqKMaQPJ8wuODDG86SjKENaEZI3fOmIkCYjJAO86YiQJiOk5fnTUfQh5YlK8tvFKp9NyR3W\n1R9l5wurh9mhO7A+DVd/bp+qVF+zr1Tp40vtLLy8G7j7Sh4Wsj6M/0eGwKOOYg+pufjr7Xf+\n8qveedthUb83vkiap4vr92xvC8mbz1VfdlR59dFRP/f4y99ZeHd596/kcSGlKh4WFgGfOvI/\npAluIR1VcjqfEnV34ddD94msfsvHRr8RMVebavWjsvJcbm53BDjVl3Opnt7UNVSfqt9r9WyF\n1F14Z3kPr+TJQvL3b98Kk1cdRR5S87/8vdr2Pl8m69sHe32xls6VJjJdWXF7i+/69jn9ZfUf\nz1ZI3YU/XLmi80qeLKRU+wn/Tr/51VHkIa31NtNJrXufX6vOvkuqVwZJ+4uf3Bq4X0l0Q3qz\nQmoW3lnewyt5tpDs5fJC5VlHkYf09JJGp+71JI5qV/+1bTfFti++59xsjF22yl6vkNqFd5b3\n8EqeLWR3v/UZOt86IqTOX63eCilv94V29exAUkeV6nXH8T6kXf1r384TFJ9WSN3lPbySZws5\nvb9YTHC864iQOn81TrcZgHOzeqg1l2zZ6kfr8nzK7kIqmv2qg565XqvDdQq777rw2/IeX8mT\nhZQfrhYTGP86IqTOX43+Zln7qV29Rig3ejtPz1yv+yGVSecX/VT9+l+nsPsuC+8u7/kruVtI\nVCebe9hR5CElT0JKeoO3n0r1iqnZ/S/1xZD6v9m9yYBqXXKdwr5zWXh3ec9fyd1CYgrJx44i\nD6mZKyu6s3Z3U3j9NUVvhXH7oiLNOkdM6yVcp7D7rgt/WN79K7lfSEQhedlR5CFt9ZbWobsn\nv7ttbtXafaRmhaEvtJzoZ3ad3g79Vc9anV6FdF14Z3nPX8ndQiLaR/Kzo8hDenJmw/p2ykKt\n3UfJbxda1qcjHNPbIdKi/0uu1zovNu2uC8/vL9x890ruF3KMZtbO044iD6naV6np39Xe3tDV\nsZ1Zy65f2Fx5uVkh6e/Z9E901bHc5gl6q6XbwrP7gTuv5HEh1QorkuNIvnYUe0jNSdjNmM93\nRtL2d/v2hUVVTns+drvx1Q2p3Q06XGaue8vrfHA/cOeVPC4kmjMbvO0o9pA+O8w981ri31K8\nPFMiLP52REgfZfP2Tvabz1/zUSRnf3vcESF9VPR3msZaf/6SjyJ5P5LPHRHSZweJdcosmyg2\n7LzuiJDgCL87IiS4wfOOCAlO8L0jQoILvO+IkOAA/zsiJNgXQEeEBOtC6IiQYFsQHRESLAuj\nI0KCXYF0REiwKpSOCAk2BdMRIcGicDoiJNgTUEeEBGtC6oiQYEtQHRESLAmrI0KCHYF1REiw\nIrSOCAk2BNcRIcGC8DoiJJgXYEeEBONC7IiQYFqQHRESDAuzI0KCWYF2REgwKtSOCAkmBdsR\nIcGgcDsiJJgTcEeEBGNC7oiQYErQHRESDAm7I0KCGYF3REgwIvSOCAkmBN8RIcGA8DsiJCwv\ngo5iCGn1gJCMiqGjCEJ67IiQjIqioyhCWnTx+CCOjggJy4qkI0LComLpiJCwpGg6IiQsKJ6O\nCAnLiagjQsJiYuqIkLCUqDoiJCwkro4ICcuIrCNCwiJi64iQsIToOoosJNUjOAh64usospBg\nRIQdERLExdgRIUFalB0REoTF2REhQVakHRESRMXaESFBUrQdERIExdsRIUFOxB0REsTE3BEh\nQUrUHREShMTdESFBRuQdERJExN4RIUFC9B0REgTQESFhPjoiJMxHR2dCwmx0VCMkzENHGiFh\nFjpqEBLmoKMWIWEGOrogJExHR1dGQzpu1/rKjOv8uNQQjwhpMXR0YzCkMu1c5TRbZIhnCGkp\ndNRhMKRcJfuTflQcEpUvMcQzhLQQOuoyGFKiTtfHJ5UsMcQzhLQMOuoxGFLvqvXvL2FPSM6j\noz7WSJiCju6Y3Uc6FPoR+0i+o6N7Jqe/s86sXVouMsQThCSPjh6YPY6U6+NIyXrLcSSf0dEj\nzmzAWHT0BCFhJDp6xmRIZV5P1W1TpbL9QkM8QUiy6OgpgyEViVLnMuEUIa/R0XMGQ9qodVn9\nsSmqpjaP099L3XCckCTR0QtGz2wo2z+qrTwOyHqJjl6RDOmY10eKslfvkdDrmUR1PpB8VS8R\nkhw6ekkupP3tXRLp4dkXbOpThLbNeULl+50kQnISHb0mFVKRqWx3qjfbyuO2elw8fslJJfnp\nvE6qkg6petrajFf1EiFJoaM3hEI6qLx7zk+RPwvlkNxmE7bSr+olQhJCR+8IhbS+P3Wu3Dz7\n1v1Gb/+tt09WWDNf1UuEJIOO3uLMBgxCR+8REoagow+EQzpmSmUfTu2eOcRYhCSAjj6RDenY\nzCTMLomQ3EJHH8mGlNZzd2WeTn89H4cYjZBmo6PPhEJqD8Gq118xe4ipCGkuOhpAKKT2bAbW\nSAGioyGEQipzndK+2Ud6e9bCQq/qJUKah44GEdtHalJi1i40dDSM4GRDu1aSQEiuoKOBRGft\nirVS69PLT0sMMR4hzUBHQ0mF1G7TnWRSIiQ30NFgQiHdjsTqlCy8qpcIaTI6Gk7qOFJn3rtK\naeaLIiQn0NEIUseRen/N3rYjJAfQ0RgLrJEEEJJ9dDSKUEhSR2LfDDEVIU1CR+MIz9oJISTb\n6Ggk3tiHJ+hoLELCIzoazezFT+YMMRUhjUZH45m8HNe8IaYipLHoaAKDF4hc9lW9REgj0dEU\nBi9ZPH+ISQhpHDqaxOBF9CWGmICQRqGjaZi1QxcdTURI6KCjqQgJN3Q0GSHhio6mIyRc0NEM\nhIQWHc1BSGjQ0SyCIaWfbh82f4gpCGkQOppHMKT6lAahlgjJNDqaSfICkfuNVEuEZBgdzSV9\no7FtKtESIZlFR7PJTzac6nuX76a9moFDjEJIH9HRfOIhHTJ9Ang28fUMGWIcQvqEjgTIhlRu\nk/pNFGVV06yrrRKSQXQkQfRtFPVkQ95cHVLNaoGQzKEjEZLHkaqV0e7yhnOVTH1F74aYgpDe\noiMZkseR1lLXhyQkY+hIiORxpFkvZNAQUxDSG3QkRfTMhvZBMmuz7t0QUxDSa3QkZoGQinkT\nDe+GmIKQXqIjOWLXteuafVMKQjKBjgRJrZHSbkezLyNESAbQkaQl9pHmI6Tl0ZEo3tgXKTqS\nJXXrS6Xfj3Rh4VW9REjP0JEwQooSHUlj0y5GdCSOMxsiREfyJGftMs618wIdLUD27G8lcCeK\nd0NMQUh36GgJkvtIRXPBBoFNPEJaDh0tQniyocgTJbCJR0iLoaNlyM/a7Zj+dhgdLUR6jaS3\n7vaTX86AIcYipA46Wor4PlKSc4FIZzUdfVl9aT68/mjaB79/fFn9+PX4bX/+qr7lrz/6cfXw\n292X/N0u5p+vq6//6q9ffZV93R4QnrXbMGvnsKajX6vVqknhLqS/VtrX/+6+7b8v+vkv9fPf\n9MOf3U//bhfz7+qvahH/6gU9qTFwoseRZm/SfRpiCkK6aLfrfqz+Wv3Qj/oh/Vx9qX7///xs\niumovuFc1/GjXvl8+3P+82P1+/bZ31/axXxbVZ9afYtzhcSZDfG47B9VG3Zfmp9JL6T/LgH9\naDO7Wt2++pte4/yny2pUaXU+r/+IcYXESavRuHT0j94C+6d+2Avpr8sG25/vf597P7ZLdl9u\nTX27frJa2l1IUa6QCCkW1/m6eqXyb1NCL6Rv3e213o/tZ7tp97O3cmr9vn502bSLcoXE2d+R\nuHb0R0/ZfVnVc3C9kN78nP6uZxu+1Oupr6t68+/f/tf2Jxv+i3KFREhxuB0/+qddvdTbdkND\n+nmdqvu5+v7n/Pvb05DOv/T09/fVr+s8eES4rl0MOsdhv+rpgt96tTEwpL/r9v78WNWrJD0T\n/v15SFq15Ns8eES4rl0EOh39t7r47y6k79d9pF9/+t/+VW8HNnMIVU9ffp7fhFStkK7z4DHh\nunbh654X9PMa0s/LHk9d19f6M+2s3b/3OzkPMwy/+1/R/Qn/Xn2/zYPHhOvaBa93fl2vnWZr\nrd52+9E5jvStffaqmf7WsxTNJMXfVS0d3Z9wvV4jpHnfwnXtnNTr6Pc1gXq2+9dKN/PPSu/Q\n/NBnNvz3/XIm3tVfq/o8u7/qPSV9esO/X5vDUBfdyfB6+WzaLfYtNoeIPKT++d63Yzy/mi4a\nzYkK37rn2nV/bM0n6jL+NKfdfe99RedL9Y4Wkw3Tv4UDso66e9/Ely/9h7++11lc4vqn+uhb\nu7Lp/dj02d/60X8/rl//JKR2hfeL6e+p30JIbuL9R6awaRcyOjKGkAJGR+ZIhrRLz+ciFZj9\nJiQRdGSQYEiHet8oqXeROI7kAjoySTCkTO3PJ5We9yqb9ZLeDDFFrCHRkVHCB2RPKpc4MktI\ns9GRWcIhrdWBkFxAR4aJbtqdDio5s2nnADoyTXayQaltvULiksWW0ZFxotPfSb2HdJ5/oVVC\nmoeOzOOAbHjoyAJCCg4d2UBIoaEjKyRD2qac/W0dHdkhGNKWt1HYR0eWCIaUqN2slzJgiCmi\nComObOGaDSGhI2sEQ1orsftRENIkdGSPYEhFksncZoyQpqEji0Q37ZhssImObAovpNWDua/G\nD3RkVXAHZB87iiMkOrIrwJDkXoZH6Mgy0ZAOa/3mvmLG6/k0xEdxhkRHtkmGlDW7RyqZXRIh\njUNH1gmGtFNZWYe0U5tZL+nNEAPEGBId2Sd6ilDZnN1gddYuwpDoyAHCpwgRknl05ALBkNJ2\njXSyese+6EKiIyfI7yMdBM4CJ6TB6MgNkrN26/a8htlX4yKkwejIEeLHkdR6/kWECGkoOnIF\nZzb4jI6cYTSk47bZ+lvnH95vQUiD0JE7pEIqc/30MVXJq6mGMu2cH/5+P4qQhqAjh0iFlOiD\nR4d3keQq2Z/0o+LQXJNV8lVdxBMSHblEKKR66rv6K0lO57K+T9IziTpdH5/qq+2LvqqLaEKi\nI6cIhZSp+kTVY30N/erP56uk3gkP789+IKSP6MgtQiE1XeTNTS9fRMIaSRAdOUY0pFR1PnhQ\n7SMdmjdYsI80Fx25RiiktN60K5r3T5Sv1jZZZ9YufXvpLkJ6j46cIxRSXk82bJo7jL1+P9Ix\n18eRkvWW40hz0JF7hEIqk+u890519oUmIqR36MhBYgdkN6rZ7VHq/e7P9CGGCT8kOnKR+ClC\nav35cqsf3/lHSK/RkZOsnLQqGVJsl7GjIzcZDEn1vf304IXGdj1IOnKUwZCOyeBWxoQ08dX4\niY5cZXLTrlyrTB+RFdy0iyskOnKW2X2kvdIntBLSNHTkLsOTDUWm1iUhTUNHDjM+a7dVyYGQ\npqAjl5mf/j6ln2flCOkRHTnNxnGkDSGNR0du8/0qQrGEREeOIyQv0JHrCMkHdOQ8QvIAHbmP\nkNxHRx4gJOfRkQ8IyXV05AVCchwd+YGQ3EZHniAkp9GRLwjJZXTkDUJyGB35g5DcRUceISRn\n0ZFPCMlVdOQVQnIUHfmFkNxER54hJCfRkW8IyUV05B1CchAd+YeQ3ENHHiIk59CRjwjJNXTk\nJUJyDB35iZDcQkeeIiSn0JGvCMkldOQtQnIIHfmLkNxBRx4jJGfQkc8IyRV05DVCcgQd+Y2Q\n3EBHniMkJ9CR7wjJBXTkPUJyAB35j5Dso6MAEJJ1dBQCQrKNjoJASJbRURgIyS46CgQhWUVH\noSAkm+goGIRkER2Fg5DsoaOAEJI1dBQSQrKFjoJCSJbQUVgIyQ46CgwhWUFHoSEkG+goOIRk\nAR2Fh5DMo6MAEZJxdBQiQjKNjoJESIbRUZgIySw6ChQhGUVHoSIkk+goWIRkEB2Fi5DMoaOA\nEZIxdBQyQjKFjoJGSIbQUdgIyQw6ChwhGUFHoSMkE+goeIRkAB2Fj5CWR0cRIKTF0VEMCGlp\ndBQFQloYHcWBkJZFR5EgpEXRUSwIaUl0FA1CWhAdxYOQlkNHESGkxdBRTAhpKXQUFUJaCB3F\nhZCWQUeRIaRF0FFsCGkJdBQdQloAHcWHkOTRUYQISRwdxYiQpNFRlAhJGB3FiZBk0VGkCEkU\nHcWKkCTRUbQISRAdxYuQ5NBRxAhJDB3FjJCk0FHUCEkIHcWNkGTQUeQISQQdxY6QJNBR9AhJ\nAB2BkOajIxDSfHQEQpqPjnAmpNnoCDVCmoeOoBHSLHSEBiHNQUdoGQ3puF2r2jo/Sg1hNSQ6\nwoXBkMpU3WRCQ9gMiY5wZTCkXCX7k35UHBKVywxhMSQ6wo3BkBJ1uj4+qURmCHsh0RE6DIak\n1KsPZgxhLSQ6QhdrpGnoCD1m95EOhX7k/z4SHaHP5PR31pm1S0uZIeyEREe4Y/Y4Uq6PIyXr\nrd/HkegI9zizYTw6wgNCGo2O8MhkSOVGqezQLsTb6W86whMmTxFKmhPtmoX4GhId4Rmj09+7\nqqZdok+zewxJdQ1eqOmQ6AhPGT0gq/8qkrTwdo1ER3jOwilCZZb5GhId4QWDIaXqchA2zfwM\niY7wisGQdmrTPipU5mNIdISXTE5/59d6Dh/mE5wMiY7wmtEDsqf15VGx8S4kOsIbnNkwEB3h\nHUIaho7wFiENQkd4j5CGoCN8QEgD0BE+8Syk1aPlXw0d4SO/QnrS0fIh0RE+8y2k5ce+R0cY\ngJA+oCMMQUjv0REGIaS36AjDENI7dISBCOkNOsJQhPQaHWEwQnqJjjAcIb1CRxiBkF6gI4xB\nSM/REUYhpKfoCOMQ0jN0hJEI6Qk6wliE9IiOMBohPaAjjEdI9+gIExDSHTrCFITUR0eYhJB6\n6AjTEFIXHWEiQuqgI0xFSDd0hMkI6YqOMB0hXdARZiCkFh1hDkJq0BFmISSNjjAPIdXoCDMR\n0pmOMB8h0REEEBIdQQAh0REERB8SHUFC7CHREUREHhIdQUbcIdERhEQdEh1BSswh0RHERBwS\nHUFOvCHREQRFGxIdQVKsIdERREUaEh1BVpwh0RGERRkSHUFajCHREcRFGBIdQV58IdERFhBd\nSHSEJcQWEh1hEZGFREdYRlwh0REWElVIdISlxBQSHWExEYVER1hOPCHRERYUTUh0hCXFEhId\nYVGRhERHWFYcIdERFhZFSHSEpcUQEh1hcRGEREdYXvgh0REMCD4kOoIJoYdERzAi8JDoCGaE\nHRIdwZCgQ6IjmBJySHQEYwIOiY5gTrgh0REMCjYkOoJJoYZERzAq0JDoCGaFGRIdwbAgQ6Ij\nmBZiSHQE4wIMiY5gXngh0REsCC4kOoINoYVER7AisJDoCHaEFRIdwZKgQqIj2BJSSHQEawIK\niY5gTzgh0REsCiYkOoJNoYRER7AqkJDoCHaFERIdwbIgQqIj2BZCSHQE6wIIiY5gn/8h0REc\n4H1IdAQX+B4SHcEJnodER3CD3yHRERzhdUh0BFf4HBIdwRkeh0RHcIfRkI7btaqt8+O0Iboh\n0REcYjCkMlU32aQhOiHREVxiMKRcJfuTflQcEpVPGeIWEh3BKQZDStTp+vikkilDXEOiI7jF\nYEhKvfpg8BCXkOgIjvFyjURHcI3ZfaRDoR/N3EeiIzjH5PR31pm1S8spQ+iQ6AjuMXscKdfH\nkZL1dsZxJDqCg7w7s4GO4CJ3QlJdL75mtaIjOMmdkAYNQUdwk18h0REc5VVIdNNzHmQAAAb/\nSURBVARX+RQSHcFZHoVER3CXPyHRERzmTUh0BJf5EhIdwWmehERHcJsfIdERHOdFSHQE1/kQ\nEh3BeR6EREdwn/sh0RE84HxIdAQfuB4SHcELjodER/CD2yHRETzhdEh0BF+4HBIdwRsOh0RH\n8Ie7IdERPOJsSHQEn7gaEh3BK46GREfwi5sh/U8Bfhn/W24gJCfHZnzGFx2fkBif8V1bmEdj\nMz7jExLjM75r4xMS4zO+awvzaGzGZ3xCYnzGd218QmJ8xndtYR6NzfiMT0iMz/iujU9IjM/4\nri3Mo7EZn/GDCQkIBiEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQ\nEiCAkAABhAQIsBxSnqgkL82Pu0uv49p6Ccf2R29l/NNGqU1hbfyyM6jZ8XeXX3jpV2A3pExf\n+j81Pm6ux01Kiy+hTJofvZXxD3b//UXSjF8YH/90udFEZ1iZV2A1pKNKTudToo6Gxz2pTVn/\nz2lj7yWc181/UjvjJ9Wg5Vrllsbf1CNX/zcz/vOvBmp+4TvDCr0CqyHl6lD9uVdbw+Oum391\n/UO19RL27U14rIy/17/IpUosja8s/fx3KmuH7gwr9AqshrRW9cr9pNZ2hq9/qJZeQnH5T2pl\n/I06XR5aGb/dqq1DNjp+9f+PNqTOsEKvwGpInf8zWVCqzNpLyFTRDGll/FSdt4nevLUz/rbd\ntNsaHv90P179l9AriDikXb1Ot/MStmp/thiSUmu9s29r/POunm1IdhbGJyRxRbK29RL0doTV\nkOrJho3xNcLVVk+Ubc+EJMNmSGWSWXsJaT3xbDWkeh+pqKd8rYy/qzftqpB3hCQjsRhSllp7\nCRs9T9QMaeVH0PndsTJ+qurds7IO2fT47UCJ+E/AgVm7wsKsXZFmhbWX0L0LvZUfQWf638r4\nyt74vVm74jZrN/sVWA1pq//XfNBzOEYdVGbxJXRDsvIjaAYt6h+ClfGblYA+jmV6/DakzrBC\nryDKMxuKa0f2zmw4Wzyzodo7Kut9lL2l8XNVn9qW2zizIswzG6qN5Vr2+QtlbW5rBFsv4fqf\n1Mr429ugVsbPrI1/2RVKpV+B3ZCas4CND9vZtLL1Eq7/Se2Mf8gug9oZ/zao4fEvIZXSr8Bu\nSEAgCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAA\nIQECCAkQQEiAAEICBBASIICQjBh/69IyT5XKdkMW3lwNaaNU3r/urv7o8OKbDvWVRfepSvX1\n3Mr21o/rV1+ODwjJhPG3Li2b26w293n9QC+8vlHL9jGk9MV/4KK+/PZR5edcXxmxuW1dHVQx\n5N+DB4RkwIRbl25UfXHyIht8JV31PIFX14bP6uVmVU36dmvlNejcwqUyg0BIy5ty61Klb9hQ\n/YYP/Q/04gtfPL1X19v13e7k2gy4HzggeghpeVNuXdoNoHqcX68FukubW92d9U6WvqdG9fn2\nwrHtDZ7vnr+sb27rnXN6uzlU9UfnE5fb3WAkQlrelFuX5mpz3VTTOz/t1anXtwtVZ5d9qLuQ\nHp6vvklvQN5Wf0e1a5bQbNrdVkj16tP8/QRCQEhGjL9RXJVDmh/br2l2p/b6fjTV735W/+Lv\n64ebeh/quoWm/+g/38zcqU29nM11NypvbmveTjYU3SmPk/mb7ASBkIyYcMfFQ33PjKS5t19z\nBx+9Qdjc604/PLb3GOqH1H++WX5zi7xbL1mzB3Y+6OnvtTpc58GbyQeMRkhGTLt16XGrZ/V6\n33W9j8btO/sh9Z9vPtrVG3XH28RGb9RT1dB1HtzSjUj9x0/NiM+3Lr3eZ6bndHfD5IkhtTfH\nK3rfcVGtkK7z4Pefw1D81Iz4fOvSfkidvp6tx3pfMiAkPZ2Qpk++t5k4vC2BkCbip2bE2FuX\nrptptcu+zvHczhisb/Nr2Yt9pOzJPlJVS3bqHLK67CM1Q526IbGPNA0hGTH2zIajUrv6HJ6s\nDuoya6en6qqH1S7PWh/mLZv7sPZD6j9/OeEhVUnn1If8Nqw+ktXZtDsyazcJIRkx+talebsv\nlOlv1oeG9EZgc4ZeUpz7x4u6f/Ser0ZJ6u879E7r68w71Cukzkl31ZqS40hTEJIR429detok\nVUD79pvXKm1PZ9hVabTHaqvY1sX5IaTe88e0CalUvZOR0kvA7akVh+v0N2c2TENI7pPY/T/0\nT2o9vDrLu1C8kWISQnKfREiZ6r+1KXuxLuTs74kIyX3zQ3rcGSvU0zc68X6kqQjJffNDSh5P\nMj9snn3hhg27iQgJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAAB\nhAQIICRAwP8Bziuu5Zjx1tMAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"ROC Curve\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC train\n",
    "f2 <- glm(EGFR~ Rad\n",
    "          +Smoking\n",
    "          +Type\n",
    "          ,data = train,family = \"binomial\")\n",
    "\n",
    "pre <- predict(f2, type='response')\n",
    "plot.roc(train$EGFR, pre,\n",
    "         main=\"ROC Curve\", percent=TRUE,\n",
    "         print.auc=TRUE,\n",
    "         ci=TRUE, ci.type=\"bars\", \n",
    "         of=\"thresholds\",\n",
    "         thresholds=\"best\",\n",
    "         print.thres=\"best\",\n",
    "         col=\"blue\"\n",
    "         #,identity=TRUE\n",
    "         ,legacy.axes=TRUE,\n",
    "         print.auc.x=ifelse(50,50),\n",
    "         print.auc.y=ifelse(50,50)\n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting levels: control = 0, case = 1\n",
      "\n",
      "Setting direction: controls < cases\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAYFBMVEUAAAAAAP9NTU1NTf9o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\ngYBEJBCQiAQCEpFAQCISCEhEAv0fO8OlOS1aSO0AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"ROC Curve\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC test\n",
    "pre1 <- predict(f2,newdata = test)\n",
    "plot.roc(test$EGFR, pre1,\n",
    "         main=\"ROC Curve\", percent=TRUE,\n",
    "         print.auc=TRUE,\n",
    "         ci=TRUE, ci.type=\"bars\",\n",
    "         of=\"thresholds\",\n",
    "         thresholds=\"best\",\n",
    "         print.thres=\"best\",\n",
    "         col=\"blue\",legacy.axes=TRUE,\n",
    "         print.auc.x=ifelse(50,50),\n",
    "         print.auc.y=ifelse(50,50)\n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting levels: control = 0, case = 1\n",
      "\n",
      "Setting direction: controls < cases\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>0.820159806082131</li><li>0.890810810810811</li><li>0.961461815539491</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.820159806082131\n",
       "\\item 0.890810810810811\n",
       "\\item 0.961461815539491\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.820159806082131\n",
       "2. 0.890810810810811\n",
       "3. 0.961461815539491\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "95% CI: 0.8202-0.9615 (DeLong)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "singular information matrix in lrm.fit (rank= 3 ).  Offending variable(s):\n",
      "Type \n",
      "\n",
      "Divergence or singularity in 1 samples\n",
      "\n",
      "n=87   Mean absolute error=0.012   Mean squared error=0.00019\n",
      "0.9 Quantile of absolute error=0.02\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAMFBMVEUAAABNTU1oaGh8fHyM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",
      "text/plain": [
       "Plot with title \"Calibration Curve\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calibration Curve train\n",
    "rocplot1 <- roc(train$EGFR, pre)\n",
    "ci.auc(rocplot1)\n",
    "cal <- calibrate(f1,  method = \"boot\", B = 1000)\n",
    "plot(cal, xlab = \"Nomogram Predicted Mutation\", ylab = \"Actual Mutation\",main = \"Calibration Curve\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting levels: control = 0, case = 1\n",
      "\n",
      "Setting direction: controls < cases\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>0.664403069452139</li><li>0.797570850202429</li><li>0.930738630952719</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.664403069452139\n",
       "\\item 0.797570850202429\n",
       "\\item 0.930738630952719\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.664403069452139\n",
       "2. 0.797570850202429\n",
       "3. 0.930738630952719\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "95% CI: 0.6644-0.9307 (DeLong)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "n=45   Mean absolute error=0.029   Mean squared error=0.00123\n",
      "0.9 Quantile of absolute error=0.058\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAMFBMVEUAAABNTU1oaGh8fHyM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\nQmCC",
      "text/plain": [
       "Plot with title \"Calibration Curve\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calibration Curve test\n",
    "rocplot2 <- roc(test$EGFR,pre1)\n",
    "ci.auc(rocplot2)\n",
    "f3 <- lrm(test$EGFR ~ pre1,x = TRUE,y = TRUE)\n",
    "cal2 <- calibrate(f3,  method = \"boot\", B = 1000)\n",
    "plot(cal2, xlab = \"Nomogram Predicted Mutation\", ylab = \"Actual Mutation\",main = \"Calibration Curve\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating net benefit curves for case-control data. All calculations are done conditional on the outcome prevalence provided.\n",
      "\n",
      "Calculating net benefit curves for case-control data. All calculations are done conditional on the outcome prevalence provided.\n",
      "\n",
      "Note:  The data provided is used to both fit a prediction model and to estimate the respective decision curve. This may cause bias in decision curve estimates leading to over-confidence in model performance. \n",
      "\n",
      "Calculating net benefit curves for case-control data. All calculations are done conditional on the outcome prevalence provided.\n",
      "\n",
      "Note:  The data provided is used to both fit a prediction model and to estimate the respective decision curve. This may cause bias in decision curve estimates leading to over-confidence in model performance. \n",
      "\n",
      "Warning message:\n",
      "\"glm.fit: fitted probabilities numerically 0 or 1 occurred\"\n",
      "Warning message:\n",
      "\"glm.fit: fitted probabilities numerically 0 or 1 occurred\"\n",
      "Warning message:\n",
      "\"glm.fit: fitted probabilities numerically 0 or 1 occurred\"\n",
      "Warning message:\n",
      "\"glm.fit: fitted probabilities numerically 0 or 1 occurred\"\n",
      "Note: When multiple decision curves are plotted, decision curves for 'All' are calculated using the prevalence from the first DecisionCurve object in the list provided.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAP1BMVEUAAAAAAP8A/wBNTU1o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\n11kXzf0/AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Decision Curve train\n",
    "Rad<- decision_curve(EGFR~ \n",
    "                          Rad, data = train, family = binomial(link ='logit'),\n",
    "                          thresholds= seq(0,1, by = 0.01),\n",
    "                          confidence.intervals =0.95,study.design = 'case-control',\n",
    "                          population.prevalence = 0.3)\n",
    "\n",
    "Clinical<- decision_curve(EGFR~ \n",
    "                         Smoking+Type, data = train, family = binomial(link ='logit'),\n",
    "                         thresholds= seq(0,1, by = 0.01),\n",
    "                         confidence.intervals =0.95,study.design = 'case-control',\n",
    "                         population.prevalence = 0.3)\n",
    "\n",
    "clinical_Rad<- decision_curve(EGFR~ Rad\n",
    "                         +Smoking+Type, data = train,\n",
    "                         family = binomial(link ='logit'), thresholds = seq(0,1, by = 0.01),\n",
    "                         confidence.intervals= 0.95,study.design = 'case-control',\n",
    "                         population.prevalence= 0.3)\n",
    "\n",
    "List<- list(Clinical,Rad,clinical_Rad)\n",
    "plot_decision_curve(List,curve.names= c('Clinical','Rad-Score','Nomogram'),\n",
    "                    cost.benefit.axis =FALSE,col = c('green','red','blue'),\n",
    "                    confidence.intervals =FALSE,standardize = FALSE,\n",
    "                    #legend.position = \"none\"\n",
    "                    legend.position = \"bottomleft\"\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating net benefit curves for case-control data. All calculations are done conditional on the outcome prevalence provided.\n",
      "\n",
      "Calculating net benefit curves for case-control data. All calculations are done conditional on the outcome prevalence provided.\n",
      "\n",
      "Note:  The data provided is used to both fit a prediction model and to estimate the respective decision curve. This may cause bias in decision curve estimates leading to over-confidence in model performance. \n",
      "\n",
      "Calculating net benefit curves for case-control data. All calculations are done conditional on the outcome prevalence provided.\n",
      "\n",
      "Note:  The data provided is used to both fit a prediction model and to estimate the respective decision curve. This may cause bias in decision curve estimates leading to over-confidence in model performance. \n",
      "\n",
      "Note: When multiple decision curves are plotted, decision curves for 'All' are calculated using the prevalence from the first DecisionCurve object in the list provided.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAP1BMVEUAAAAAAP8A/wBNTU1o\naGh8fHyMjIyampqnp6eoqKiysrK9vb3Hx8fQ0NDZ2dnh4eHp6enr6+vw8PD/AAD///8WMyfq\nAAAACXBIWXMAABJ0AAASdAHeZh94AAAgAElEQVR4nO2di3aqvBZG6UGtf63Vrbz/sx4BQe4E\nWEnIypxj7F1Uyickswm3kGQAsJnE9xcA0AAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgE\nIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAAC\nIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAi\nAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKA\nAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiA\nSAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgE\nIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAACIBKAAIgEIAAiAQiASAAC\nIBKAAIgEIIBPkf55zCaffNF8RCKffAEQiXzyBUAk8skXAJHIJ18ARCKffAEQiXzyBUAk8skX\nAJHIJ18ARCKffAEQiXzyBUAk8skXAJHIJ18ARCKffAEQiXzyBUAk8skXAJHIJ18ARCKffAEQ\niXzyBUAk8skXAJHIJ18ARCKffAEQiXzyBUAk8skXAJHIJ18ARCKffAEQiXzyBUAk8skXAJHI\nJ18ARCKffAEQiXzyBUAk8skXAJHIJ18ARCKffAEQiXzyBUAk8skXAJHIJ18ARCKffAEQiXzy\nBUAk8skXAJHIJ18ARCKffAEQiXzyBUAk8skXAJHIJ18ARCKffAEQiXzyBUAk8skXAJHIJ18A\nRCKffAEQiXzyBUAk8skXAJHIJ18ARCKffAEQiXzyBdgq0iVNDpeVv6tqQ5Ifd/5qkW6nJL1k\nP0nOcd0iVG1I8uPOXyvSrTDonHw/svspWdcmqdqQ5Medv1ak7+ScZeckzacfyWHVMlRtSPLj\nzl8rUlL8YnJqvFiMqg1Jftz520T6Lft0ZcO0GFUbkvy489d37V57RyWPopu3AlUbkvy489eK\n9Ejr/lyyskHStSHJjzt//Xmkc6VPuq49UrYhyY87nysbyCdfAEQin3wBEIl88gUQEYnzSOTH\nnm9JpKTBvzG+vkY/AvCJJ5FWRnx92Q+fQtVfRPL95vsU6Z9nk1QVJPl+8z2L5NUkVQVJvt/8\n9SL9/ZyKXaDT+W9lxD/PnTtVBUm+3/zVlwgdGocTpm/smxDJr0mqCpJ8v/lrRTon6e+tmLpf\n0+mLVqdF8miSqoIk32/+WpHS5FZP36avWp0SyatJqgqSfL/52+5HGnphHlGsCCKRryHfd4vk\n0yRVBUm+3/wN+0jXezG1aR8p89m5U1WQ5PvNX334+9g4and4TM05I5K/JklVQZLvN3/DeaRz\ncR4pPf2sP49U4sskVQVJvt98r1c2vH/66typKkjy/ebvQSRfTZKqgiTfb/4uRHr6MUlVQZLv\nN38XInnq3KkqSPL95u9DJD9NkqqCJN9v/j5E8mOSqoIk32/+fkRyb5KqgiTfb/5ORPJikqqC\nJN9v/m5E8tC5U1WQ5PvN34tIPkxSVZDk+83fjUgeOneqCpJ8v/n7Eel/zk1SVZDk+83fj0iF\nSba/y1S+c8hXlL8jkZzvJqkqSPL95u9JJNedO1UFSb7f/D2J5NokVQVJvt/8XYn0n9vdJFUF\nSb7f/F2J5NgkVQVJvt/8fYmU/e9/Djt3qgqSfL/5OxPJaZOkqiDJ95u/M5GcmqSqIMn3m783\nkVweuVNVkOT7zd+dSA6bJFUFSb7f/N2JlP3PmUmqCpJ8v/n7E+k/Z507VQVJvt/8/YlUNkku\nVFJVkOT7zd+hSK8myY1JqgqSfL/5OxQpb5KcqKSqIMn3m79Hkf5zZJKqgiTfb/4eRXo1SblJ\n1lVSVZDk+83fpUivJim/zc+2SaoKkny/+bsUqTbJrkqqCpJ8v/k7Fsm6SaoKkny/+fsU6W1S\n1b8T/0qz+U4gX1H+zkXKsdYoqSpI8v3m71QkJyapKkjy/eaHIJKtPSVVBUm+3/y9iuTCJFUF\nSb7f/CBEeplk4+mYqgqSfL/5uxXJgUmqCpJ8v/m7FqnZuctNkvtOBvn2IV9R/n5F6jZJ8iOD\nqypI8v3m71mkrGOSdOdOVUGS7zd/xyK1O3fvY3cyX8ks3zbkK8rfs0jtzp28SaoKkny/+fsW\nKWubJPx4TFUFSb7f/F2L1G2Sskz00J2qgiTfb/7OReo2SaImqSpI8v3m71uk7vGGLJM8L6uq\nIMn3m793kYaaJCmTVBUk+X7zdy6S1c6dqoIk32/+3kUa6NyJmaSqIMn3m79/kex17lQVJPl+\n83cv0ohJEi6pKkjy/eYHKVJp0maXVBUk+X7z9y/SoEnlPbMbTVJVkOT7zQ9WpOf2fSVVBUm+\n3/wARLJmkqqCJN9vfqgi5Tefbz0QrqogyfebH4JItkxSVZDk+80PWaStnTtVBUm+3/wgRLJk\nkqqCJN9vfjgiyZukqiDJ95sfhkjDTdLW3SRVBUm+3/xgRLJgkqqCJN9vfiAijTZJT0Qifw/5\n4Yg0ahIike8/PxSRRpqkDJHI30V+MCJZMElVQZLvNz8skYY7d19fzw8LlqiqIMn3mx+OSONN\nEiKR7z0/KJGGTWp17ZaYpKogyfebH5BIY01SyyREIt9LflgizTdJiES+l/yQRBptkhCJfN/5\nQYk0ZhIike87X4VIDZMQiXwv+WGJNN8kIRL5XvK1ibTEJFUFSb7f/MBEGjEJkcj3nK9DpHV9\nO1UFSb7f/NBEmm2SEIl8H/mI5A/yFeUHJ9KwSYhEvt98JSJ9TEIk8n3khyeSXJOkqiDJ95uP\nSP4gX1F+gCJNPXrs5RMike8hX51IX4hEvof8EEUav8OvEAqRyHefr02kJWOBqypI8v3mBynS\njEmmS1FVkOT7zdcnkrlJqgqSfL/5oYok0blTVZDk+80PU6TJJulpapKqgiTfb36wIo2b9DQ1\nSVVBku83P1CRppok49HAVRUk+X7zwxVp1CTjZyapKkjy/eaHKtLYmPpZJZKBSaoKkny/+QGL\nNNMkzZukqiDJ95sfrEgTJr2fLFtedDdhlKqCJN9vvlqRCpOqK8Jt5G+HfEX54Yo0blLdFJUW\nIRL59vP1ivSs2iJEIt9+fsAijZpUiJT/VzVNlvK3Qr6i/AhEGjVJVUGS7zc/ZJGmTKplyhCJ\nfAf5qkWa6dupKkjy/eYHLdKYSfXTzRGJfEf5MYg0ZpKqgiTfb37YIo2YVHmESOS7ytct0nTf\nTlVBku83P3CRhk36DG2HSOS7yVcp0ofJvp2qgiTfb37oIpmZhEjkW85HJH+Qryg/eJFmTEIk\n8p3kxyHSsEmqCpJ8v/nhi7S+SVJVkOT7zY9NpKZQqgqSfL/5CkSaNqklUvu+c1UFSb7ffPUi\nNUzqDuCgqiDJ95uvQSTDJqm0qGGSqoIk329+PCL1R3BQVZDk+81XIdLEsKvVFayfTt3HJFUF\nSb7f/NUiPb6T5Hh9L2RyKU5EmmySWk+VRSTyLeSvFemRJjmnciG+RVpnkqqCJN9v/lqRzsnl\nZdMlPRYL2bdIHZMQiXz5/LUipeUv3tPDfQ8irTJJVUGS7zd/rUiVO4/jMQCR2iYhEvni+WtF\nOiSPauq4B5HWmKSqIMn3m79WpEvy/Z66J8cARGqZ9D4Urqogyfebv/rw97m255rsRKSlJqkq\nSPL95q8/IXs7VVP37x2INNsk9U1SVZDk+83XcWVDzmyT1DNJVUGS7zdfj0jzTVLXJFUFSb7f\nfF0iLTRJNn8xqipS7PkiIu3hYENm0iR1TBLOX4qqihR7viWRkgb/XPHf69///jc9z8ukevrV\nuQMYwpNIKyPE/yIZNEnNNsl3k6TqL3Ls+dpEMjGpmhoQqX2duGVUVaTY81WJZNQkfWT51zfp\n6dIkVRUp9vz1Iv39nMpbks5/KyOsiLSgc9cX6YlI5K9j9Y19h8bhhOO6CAsb0rBJKm3pifRE\nJPJXsv7GvvT3Vkzdr2lyXhVhRyRzk/5195Lytx2apKoixZ6//sa+Wz19S9JVETY2pFGTlA2L\nVLyLSOSvYeuNff0X5hGWRDI2CZHIF0Nbi7SkSfrXPgJeKoRI5K9hwz7S9V5M7WofaVGT1Bap\nOs7gziRVFSn2/NWHv4+No3aHx9ScbkUybpKePZHaP+2jqiLFnr/hPNK5OI+Unn72cx4px0yk\nrCtS7Q8ikb8CXVc2FBiblOdXJj0RifwtIFL1qvGBne/VQ1VFij1fp0imnbtapKY8iET+chSK\nZN4k1SY9EYn8bSgVydykt0jt9+18ry6qKlLs+RpFMm2S8ttlC5E65iAS+YuJWqTicu+vr+4l\n34hE/mJUimRo0r+id5eL1H4fkchfTOQivax5idT9wJFJqipS7PlqRTIwqcwfGN8Okchfik6R\nzJqkd37fJEQifyl6RZo3qcrvmeTohnNVFSn2fKUiGTVJdb4nk1RVpNjzESlnyCT7KqmqSLHn\naxXJxKRGvheTVFWk2PMRqcTHWJGqKlLs+ZpFmjOple/BJFUVKfZ8tSIZNEkzIlnv3qmqSLHn\nqxZpxqQ5kWybpKoixZ6vV6T5JmlWJMsqqapIsefrFmnaJAORrJqkqiLFnq9YpNkmyUQkm9cL\nqapIsecrF2nSpHa+e5NUVaTY8xGpApHI34BmkeZMQiTyxUCkivFHM9sySVVFij1ftUgzJiES\n+WIgUgUikb8B3SJNm9TJd76TpKoixZ4/+OC9dPLBYVsiWoQiki2TVFWk2POHRLpPP8pyS0QL\nFxtyyiREIl+MqpZfkyYHGxE9EMkv5AtS1/JD06OZR4etjOjiZENOmIRI5Ith/nBykYgWwYhk\nySRVFSn2fOVH7bKpS1e7+a6bJFUVKfb8qpbnrVGjc2cjooczkUZMQiTyxdAv0niTZC6SHZNU\nVaTY86tafv6RXOpgRA93Ig2bhEjki9FukSSX3IvogUh+IV+Qj0h3rSKNmoRI5ItR1fLv1glZ\nVftIxiK5NklVRYo9v6rlj5NekcYONyAS+WKoPyGbM9IkIRL5YiBSgwmRBp7rtxlVFSn2fP1X\nNuQMm7RQJHGTVFWk2PPbtfx6ylul010yIUSR+s58WTBJVUWKPb9Vy4/lcYYkFTVpByINm9TP\nf7vSdyZ/Q1wlVRUp9vxmLb8kx0cu0iX5thXRYs8ifQ18IG2SqooUe36zlqfJozzgoO3wdylS\nz6QxkV7/d5SpXsmapKoixZ7fPWqnVKTBJmlCpI4yn50nSZNUVaTY85u1/PBukW6KbjWvGGqS\nRkSqbPrqvJ/13t6IqooUe/7APtI1TS62Ilo43ZADTdKkSE1lGvI8BXeUVFWk2PNbtby6TOgo\nmbAjkTomDeQXu0f19GeqnuEpaJKqihR7fv88UnL6lQwIV6TapJY4z6dY905VRYo9P44rG3L6\nJg2K1N41qg8+1OQX3QmZpKoixZ6PSC2+ukfryiN4jbeKq1dlTFJVkWLPj0ek/uGGYZG6rztv\nlZeBi5ikqiLFnt+q5T8HnfcjlfSaJAORBi5nkDNJVUWKPb9Zy3+03tj3ptskmeV3jXnfmSRg\nkqqKFHt++xIh0fNHQxEtfIjUNGldfnWL33aTVFWk2POjuLHvjaxI201SVZFiz2/W8lPykFz0\nUEQL5xuy07dbmd80aZNKqipS7PnNWn5Pj6KPoRiIaOFFpIZJW0XaapKqihR7frtrp/tgQ7dJ\n2izSRpVUVaTY8+MT6WPS2nwpk1RVpNjzIzohm9NqkiRE2nLMQVVFij0fkZbTGeNutUmqKlLs\n+XGMIvShaZKQSKtNUlWRYs+PZBShGgmRpExSVZFiz49kFKGa5uEGMZFWmqSqIsWeH8koQh8a\nTZKcSOtMUlWRYs+PZRShGhmRBkxavhhVFSn2/FhGEapp9O3W58uYpKoixZ4fzShCNZ8maUN+\n3yREijs/mlGEaj5N0pb8nkmIFHd+NKMIfaibpE35Aiapqkix50d2ZUOOjEg9kxAp6vxIRSpM\n2pj/7F5zt3QBqipS7PndWv53TNKz7P19exOpbpK25m81SVVFij2/ruW3l0GX7FYcbEhFTdql\nSLlJm/PbJiFSzPlVLf8rDDof01v2OCZnGxE9ghcpN+mjEiLFnF/V8kKec5JcX9OPJLUR0cPb\nhnz37QTymyYhUsz5VS0vrwp6Xxuk+RKhnHeTJJK/wSRVFSn2/ChFejdJMvnPxqhCy35TVUWK\nPT9ekfoPZ17J8zNk5LJfVFWRYs9HpO2sNUlVRYo9/yNSCxsRPTxuyKFnM6/nWY8HvujXVFWk\n2PMjFkneJESKNz/CS4RyhEV6m4RI8eZHKlJh0j/PJqmqSLHnI5IQz/Lhskt+RVVFij0/VpFy\nk/5Jdu4KkxAp2nxEEiO/XGiRSaoqUuz5cYskbhIixZofrUgvk6RFeqmESLHmI5IoiwaKVFWR\nYs+PXCSvJqmqSLHnDz6MOVV+P1LJfzZEWmKSqooUe/6QSHf9lwjlFCL5NElVRYo9v6rl19al\ndoqHLG4gfZ3QG2OTfK8/+YLUtfzQ9Ej04eb7FsmjSb7Xn3xBBveRZIlOJFOTfK8/+YLEe9Tu\nlW+pb2dqkvf1J1+O2J4h28q31iSZmeR9/cmXI7ZnyLbzrTVJRpev+l9/8sWI7Rmy7Xx7TZKJ\nSf7Xn3wxonuGbCvfqkgtk/qP+NvD+pMvRnTPkG3n2+vbdUwaeFjmHtaffCmie4ZsO99ik9Q2\nCZGU58f3DNl2vsUmqWUSIinPj+8Zsu18myI9GyYhkvL8CJ8h2863aFJ+w+xbpSciKc+P+sqG\n/D+rTVJt0kujvkm7WH/yZUCkYtJWk1TtKCGS9vzoRbLbt8veZ2YRSXt+pGN/N/Lt9u2ywqT8\nJyKpzkckF00SIqnP79fy2yFJr3Yj3uxkQyIS+dvp1vLHd5L8SAYg0lukvkk7WX/yJejU8kuS\nCN+NtH+RHJj0rKeH8n1BviCtWv53EB6uoR/RZC8bEpHI30yjlt9PiexFdv2INnvZkBZPJSFS\nLPmfWv6TJN8PyUX3IzrsZkNabpIQKYL8ely7NDncJBfcj+ixmw1pv0mqJwfzPUG+IJxHyqyL\n9KyvXB3J9wT5giBSjr2+HSJFks+1djl2myREiiAfkQqsNknVg/wQSXH+WpEWdAWDEcmKSc/P\nQ5oRSXH+WpEuiGRI42nnHZN2tP7kb2V11+6Wmo7sEIJIVvt2iBRB/vp9pFty3hixpw1ptW83\nciZpT+tP/kY2HGy4JGZncKMXKUMk/fkctXtjc+wGRNKfj0hvEIn8LSDSG7siDZq0q/UnfxuI\nVOG+SdrX+pO/CRGRwj+PlCES+ZuwJFLzZO2/QPiv+P9//7Oy8K+v/P/n08rCQRpPIq2M2Nlf\nJKtNUj3g6ni+c8gXBJFqbPftvhBJcT4ifXBt0t7Wn/wNrBfp76d8nNLpPDPwECKVfFUD3I3l\nu4Z8QdaK9Dg0DidMX76KSG9eJiGS1vy1Ip2T9Le81O5+TacvXw1GJCcmTeU7hnxB1oqUNq5Y\nvSXpqojdbUjbInWedL679Sd/PevvkB17YR6xvw3p1qT9rT/5q6FFamJdpJZJ+1t/8lezYR/p\nWo62r2gfyYFIzSed72/9yV/N6sPfx8ZRu8PkUMcBiWTfpGpEoZF8p5AvyIbzSOfiPFJ6+tFy\nHilzIlJWm7TD9Sd/LVzZ0OY9CIrdJulrPN8l5AuCSG3cNElfo/kuIV8QRGpTimTRpM+YQrtc\nf/JXgkgdbDdJjUGFdrn+5K8DkTo4bJJ2uf7krwOROtQi2TIJkVTmI1IX200SIqnMR6Qub5Gs\nmfTZSdrn+pO/CkTqYlukT5O0z/UnfxWI1MNFk4RI2vIRqcdHJDsmIZLGfETqUYlkq0mqd5J2\nuv7krwGR+rgwCZGU5SNSn4ZIVkxCJIX5iNSnFslSk9QWqTWMg1P2uv2DzEekASybVO0kVSL5\nMml2+z+bfDVxk28ZRBLCSCRbJlUiiVXL5cxs/3GNEKkPIg3wEcmOSaGI1HjRHpFP5ivvtvzX\ngEhDtEySD26I9PqBSJ5AJCGMRLJh0rM06S2SN5MWiiRu0n7LfwWINERbJDsmFSIVFRKR/IBI\nQkzkW26SsmL3PSyRWq8RqQsiDdIUyZJJuUhlfQxQJJHvvOPyXw4iDWJdpHKkyHd19GQSIgmC\nSMM4MOlz4DsMkcT7dnsu/8Ug0jAdkWyYFLRIEt95z+W/GEQapiWSnSbp89ixQESSbpL2XP6L\nQaQR7JvUeBKmH5MQSRBEGsFBk/SvuJwtnwpQJIHvvOvyXwoijdAWyYpJ/7LKpEBEEm6Sdl3+\nS0GkMbpNkrxJRf6zMRi4YxBJEEQaw36TVOb7a5KWiyRr0r7LfyGINEZHJAsmvfO9NUmrRBq9\nRWn5nUr7Lv+FINIovSZJ2qR9i9TyCJHmQKRRrDdJAYk0//3esy9ZkZ2X/zIQaZSuSOImNUTy\nYhIiCYJI49hukqp8X02SqEjZ8jNiey//RSDSOLabJI0iLVmRvZf/IhBpnAGRRE1CJL8gkhCz\n+ZabpKZIPkySFWl532735b8ERJqgJ5KsSXW+pybJikgLVmT35b8ERJpgSCRBk5SJtLhJ2n35\nLwGRprDbJLVEalZAR2NGIpIgiDRFXyRJk6ZEcmGSHZHMTdp/+S8AkSax2iR98jt9O7nhtQ3z\nh1hx89HCJimA8jcHkSax2iS1RWpUwGJ8IfsmIZIgiDTJoEhSJo2J9B410rpJ4iItNCmA8jcH\nkaax2SRNiuTAJEQSBJGmGRBJzKRGfmsnqR420rJJiCQIIk3jUKTeuKu2DzlMrv/g7UizLDIp\nhPI3BpFmsGjSnEi2TbIi0vgtf2Y8+6xfw2kQSYjdi2RZJUQSBJHmsGdSM/9jUrvO2jTJXCQ7\n32EofyDJmkmIJEQIItm8CA+RBEGkOYZEkjHJTCR7Ju1RJJcmIZIQhvnWmiREGgCRlkcELJKI\nSa38yqR+RUIkRJqOQKQGiDSeZckkRBLCNN+WSYYiWTMJkQRBpHmciFSZNFBlEQmRJiOCFknA\nJEQaxJlJiCQEIk2ASMtAJAMsmdTJHx8CxZJJiCQIIhkwJtJGkwZEGiYukZyZhEhCbBRpc5OE\nSMMg0sKIYESyZBIiDYNICyMUiLTJpG6+a5Om1n/dXRRC+Y5MQiQhNou0sUlCpBEGb1RymL8G\nRDLCikmINAIiLYtApA6jJkUm0hAWvgIiCSEg0iaTzEWyU5ONRfJxsKMLImkXab1JiLQE+S+B\nSEIsybfRJCHSEhBJt0gbTOrnu91JQiRBEMkMzyJZqcthiST/LRBJiEX5FkxCpEUg0tgHiNTD\nad8uMJHEvwYiCSEk0mqTlonk9vAvIi0EkUyRb5KWiGTDJEQSBJFMkW+SBkVyaFJoIkl/D0QS\nYocizZjkriIh0kIQyRhxkwbzHZoUnEjCXyQ4kVTgSqTJO28WmTQ780RFcnHNKiLtL8I+0yKt\nM2mkIKVMmm3AEEkQRDIjXwnpJmlUJBmTZu/iCU8k2W+CSB6YEWmVSWMFKWRSMf7x1Nz/xheI\nSEtBJDPmRVpu0mhBypiUzzdpUi1Sfx5EWgoimVGshHCTNF6QIiaVs03M3RCpO89eRRL9Kojk\ngVmRVpg0uY+y3aT3XONz/6tn66nk4ug3Iu0vwj7lSsg2SeZjJnQwM6maaXTuj0i9/t1uRZL8\nMojkgXmRlps0WZDbTarnGZu7IVL2bJuESEtBJDM8iLTVpM8sI3O3RHq2VEKkpSCSGe+VEDVp\nuiDnTJpbenOOYZM6IjX7d/sVSfDbIJIHzERaZtJMQW40qTXDoEkdkZ6Ngw6ItBREMsNEpKVN\n0lxBbjt01/58aPa+SLVKiLQURDKjWglJk2YLcpNJnY8HZh8S6W3SjkWS+zqI5AEvIm06dNf9\ntD97W6Q6LFcJkZaCSGaYibTMJIOCnDLpOW1S78PeWdd/jdkaImWItAJEMqNeCcEmyUikcZOe\n0yb1P+uaNCZS/uaORRL7PojkAUORFplkUpATJr0PWI99PPRJe/aOSA2TugcqDL7oChBpdxH2\n8SXShEmt49V9Bj9AJIH8QRDJDFORlphkVpBjJn2Osg3/3vD7zXfHRXp2TkIZfdHFrK7IQl8o\ndpFu32nyfS0XnZT/GmFjaaMfmPH5bbkmybAgp0UaM2mksk2J9EnavUgDPJvYzR8iPJHO5WAk\nh3u2S5EWmGRakMP1onmQbeDjZSKVC0Ok9QQn0k+Svlqjx+vHfZEdQYs0VDGaezSDxxUGl6VC\npCHG7wJxkx+aSPdCoBffybcfkeRMMi7IQZNaR6v7H68RqV7ms2USIhkQmkjn5KeceJwun65d\nktxPSZp/UvpyTpNj4dv1lCTpOcvCFmmoYsydMpUTyZZHwgOELjdJg0j/M2LgF4/JrbnoWqQ0\n32/6eftyzF+kj7wfWHDOXIpkbNKCguxXjI5IA2dfh5fUeB+RBPEjkplHQ/Wxf2ih/Hd8ZJfk\nUL76zV995/okyW/+Mun94qaVkGqSFonUrRnd4Un6FwQNL6kjUue2iVqo8ERqfc9YRNqwsBGR\n/hqvTvmrR5K2fsehSKYmLSnInkmd1z2TNorUur92wfdcgkWRjExCpNarzyHw9quS+/XnqEGk\nnkndmtI1CZHc5ocm0qneR7o+DEQ6ViPgBy9Sx6R+RemYtFak6jh4eCIt7ttFLdJPddTur9oj\nmhLpOzlcrndpkaRMWliQMyJ17y0fWcq0SO8b/MrL+OYWtRWbIpmYFLVI9XmkY3IZE+lY7yMV\n72sRqVU3huqJ0bmfGZFKk8IUaWmTFLVIr0Ymv7IhP22UjYl0yY/ancujdn/ZTXwfaV4kM5OW\ni/RsTPc/b3buTG6vGI2U5mcAABFqSURBVBSpVAmRlhKcSNVuT+tau7ZIn/NI7+vy8hZKUiSh\nJmlxQT6bDcfA518mOzazIr1jAhfJwKTIRcp+T0ly/C0XPSxSLtCp6AF+v2b9uyYn1yIZmbS8\nIJ+do2tdvgxq/7xInRnDEWlhkxS7SF7Yh0i1SWO1pL5HfJlII+3b3KI2gki7i7BPZyVETFpT\nkDMiGdR+zSItMwmRPLAXkbo3PHSpmiQRkb5mFrURKyLNYSsfkcxYLJKBSStFat3L2qUaSctQ\npIldpLJWTi5qI4i0uwj77EakrDrPM0xxp2g2Wfvrj+ZEepsUkkiz9G4jkQKRzOiuhIRJKwty\nUqR3kzRV+c1Fqv7Cr/uesyDS7iLssyORsslBCV41pXWl3OAcdf6cSBkiGYJIZqwSacYkeyI9\nxUSy2LPzIlLvxkYp1tfyv59TcdXA6fxnK2JHrBBptklaXZBz9V5UJHsg0ovHIflwtBKxK3or\nIWCSnYpUmIRIY+xNpHOS/pZ3Bt2vaTEognjEyNLe7o40g60rgR7nl+7Hi0hs941diyS3j2QT\nLyJ1zqOJsbaWp41BSG6fu7olI0aWlnwuRB38+DP9SMs588tXN8d23zARacYkqyIZ7PUgkiBr\na3nrL//0BaHSIhU/ziP9yeZX+S7G5LofpxtMw9jeO9ubJLsiTR6PqPIRSYoAW6T2z5GPy+mi\nKXpsvPC7XFbvne1NkqWKVJ5ERaQxml1bOTbsI13LW1Wd7yM1f34GgCxGhTx3RGr+Yj1oZHY5\nJIdLOcPjkN9ikb+TTu9KBSfS5LmhKt+rR4hUcGwctTtM7oQMRTyNGFxas2vXGACy/EKnpjzn\n5Pve/r7F3tKxPtJYzH/OR1SZPfa4UqRJk+yJVB4Dn7tCHJEE2XAe6VzUv/T0s/w8kplHIyK9\nuRUv6gEgf5P0lt3SViv0UubwPsv1GTSymvE3K8eVfDVr+Y/HMbkuW4nNTZJdkeZvtYhUpGbX\nVo7grmyoDn/fmm9l71EhX0600q7fuerXrDlo5KkQ5po3QO9Df6diX+pRdPIWrMTmJslWRaqu\n6hm/+6/KRyQxAhTp9d8hrVqP7gCQvQMLfz9pe8iGxoz1ZFIPfzca238rWJGqmoRIgoQp0l+S\nVINydQaAfP3oSnGrRsBrLMCdSBMmIZIn6vUXRKSWuz+PdCr7Yf0BIBsi1d+qPWjkkEgGsQPv\nmTZJrslFyn8+n6MztCdGZ1RKQCIlTSQiOlm36mBDVg0AWe76/DXSTkl5RLvYMzr29pE+Iwud\nJg8zDKzQm/+G3uzjo6Ig0hRfX/9mWFEvl/+K34i68p+yrDkA5LV31O4l1eXx+lGMyfoZNLJ1\n1K6YsXjnNcfCgw2mfbtRrHVt6Nq5zw9VpEfRJDUGgCxPBn23zyMlnxNEw+eRyhnLd9J7Ns7g\nSmw0ybZIc4ftEEmQUEV6WZI3IJ8BILPsp3tlQ3b7TuuxJD+DRmaXtL6y4T3j5ZA0T94OxQ69\nGa5IzzIfkcTgxj4zAhOp/DkmyLM0CZEE4cY+M4ZXYptJ1ivSlEivj/7NCWcZRMo83tjnCUSS\nB5Eyj7dReEKbSPln//z27BCp+D1fN/Z5YmQlNplkvyKNKPIsmqQnIglCi2SGOpEag3YhkgDB\n3djnCX0iZYgkuTBPN/YFh0KRMkQSxM+NfeExthJbTPIoUmv0O0QSILgrGzyBSPIg0u4i7BOm\nSCOOvEXiqJ0goYmUVLc8CN+dMZs79sEGk3yJVD33BZEECU+ktJqQXOx87tgHgYpUDpEy80Ba\nyyCSx4gkSX7eE5KLnc8d+yBskaYfSGsZRPIYkSSHcriGvYi0wSRE8kvkIt3Ku49KkRqDpmY/\nSfpT3HZUnh6uP2qMwVoNrfoZoLX/e0tXApHWgkjbI+afPj3yyMVXxf8ubogtRGre7FqMunot\n3jm3Pionv0uRiqFVGwO09n5vwUqU7FqkQUmq554jkiB+RDLzaESkRz68ViFSd9DUy/v/tPVR\nYzSH99CqjQFau7+3YCUqVpvkX6Ri0pdHiOQzIq/9l3w0k3yiN2hqOdxd56NqMqnmaiyq93sr\nVgKRVoJIHiPKgVaTR3dcunLy8//w6HW1Kp0BWpv/L14JRFoJInmMeA+0+r1NpO4ArZtEWm2S\nX5HKIVJe04gkQZAivXprty0i9QZoRSQfIJLHiLKy35NDc0fo1Bei8VFrH+mzkDhEGtIEkSzk\nhylScQB7YNDUz/9jR+3eC6kHaI1QpOqd6iFKiCRBoCJlae88Utb+v3seKWmI1BigVUKktSZ5\nFqnMH3ugmwMQyWNEVdnfTxTrDJra/L/+qHx87F/zYMNngFZEQiQRQhNpPTPDWM799tSHiLQK\nRNpdxHR+vqP0OE2P0DK7kKkPQxZp4qHNtkGk3UVM8r6ybnLIsFmmV2KdSW4qUs8TRLKRH4FI\n2aV4uvm2ZSCSPIi0uwj7aBbJydcYAJF2F2GfmZVYZdI+RPKHqnxEMgOR5FGVj0hmIJI8qvIR\nyQw9In1eq6rIvvMRyYy5lVhjkqOK1DEJkazkI5IZiCSPqvwQRUqbg0Q6GpYLkeRRlR+gSNek\nGrcYkUxAJBf5AYr0nZyT73LJ+xFpjUmuKlLbJESykh+gSK+OXdq7e9w2iCSPqvzwRPpNztk5\nv6AbkQxBJAf54Yl0TP6yv/Lmoj2JtMIkRFKU70ek/8wYWtqjOGSXJuWIqYhkQsskRLKSH1yL\n9FvcoFf27RDJDESynx+cSIdijOFbPf73bkRabhIiKcoPTaR7UnFHJFOaIjWmVVVk3/mhifRT\ni/SDSKYgkv380ER6P68vH2sVkYxp2INIdvIDE+n9uL4sPwp+25lIi01CJEX5gYl0fl9ll19x\nd0YkUxDJen5gIqVpcxKRDEEk6/mBieQNo5VYaJLDivTRB5Hs5COSGYgkj6p8RDIDkeRRlY9I\nZpitxDKTEElRPiKZEbhIH38QyU4+IpmBSPKoykckM7SI1LxaSFVF9p2PSGYYrsQikxBJUT4i\nmRG+SM/3Tz/5Q6jKRyQzQhepMgmRLOWHJlI1pJ2rS4PqXMP5lpjktiIhktX88ERqjLLqkPBF\nKh1CJEv54YmU39GXIdJyis4dIlnKD0+k9619iLSY3CREspQfnkjve/tKkS6H5HApX95PSVo2\nVq8304tkaLZgJRaY5LwiIZK9/PBEyr6LcYQKkY7F6A3H4mX6HsghO9VvSuaazrhnkfI2CZHs\n5PsRKTFjaGFJ9qiH4vpN0lt2S/Mx7l7mPLJL/sk1n3oc61tpra3EMIhkjKr8AFukly+XcuJU\n2HLNW5+kbqZOxSCsj3pwB6Fc0xl3LVJ7hDtVFdl3fogiZYeXK43bzD+T5dR4e7Yh13hOc5NU\nVaTY84MU6S/5RqTtkC9IkCK9um+3CZEk8+pc4zkRKcr8MEW6J4fmPtKpKdJJ+DDDO9d4TkSK\nMj9MkYqRiztH7apPizezi6+DDQtMUlWRYs8PVKQs7Z1Hqj8t30zvkrGIRP40oYp0fV/ZkNZX\nNnz+v7z6fd+yHiES+dOEJpIvEIn8SRDJjCUrYWqSqooUez4imYFI5E+CSGYgEvmTIJIZiET+\nJIhkxqKVMDRJVUWKPR+RzEAk8idBJDMQifxJEMkMRCJ/EkQyY9lKmJmkqiLFno9IZiAS+ZMg\nkhmIRP4kiGQGIpE/CSKZsXAljExSVZFiz0ckMxCJ/EkQyQxEIn8SRDIDkcifxIVIMfKfwTz/\nrH8L8ifzRWu55MICyiaffNkxECQXFlA2+eQjEvnk7y0fkcgnf28LCyibfPIRiXzy95aPSOST\nv7eFBZRNPvmIRD75e8tHJPLJ39vCAsomn3xEIp/8veX7XhkAFSASgACIBCAAIgEIgEgAAiAS\ngACIBCAAIgEIgEgAAiASgACIBCAAIgEIgEgAAiASgACIBCAAIgEI4Fykc5qk58fUG47zLwe/\n+S/+HJZCL//2nSTfd2/5D8fl/yrw9tYWynct0rF4DsBh4g3H+efijdRVSQ6t7iN1Vwq9/Kvf\n9b+nZb47k29Ja2tL1T/HIv0l6S27pcnf6BuO82/J9yP/I/XtKT/nlDgrhX5++nrjcUrOnvK/\ni+Szq+2f5eHNrS1W/xyLdE6ur/9/k5/RNxznn8oN4KoqD63ub+JOpF7+b1GRH0nqKT9xu/1f\nfzKPrSyx+udYpFOSt+G35DT6huP8N64KciD/3ilat/nfyc1V9mD+u1frSuTs9XejtbXF6p9j\nkXp/gBz/RRqJeyRHb/nH5O5OpF7+Icl+0qJ76yf/5921c9QjyW6dwherf4iUcykaeC/5P8mv\nu47N0PY/FTv7vvKzS360Ib04yu+EI5JYfsE9ddSz7OcXnQqvIuUHG75dtQhDf0hyXDVInXBE\nEsvPeaSOOnZDXav8wLNXkfJ9pLur8w+9/EvetXuJ7LBJUiFS2v3evTcc5+ccnZ3F6uV/F31K\ndyL11t/xH7Je/iHJd88e7k4kdtZVrP55OWp37x61u7s9ateKux+O7s4GdvM/z6r3k+/68H8v\n3/Xh726WWP1zLNJP8Rf4+jn/13vDcf5r2lm/biDftUgj2//uaiP08ssWwdl5rJzWtharf7Ff\n2eCsCo3kF3i8suG1d/TI91F+PeWfk/w6t7OrP6Q5Kq5sePWJc4rKW65Q4w0f+d9uW4T++ren\n3Of/+N3+72vdXP41q7a2bP1zLVJ5sW8ZnXTe8JHvuGvVX//2lIf869Hn9n9ffe0sP+uKJFX/\nXIsEoBJEAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAARAJQABEAhAA\nkQAEQCQAARAJQABEAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAARAJ\nQABEAhAAkQAEQCQAARAJQABEckH9SL58ovN8vt7j+t6PEDz+9T/t/WrjYc5mj/0bnKvxpsvH\ni6uC7eaCNSIlxQOCESkQ2G4uaIk09lnnjfPAE4KHqnn7kajG32TkTURaCdvNBWtEMpp3evap\nbzLyJiKthO3mgoGu3TlNztXLc5L+9Gd+P3U9Kx48nhyv9UfnpDF3c/b3cpLkcUhOr4nLIUkv\nxYfNJdRpr48Pl88i6m8EK2C7uaAv0jHfr/kuX57y6Ut35rJrl7+4lHtBl/dHnT7fR6RTPddr\n6pxlp/KgRdZeQp12rD/+fKMTIq2E7eaC5kGBotpek/SW3dLy5fHxqumH/sy3rJw5zad+8zny\nV919p49I1XKKqTzj9eNxTK7tJVRz/b6/wm+5iN/PN4IVsN1c0BPplFfvV1UvXnaOzlWHv29Z\nVjVh1/qj/jGIj0h/9fzFkfNTkuv0yDt5zSVUc1Vf4Vi9/Ku+EayA7eaCXtfu/cZnsru/f0iv\n9Yvzq8t1e2t1fFvSX/ZnOfXCa3ebS+jN1XsJK2C7uWC5SH9Jcq9f/Lx6XEl6L9xIG53A1rKn\nRGouoTcXIknAdnPBcpFeXa1T4/3r+VDu4fzdknyvZmDZg4p8qJcwMBcibYft5oKeSK19pNYc\n1eTtc7Ch/as/STq47J4ip3rHqLOE9lc4NV/+IdJK2G4u6InUOmrXmqPhwen94pC3QfVRu9fL\nn6Fl90QqjsNll3wxnSW0DtP99r4RrIDt5oKR80jJlEiPokkqq3x16V3x0autegwsuyfSOyPf\nM+osofkV6vNIp/rMFqyA7eaCvkj5dQTHvymRsnPV6yqvS/gcJP8pd586s/dFyi9dSL6LYxad\nJZT/X9LWlQ0/XNmwAbabTwauS4UwQSQvFIfeHqf8Qh5QASJ54afcaUnn54QwQCQ/XF47LQfa\nIz0gEoAAiAQgACIBCIBIAAIgEoAAiAQgACIBCIBIAAIgEoAAiAQgACIBCIBIAAIgEoAAiAQg\nACIBCIBIAAIgEoAAiAQgACIBCIBIAAIgEoAAiAQgACIBCIBIAAIgEoAAiAQgACIBCPB/cX/X\nQkebaIMAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Decision Curve test\n",
    "Rad1<- decision_curve(EGFR~ \n",
    "                     Rad, data = test, family = binomial(link ='logit'),\n",
    "                     thresholds= seq(0,1, by = 0.01),\n",
    "                     confidence.intervals =0.95,study.design = 'case-control',\n",
    "                     population.prevalence = 0.3)\n",
    "\n",
    "Clinical1<- decision_curve(EGFR~ \n",
    "                          Smoking+Type, data = test, family = binomial(link ='logit'),\n",
    "                          thresholds= seq(0,1, by = 0.01),\n",
    "                          confidence.intervals =0.95,study.design = 'case-control',\n",
    "                          population.prevalence = 0.3)\n",
    "\n",
    "clinical_Rad1<- decision_curve(EGFR~ Rad\n",
    "                              +Smoking+Type, data = test,\n",
    "                              family = binomial(link ='logit'), thresholds = seq(0,1, by = 0.01),\n",
    "                              confidence.intervals= 0.95,study.design = 'case-control',\n",
    "                              population.prevalence= 0.3)\n",
    "\n",
    "List1<- list(Clinical1, Rad1, clinical_Rad1)\n",
    "plot_decision_curve(List1,curve.names= c('Clinical','Rad-Score','Nomogram'),\n",
    "                    cost.benefit.axis =FALSE,col = c('green','red','blue'),\n",
    "                    confidence.intervals =FALSE,standardize = FALSE,\n",
    "                    legend.position = \"bottomleft\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}