02_FortCollins_ProbabilityDistributions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Master's Thesis: Investigating Maximum Rainfall Trends in Colorado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Institution:</b> University of Georgia <br>\n",
    "<b>Department:</b> Statistics <br>\n",
    "<b>Student:</b> Jeremy Mulcahey <br>\n",
    "<b>Advisor:</b> Dr. Lynne Seymour <br>\n",
    "<b>Last Edit:</b> 9/27/2016"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Section 0: Preparing data <br>\n",
    "Section 1: Parameter Estimation<br>\n",
    "Section 2: Probabilities of Interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Section 0: Preparing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "require(fitdistrplus); # required for parameter estimation\n",
    "require(caTools);      # required for smoothing\n",
    "require(repr);         # required for resizing plots in IRKernel\n",
    "require(\"IRdisplay\");  # graphic embedding\n",
    "require(MASS);         # testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# establish working directorys\n",
    "setwd(\"C:/Users/jerem/Desktop/UGA/Master'sThesis/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data files for individual station names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = read.csv(file = \"data/FORT_MonMax_GHCN.csv\", head=T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adjust the data for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "11"
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       "11"
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      "text/markdown": [
       "11"
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      "text/plain": [
       "[1] 11"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an index as x - used as needed\n",
    "month = seq(1,nrow(data),by=1)\n",
    "data = cbind(data,month)\n",
    "\n",
    "# first year of the data (changes by station name)\n",
    "firstYear = data$YEAR[1]\n",
    "\n",
    "# station name for plotting\n",
    "stationName = as.character(data$STATION_NAME[1])\n",
    "\n",
    "# -999 messed with the plotting - change to NA\n",
    "data[data==-999] = NA\n",
    "\n",
    "sum(is.na(data)) #print number of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1896"
      ],
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       "1896"
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      "text/markdown": [
       "1896"
      ],
      "text/plain": [
       "[1] 1896"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "firstYear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check number of years in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "118"
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       "118"
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       "118"
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      "text/plain": [
       "[1] 118"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data$YEAR[nrow(data)]-firstYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  1   2   3   4   5   6   7   8   9  10  11  12 \n",
       "119 119 119 119 119 119 119 119 119 119 119 119 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(data$MONTH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a data frame of all the 10 year periods following the first year for data collected at the station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#create the first decade (first year and the following 9 years)\n",
    "max_PRCP = data.frame(data[which(data$YEAR < (firstYear + 10)),6])\n",
    "\n",
    "# add all the subsequent decades to the data frame incrementing by 1 year at a time\n",
    "for(year in c((data$YEAR[1] + 1):2005)){\n",
    "    max_PRCP = cbind(max_PRCP,data[which(data$YEAR >= year & data$YEAR < year+10),6])\n",
    "}\n",
    "\n",
    "# name the columns as the first year of the observed 10 year periods\n",
    "colnames(max_PRCP) = as.character(c(firstYear:2005))\n",
    "\n",
    "# set the missing values to NA\n",
    "max_PRCP[max_PRCP==-999] = NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 1: Parameter Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=8) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mleGamma = data.frame(alpha = double(),\n",
    "                 theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   gamma1 = fitdist(as.vector(testDist),\"gamma\",method = \"mle\")\n",
    "   mleGamma[i,1] = gamma1$estimate[1]\n",
    "   mleGamma[i,2] = gamma1$estimate[2]\n",
    "   \n",
    "   gof = gofstat(gamma1)\n",
    "   mleGamma[i,3] = gof$ks\n",
    "   mleGamma[i,4] = gof$kstest\n",
    "   mleGamma[i,5] = gof$cvm\n",
    "   mleGamma[i,6] = gof$cvmtest\n",
    "   mleGamma[i,7] = gof$ad\n",
    "   mleGamma[i,8] = gof$adtest\n",
    "}\n",
    "\n",
    "mleGamma[\"invTheta\"] = 1/(mleGamma$theta)\n",
    "mleGamma[\"mean\"] = mleGamma$invTheta*mleGamma$alpha\n",
    "mleGamma[\"var\"] = mleGamma$alpha*(mleGamma$invTheta^2)\n",
    "mleGamma[\"sd\"] = sqrt(mleGamma$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mleExp = data.frame(theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   exp1 = fitdist(as.vector(testDist),\"exp\",method = \"mle\")\n",
    "   mleExp[i,1] = exp1$estimate[1]\n",
    "   \n",
    "   gof = gofstat(exp1)\n",
    "   mleExp[i,2] = gof$ks\n",
    "   mleExp[i,3] = gof$kstest\n",
    "   mleExp[i,4] = gof$cvm\n",
    "   mleExp[i,5] = gof$cvmtest\n",
    "   mleExp[i,6] = gof$ad\n",
    "   mleExp[i,7] = gof$adtest\n",
    "}\n",
    "\n",
    "mleExp[\"mean\"] = 1/mleExp$theta\n",
    "mleExp[\"square\"] = mleExp$theta^2 \n",
    "mleExp[\"var\"] = 1/mleExp$square\n",
    "mleExp[\"sd\"] = sqrt(mleExp$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mleWB = data.frame(alpha = double(),theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   WB1 = fitdist(as.vector(testDist),\"weibull\",method = \"mle\")\n",
    "   mleWB[i,1] = WB1$estimate[1]\n",
    "   mleWB[i,2] = WB1$estimate[2]\n",
    "   \n",
    "   gof = gofstat(WB1)\n",
    "   mleWB[i,3] = gof$ks\n",
    "   mleWB[i,4] = gof$kstest\n",
    "   mleWB[i,5] = gof$cvm\n",
    "   mleWB[i,6] = gof$cvmtest\n",
    "   mleWB[i,7] = gof$ad\n",
    "   mleWB[i,8] = gof$adtest\n",
    "}\n",
    "\n",
    "\n",
    "#mleWB[\"mean\"] = 1/mleWB$theta\n",
    "#mleWB[\"square\"] = mleWB$theta^2 \n",
    "#mleWB[\"var\"] = 1/mleWB$square\n",
    "#mleWB[\"sd\"] = sqrt(mleWB$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>33</li>\n",
       "\t<li>34</li>\n",
       "\t<li>35</li>\n",
       "\t<li>36</li>\n",
       "\t<li>37</li>\n",
       "\t<li>38</li>\n",
       "\t<li>39</li>\n",
       "\t<li>52</li>\n",
       "\t<li>53</li>\n",
       "\t<li>54</li>\n",
       "\t<li>61</li>\n",
       "\t<li>62</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 33\n",
       "\\item 34\n",
       "\\item 35\n",
       "\\item 36\n",
       "\\item 37\n",
       "\\item 38\n",
       "\\item 39\n",
       "\\item 52\n",
       "\\item 53\n",
       "\\item 54\n",
       "\\item 61\n",
       "\\item 62\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 33\n",
       "2. 34\n",
       "3. 35\n",
       "4. 36\n",
       "5. 37\n",
       "6. 38\n",
       "7. 39\n",
       "8. 52\n",
       "9. 53\n",
       "10. 54\n",
       "11. 61\n",
       "12. 62\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 33 34 35 36 37 38 39 52 53 54 61 62"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(mleWB$KStest==\"rejected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected     rejected \n",
       "          98           12 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "         110 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected     rejected \n",
       "          92           18 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleWB$KStest)\n",
    "table(mleExp$KStest)\n",
    "table(mleGamma$KStest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>30</li>\n",
       "\t<li>31</li>\n",
       "\t<li>32</li>\n",
       "\t<li>33</li>\n",
       "\t<li>34</li>\n",
       "\t<li>35</li>\n",
       "\t<li>36</li>\n",
       "\t<li>37</li>\n",
       "\t<li>38</li>\n",
       "\t<li>39</li>\n",
       "\t<li>52</li>\n",
       "\t<li>53</li>\n",
       "\t<li>54</li>\n",
       "\t<li>55</li>\n",
       "\t<li>56</li>\n",
       "\t<li>57</li>\n",
       "\t<li>61</li>\n",
       "\t<li>62</li>\n",
       "</ol>\n"
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      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 30\n",
       "\\item 31\n",
       "\\item 32\n",
       "\\item 33\n",
       "\\item 34\n",
       "\\item 35\n",
       "\\item 36\n",
       "\\item 37\n",
       "\\item 38\n",
       "\\item 39\n",
       "\\item 52\n",
       "\\item 53\n",
       "\\item 54\n",
       "\\item 55\n",
       "\\item 56\n",
       "\\item 57\n",
       "\\item 61\n",
       "\\item 62\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 30\n",
       "2. 31\n",
       "3. 32\n",
       "4. 33\n",
       "5. 34\n",
       "6. 35\n",
       "7. 36\n",
       "8. 37\n",
       "9. 38\n",
       "10. 39\n",
       "11. 52\n",
       "12. 53\n",
       "13. 54\n",
       "14. 55\n",
       "15. 56\n",
       "16. 57\n",
       "17. 61\n",
       "18. 62\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 30 31 32 33 34 35 36 37 38 39 52 53 54 55 56 57 61 62"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(mleGamma$KStest==\"rejected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "integer(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(mleExp$KStest==\"rejected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>alpha</th><th scope=col>theta</th><th scope=col>KS</th><th scope=col>KStest</th><th scope=col>CvM</th><th scope=col>CvMtest</th><th scope=col>AD</th><th scope=col>ADtest</th><th scope=col>invTheta</th><th scope=col>mean</th><th scope=col>var</th><th scope=col>sd</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.7702459   </td><td>0.005838670 </td><td>0.11823885  </td><td>not rejected</td><td>0.3945848   </td><td>NA          </td><td>2.578290    </td><td>NA          </td><td>171.2719    </td><td>131.9215    </td><td>22594.44    </td><td>150.3145    </td></tr>\n",
       "\t<tr><td>0.7592384   </td><td>0.005623842 </td><td>0.10957672  </td><td>not rejected</td><td>0.3993983   </td><td>NA          </td><td>2.545615    </td><td>NA          </td><td>177.8144    </td><td>135.0035    </td><td>24005.57    </td><td>154.9373    </td></tr>\n",
       "\t<tr><td>0.7329028   </td><td>0.005426065 </td><td>0.11023874  </td><td>not rejected</td><td>0.3867091   </td><td>NA          </td><td>2.377635    </td><td>NA          </td><td>184.2956    </td><td>135.0708    </td><td>24892.95    </td><td>157.7750    </td></tr>\n",
       "\t<tr><td>0.7228140   </td><td>0.005126209 </td><td>0.10648805  </td><td>not rejected</td><td>0.3718661   </td><td>NA          </td><td>2.266249    </td><td>NA          </td><td>195.0759    </td><td>141.0036    </td><td>27506.41    </td><td>165.8506    </td></tr>\n",
       "\t<tr><td>0.7894197   </td><td>0.005429997 </td><td>0.10174311  </td><td>not rejected</td><td>0.2569872   </td><td>NA          </td><td>1.422317    </td><td>NA          </td><td>184.1622    </td><td>145.3812    </td><td>26773.72    </td><td>163.6268    </td></tr>\n",
       "\t<tr><td>0.7953316   </td><td>0.005407428 </td><td>0.09718581  </td><td>not rejected</td><td>0.2725329   </td><td>NA          </td><td>1.497542    </td><td>NA          </td><td>184.9308    </td><td>147.0813    </td><td>27199.86    </td><td>164.9238    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       " alpha & theta & KS & KStest & CvM & CvMtest & AD & ADtest & invTheta & mean & var & sd\\\\\n",
       "\\hline\n",
       "\t 0.7702459    & 0.005838670  & 0.11823885   & not rejected & 0.3945848    & NA           & 2.578290     & NA           & 171.2719     & 131.9215     & 22594.44     & 150.3145    \\\\\n",
       "\t 0.7592384    & 0.005623842  & 0.10957672   & not rejected & 0.3993983    & NA           & 2.545615     & NA           & 177.8144     & 135.0035     & 24005.57     & 154.9373    \\\\\n",
       "\t 0.7329028    & 0.005426065  & 0.11023874   & not rejected & 0.3867091    & NA           & 2.377635     & NA           & 184.2956     & 135.0708     & 24892.95     & 157.7750    \\\\\n",
       "\t 0.7228140    & 0.005126209  & 0.10648805   & not rejected & 0.3718661    & NA           & 2.266249     & NA           & 195.0759     & 141.0036     & 27506.41     & 165.8506    \\\\\n",
       "\t 0.7894197    & 0.005429997  & 0.10174311   & not rejected & 0.2569872    & NA           & 1.422317     & NA           & 184.1622     & 145.3812     & 26773.72     & 163.6268    \\\\\n",
       "\t 0.7953316    & 0.005407428  & 0.09718581   & not rejected & 0.2725329    & NA           & 1.497542     & NA           & 184.9308     & 147.0813     & 27199.86     & 164.9238    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  alpha     theta       KS         KStest       CvM       CvMtest AD      \n",
       "1 0.7702459 0.005838670 0.11823885 not rejected 0.3945848 NA      2.578290\n",
       "2 0.7592384 0.005623842 0.10957672 not rejected 0.3993983 NA      2.545615\n",
       "3 0.7329028 0.005426065 0.11023874 not rejected 0.3867091 NA      2.377635\n",
       "4 0.7228140 0.005126209 0.10648805 not rejected 0.3718661 NA      2.266249\n",
       "5 0.7894197 0.005429997 0.10174311 not rejected 0.2569872 NA      1.422317\n",
       "6 0.7953316 0.005407428 0.09718581 not rejected 0.2725329 NA      1.497542\n",
       "  ADtest invTheta mean     var      sd      \n",
       "1 NA     171.2719 131.9215 22594.44 150.3145\n",
       "2 NA     177.8144 135.0035 24005.57 154.9373\n",
       "3 NA     184.2956 135.0708 24892.95 157.7750\n",
       "4 NA     195.0759 141.0036 27506.41 165.8506\n",
       "5 NA     184.1622 145.3812 26773.72 163.6268\n",
       "6 NA     184.9308 147.0813 27199.86 164.9238"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mleGamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>theta</th><th scope=col>KS</th><th scope=col>KStest</th><th scope=col>CvM</th><th scope=col>CvMtest</th><th scope=col>AD</th><th scope=col>ADtest</th><th scope=col>mean</th><th scope=col>square</th><th scope=col>var</th><th scope=col>sd</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.007582251 </td><td>0.06135990  </td><td>not rejected</td><td>0.07771479  </td><td>not rejected</td><td>1.154525    </td><td>not rejected</td><td>131.8870    </td><td>5.749053e-05</td><td>17394.17    </td><td>131.8870    </td></tr>\n",
       "\t<tr><td>0.007406461 </td><td>0.05409077  </td><td>not rejected</td><td>0.07202469  </td><td>not rejected</td><td>1.120545    </td><td>not rejected</td><td>135.0172    </td><td>5.485567e-05</td><td>18229.66    </td><td>135.0172    </td></tr>\n",
       "\t<tr><td>0.007404126 </td><td>0.05999186  </td><td>not rejected</td><td>0.06899385  </td><td>not rejected</td><td>1.086094    </td><td>not rejected</td><td>135.0598    </td><td>5.482108e-05</td><td>18241.16    </td><td>135.0598    </td></tr>\n",
       "\t<tr><td>0.007092198 </td><td>0.05165822  </td><td>not rejected</td><td>0.05439701  </td><td>not rejected</td><td>1.053790    </td><td>not rejected</td><td>141.0000    </td><td>5.029928e-05</td><td>19881.00    </td><td>141.0000    </td></tr>\n",
       "\t<tr><td>0.006879263 </td><td>0.07186013  </td><td>not rejected</td><td>0.08074364  </td><td>not rejected</td><td>0.770200    </td><td>not rejected</td><td>145.3644    </td><td>4.732426e-05</td><td>21130.81    </td><td>145.3644    </td></tr>\n",
       "\t<tr><td>0.006798410 </td><td>0.06769829  </td><td>not rejected</td><td>0.08938361  </td><td>not rejected</td><td>0.817524    </td><td>not rejected</td><td>147.0932    </td><td>4.621838e-05</td><td>21636.42    </td><td>147.0932    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       " theta & KS & KStest & CvM & CvMtest & AD & ADtest & mean & square & var & sd\\\\\n",
       "\\hline\n",
       "\t 0.007582251  & 0.06135990   & not rejected & 0.07771479   & not rejected & 1.154525     & not rejected & 131.8870     & 5.749053e-05 & 17394.17     & 131.8870    \\\\\n",
       "\t 0.007406461  & 0.05409077   & not rejected & 0.07202469   & not rejected & 1.120545     & not rejected & 135.0172     & 5.485567e-05 & 18229.66     & 135.0172    \\\\\n",
       "\t 0.007404126  & 0.05999186   & not rejected & 0.06899385   & not rejected & 1.086094     & not rejected & 135.0598     & 5.482108e-05 & 18241.16     & 135.0598    \\\\\n",
       "\t 0.007092198  & 0.05165822   & not rejected & 0.05439701   & not rejected & 1.053790     & not rejected & 141.0000     & 5.029928e-05 & 19881.00     & 141.0000    \\\\\n",
       "\t 0.006879263  & 0.07186013   & not rejected & 0.08074364   & not rejected & 0.770200     & not rejected & 145.3644     & 4.732426e-05 & 21130.81     & 145.3644    \\\\\n",
       "\t 0.006798410  & 0.06769829   & not rejected & 0.08938361   & not rejected & 0.817524     & not rejected & 147.0932     & 4.621838e-05 & 21636.42     & 147.0932    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  theta       KS         KStest       CvM        CvMtest      AD      \n",
       "1 0.007582251 0.06135990 not rejected 0.07771479 not rejected 1.154525\n",
       "2 0.007406461 0.05409077 not rejected 0.07202469 not rejected 1.120545\n",
       "3 0.007404126 0.05999186 not rejected 0.06899385 not rejected 1.086094\n",
       "4 0.007092198 0.05165822 not rejected 0.05439701 not rejected 1.053790\n",
       "5 0.006879263 0.07186013 not rejected 0.08074364 not rejected 0.770200\n",
       "6 0.006798410 0.06769829 not rejected 0.08938361 not rejected 0.817524\n",
       "  ADtest       mean     square       var      sd      \n",
       "1 not rejected 131.8870 5.749053e-05 17394.17 131.8870\n",
       "2 not rejected 135.0172 5.485567e-05 18229.66 135.0172\n",
       "3 not rejected 135.0598 5.482108e-05 18241.16 135.0598\n",
       "4 not rejected 141.0000 5.029928e-05 19881.00 141.0000\n",
       "5 not rejected 145.3644 4.732426e-05 21130.81 145.3644\n",
       "6 not rejected 147.0932 4.621838e-05 21636.42 147.0932"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mleExp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trueEst = NULL\n",
    "for(i in 1:nrow(mleExp)){\n",
    "    if(mleExp$KStest[i] == \"not rejected\" & mleGamma$KStest[i] == \"not rejected\"){\n",
    "        trueEst = c(trueEst,\"plot\")\n",
    "    }\n",
    "    if(mleExp$KStest[i] == \"not rejected\" & mleGamma$KStest[i] == \"rejected\"){\n",
    "        trueEst = c(trueEst,\"exp\")\n",
    "    }\n",
    "    if(mleExp$KStest[i] == \"rejected\" & mleGamma$KStest[i] == \"not rejected\"){\n",
    "        trueEst = c(trueEst,\"gamma\")\n",
    "    }\n",
    "    if(mleExp$KStest[i] == \"rejected\" & mleGamma$KStest[i] == \"rejected\"){\n",
    "        trueEst = c(trueEst,\"fail\")\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trueEst\n",
       " exp plot \n",
       "  18   92 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "not rejected     rejected \n",
      "          98           12 \n"
     ]
    }
   ],
   "source": [
    "table(trueEst)\n",
    "print(table(mleWB$KStest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. FALSE\n",
       "2. FALSE\n",
       "3. FALSE\n",
       "4. FALSE\n",
       "5. FALSE\n",
       "6. FALSE\n",
       "7. FALSE\n",
       "8. FALSE\n",
       "9. FALSE\n",
       "10. FALSE\n",
       "11. FALSE\n",
       "12. FALSE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mleGamma[which(mleWB$KStest==\"rejected\"),\"KStest\"] == \"not rejected\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
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       "\t<li>TRUE</li>\n",
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       "\t<li>TRUE</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mleExp[which(mleWB$KStest==\"rejected\"),\"KStest\"] == \"not rejected\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>33</li>\n",
       "\t<li>34</li>\n",
       "\t<li>35</li>\n",
       "\t<li>36</li>\n",
       "\t<li>37</li>\n",
       "\t<li>38</li>\n",
       "\t<li>39</li>\n",
       "\t<li>52</li>\n",
       "\t<li>53</li>\n",
       "\t<li>54</li>\n",
       "\t<li>61</li>\n",
       "\t<li>62</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 33\n",
       "\\item 34\n",
       "\\item 35\n",
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       "\\item 37\n",
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       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 33\n",
       "2. 34\n",
       "3. 35\n",
       "4. 36\n",
       "5. 37\n",
       "6. 38\n",
       "7. 39\n",
       "8. 52\n",
       "9. 53\n",
       "10. 54\n",
       "11. 61\n",
       "12. 62\n",
       "\n",
       "\n"
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      "text/plain": [
       " [1] 33 34 35 36 37 38 39 52 53 54 61 62"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(mleWB$KStest==\"rejected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s5hl1LEccUcRVdkYRRQVwWcwgG+XRVJ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",
      "text/plain": [
       "Plot with title \"P-P plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testDist = na.omit(max_PRCP[,1])\n",
    "\n",
    "    for(j in 1:length(testDist)){\n",
    "        if(testDist[j] < .1){\n",
    "        testDist[j]=.0001\n",
    "        }\n",
    "    }\n",
    "\n",
    "plot(fitdist(as.vector(testDist),\"weibull\",method = \"mle\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mleGamma[1,\"KStest\"];mleExp[1,\"KStest\"];mleWB[1,\"KStest\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CJVchQvjtcBVlW19E3tvcrlTB3HWXUg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\nRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"P-P plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testDist = na.omit(max_PRCP[,1])\n",
    "\n",
    "    for(j in 1:length(testDist)){\n",
    "        if(testDist[j] < .1){\n",
    "        testDist[j]=.0001\n",
    "        }\n",
    "    }\n",
    "\n",
    "fw = fitdist(as.vector(testDist), \"weibull\", method=\"mle\")\n",
    "fg = fitdist(as.vector(testDist), \"exp\", method=\"mle\")\n",
    "fln = fitdist(as.vector(testDist), \"gamma\", method=\"mle\")\n",
    "par(mfrow = c(2, 2))\n",
    "plot.legend <- c(\"Weibull\", \"exp\", \"gamma\")\n",
    "denscomp(list(fw, fln, fg), legendtext = plot.legend)\n",
    "qqcomp(list(fw, fln, fg), legendtext = plot.legend)\n",
    "cdfcomp(list(fw, fln, fg), legendtext = plot.legend)\n",
    "ppcomp(list(fw, fln, fg), legendtext = plot.legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mleGamma[2,\"KStest\"];mleExp[2,\"KStest\"];mleWB[2,\"KStest\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mleGamma[3,\"KStest\"];mleExp[3,\"KStest\"];mleWB[3,\"KStest\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yajgePcOVYFYAvVk29EI0kcmbt+V0bc5SiNu88ufGL/pYy88vFrXnEvXHK/VXMg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\nQDSIAwCiQRwAEA3iAIBoEAcARPN/EskBdx8UOeQAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"P-P plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testDist = na.omit(max_PRCP[,3])\n",
    "\n",
    "    for(j in 1:length(testDist)){\n",
    "        if(testDist[j] < .1){\n",
    "        testDist[j]=.0001\n",
    "        }\n",
    "    }\n",
    "\n",
    "fw = fitdist(as.vector(testDist), \"weibull\", method=\"mle\")\n",
    "fg = fitdist(as.vector(testDist), \"exp\", method=\"mle\")\n",
    "fln = fitdist(as.vector(testDist), \"gamma\", method=\"mle\")\n",
    "par(mfrow = c(2, 2))\n",
    "plot.legend <- c(\"Weibull\", \"exp\", \"gamma\")\n",
    "denscomp(list(fw, fln, fg), legendtext = plot.legend)\n",
    "qqcomp(list(fw, fln, fg), legendtext = plot.legend)\n",
    "cdfcomp(list(fw, fln, fg), legendtext = plot.legend)\n",
    "ppcomp(list(fw, fln, fg), legendtext = plot.legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'not rejected'"
      ],
      "text/latex": [
       "'not rejected'"
      ],
      "text/markdown": [
       "'not rejected'"
      ],
      "text/plain": [
       "[1] \"not rejected\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mleGamma[45,\"KStest\"];mleExp[45,\"KStest\"];mleWB[45,\"KStest\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j8qXT6DUoow0req7CdTnxT+NGy94PD/soi2/94tjskBTz/WB01rlyzu7McBD8U7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\nOela3JsMuQAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"P-P plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testDist = na.omit(max_PRCP[,45])\n",
    "\n",
    "    for(j in 1:length(testDist)){\n",
    "        if(testDist[j] < .1){\n",
    "        testDist[j]=.0001\n",
    "        }\n",
    "    }\n",
    "\n",
    "fw = fitdist(as.vector(testDist), \"weibull\", method=\"mle\")\n",
    "fg = fitdist(as.vector(testDist), \"exp\", method=\"mle\")\n",
    "fln = fitdist(as.vector(testDist), \"gamma\", method=\"mle\")\n",
    "par(mfrow = c(2, 2))\n",
    "plot.legend <- c(\"Weibull\", \"exp\", \"gamma\")\n",
    "denscomp(list(fw, fln, fg), legendtext = plot.legend)\n",
    "qqcomp(list(fw, fln, fg), legendtext = plot.legend)\n",
    "cdfcomp(list(fw, fln, fg), legendtext = plot.legend)\n",
    "ppcomp(list(fw, fln, fg), legendtext = plot.legend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tried failed fits with normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DzCM+JQ0lCCEH3jSbrGCLNXxYqjWThCswmaPr8TrRyJtDR301CXgWx4RBxTb9w8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\nHADgzf8B7miAnR4v7+oAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"P-P plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testDist = na.omit(max_PRCP[,45])\n",
    "\n",
    "    for(j in 1:length(testDist)){\n",
    "        if(testDist[j] < .1){\n",
    "        testDist[j]=.0001\n",
    "        }\n",
    "    }\n",
    "\n",
    "fw = fitdist(as.vector(testDist), \"weibull\", method=\"mle\")\n",
    "fg = fitdist(as.vector(testDist), \"exp\", method=\"mle\")\n",
    "fln = fitdist(as.vector(testDist), \"gamma\", method=\"mle\")\n",
    "norm = fitdist(as.vector(testDist), \"norm\", method=\"mle\")\n",
    "\n",
    "par(mfrow = c(2, 2))\n",
    "plot.legend <- c(\"Weibull\", \"exp\", \"gamma\",\"normal\")\n",
    "denscomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "qqcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "cdfcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "ppcomp(list(fw, fln, fg,norm), legendtext = plot.legend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try good fits with normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XpjM8ZhbjfFMbo8pD/RxCEWH+6liDhAEVccjRPfyLfrxtpm511VOSsOKivQRRWH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\nDgDwBnEAgDeIAwC8QRwA4A3iAABv/g97CpwQ9pahMgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"P-P plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testDist = na.omit(max_PRCP[,3])\n",
    "\n",
    "    for(j in 1:length(testDist)){\n",
    "        if(testDist[j] < .1){\n",
    "        testDist[j]=.0001\n",
    "        }\n",
    "    }\n",
    "\n",
    "fw = fitdist(as.vector(testDist), \"weibull\", method=\"mle\")\n",
    "fg = fitdist(as.vector(testDist), \"exp\", method=\"mle\")\n",
    "fln = fitdist(as.vector(testDist), \"gamma\", method=\"mle\")\n",
    "norm = fitdist(as.vector(testDist), \"norm\", method=\"mle\")\n",
    "\n",
    "par(mfrow = c(2, 2))\n",
    "plot.legend <- c(\"Weibull\", \"exp\", \"gamma\",\"normal\")\n",
    "denscomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "qqcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "cdfcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "ppcomp(list(fw, fln, fg,norm), legendtext = plot.legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "integer(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(trueEst==\"fail\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>2</li>\n",
       "\t<li>3</li>\n",
       "\t<li>4</li>\n",
       "\t<li>5</li>\n",
       "\t<li>6</li>\n",
       "\t<li>7</li>\n",
       "\t<li>8</li>\n",
       "\t<li>9</li>\n",
       "\t<li>10</li>\n",
       "\t<li>11</li>\n",
       "\t<li>12</li>\n",
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       "\t<li>14</li>\n",
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       "\t<li>18</li>\n",
       "\t<li>19</li>\n",
       "\t<li>20</li>\n",
       "\t<li>21</li>\n",
       "\t<li>22</li>\n",
       "\t<li>23</li>\n",
       "\t<li>24</li>\n",
       "\t<li>25</li>\n",
       "\t<li>26</li>\n",
       "\t<li>27</li>\n",
       "\t<li>28</li>\n",
       "\t<li>29</li>\n",
       "\t<li>40</li>\n",
       "\t<li>41</li>\n",
       "\t<li>42</li>\n",
       "\t<li>43</li>\n",
       "\t<li>44</li>\n",
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       "\t<li>46</li>\n",
       "\t<li>47</li>\n",
       "\t<li>48</li>\n",
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       "\t<li>50</li>\n",
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       "\t<li>58</li>\n",
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       "\t<li>60</li>\n",
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       "\t<li>64</li>\n",
       "\t<li>65</li>\n",
       "\t<li>66</li>\n",
       "\t<li>67</li>\n",
       "\t<li>68</li>\n",
       "\t<li>69</li>\n",
       "\t<li>70</li>\n",
       "\t<li>71</li>\n",
       "\t<li>72</li>\n",
       "\t<li>73</li>\n",
       "\t<li>74</li>\n",
       "\t<li>75</li>\n",
       "\t<li>76</li>\n",
       "\t<li>77</li>\n",
       "\t<li>78</li>\n",
       "\t<li>79</li>\n",
       "\t<li>80</li>\n",
       "\t<li>81</li>\n",
       "\t<li>82</li>\n",
       "\t<li>83</li>\n",
       "\t<li>84</li>\n",
       "\t<li>85</li>\n",
       "\t<li>86</li>\n",
       "\t<li>87</li>\n",
       "\t<li>88</li>\n",
       "\t<li>89</li>\n",
       "\t<li>90</li>\n",
       "\t<li>91</li>\n",
       "\t<li>92</li>\n",
       "\t<li>93</li>\n",
       "\t<li>94</li>\n",
       "\t<li>95</li>\n",
       "\t<li>96</li>\n",
       "\t<li>97</li>\n",
       "\t<li>98</li>\n",
       "\t<li>99</li>\n",
       "\t<li>100</li>\n",
       "\t<li>101</li>\n",
       "\t<li>102</li>\n",
       "\t<li>103</li>\n",
       "\t<li>104</li>\n",
       "\t<li>105</li>\n",
       "\t<li>106</li>\n",
       "\t<li>107</li>\n",
       "\t<li>108</li>\n",
       "\t<li>109</li>\n",
       "\t<li>110</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 2\n",
       "\\item 3\n",
       "\\item 4\n",
       "\\item 5\n",
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       "\\item 108\n",
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       "\\item 110\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1\n",
       "2. 2\n",
       "3. 3\n",
       "4. 4\n",
       "5. 5\n",
       "6. 6\n",
       "7. 7\n",
       "8. 8\n",
       "9. 9\n",
       "10. 10\n",
       "11. 11\n",
       "12. 12\n",
       "13. 13\n",
       "14. 14\n",
       "15. 15\n",
       "16. 16\n",
       "17. 17\n",
       "18. 18\n",
       "19. 19\n",
       "20. 20\n",
       "21. 21\n",
       "22. 22\n",
       "23. 23\n",
       "24. 24\n",
       "25. 25\n",
       "26. 26\n",
       "27. 27\n",
       "28. 28\n",
       "29. 29\n",
       "30. 40\n",
       "31. 41\n",
       "32. 42\n",
       "33. 43\n",
       "34. 44\n",
       "35. 45\n",
       "36. 46\n",
       "37. 47\n",
       "38. 48\n",
       "39. 49\n",
       "40. 50\n",
       "41. 51\n",
       "42. 58\n",
       "43. 59\n",
       "44. 60\n",
       "45. 63\n",
       "46. 64\n",
       "47. 65\n",
       "48. 66\n",
       "49. 67\n",
       "50. 68\n",
       "51. 69\n",
       "52. 70\n",
       "53. 71\n",
       "54. 72\n",
       "55. 73\n",
       "56. 74\n",
       "57. 75\n",
       "58. 76\n",
       "59. 77\n",
       "60. 78\n",
       "61. 79\n",
       "62. 80\n",
       "63. 81\n",
       "64. 82\n",
       "65. 83\n",
       "66. 84\n",
       "67. 85\n",
       "68. 86\n",
       "69. 87\n",
       "70. 88\n",
       "71. 89\n",
       "72. 90\n",
       "73. 91\n",
       "74. 92\n",
       "75. 93\n",
       "76. 94\n",
       "77. 95\n",
       "78. 96\n",
       "79. 97\n",
       "80. 98\n",
       "81. 99\n",
       "82. 100\n",
       "83. 101\n",
       "84. 102\n",
       "85. 103\n",
       "86. 104\n",
       "87. 105\n",
       "88. 106\n",
       "89. 107\n",
       "90. 108\n",
       "91. 109\n",
       "92. 110\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19\n",
       "[20]  20  21  22  23  24  25  26  27  28  29  40  41  42  43  44  45  46  47  48\n",
       "[39]  49  50  51  58  59  60  63  64  65  66  67  68  69  70  71  72  73  74  75\n",
       "[58]  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94\n",
       "[77]  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(trueEst==\"plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mleWB2 = data.frame(alpha = double(),theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "    \n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "    \n",
    "   WB1 = fitdist(as.vector(testDist),\"weibull\",method = \"mge\", gof=\"ADR\")\n",
    "   mleWB2[i,1] = WB1$estimate[1]\n",
    "   mleWB2[i,2] = WB1$estimate[2]\n",
    "   \n",
    "   gof = gofstat(WB1)\n",
    "   mleWB2[i,3] = gof$ks\n",
    "   mleWB2[i,4] = gof$kstest\n",
    "   mleWB2[i,5] = gof$cvm\n",
    "   mleWB2[i,6] = gof$cvmtest\n",
    "   mleWB2[i,7] = gof$ad\n",
    "   mleWB2[i,8] = gof$adtest\n",
    "}\n",
    "\n",
    "\n",
    "#mleWB[\"mean\"] = 1/mleWB$theta\n",
    "#mleWB[\"square\"] = mleWB$theta^2 \n",
    "#mleWB[\"var\"] = 1/mleWB$square\n",
    "#mleWB[\"sd\"] = sqrt(mleWB$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "         110 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected     rejected \n",
       "          98           12 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleWB2[,4])\n",
    "table(mleWB[,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mleGamma2 = data.frame(alpha = double(),\n",
    "                 theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   gamma1 = fitdist(as.vector(testDist),\"gamma\",method = \"mge\", gof=\"ADR\")\n",
    "   mleGamma2[i,1] = gamma1$estimate[1]\n",
    "   mleGamma2[i,2] = gamma1$estimate[2]\n",
    "   \n",
    "   gof = gofstat(gamma1)\n",
    "   mleGamma2[i,3] = gof$ks\n",
    "   mleGamma2[i,4] = gof$kstest\n",
    "   mleGamma2[i,5] = gof$cvm\n",
    "   mleGamma2[i,6] = gof$cvmtest\n",
    "   mleGamma2[i,7] = gof$ad\n",
    "   mleGamma2[i,8] = gof$adtest\n",
    "}\n",
    "\n",
    "mleGamma[\"invTheta\"] = 1/(mleGamma$theta)\n",
    "mleGamma[\"mean\"] = mleGamma$invTheta*mleGamma$alpha\n",
    "mleGamma[\"var\"] = mleGamma$alpha*(mleGamma$invTheta^2)\n",
    "mleGamma[\"sd\"] = sqrt(mleGamma$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "         110 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleGamma2[,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected     rejected \n",
       "          92           18 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleGamma[,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mleExp2 = data.frame(theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   exp1 = fitdist(as.vector(testDist),\"exp\",method = \"mge\",gof=\"ADR\")\n",
    "   mleExp2[i,1] = exp1$estimate[1]\n",
    "   \n",
    "   gof = gofstat(exp1)\n",
    "   mleExp2[i,2] = gof$ks\n",
    "   mleExp2[i,3] = gof$kstest\n",
    "   mleExp2[i,4] = gof$cvm\n",
    "   mleExp2[i,5] = gof$cvmtest\n",
    "   mleExp2[i,6] = gof$ad\n",
    "   mleExp2[i,7] = gof$adtest\n",
    "}\n",
    "\n",
    "mleExp2[\"mean\"] = 1/mleExp2$theta\n",
    "mleExp2[\"square\"] = mleExp2$theta^2 \n",
    "mleExp2[\"var\"] = 1/mleExp2$square\n",
    "mleExp2[\"sd\"] = sqrt(mleExp2$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "         110 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "         110 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleExp2[,3])\n",
    "table(mleExp[,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zeJQ1pD+UGI/KsoYY7oseJoVs7QYoEmOa2bmoJ6Ze2mZiiHQDHcIw7jjaoRGfvC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\nAHjzf/nwhncheC0IAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"P-P plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testDist = na.omit(max_PRCP[,45])\n",
    "\n",
    "    for(j in 1:length(testDist)){\n",
    "        if(testDist[j] < .1){\n",
    "        testDist[j]=.0001\n",
    "        }\n",
    "    }\n",
    "\n",
    "fw = fitdist(as.vector(testDist), \"weibull\", method=\"mge\",gof=\"ADR\")\n",
    "fg = fitdist(as.vector(testDist), \"exp\", method=\"mle\")\n",
    "fln = fitdist(as.vector(testDist), \"gamma\", method=\"mle\")\n",
    "norm = fitdist(as.vector(testDist), \"norm\", method=\"mle\")\n",
    "\n",
    "par(mfrow = c(2, 2))\n",
    "plot.legend <- c(\"Weibull\", \"exp\", \"gamma\",\"normal\")\n",
    "denscomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "qqcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "cdfcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "ppcomp(list(fw, fln, fg,norm), legendtext = plot.legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EstltwF4gCJvMhtYS2l8sWN53rjjTmm/xAHPIP13BZ6o3l0NSVSHMGfiTh2x0Zz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\nAIBoEAcARIM4ACAaxAEA0fwfHw7r5hiGkJIAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"P-P plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testDist = na.omit(max_PRCP[,3])\n",
    "\n",
    "    for(j in 1:length(testDist)){\n",
    "        if(testDist[j] < .1){\n",
    "        testDist[j]=.0001\n",
    "        }\n",
    "    }\n",
    "\n",
    "fw = fitdist(as.vector(testDist), \"weibull\", method=\"mle\")\n",
    "fg = fitdist(as.vector(testDist), \"exp\", method=\"mle\")\n",
    "fln = fitdist(as.vector(testDist), \"gamma\", method=\"mle\")\n",
    "norm = fitdist(as.vector(testDist), \"weibull\", method=\"mge\",gof=\"ADR\")\n",
    "\n",
    "par(mfrow = c(2, 2))\n",
    "plot.legend <- c(\"Weibull\", \"exp\", \"gamma\",\"WMGE\")\n",
    "denscomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "qqcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "cdfcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "ppcomp(list(fw, fln, fg,norm), legendtext = plot.legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2wLHqrNPNy7KRM+nbZp1423KcNQNRu9mZbicN9SiyVPkag41t6s6zY3LW7jhDiS\n5MNpmwVFX6iNzhmb5pj+QxwfljQZaiIPxJEku6dt3lP0jd7wE4fzZyKO86XyiOL4uFcRxKGx\nwdKCqTtSZu+0ud6o3uqNa+LoLkyRJQ61ePIpxYD7ovOWlQfiSJKd0zZvg32nN3ZiHLO6iWsM\nNb0uJYpDrT7dTTHwvrR5qqRbeqJsn+FZceO13thuVVkTR/cUcRykZDhcmmyc4UUP8/dqY489\ncUhvjpUhjnZR09hictgb2+xSZf0MTxtTOJ2bqMlftftwbQshl5wt/0SMY2XR2tZcKH0kwdoZ\nnjWm4I1tdsUhvcv5U60qO4vqfmK2kymlD9kszrBT2LC1lCf2CgYy68dxcNGy0gJpSyD4Qyjz\n0zbxBrGN58lfHDtUVVcAKW1n9eOpQmRm52IeFMUbT/OuqsoG5s49ddF6pKioxYhgdtqmxQ2q\nKc/znuDowUV1DLX9favNXxzyFNPTRnRDHPk3x55ftLL+KGw52dZojqcGV1gXB20pYkAcHotW\nXRYuCvvPVHCKsiz3Aik+HN/XxPGsxtIGKw7E4Sx65hK3pZLKFkyKeiqWorZiCb+vaeNZjS07\nbWANORDjiLloZcooXeFER17rrpzSRVI2fDNGaisbaal191fdCaX/e3xPT/owKr4/Ku1hrAhu\nyOL+VpXtbOqTw9/EeDtUZf9NJXNaSGNhqY/kPC0O50u7b+sDwJAjWTzZjwNeineJQ1W6vhJx\nj8AXxAH341eNbctbJRUVYSAOeIC1Csl0gelTvCGNO8SBcsCXsUxC7hEJJQ6QiJr9BWEgDpBI\nP1/Ew7sBWyAOkIjObWhDME+OjgXYQvFLJZsne44CbMEPjnCeHKsCsAV5RjiSxHFnN+434Xfi\nZPD0MYNPHD+Vnmfef80oOTzOZUOq9xL7O0RO/9W7Hz/GgTgSSvVeXn3lPZ7+veLwb1VBHAml\nei+vvvIeT/9mccjYQkoXY0qp3surr7zH00ccpJoor77yHk8fcZBqorz6yns8fcRBqony6ivv\n8fQRB6kmyquvvMfTRxykmiivvvIeTx9xkGqivPrKezx96eIAgOxAHADgDeIAAG8QBwB4gzgA\nwBvEAQDeIA4A8AZxAIA3iAMAvEEcAOAN4gAAbxAHAHiDOADAG8QBAN5EHxoc8M5Bw+TqQ6Jh\nUp8nFiLVZWJyU32ISLseK5eMG5ilGjb5yKc4WPI3jPkPtYkhreWTa+mqjeSvpNmnGz7V9Sfp\nEWnXox+jGNnFSb2JkXGmya8/OZFSPJTzGCyt5ZOL6Q4HMFiqcfY11hF4hEi7Hv0Yxcguburz\nZIOmHzD5dMQxpLCCqN4AACAASURBVJiMOOKkijgOJZ6yOKKljzjCHdM+ncCXeFdzDL6vTtES\ncewlHu/KjiuOKBnHST9UDkpMHOEvmzjiiHSJx81VtxJz1yPKNbY4Yv82BMtBbxfHkGDwVOOU\njihxHEw7zjGKk12m6UcuML2yxBH8nKnpg2xxxM5VtxJx1+Nd2ZGyy2wD8U5xwOSTEoeaPoYQ\nR0cSlzji8E45tDjiZBdnA4tkEUeo1MIfU0ocDxBt12PlkukGEEeYPdpLPrA3nEQDpT4evGCp\nLhOTm+pDRNr1aLlktoVIyUc+xeGSj53rwvWXHUqJTejeuPPE5HYOp8v5gUQj5ZJxE7NU6XIO\nAHAIxAEA3iAOAPAGcQCAN4gDALxBHADgDeIAAG8QBwB4gzgAwBvEAQDeIA4A8AZxAIA3iAMA\nvEEcAOAN4gAAbxAHAHiDOADAG8QBAN4gDgDwBnEAgDeIAwC8QRwA4A3iAABvEAcAeIM4AMAb\nxAEA3iAOAPAGcQCAN4hjA7XxHFJH9Xic1+17uvvkDdXspZQWOXyHKCCOnFHO4/Hl/T7ZXjaH\n/JTDd4gC4sgZxHGVHL5DcIZirCnQdo/9H8iAXhzDOR3ObfdENcNJb7pTv1xwWu3olnGcNCzi\n5CPlfOimkxop7nNsbFZp+hM8/uVw5UJ/zfbP3bM9nv/pZ5PPp3mjcdaarmgWcZdd+5ditkpw\nl2MznPnJE9/CLUhGTR93njSLS35twWNpOaks1k+NVPc7IhNFKOVmjiQLlbDkmjj6RCZ5Y21N\ndxHEkTtOWbM77WOdlMOVB3MvdO2zi8vffX8qjkneaJZrLhaZiaNPmRhHNlBVyZ/VEsfyncn7\nav7G9arKNLWkSHCXY4M48udaVWWtqnFKHNOVkyLBXY6OLUAONRQnyyRZqIQlMy84Z9h9MX25\n/HxSeV3NNc1WjGOZamIkuMvxGdrb+6b5SSs8pM9cHCv9OKYvF/04hrwxpjnLNbNFlLPEIqH0\nSHGfAYTynsvpPd8UIDrvuZze800BovOey+k93xQAgoE4AMAbxAEA3iAOAPAGcQCAN4gDALxB\nHADgDeIAAG8QBwB4gzgAwBvEAQDeIA4A8AZxAIA3iAMAvEEcAOAN4gAAbxAHAHiDOADAG8QB\nAN4gDgDwBnEAgDeIAwC8QRwA4A3iAABvEAcAeIM4AMAbxAEA3iAOAPAGcQCAN4gDALxBHADg\nDeIAAG8QBwB4gzgAwBvEAQDeIA4A8AZxAIA3iAMAvEEcAOAN4gAAbxAHAHiDOADAG8QBAN4g\nDgDwBnEAgDeIAwC8QRwA4A3iAABvEAcAeIM4AMAbxAEA3iAOAPAGcQCAN4gDALxBHADgDeIA\nAG/EiUONfF5y48XOcocT/O616oxPK08/+t93pb58/919oPn2s3Fend0LuEB34L/++Lv/3nSV\n+Vvfo+3f04jLkjLE8euL8lp1wueVJx/96L7uj8b59l//NojjSYZD/+Xv7nvTVaZvdBkhS8R9\ns+Pi8EjRe8Hu6bmd8HPev8P3/Z/77f9pEMeTjMf+x+5701U+vJER4r5Z+IMtWxx/2udtxeT3\nt7aU0X/w92f791feGU863bH/ry1e7L63XGXnjYwQ983mFZC/39WXn/q6+vI/+8af753wzZL6\nCvuqvvWr/e8fpWy04He7mPr2a5bi9N0fX9RXG01on33/Oy7Y/8zrh3/bhf7XL/Slq97++tYl\nM+zA+LG78udd+rf7Nn++/vtn/OCneXdyLH616XQJQXz6Y++eg5X3FhnSyRtZFxbFfa+5OL6Y\ncrtTlDdvfGtGcXzR5Xq7WhcuaK+uX12Zcvq7PX33m3muzWGefVkVh13ov/ZNs2Fbve22828z\n7sD48VQcn3apTf/P8tv/MV/RPRb/jWvDDRwscSwypJM3EMedqAH74nvzP/P4X1+U//ZXX2z/\njeJoz9lf++J/5tMf+p2veon/5pff9F2zrE71Z5/qSlWl/eCnWeFffdH+NDnit3777zejg34H\nnI/HlQ/s0jRrDa/6Lzccii9aML/M7sINjPlwN8axyJCzvPHIvt+BuG82F8cf59FWDMxv9z+T\nN7oX/5jT9fef305iW1VPWwqwz7/1qa6Iw13IPG2v++/98t/HHXA+dsXxcZeOikPZshHcxHDs\nv+2+t8iQs7zxwJ7fg7hvNhfH9NG9JieX4FIRf39+//b53cXzpnGezhbq9+tL//aXyQ70H68k\nvb1LR8Vhamvf/nf6uIIfvSJ+jC+m7w2LDX8280aOiPtmk4N9QRz/zK/j9XdPiWPNDAfEsbpL\ntlA7//Z/nDaWjp9fTab1PZ5wjnk+7MSxsdgiQy5/NTJD3DcLJI4f7TX239/P73qJo/90o8Sx\ntvKRXTI/YL/nrSrLou6ff20YFm7grDgocTzER3EsYhzjkv+YT01AYSmbZpHSbL31GMfwx2n+\nWKvHOh8vk97Zpd/K9uP46oZH/vdlvR/Hn5yzoizWjvS6OIhxiECNrItj0arSrzY2YXw14v9l\nWsbmRYXZu+bPf30kfE8c/+q8YHPIr5XIufOxu9bHXRp6nPeq6Bjb9yxf9ee/aVW5i+PimGbI\ntbyRI+K+2UdxfJ1cVtOawo/hCrQduU0bpnP6lu/aP/N+HPp3w/nJMH/+frUr/xm386MZd8D5\neLLyp12yizupWfqxKsNSv0e7wA0cF8c0Q7p5o8sIWZKeONrT8WXSc7QZXrQ/8N/63pU/da/Q\nv/rEuad78W73pz3b3916wJ9/FmWH9sr/qpdq+u3YJo5xpeHj6cqfdsn2Ld0aHTsu9ef7F3qO\n3sdxcUwzpJs3uoyQJeLEsU/OhT9IkNdmyMS+9mvPE8jktRkysa/92vMEMnlthkzsa7/2PIFM\nXpshX/q1AeAKiAMAvEEcAOAN4gAAbxAHAHiDOADAG8QBAN4gDgDwBnEAgDeIAwC8QRwA4A3i\nAABvEAcAeIM4AMAbxAEA3iAOAPAGcQCAN4gDALxBHADgDeIAAG8QBwB4gzgAwBvEAQDeIA4A\n8AZxAIA3iAMAvEEcAOAN4gAAb8KLQ4Fwgp/y+aknbyTL8XMdPnP4pgj3Eu0EqcWT2zYNAVA+\nJ8j3VJI5kifWCVKrT2/ZNARANRHFQeZIH8QBS2wdAnHAJogDOqqq6p6pyZ8DII7XQYwDrDO0\nNYw5hpAlMQ7Y5MlWlWibBj8GZ+jH8bTQqgKbPHiCyBtCqMpSFzqKomjFcaAasSRCP47gKUJQ\nEMfrKaumdUbV6qMsJmUAxAE9i9Ih4ng7ZVMV7WOh6yqqcD9BHNCxrFfecIJmGzzRMREiUjZF\nVbfFjqJqz8jt4iBzbHDbIahaisb+008L+0/Hywv7r6Uuy9kOUeJ4O2VtskxR6PNBiUMIp+Wp\n29SrbRmUhf2nn5ZaCFXR1lDrVgwt9erfehD6ZAfDfM8zvD1vCKEui7K1R2m8UbqfII7H2C92\nzYoDri02DVAH3h3E8XYKg1Jl+zjxBuJ4CqfG1spAPxTKLTpslA0uDWg8wHQfHzo2z24aBtpi\nqvaGjo+WU28gjmewiuhsUWizzwTxxE4tij+I490UZa2LG+3v2PIzxHEXfU1D1z2UPh9KPW2K\nT0Qbq7JVxIm/afCgFUfVaqOs7hQHmaOnq3801ttu3WP70Mgg/liV+zcNxylab5RlVdfV8rMb\nxqoESzEtbDCzNYYyxqiHksVno0oh4liV5zYNRzGht0IXOG4Vx2szR9f80cyKGMPnyXgj5gn6\nlLT4Y5M9hY3W2yb+5ccxYxxvyxxdxHMmjIUjkvEGwdEXU1S1av/pHH27OO5P8Tm6Ekarg1kJ\n41C7p1AQx2vRWdk6w3YiWiyAOC7S9eC0xYx6pdv2Wv+INLyBON6K7llkM7bufdwgjsCMgYyu\nneRYv6pkQBzvpDLFjaqstTlK+84cxHEO2yNjCGbsNT8n6w3E8U5ab+gxTv3QyGbNG4jjBKbO\nN5Y0zHvpFy9WQByvRHcg0IWNojRdv1Zjo4jDj8qRhi1pdOToDcTxStocXOrB1YUpdmwudTy9\nEDsVOcXITGMaH2OgyYM4XohqSxtlXfbtKZuLHU8wwE7FTjEiY1HDlDQ2Omc8tXdxQByvw46g\n18GNctcbiOMgQ1DDxjQWnkAc2Wz6reiR86puvdGUhXksm5XBbT2I4zNV5QQ1rB2yanXdBHG8\nCP2DqL2hZwssdWC0QRxX6ApstVvUWJ8uKztvII4XoUdjtt4oW2tU3S8l4jiLcUahp+rrm08G\nYeRYwFiAOF5DUVbKzl5bV6W5m8q+NxDHFp00+l7k3b7mXDFZgjjeQmFuCmunuDXFjkrHSPfW\nQByrGG0oPUdXU7qaeIsyLIjjLRSq0rdQKas+orfXoGJAHGsMVZT1QOjTu3cXiOMl1KppCxpF\n1ejZvowzEMdhRiFU0yrK1BYv8gbiyJrxdhrmLm1VrWsqpb3d40dvII6eUQ66f75TRXlZKcMF\ncWSMtkZt2gx1Y0rZz/RVVBuDU2YgDou1gx3z2rVpj8J4qTcQR5aUJu6pvVHo30g9NEW/q99Z\nn+xrlbeLYxL1rKwr3Hk1Ht69Z0EcGVLallajiaYuVFMZZzSeN+h4uTjcQoWm0LdYdT58bs8k\ngDiyo9YdQ609irIVh+68UZp4B+I4vn23WKHnglcXb7+aG4gjN+quW1f7qDtr2KioVQbiOLz5\nGab99dldEgbiyAgb2uhqKaZbeW07bzRdjMOPd4pDlzIcZZiIkNT7MD4I4siHtopSl/Vwo5Si\nNH1Fu2Do4pbSn3mlOKYlDTs9T+3t3PxBHNlgQhu1bjK06mhK1VQfepXv8kZxzLyh3yK0sQbi\nyAYjDj0ApTHjUZpKlU115bfy7eJour7lD+2KbBBHLtRlY+f10rMQN3oEvY5zXMn1LxTH4Iy+\nuIE2NkAcmVDXte5TXtVmEJse0rZ6dzYf3ieOSRtsW+fzbcB+EYgjE+pGz0Bc6CqK6XfQ7E9E\nfITXiqMbAkvHjR0QRx60mbyVRncjWHMP+utpvk0cY3BDt6UQ3NgFcaRN0d/LQ08lqu/KpptT\nAh3Zl4lj7o379yAlEEfSaGvouQDb2nihx8x3fUXD8C5xTBph6bnxCcSRMm01XHfcqO0kG/bO\n8ypAJcXyGnEoZ04eo12CGx9BHCmjb+Goo6K6o5eZ2au0Be0wvEUcQwNs11e0oLTxGcSRMHXR\nljPaKkorjlLXUtpqytUmWJeXiMPpumE7feGNA8Q7Qf1drR7Y9Fvo5iq3c3rpG7SFaU3piSgO\nSZlj1secxpRDRDtButZoUt/cAuK4SuuMtopi7NGWOIJWUzTxxCEpczihDaopx4l1gtSY+NYm\nEMclytIGRM1wWN27XE/YE3QL0cQhKXM4MdGKaspxEEeimPHzlW5VMXWV9nAGraZoXiAOd0hb\nZbrRwTEQR5LoeTfMjZXK0g5kU+r88Pkt8hfHbCgsxY3jEONIkbrRzbC6LaXQQQ5zLMPn+uxj\nHNOoKN7wgVaVhDD3Ril0N9FSz0LcmPs5tkVsFcUbubeqzKoptKZ4QT+OdCj1/MNlYyfdqPQN\nxezQeXVmXsAD5N2Pg2rKJRBHMpRmEuK2rKFHpeh6io2PRq1tBl/yuRTnG3C1gTe8QRzJYMSh\n6yi6tlKZWcybrtAfh5zF4XqjojXFnzvqktNXU9XDYezND0ozq2itO4rqN2MexDvE8VDmcLVR\nMabtBJQ40qHQ5ihtf6/K3HUp7vWVcYmDXl9XQRzJoO/oqAe16UbYzhtxN5i/OMxzvHEGxJEI\nRT8tYGFvuRTfGxmLw60P0Qx7ijMn6FMbfLfUp8oq4vCg0MWM2vT4Ku3Q+fgxoqj9OJ7MHG4g\nBW2c48QJUs6/AwnTczQAZvZyW9jobntwe1Q7zJKz5R/JHJPuohG3kzWxxKFWn17d9DvRzSm1\njmuY6oppB7ilSSruWJX9Ne8QB964AOIQj+4vagIc5XADhJuGgEVYcr74A5nDqafQDnsaxCGd\n0lRUTDNs3Y2cv+nI5S2Oa/fVfT2ngqPqc4iDGEcoSjvLV9eq0ujDH3jejS3yjHGMFRXqKReI\nf4Ie2HRWlEWp+4l21tBDU27yRqatKkOBA29cgX4c4tH3QNB1lborbtzljTz7cQwFDuoplzh1\ngrSwr59ZxHEMM8tXre+6FHEk7Bo5i4MAx0VOdgBTAU4t4jhEWXfxDXvblBvJWRwEOC4SrQNY\nlE2/j0J3Lu/Ecfd44pzFgTcugjhEUzS1nn2j0feVvqsxZSBHcdh6iqIDx1UQh2QKfRf6stC3\nQLgzKtqRoTh6bxDguAoxDsHURa2Hz/fzAyKOy6laaioqlznbqhKgvo049tDTmesuHK0vitJG\nRRHH1UTxRjDoxyGTQpc3+hk4bDXlbm9kKo6KekoIEIdI9ByBejrzSo9qU3oGjtsjHPmJg8Bo\nQBCHRFpttOUNffOUtpaibFfz+8lMHL03qKiE4MTwJHf+pDs3/R4K05xiqim1Kh+ppmgyFEdb\nesMbQaDEIZDC9PsyQ+lV9Uw1RZOhOFRJrgsD4hBI0U1nbqspz1ijyVIcdUGuC8PJDmAnV726\n6ZegxdGWM0rVT8HxDNmJo1IFtwELBOIQiKmqtMUNXe54cDfyEocJjIbofwQa7+Cow82bfg9F\nXdaVqsyo2Ad3IytxmIpKkI6LoLlQ4nhg0y/BlDcqc1dpxBEqQRPgwBuhIDgqB3N7NjtCRami\nrOpnvZGVOHSPUbwREMQhhsL+K/XQlKos6vrJwKgmI3GYHhx4IyB0AJOC9kbZWqNpqym6q3n9\nYEOsJR9xmDlG8UZIKHEIQddTzC1UdIthWT/WXdQhG3EoKirBQRwy0L3M7a1hVVXoeUafrqc0\nmYmjpAtHUBDH05iQqIlv6LsgKKVvSN++9bg28hFH3xIbMEmgH8fDWGXo6TeMN2rdmFI+X03R\nZCKOrqs52S0olDiexYRE66Iom7KpCqX1UT7az9whD3EoKioxQBzPottfax3faB/bDF5ofYiw\nRpOJOBQtKlFAHM+ixdGYRthC3xRW3+tRijfyEQfeCA/9OO6n6yHavdCdNxo9gr4xXb5k1FIM\nyYvD3KrUDooNkRw4UOK4HRMOHbuFmiaUyt41RdY0uqmLY7i9NAGO8CCOu7HjUSr7z5Q4Wono\n1pSqlOWN1MXRe4MxsTE4dUS5r8oF7GQbuoOXNoeJcShV60aVp/dsTibiYPaeGJw5pOr0mpc3\nnQFF0d193lRWdPnD9BWV0XVjQhbiqOjCEQVmALsbHRk1Q1FKK442W1emyxfiCJuiFYew6l82\nII7Y2DaUcghg2KpKVdfGHuYoFLJaU3riicOUAXbru4hDONHEcUveSAA7y4bOvuX4RmliHGWl\nD4IRi0RvxBRHv87mihczR9cQayIc11KCdaLFOKLnjTSw2bZoyxe9ObQnyq6qIrv2HU0cQ97Y\nXvPagRm6GlXcfykSsQa5Rc8bidCJwxQp+iLHEB0VfgSSFceYRWvZak6YWMcVcVi6WUSNJ4Yo\nh56zR3pxo8lDHBQ4IoE4omICGKW5y0HthDHKtuKiaulxu2RjHH2JmAhHPE7FOI6MVSHG0XSR\n0aKoq7KlduKfZS3fGzGbYz9moSDiqMQf4nQ516rilCe2F4uaN5Kgm6Snrak0pR7CNn6i79L2\n2G4dJdV+HEPOE9hSlQ3nxUE/jo+YOQFLE+BoKydOVSWNr56oOEZv0KQSj5P9OI4UOSJsOjEK\nczfHat7DS3xUtCNtceg+/IgjGogjHjoyWjR60GtRut54cp98uEMc0zUPtvXvpzgkUJ9OAz5y\nsgOYjzjC5400KMqy1D02zF2WUqumaNIsceCNWzh1gpTt0/vEptOhMN3MdSWlcFpUUtJl2uKo\n6MMRkwfzcUKXkAemfmJ0YRti9TgUE+cwJPWdExcHEY6YII6w2DFtle1kricfbpwRbCkVN5pE\n+3H0Kctv7U6bc1WVIxGKV/bj6L3R9RbV1ZS67jsipfaF4/Yc3V/x7LEach3iiMvJ4OjnNePl\nDcn04ihrfaMDPUWg7jNqCxzJfd+oY1U+rHlRHFRUYnOyOfbjqhHzhmQ6cWhZFOaxqjtvJFZN\n0SQojr7AQWQ0NogjLF1MQ4+HLcw8ol2AI8Uvm6446GweHcQRGN1xw063URdFio2wI+nFOMYC\nx6nV4TjEOELTVk7qptRNsFWSjbAj6bWqEOG4De/fisM9P9/ZqtKWMlpx1LV7d6VUv2h6/TiG\npli8ERv6cYTETApYm3mIB2+kWU3ReIsj4De9KA6IDeIIiLkRQjdNYJ95E/6W58QR5gtfEUdJ\naDQ+8TqAxdm0XIruBirdTdrqNDt9uaQqDiIcNxAtOBpn03KxPThMz6+yKUsrjnSrKZpExVEr\nxBGfWM2xcTYtF5NVCzNCRXcWNbNwpP4NkxOH6sUB0UEcIbC1FF1D0V04TI/Rsky7uNGkKo6a\nCMcdII4AFLawoacVtYUOfQ+V9L9eauLoIhzcEuEOiHFcx1ZTWm/oplhT3GifZ9Am6C+Ow518\nAm57XKVvUkEcN0CryiVMHcXeGdYUNnS/UT2KPou+BGl1AOt7m+eQrRKAfhxX6AbD2rCGbkfp\nZvtK4a4pn0lKHGoocMAdXIhxPLBpWZjihrZGocvHprOomfYr+ahoxxlxBKmneG17smF6m98F\n4jiNvvdBoWcHNMNh68bGNnKIinacEEewvudnxVEjjntAHGcxt2mrilr/39RGGY3uwZH413Lw\nF8fnIdHht92vQFvsrZxvVXlk04Kw7a6FHdGmM6wJa+RSTdFcGeR2e6sKvc3v5XSz1yMtbmIw\nw2D1v8rcpE3HN6w3nt6xkKQnjpLe5ndBq8oZTD/R9qHuuonqxyav4kaTljhULw64B8ThT9fB\n3JQ6qq6qYrzx9I4FJiFx0BZ7N3QA86awA+irotbTA7ZVlNJGRlP9PpskKA4qKndBl3NfzC3o\n9YO580HV1NYbmVVTNOmIQxEavRsGuflie4vaSkoXGs2qEXYknbEqtMXeDuLwpRvSZsfQ68aU\nMruoaEc6Xc77zl9wG4jDl2HqDd1RtC4zmLBni8TEQVvsnVyIcbxUHF1pw0RG6zyjoh3pdDk3\n3qgJjd7IlQ5gF2uyqV5vpuNXowsbOrqR/kRfm6TT5dw2qWR7IiRCPw4vCntbx7orcWhvPL1L\n8UiqVaWs8zW4RBCHDya80Xqjj3HkMWHPFsmIw9ZUmDLwThCHByZnduPaTJdR1Q1RyZOkxFEz\n1+itIA4PbM40tRTTf0M3ACKORoo4Lm4SfEAcHhQmxmFLHE2tMvdGUuJovZFefkoZxOGD7cJh\nW2KVnrYnZ2+k06pih6kkmJ8SBnEcx4xuK8zgtrL9jcvbGk1C/ThsaDS5/JQ0iOMwZnBbY/t+\nKX2LR8SxXPKRyYp1r9EgW4XD+B7tx8YxPY4JcHTtsHrmhzp3byTT5dz2Gk0sO6UOJY6jmFsv\ntcWNotI16szDG4bo4thZzVMcFTWVm4l9uAPlDQF0TSpFrXRt5QXeSEgcNTWVuzk1yM2jqpKR\nOOx8gbUuF9d1/hWViOI4UN9FHMI5N6xefVwzcN4QgC1x6NaU6g2R0SZmieNz44uXOHSvUcRx\nL+fF8ckcH9NP7EybEoeyQ+obqionl+xX+JSDvMShO3Egjns5J44DRY6weUMAbYmj/Up2jEru\nXb8sUWMcSoUTRxWoDRiOE08cQfOGBAqlb4fQD6dHHKeWnKwUKHOUiON+TgVHD4ojYN4QgPaG\nHahSdzN/5U7sVpW9a91PHAXiuJuTvxXN0UaVbMTRfpOCEsflJWOkWCrEcTt0ADuG6icpbssa\niOP8kjFSrEP1c4fjII4j6Fzpzm6OOM4uub/mufEMFeK4n7NVldO5KtxYl/swe9oNjbX3ikUc\nJ5eMkGJtQxzpZKcsOBkcPbnm5U0/gt1ROzDW3J2+ITh6dskIKXYhjmSyUx6cbI49uerVTT+B\nyZFmYKyepNhMGUgHsNNLhk+xJMTxBIjjA0M1pdKPZji9qaggjlNL9it8qq16iIMQxxPEE0fA\nvPEcqvNGY8a36WqKHt+W+VyjPdHHquyseDjFkhDHI0SLcQTMG8/RFzf0n6oo69LUV94RGo0o\nDrX69FyKZioOxHE751pVPp+okHnjMaw3bImjvwVT+YrwhiEJcdRVam10sZnXCGZHe3mg9keG\nfNhKaDIQR1dN0cooiqosBPXgkJY3nhQH3pjT1wjWawaIIzLd7pl7xVp16HpKJcEb4vLGgzGO\nmprKHGGZ42zCacY4+qiovcd0qcXR6Mhode9u1HW9+r6wvPFcq0pJaHTBSuZQ4yQXyn3R54z4\n4lDjSc+5VcXuW9eYUpdaH1ocuvPXndTdvwXC8kb4U3kwxbKmMXaJmv2bvjH55DZxBETkybZN\nKGp4agIcRVnpWkpVN/d7Y8cccvLGg+LIzBu1FxuJzDOHmw2mL24VR6izJPFsa2u0ZV89et7c\nf6n3Rdm1qty7Nx7ieDZvPCWOkgjHGrIyx5VVoqYTENNhQ1Vm4FRZFm09pal1a4rpLnr/7iCO\nDwv1vb+Cbz5xxjpsX4u1RwlxRKLQB1nbQoc0tDN0scMManvmdgibMQ5heeMpcTDb6DrzzDG+\n/6Q4QmUTgee7MNOYm7tKa3G0JQ4tEdNb9Jkd2qvHCsobT4nDhkaDbzx5VjPH8yWOQPNpSDvh\nWhm6mmK7h5pyh+3E8Uw95QOi8sYz4tAFDu6LsIqa/p1Uaie1W3VniSMQgk64GTVfmGpK34Ri\n3jQx0ad3bgNJeeMRcXS3jEUcK6j535W2elMCuLWqEgg5J9z212h3qDbljNLcHLYWPnReUt54\nShxEOB4DcYw3dzRBDR1VMPENifWTe5EujpJbxj6H73FXGcY4dD2lLfSamQHN1IDlW2bc2Ee8\nOLhl7HNQ4jADYNud0YWNqjBTA5Yiw6G3I18cRDgeA3F01RQzf3mhpfGeCTc+IFscStWEOJ7j\n1HEPc8JkKBdFlgAAEANJREFUnHI9najqpdE1vSIOQxLiCL5lOMSZA6+cx5s3HZzCNMKa3htV\naUbNl4Q3OoSLo6am8iBnOoCdX/XqpsPTekPfEbYodFy0SaAV9kZki6NEHE/yenHovejuQV+Z\nu9AT4egRLg7GqTzJy8WhlO38ZcTR3TEFOkSLo5/CJ/iW4RAXYhwZiEPZO8IWZiB9jTimSBcH\nI+of5EyJw+HmTQdG9b1GbYmjbGhScREuDoU4HuTF/TiG+YjbAocey6abY3WM4+HdkoNocTS5\nTRqYGO8VR7d5Iw5LSSeOCaLFwSzFz/JWcegcpwfT27s7mhiH6TVKiWNEuDiYbfRJTgVH0x/k\nprdtJia2fUaJcawhWRxlXVDieJILzbEPbDoUvTfMNBz6Pm2NHRRLjMNFtDjC/HjBWd4ojv6m\nsPamKXVd6k6jpucoJQ4XweLgRkxP80Jx9I0pjZ1fdAiONgKn4Xi0X4lscdAY+yjnO4A9sukA\nW3UaYU2fUTMxsb13irhpOMrmSZNJFgeh0T3UMOusfhxnkgy5iTPrJBwctRstho7mui1F33zp\n2Ut0g9J5nCMqb9wujmxrKn3n5aN/V1HTf05OCcfLqip95w390DXCdvdREeiNPXHIyhuIQxJq\neFSNmrwOvpHYq0RNx2OL/SZN1URPZV4OvTeEVVIM2+IQljfuFweNsdsIyxxXVomajucGu3uo\n6EZY29FccH/RzQqUsLxxtzhK7jW9gxNJGB8EiCPR4GjXV7Qw41JMcLQ0d3m0vTfu3ZfDbBWE\nhOWN28VBTWUH5TwT8KsyrpJkcLTr86UbU8q+EVb33mjKFOfuEZY3bhYHc3HsIqw4GpxbN603\nZooalb6vo26EbcsaXSPsnfsRCGF5w3vDw1W/teYHcVBT2UNW5Dw4N25aKTPrhmmCLeyAtqay\n3cuTK2xYZOUN3y2bvXXqWJ4pEuL4gKi2+sXyH1a99qMSFGX6lxdWGUXd2DhHXYvr8OWBqLzh\nuWknY5zKHNRUvIhyoE6J40jB+OKPSkCUMvN8dbP1tEWOohTZvfwKD+eNc+IwRaUzKSIOL5IS\nx9UflXCYqGg3Brbo7qDSlEmXNpakKY4uQOOdIv3NvUhRHKd/VIKhhjZYc3NY3foqt9/GadIS\nh2OOM5mDgSoCiC2Osz8qgTDVFN1T1BQ09Gw9urDR5FRLiUfMVpVPa+6kSGOsBKLGOOyTB8Wh\nhmnMTYxDt8HWJrqBOA4QURxXUiyZplgA0cRx6UclDKoPbwwDYeuqfnqsekLIFEfZ0ItDAPHE\nEXzT59LvBsJ20/WUpRmagjeOIFQcTFMsAW9xONy8af/k+5EpdTeC3sQ3kMZR7hDHdM1DmYsh\n9RJ48PBH3bQZ0taYyYiLbhrzUvRAWIHILHFwJyYR3BOhdF8FK7F82qbT68veT9oOh0UcR5Ep\njpr+5hLIssTRzys69vqyg9r0AHrEcRiR4iDEIYPsxFEUhU24LlpLVG25o1VG0w2EpUHFh5j9\nOD4VOvfEQVVFALmJoy1lKDtneasQPTNgMURFmzSn3niOO3qO+qZYKrwhgpj1hbM/KhcodDXF\njJ9vn5d6li8T47C9vpCGH/HHqvh38uGGKkKIdgbO/6hcoVBdQLRuZWFqKaWRyP508rCKUHFQ\n4JBArDNwIW9c2arqAqL63gfl2I/DzmMefnt5I1EcNMYKIStx2JGw3b3n6xx6bRRF8dzGJcY4\nEIcQchKH6m7taEscdXfDlNBbuZOi+/cMEltVaIw9gj5Aqnui/3YTxK28GBbx3kS4vd1I+K4Y\nhx3S1uiQRlfiKFMPbBTO4xxReeO2fhzZN8YWhd+/VUwWUM04sky570xeuIPPvMimVcWm5pQ4\nmvRn7NkRh6y8caM44vc6Th7VP06fqM0XssRx76a7xCYxDnOnpTzFISxvIA5JTE64LXoOb8xf\nDIuc28gjBNz0eFPYsVUlg6rKdoxDWN64TxwVN439yJg5+pyxkznGzHNmI48QbtNjSsPAtixi\nHNutKsLyxo3ioMTxEWHF0eCE2rQawhv95Bt9jKPOdiyssLxxmzh6byCOHYRljuAE2nQf3WjM\nkLaiarrJNzKIcWwjLG/cLo7g28sKW8zsc8DnzPHKGMfgja6OomOjdu6N9FtVdpCVN+4TB704\njtC31avuyX49dvtGBHubCLWvz2x6ERXVFRUzsE07I9uaSiMsb9wljpLQ6GEiH6S0xTFqo6up\n2Mk37Hw9md2sbQUxeeM2cVBTOcD4sxF7I89wedNjcUPfcsm5V1v+t1wSljduEgdDY49xxzFK\nWBzD+k5c9DWTb8jKG3eJgxCHGNIVx7i6ncjcOqPpbg6buTduQZ44CHGIIVVxuLnHdODo7rhU\n1XWNN8IgThz0N5dDouLoG2Ftp6+iruqiKMe4aIjdA3niyHxobEokKY4+69jghq2o1IWZlPgF\n4Y3bkCqO4BsDf1IUh1nRVk1ME6wudfRx0Zw7fd2NNHGUFDjkkKA4rDeaphsEWzSlnsy8sBMT\nc+eUcAgVR/BtwQmSE4cavKGDonX7qDuY2+JHyaTEIREmDnpxSCI1cYxDU7oWWN3fq49thNw7\nkCcOenHIIS1xuENTuhbY7v4HtMCGR5o46MUhiKTE4fT5MlOL6oYU4qHRECYOGmMlkZI4Jn2+\n+qlFaYGNBuKATdIRxyTHFN2otjymFhWKNHFQVTmEWvxVs8+Go9jPpxZ1zoXg+G16unQX47B9\nvt4nDml54xZxFHQ4P0Z34oe/42+uGkdVXx1fnYg45tllMq7thb3MheWNW8TBtIEH+ZA51LhU\n9uJYLDq5fUrGJQ7txjWE5Y07xMEE50dZZg73LbVY8sJGHuFKvnRbVTIWR9X9WyAsb9whjoqq\nylHU4t8rxTHLKnaQinaGIedWlcp5nCErbyCOUOgSps3Xx56usswczp9J5jh/SOWLYxHd0INU\niqoY7oKQbz8OL3E8mTduEccbvBGGz5mjO5IZlzi2oqK6PaUu6ryn4Lgkjjvzxh3iYBaf40zK\nn26ucDNE1uJYiYr27bBlVZjbp+Q8IHYzxiEsb9whjlfUVAKxzBzd07eIYyX72Kl7dH3FVFUy\nHxC71aoiLG9QVZHFXuboD63z9NwmHuPjpheZpJu7p3VGaZyRa1D0CKLyxo3iCL6lLFGTv2r3\n4doWHuDTppfVFDPfV9XFODIOih5CUt7w3sjHkO3iE+7F5MNu5si7y/nyyxRNPwWHDm6YGzxm\nG9w4gKS84Zu4Wjz5lKIixCGLeCfC/0flw4dm9g3T8asb29a82Rs3EE0cavXpXopKEeKQRbQT\n4f+j0mFinmrFCIVpYCjM1KJmvq+Mg6IikCMOhTikEetE+P+odBRV3eaTeqUsMZQ4uqaUAHsJ\ne0gUh9+GIBrSxGG8YR4XYhhiHGX97rDoXYiJcahBHJ7bgWjIEYftstAqo62mrIqjb1VpGmYm\nvgUxrSqdOHw3AhERE+PoOkkWbf7YEkfXj8PeHTbcnsIGYvpxtN6oS8QhCimtKt2t2BqlnbHh\nDbgXSeIoqafIQko/DlNPMcVR25McbzyPJHFUiEMWYsRh3tkamAFPcIc4Fq0nawVVxCEPMeKo\ntDcQhyTElDia/TovPMANp+PQj0qlOwduDSGHR5AjjgZvSENKicO8whuiECQOkIYQcbQ/KNtT\nT8AziOnHAfKQIQ6yiUTE9BwFeYjox0EuEYmcsSogDgE9R4l7CQVxwCbPj1Uhi0gFccAmT4uD\n4oZciHHAJg+Lg/whGFpVYJNnYxxkD8k82o8DhBP8lM9PPXkjWY6f6xAZ5v4txNltUr2ZoF/i\nJYkJ2TXEQarPIeMaSCsxIbuGOEj1OWRcA2klJmTXEAepPoeMayCtxITsGuIg1eeQcQ2klZiQ\nXUMcpPocMq6BtBITsmuIg1SfQ8Y1kFZiQnYNcZDqc8i4BtJKTMiuIQ5SfQ4Z10BaiQnZNcRB\nqs8h4xpIKzEhu5ZF/gOAe0EcAOAN4gAAbxAHAHiDOADAG8QBAN4gDgDwBnEAgDeIAwC8QRwA\n4A3iAABvEAcAeIM4AMAbxAEA3sQWh889Xg6kpaaJhkl9nliIVJeJyU31dqY7ffErLBK7ktp8\n7bB7diWxwKk1VxOLnO1UwE0MaS2fXEtXbSR/Jc0+3fCprj9JielOX/wKERNrrt0fe2XPpHzP\n6a6cSixurlPOY7C0lk8upjtcgsFSjbOvsY7AzUx3+uJXiJjYkDVCJHb1TIVNbfLNziWWjjiG\nFJMRR5xUEcd2Ys3KqwuJBRdH2NQuaQhxXEy9TyfwJd5VG4Pvq1OuRBwbq4dKTF06tPOveTH4\nMknNyQXXElxJ2nv9GATP2OEvmzjiiHSJR9HR3cQWx6UfYnf9oOK4eKkvCkPhgqOI41KCwVON\nUzqixLGX2LW0VvYsYInDfXU1NUocZ9ILedmo6YNscUTS0d1EFkeoa93JFdcTCyyOUDn/QmJJ\niUNNH0Mcvo4kLnHE8SmxKynNExuzRoDEEMeZ1ENtw/0pCHnZUOK4lajiCHAxxSi+II4TyQf2\nhpNooNTHIxcs1WViclO9nelOX/wKy8QusNiXq+WXYF8zcGqLQpp3YrFzXbgu0U7JkS7neXQ5\nV5NXARK7WLuY79nVH/WQXzNwas3VxBLMdgDwNIgDALxBHADgDeIAAG8QBwB4gzgAwBvEAQDe\nIA4A8AZxAIA3iAMAvEEcAOAN4gAAbxAHAHiDOADAG8QBAN4gDgDwBnEAgDeIAwC8QRwA4A3i\nAABvEAcAeIM4AMAbxAEA3iAOAPAGcQCAN4gDALxBHADgDeIAAG8QxwZq4zkkierxOJnbd3H/\nlMbyc+U+UWPayWatZHc8NogjO6Y3oj+6vN8nW5+rlRdJZ6ukdz4miCM7EEdAkt75WAwlWlO2\n7R77P5AqvTiGEzmc0O6JaoYz3XTne7mgW8FQ62uO7ztrDB/1VZWV9BPKYqns553YHNA96Suk\nzgOkSX9x9s/dUzye9Olnk8+nGaJxcsl8zeUas0TXtz/upXgS2c07GfLA5IlvORfEoaaPO0+a\n5cW9suD+mjtPZmkvkk2BlPb1JiaKsHWVxnkBqXJNHH0ikwyxs+ZaFkIcOeOUOrtTbouQKrFT\nCzPmV3fXPru43t33p+KYZIhmueby5XSNDXH0GyTGkTRUVTJl9epevjN5X83fiFZVmW5EPIns\n5p0gjky5VlVZLhhWHNM0xZPIbt6KLTQONRTn7CZTkIQVZle3c1rdFysX9fSFK46tNZdrzBLd\nST/od45GIrt5L30De9/SPvbm4HAlzFwcK/04pi/X+1lMShwba66ssejHsZV+GqSynwDyePHV\n8+KvDnCRF189L/7qABd58dXz4q8OAGdBHADgDeIAAG8QBwB4gzgAwBvEAQDeIA4A8AZxAIA3\niAMAvEEcAOAN4gAAbxAHAHiDOADAG8QBAN4gDgDwBnEAgDeIAwC8QRwA4A3iAABvEAcAeIM4\nAMAbxAEA3vwff6Dcx4q4oWQAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"P-P plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testDist = na.omit(max_PRCP[,45])\n",
    "\n",
    "    for(j in 1:length(testDist)){\n",
    "        if(testDist[j] < .1){\n",
    "        testDist[j]=.0001\n",
    "        }\n",
    "    }\n",
    "\n",
    "fw = fitdist(as.vector(testDist), \"weibull\", method=\"mle\")\n",
    "fg = fitdist(as.vector(testDist), \"exp\", method=\"mle\")\n",
    "fln = fitdist(as.vector(testDist), \"gamma\", method=\"mle\")\n",
    "norm = fitdist(as.vector(testDist), \"weibull\", method=\"mge\",gof=\"ADR\")\n",
    "\n",
    "par(mfrow = c(2, 2))\n",
    "plot.legend <- c(\"Weibull\", \"exp\", \"gamma\",\"WMGE\")\n",
    "denscomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "qqcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "cdfcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "ppcomp(list(fw, fln, fg,norm), legendtext = plot.legend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "YET AGAIN, but this time with gamma MGE as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CJ7UptTv9GpQW3RtwoGEhMHRRVQ7EhtYw+OtsEbV4M4IEXepza351eHN64mMXEE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\nHADgzf9h58bVkZuiWAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"P-P plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testDist = na.omit(max_PRCP[,3])\n",
    "\n",
    "    for(j in 1:length(testDist)){\n",
    "        if(testDist[j] < .1){\n",
    "        testDist[j]=.0001\n",
    "        }\n",
    "    }\n",
    "\n",
    "fw = fitdist(as.vector(testDist), \"weibull\", method=\"mle\")\n",
    "fg = fitdist(as.vector(testDist), \"gamma\", method=\"mle\")\n",
    "fln = fitdist(as.vector(testDist), \"gamma\", method=\"mge\", gof=\"ADR\")\n",
    "norm = fitdist(as.vector(testDist), \"weibull\", method=\"mge\",gof=\"ADR\")\n",
    "\n",
    "par(mfrow = c(2, 2))\n",
    "plot.legend <- c(\"Weibull\", \"gamma\", \"gammaMDE\",\"WMGE\")\n",
    "denscomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "qqcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "cdfcomp(list(fw, fln, fg,norm), legendtext = plot.legend)\n",
    "ppcomp(list(fw, fln, fg,norm), legendtext = plot.legend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Probabilities of Interest "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1 inch of rain approx 25.4mm, 2 inch approx 50.8, 3 inch approx 76.2mm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameter estimates for frequentist probability checks are found in mleGamma2 (using maximum goodness of fit with ADR distance for right tailed data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "probability of seeing or exceeding 1 inch of rain in a 24 hour day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.283296322069051"
      ],
      "text/latex": [
       "0.283296322069051"
      ],
      "text/markdown": [
       "0.283296322069051"
      ],
      "text/plain": [
       "[1] 0.2832963"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "1-pgamma(254, mleGamma2[1,1], mleGamma[1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0699997347128729"
      ],
      "text/latex": [
       "0.0699997347128729"
      ],
      "text/markdown": [
       "0.0699997347128729"
      ],
      "text/plain": [
       "[1] 0.06999973"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "1-pgamma(508, mleGamma2[1,1], mleGamma[1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0167967927809439"
      ],
      "text/latex": [
       "0.0167967927809439"
      ],
      "text/markdown": [
       "0.0167967927809439"
      ],
      "text/plain": [
       "[1] 0.01679679"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "1-pgamma(762, mleGamma2[1,1], mleGamma[1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "oneInch = NULL\n",
    "twoInch = NULL\n",
    "threeInch = NULL\n",
    "for(i in 1:nrow(mleGamma2)){\n",
    "    oneInch = c(oneInch,1-pgamma(254,mleGamma2[i,1],mleGamma2[i,2]))\n",
    "    twoInch = c(twoInch,1-pgamma(508,mleGamma2[i,1],mleGamma2[i,2]))\n",
    "    threeInch = c(threeInch,1-pgamma(762,mleGamma2[i,1],mleGamma2[i,2]))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "110"
      ],
      "text/latex": [
       "110"
      ],
      "text/markdown": [
       "110"
      ],
      "text/plain": [
       "[1] 110"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(oneInch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "starter = firstYear+9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "oneInchTS = ts(oneInch, start=starter)\n",
    "twoInchTS = ts(twoInch, start=starter)\n",
    "threeInchTS = ts(threeInch, start=starter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=7,repr.plot.height=5) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qtJJE2rxzBa26uSeURyzH+6Bpq/o9pn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\nEAlAAEQCEACRAARAJAABEAlAAEQCEOB/nLUxQEeDnGsAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(oneInchTS, ylim=c(0,.26), lwd=2)\n",
    "lines(twoInchTS, col=2, lwd=2)\n",
    "lines(threeInchTS, col=4, lwd=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rollSmall = ts(runmean(oneInchTS,15),start=starter)\n",
    "rollSmall2 = ts(runmean(oneInchTS,30),start=starter)\n",
    "rollSmall3 = ts(runmean(oneInchTS,50),start=starter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KEjRSovvw6T5hQ92AzPebsz5F57NE7iGOuom0aktCCSHTpWJKdJM4vuqBowZ5uu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\nAEQCEACRAARAJAABEAlAAEQCEOB/KeJRKKv/Qd8AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(oneInchTS)\n",
    "lines(rollSmall, col=3,lwd=2)\n",
    "lines(rollSmall2, col=2,lwd=2)\n",
    "lines(rollSmall3, col=4,lwd=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rollSmall = ts(runmean(twoInchTS,15),start=starter)\n",
    "rollSmall2 = ts(runmean(twoInchTS,30),start=starter)\n",
    "rollSmall3 = ts(runmean(twoInchTS,50),start=starter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yKlhsgR33GcuXCYtKwxNup+ydqgH9Xhydw5P9CRRTIUQqTEEFnCR4p0bzkLTns3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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(twoInchTS)\n",
    "lines(rollSmall, col=3,lwd=2)\n",
    "lines(rollSmall2, col=2,lwd=2)\n",
    "lines(rollSmall3, col=4,lwd=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rollSmall = ts(runmean(threeInchTS,15),start=starter)\n",
    "rollSmall2 = ts(runmean(threeInchTS,30),start=starter)\n",
    "rollSmall3 = ts(runmean(threeInchTS,50),start=starter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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74Ot5yUGpGkySiS3AnZBCI5V+izws2xlVmkASEujYXrBr2tIpk1Gm86Kbbytlkh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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(threeInchTS)\n",
    "lines(rollSmall, col=3,lwd=2)\n",
    "lines(rollSmall2, col=2,lwd=2)\n",
    "lines(rollSmall3, col=4,lwd=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.3.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}