04_Lakewood_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": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: fitdistrplus\n",
      "Loading required package: MASS\n",
      "Loading required package: survival\n",
      "Loading required package: caTools\n",
      "Loading required package: repr\n",
      "Loading required package: IRdisplay\n"
     ]
    }
   ],
   "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": 2,
   "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": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = read.csv(file = \"data/EVERGREEN_MonMax_GHCN.csv\", head=T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adjust the data for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "46"
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       "46"
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       "46"
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      "text/plain": [
       "[1] 46"
      ]
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "Check number of years in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "52"
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       "52"
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      "text/plain": [
       "[1] 52"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data$YEAR[nrow(data)]-firstYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1  2  3  4  5  6  7  8  9 10 11 12 \n",
       "53 53 53 53 53 53 53 53 53 53 53 53 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(data$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": 7,
   "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": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=9, repr.plot.height=8) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mleWB = data.frame(alpha = double(),theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   WB1 = fitdist(as.vector(testDist),\"weibull\",method = \"mle\")\n",
    "   mleWB[i,1] = WB1$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$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
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      "text/markdown": [],
      "text/plain": [
       "integer(0)"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(mleWB$KStest==\"rejected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "          44 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected     rejected \n",
       "          10           34 "
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "          44 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleWB$KStest)\n",
    "table(mleExp$KStest)\n",
    "table(mleGamma$KStest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
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      "text/plain": [
       "integer(0)"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "which(mleGamma$KStest==\"rejected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "<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>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>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>40</li>\n",
       "\t<li>41</li>\n",
       "\t<li>42</li>\n",
       "\t<li>43</li>\n",
       "\t<li>44</li>\n",
       "</ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 2\n",
       "\\item 3\n",
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       "\\item 43\n",
       "\\item 44\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. 18\n",
       "9. 19\n",
       "10. 20\n",
       "11. 21\n",
       "12. 22\n",
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       "14. 24\n",
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       "18. 28\n",
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       "22. 32\n",
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       "24. 34\n",
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       "26. 36\n",
       "27. 37\n",
       "28. 38\n",
       "29. 39\n",
       "30. 40\n",
       "31. 41\n",
       "32. 42\n",
       "33. 43\n",
       "34. 44\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]  1  2  3  4  5  6  7 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n",
       "[26] 36 37 38 39 40 41 42 43 44"
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   "source": [
    "which(mleExp$KStest==\"rejected\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 16,
   "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>1.251003    </td><td>0.008698314 </td><td>0.10891072  </td><td>not rejected</td><td>0.3103006   </td><td>rejected    </td><td>2.0625315   </td><td>rejected    </td><td>114.96481   </td><td>143.8213    </td><td>16534.39    </td><td>128.5861    </td></tr>\n",
       "\t<tr><td>1.266897    </td><td>0.008416959 </td><td>0.11706474  </td><td>not rejected</td><td>0.3299521   </td><td>rejected    </td><td>2.2218374   </td><td>rejected    </td><td>118.80775   </td><td>150.5171    </td><td>17882.60    </td><td>133.7259    </td></tr>\n",
       "\t<tr><td>1.694246    </td><td>0.011020722 </td><td>0.07668986  </td><td>not rejected</td><td>0.1186088   </td><td>not rejected</td><td>0.7498934   </td><td>not rejected</td><td> 90.73815   </td><td>153.7327    </td><td>13949.42    </td><td>118.1077    </td></tr>\n",
       "\t<tr><td>1.749939    </td><td>0.011064360 </td><td>0.08802200  </td><td>not rejected</td><td>0.2087449   </td><td>rejected    </td><td>1.1536578   </td><td>rejected    </td><td> 90.38028   </td><td>158.1600    </td><td>14294.54    </td><td>119.5598    </td></tr>\n",
       "\t<tr><td>1.723695    </td><td>0.010571996 </td><td>0.09304209  </td><td>not rejected</td><td>0.1550119   </td><td>rejected    </td><td>0.8987221   </td><td>rejected    </td><td> 94.58952   </td><td>163.0435    </td><td>15422.20    </td><td>124.1862    </td></tr>\n",
       "\t<tr><td>1.689992    </td><td>0.010158564 </td><td>0.08569737  </td><td>not rejected</td><td>0.1013708   </td><td>not rejected</td><td>0.6046450   </td><td>not rejected</td><td> 98.43911   </td><td>166.3613    </td><td>16376.46    </td><td>127.9705    </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 1.251003     & 0.008698314  & 0.10891072   & not rejected & 0.3103006    & rejected     & 2.0625315    & rejected     & 114.96481    & 143.8213     & 16534.39     & 128.5861    \\\\\n",
       "\t 1.266897     & 0.008416959  & 0.11706474   & not rejected & 0.3299521    & rejected     & 2.2218374    & rejected     & 118.80775    & 150.5171     & 17882.60     & 133.7259    \\\\\n",
       "\t 1.694246     & 0.011020722  & 0.07668986   & not rejected & 0.1186088    & not rejected & 0.7498934    & not rejected &  90.73815    & 153.7327     & 13949.42     & 118.1077    \\\\\n",
       "\t 1.749939     & 0.011064360  & 0.08802200   & not rejected & 0.2087449    & rejected     & 1.1536578    & rejected     &  90.38028    & 158.1600     & 14294.54     & 119.5598    \\\\\n",
       "\t 1.723695     & 0.010571996  & 0.09304209   & not rejected & 0.1550119    & rejected     & 0.8987221    & rejected     &  94.58952    & 163.0435     & 15422.20     & 124.1862    \\\\\n",
       "\t 1.689992     & 0.010158564  & 0.08569737   & not rejected & 0.1013708    & not rejected & 0.6046450    & not rejected &  98.43911    & 166.3613     & 16376.46     & 127.9705    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  alpha    theta       KS         KStest       CvM       CvMtest      AD       \n",
       "1 1.251003 0.008698314 0.10891072 not rejected 0.3103006 rejected     2.0625315\n",
       "2 1.266897 0.008416959 0.11706474 not rejected 0.3299521 rejected     2.2218374\n",
       "3 1.694246 0.011020722 0.07668986 not rejected 0.1186088 not rejected 0.7498934\n",
       "4 1.749939 0.011064360 0.08802200 not rejected 0.2087449 rejected     1.1536578\n",
       "5 1.723695 0.010571996 0.09304209 not rejected 0.1550119 rejected     0.8987221\n",
       "6 1.689992 0.010158564 0.08569737 not rejected 0.1013708 not rejected 0.6046450\n",
       "  ADtest       invTheta  mean     var      sd      \n",
       "1 rejected     114.96481 143.8213 16534.39 128.5861\n",
       "2 rejected     118.80775 150.5171 17882.60 133.7259\n",
       "3 not rejected  90.73815 153.7327 13949.42 118.1077\n",
       "4 rejected      90.38028 158.1600 14294.54 119.5598\n",
       "5 rejected      94.58952 163.0435 15422.20 124.1862\n",
       "6 not rejected  98.43911 166.3613 16376.46 127.9705"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mleGamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>0.006952153 </td><td>0.1565358   </td><td>rejected    </td><td>0.6555035   </td><td>rejected    </td><td>3.833047    </td><td>rejected    </td><td>143.8403    </td><td>4.833243e-05</td><td>20690.04    </td><td>143.8403    </td></tr>\n",
       "\t<tr><td>0.006644725 </td><td>0.1670687   </td><td>rejected    </td><td>0.6798898   </td><td>rejected    </td><td>4.003104    </td><td>rejected    </td><td>150.4953    </td><td>4.415236e-05</td><td>22648.84    </td><td>150.4953    </td></tr>\n",
       "\t<tr><td>0.006506141 </td><td>0.1536210   </td><td>rejected    </td><td>0.6583862   </td><td>rejected    </td><td>3.815963    </td><td>rejected    </td><td>153.7009    </td><td>4.232987e-05</td><td>23623.98    </td><td>153.7009    </td></tr>\n",
       "\t<tr><td>0.006322382 </td><td>0.1574944   </td><td>rejected    </td><td>0.6989309   </td><td>rejected    </td><td>4.104749    </td><td>rejected    </td><td>158.1682    </td><td>3.997252e-05</td><td>25017.19    </td><td>158.1682    </td></tr>\n",
       "\t<tr><td>0.006134266 </td><td>0.1518525   </td><td>rejected    </td><td>0.6212355   </td><td>rejected    </td><td>3.709109    </td><td>rejected    </td><td>163.0187    </td><td>3.762922e-05</td><td>26575.09    </td><td>163.0187    </td></tr>\n",
       "\t<tr><td>0.006008198 </td><td>0.1387032   </td><td>rejected    </td><td>0.5395451   </td><td>rejected    </td><td>3.261619    </td><td>rejected    </td><td>166.4393    </td><td>3.609844e-05</td><td>27702.02    </td><td>166.4393    </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 0.006952153  & 0.1565358    & rejected     & 0.6555035    & rejected     & 3.833047     & rejected     & 143.8403     & 4.833243e-05 & 20690.04     & 143.8403    \\\\\n",
       "\t 0.006644725  & 0.1670687    & rejected     & 0.6798898    & rejected     & 4.003104     & rejected     & 150.4953     & 4.415236e-05 & 22648.84     & 150.4953    \\\\\n",
       "\t 0.006506141  & 0.1536210    & rejected     & 0.6583862    & rejected     & 3.815963     & rejected     & 153.7009     & 4.232987e-05 & 23623.98     & 153.7009    \\\\\n",
       "\t 0.006322382  & 0.1574944    & rejected     & 0.6989309    & rejected     & 4.104749     & rejected     & 158.1682     & 3.997252e-05 & 25017.19     & 158.1682    \\\\\n",
       "\t 0.006134266  & 0.1518525    & rejected     & 0.6212355    & rejected     & 3.709109     & rejected     & 163.0187     & 3.762922e-05 & 26575.09     & 163.0187    \\\\\n",
       "\t 0.006008198  & 0.1387032    & rejected     & 0.5395451    & rejected     & 3.261619     & rejected     & 166.4393     & 3.609844e-05 & 27702.02     & 166.4393    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  theta       KS        KStest   CvM       CvMtest  AD       ADtest   mean    \n",
       "1 0.006952153 0.1565358 rejected 0.6555035 rejected 3.833047 rejected 143.8403\n",
       "2 0.006644725 0.1670687 rejected 0.6798898 rejected 4.003104 rejected 150.4953\n",
       "3 0.006506141 0.1536210 rejected 0.6583862 rejected 3.815963 rejected 153.7009\n",
       "4 0.006322382 0.1574944 rejected 0.6989309 rejected 4.104749 rejected 158.1682\n",
       "5 0.006134266 0.1518525 rejected 0.6212355 rejected 3.709109 rejected 163.0187\n",
       "6 0.006008198 0.1387032 rejected 0.5395451 rejected 3.261619 rejected 166.4393\n",
       "  square       var      sd      \n",
       "1 4.833243e-05 20690.04 143.8403\n",
       "2 4.415236e-05 22648.84 150.4953\n",
       "3 4.232987e-05 23623.98 153.7009\n",
       "4 3.997252e-05 25017.19 158.1682\n",
       "5 3.762922e-05 26575.09 163.0187\n",
       "6 3.609844e-05 27702.02 166.4393"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mleExp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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$KStest[i] == \"rejected\"){\n",
    "        trueEst = c(trueEst,\"fail\")\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trueEst\n",
       "gamma  plot \n",
       "   34    10 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "not rejected \n",
      "          44 \n"
     ]
    }
   ],
   "source": [
    "table(trueEst)\n",
    "print(table(mleWB$KStest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "logical(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mleGamma[which(mleWB$KStest==\"rejected\"),\"KStest\"] == \"not rejected\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "logical(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mleExp[which(mleWB$KStest==\"rejected\"),\"KStest\"] == \"not rejected\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "integer(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(mleWB$KStest==\"rejected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lUOs3lxuAZo1IZ/56iJUB29Lw4rCY0n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\nB5lh9rkAGjRaAAAAAElFTkSuQmCC",
      "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": 24,
   "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": [
       "'rejected'"
      ],
      "text/latex": [
       "'rejected'"
      ],
      "text/markdown": [
       "'rejected'"
      ],
      "text/plain": [
       "[1] \"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": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cu5rRoKG0NfSrfUBskbEkniSNXG0W1oRgad3QHici23DQ2i3UBRO0GFZI0A4phs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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",
    "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": 26,
   "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": [
       "'rejected'"
      ],
      "text/latex": [
       "'rejected'"
      ],
      "text/markdown": [
       "'rejected'"
      ],
      "text/plain": [
       "[1] \"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": 27,
   "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": [
       "'rejected'"
      ],
      "text/latex": [
       "'rejected'"
      ],
      "text/markdown": [
       "'rejected'"
      ],
      "text/plain": [
       "[1] \"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": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3fk1Her+reoHOLkNlvIEENRo+6yhoFWlWs7EvjdZMsshY5MiJDbdFlDBUVN62tb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\nAABJRU5ErkJggg==",
      "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": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'NA'"
      ],
      "text/latex": [
       "'NA'"
      ],
      "text/markdown": [
       "'NA'"
      ],
      "text/plain": [
       "[1] NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'NA'"
      ],
      "text/latex": [
       "'NA'"
      ],
      "text/markdown": [
       "'NA'"
      ],
      "text/plain": [
       "[1] NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'NA'"
      ],
      "text/latex": [
       "'NA'"
      ],
      "text/markdown": [
       "'NA'"
      ],
      "text/plain": [
       "[1] NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mleGamma[45,\"KStest\"];mleExp[45,\"KStest\"];mleWB[45,\"KStest\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in `[.data.frame`(max_PRCP, , 45): undefined columns selected\n",
     "output_type": "error",
     "traceback": [
      "Error in `[.data.frame`(max_PRCP, , 45): undefined columns selected\nTraceback:\n",
      "1. na.omit(max_PRCP[, 45])",
      "2. max_PRCP[, 45]",
      "3. `[.data.frame`(max_PRCP, , 45)",
      "4. stop(\"undefined columns selected\")"
     ]
    }
   ],
   "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": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in `[.data.frame`(max_PRCP, , 45): undefined columns selected\n",
     "output_type": "error",
     "traceback": [
      "Error in `[.data.frame`(max_PRCP, , 45): undefined columns selected\nTraceback:\n",
      "1. na.omit(max_PRCP[, 45])",
      "2. max_PRCP[, 45]",
      "3. `[.data.frame`(max_PRCP, , 45)",
      "4. stop(\"undefined columns selected\")"
     ]
    }
   ],
   "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": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PadoCsrEAfFqsq5LSHGkY1SZbVqxLHM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",
      "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": 33,
   "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": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\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",
       "\t<li>13</li>\n",
       "\t<li>14</li>\n",
       "\t<li>15</li>\n",
       "\t<li>16</li>\n",
       "\t<li>17</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 8\n",
       "\\item 9\n",
       "\\item 10\n",
       "\\item 11\n",
       "\\item 12\n",
       "\\item 13\n",
       "\\item 14\n",
       "\\item 15\n",
       "\\item 16\n",
       "\\item 17\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 8\n",
       "2. 9\n",
       "3. 10\n",
       "4. 11\n",
       "5. 12\n",
       "6. 13\n",
       "7. 14\n",
       "8. 15\n",
       "9. 16\n",
       "10. 17\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]  8  9 10 11 12 13 14 15 16 17"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "which(trueEst==\"plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "          44 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "          44 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleWB2[,4])\n",
    "table(mleWB[,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mleGamma2 = data.frame(alpha = double(),\n",
    "                 theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   gamma1 = fitdist(as.vector(testDist),\"gamma\",method = \"mge\", gof=\"ADR\")\n",
    "   mleGamma2[i,1] = gamma1$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$var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "          44 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleGamma2[,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected \n",
       "          44 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleGamma[,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mleExp2 = data.frame(theta = double(),\n",
    "                 KS = double(),\n",
    "                 KStest = character(),\n",
    "                 CvM = double(),\n",
    "                 CvMtest = character(),\n",
    "                 AD = double(),\n",
    "                 ADtest = character(),\n",
    "                 stringsAsFactors = FALSE)\n",
    "                \n",
    "for (i in 1:length(max_PRCP)) {\n",
    "\n",
    "    testDist = na.omit(max_PRCP[,i])\n",
    "        \n",
    "        for(j in 1:length(testDist)){\n",
    "            if(testDist[j] < .1){\n",
    "            testDist[j]=.0001\n",
    "            }\n",
    "        }\n",
    "\n",
    "   exp1 = fitdist(as.vector(testDist),\"exp\",method = \"mge\",gof=\"ADR\")\n",
    "   mleExp2[i,1] = exp1$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": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected     rejected \n",
       "          13           31 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "not rejected     rejected \n",
       "          10           34 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(mleExp2[,3])\n",
    "table(mleExp[,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in `[.data.frame`(max_PRCP, , 45): undefined columns selected\n",
     "output_type": "error",
     "traceback": [
      "Error in `[.data.frame`(max_PRCP, , 45): undefined columns selected\nTraceback:\n",
      "1. na.omit(max_PRCP[, 45])",
      "2. max_PRCP[, 45]",
      "3. `[.data.frame`(max_PRCP, , 45)",
      "4. stop(\"undefined columns selected\")"
     ]
    }
   ],
   "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5DuG2eOZI8m1OSJfVj5pQ5xBP9VeUIcRMc4m9ZKqa0q4xm3RGP/dYGNKScPJlwA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\ntcdplQAAAABJRU5ErkJggg==",
      "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": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.246899231597615"
      ],
      "text/latex": [
       "0.246899231597615"
      ],
      "text/markdown": [
       "0.246899231597615"
      ],
      "text/plain": [
       "[1] 0.2468992"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "1-pgamma(254, mleGamma2[1,1], mleGamma[1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0374935685800497"
      ],
      "text/latex": [
       "0.0374935685800497"
      ],
      "text/markdown": [
       "0.0374935685800497"
      ],
      "text/plain": [
       "[1] 0.03749357"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "1-pgamma(508, mleGamma2[1,1], mleGamma[1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0050753574795539"
      ],
      "text/latex": [
       "0.0050753574795539"
      ],
      "text/markdown": [
       "0.0050753574795539"
      ],
      "text/plain": [
       "[1] 0.005075357"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "1-pgamma(762, mleGamma2[1,1], mleGamma[1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "44"
      ],
      "text/latex": [
       "44"
      ],
      "text/markdown": [
       "44"
      ],
      "text/plain": [
       "[1] 44"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(oneInch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "starter = firstYear+9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width=7,repr.plot.height=5) #reduces the plot sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Eq6EKRVMD1i1zSf5hzpKH52w4JXKOxl\nlCPnXrvytgqsnv39u7++vKwE348EJERyHcmUPxbz1ZvO357x4BEkLEzAkQ2VKX6uX8cs+g5Z\nIBkBgyT/VnMgGQGDZN77zfVNAJG4BmnCs7/5REK+HIM05dnff+dIx+uIPM6RkBvnIUITrh9t\nO712m9EntBIkLIxgiJC936q9jlSUe64jIS+OQQr+7G8gSY5BCv3sbyBNPLIYECBIgAAPPwEE\ngo5ssB4yT5CwMM5B+tnajv4+ECRkS/IUIcv7kU6F1V1L86oCYnK+Q7Y4/v1jeYfsaXxgkFNV\nQEzOz2y4DkS1fGbDoTNu9WOxmdxzjFVSDRGi+xurJvtEGr0twqUJYAGCniPNagJYgJC9djOb\nANLnfh3J+ilCrwvhOhJyEmuIEEFCVhhrBwgQJECAIAECQYPEs7+Rq4BB4tnfyBfP/gYEePY3\nIMCzvwEBPpEAgbDnSDz7G5kK2f3Ns7+RrbDXkXj2NzLFyAZAgCABAgQJECBIgABBAgQIEiBA\nkAABggQIECRAgCABAgQJECBIgABBAgQIEiBAkAABggQIECRAgCABAgQJECBIgABBAgQIEiBA\nkAABggQIECRAgCABAgQJECBIgABBAgQIEiBAkACB8EE6bIwpj16bAEIL/mXMt6/tG/3mS4KE\npQkdpMpU9eVyrszBRxNAJKGDVJj2y2Nrs/HRBBBJ6CAZ0/lB3gQQSegg7e5BKnw0AUQSNEjl\n/nA0P3+TdTXe20CQsDBBg3TVTha1jyaASEJeRzqdDoeybLscqtEcESQsDSMbAAGCBAgEDdLv\nvmzPksrq11cTQBQBg1RvzNPWSxNAJAGDVJni59ROnY8F3d/ISsAgFeb0mD5xQRZZCT76u+8H\nWRNAJHwiAQJhz5GO53aKcyTkJmT397bTa7f5GNpguuY2AcQR9jpS1V5HKso915GQF0Y2AAIE\nCRAgSIBArCBxHQlZIUiAAId2gABBAgQIEiDAjX2AADf2AQLc2AcIcBsFIMCNfYAAn0iAADf2\nAQLp3NgnaQKIgxv7AAFGNgACBAkQIEiAAEGy8i92AUgcQfrqXyt2FUgbQRr17yl2KUgaQRrW\njRBJwiiC1O/jc4ggYQxB6tVzNEeSMIIg9eg9JeLgDiMI0qeBrgWShGEE6cNgYEgSBhGkN2M9\n3bOS9O+NS3FIFkF6Nb6rT83Be4hIUrYIUtfX/XxCDgaCQ5TyRJA6LHbyvhf0/Wr404ckZWm9\nQZrZwf1xdWnQyCJIUnbWGaS+/d3yk+JtjskpmtAUFmR9QZq793fm7y5mdg2zZkSqVhakz91/\nYoxmzTGwEIfZkZoVBUmw+3cXJFiIcyVIxlqCJEuRTlrVwM0qgpReiFop1oSZ8g9SoilqfO/e\nc1v25I4UzJZ5kFLfi0a6EF0yoFkKJsg5SEvYg6ySNO1d9M6Q/IpYumyDtIQU2ZmUpsEXZrIy\nkpVpkLJJ0Z1VlMZfk9kaSUyOQcouRXdfczL+tjNdK2nIL0jZxqgx8OZsD/0yXjOxhf1al337\ntS6mrHx9rUvWKbp6f4eTuiOyXzvRBAxSvel80djWRxMriFGj8zbn9el5qmvVwn715c/1W2TV\nX305q5d4yWZ2jD/m9VLUui37y5j/vZlZ2RI5vGO3NbWmlTxBwCAZM/TD5CZWHCCFb2vtffX2\nCFjtMizsE4ntqTG4/iwyNLr2Q20Rp3vBZFV0hD1HOp7bqfnnSARI531dTvn7FO/jyqFJr+WG\n7P7ednrtNrWXJjCJ217lOUvjYdUbqMKy2rDXkar2OlJR7n1dR8Jk0p3/ZYmKmiz3d6eouKft\nkuPIBiRgfjhtdlovCBLSNGk/fJshUIlCmQ0R8uW/2AUs1KTDpqVmqJXVECGh//rELmrx8sxQ\nK4shQkK9AfoSpvGAkcZVWNgFWRdfM/I1MEo+3iHiWeYQITs+dnDnJUqrmVDo/CXBxtI+kWbs\nyEnuSy7vQ8FbcaHWX6R5By1siFCSG9Uf3R7tsiQ8DW6qdIYIma65TcDO3N3FdcGLN/jGGSIE\nCDCyARAgSIAAQQIEYgUpxHUkIBiCBAhwaAcIECRAgCABAtzYBwhwYx8gwI19gMDSbqMAkpTo\njX3AwswIxPRZWhM+kQSt+ZRiTRRlLZWiAtzYJ2jNpxRroihrqRQV4NnfitY8SrEmirKWSlEB\nbuyTtOZPijVRlLVUigpbRyrvuivFmijKWipFEaQUa6Ioa6kURZBSrImirKVSFEFKsSaKspZK\nUQQpxZooyloqRRGkFGuiKGupFEWQUqyJoqylUhRBSrEmirKWSlGp1AEsGkECBAgSIECQAAGC\nBAgQJECAIAECBAkQIEiAAEECBAgSIECQAAGCBAgQJECAIAECBAkQ8Bqkw33pVWG2x7a57lPK\n/35bVLYPafVX06XuFBKlpr6i3iaDF3XY9K2TyGuqv6jX9Rdh8zV8Bul0f6r/9fHG+8szSMXj\ntxuPBdjVdC6uJZ1j1dRX1Odk2KKq6zqpX5vvn4xc1Mf6C735Wh6DdCpu7+9gtvWl3j2/v+Jo\nfi+XX1OcmtfYP/DYU0279jsAKrOLVFNvUZ3JGEWdzK5uinhdJ/2TkYvqrL84m+/KX5D+doXb\n+9u27+z8+M6KuigvzZ7bHLj8XP/oBtJb0+03zT8xauovqjMZo6iyd530T0YuqrP+omy+G39B\n+tsHOrto88/9q2ZLU7f/3xxLnUzprQLLmorbZBGnpv6iOpNRiro8Cuo03z8ZuajO+otYlMcg\nnS7v+8etrdP1o+ntt0H01rS/Hdrt49TUX1T/ZGB1N9KpFjW8owXltc3bO9q0fyh+7+/v+oEU\n61331HRoehuKQ7yaeorqTMbbPQ7NoVJqQXovqvNv7kHam7K+nO4HsqfmZPESO0jdmvadTsUo\nNfUU1ZmMtnuc23PZxIL0UdTl48dsg3Rp+5fLlzPC2EHq1HRoDu3qnTlED1J3RT0nYxVVF9tO\nu4kE6bOozx/zDdLfXlrs7z8Vj53m5TWhfNa0aQ816+byQ6Sa+lbUczJWUdvr9ZhO8/2TkYvq\n/BurqLZRrwvvLv10vVD26FO5drGcQ3exfNbU+TsWqabeFfWYjFPUebO9ftl2p/n+ychFNV56\n7cJvvrYErwu//6Fo/uQfru/v0BxCNfbtId7R+hvRvdV0/TtWN93fkWrqLeoxGaWo4+NiRaf5\n/snIRTUe55hxNl9bgteF38+KmmvRG/PT/FDeBzhEugz9WVNlmtFZVbP6Y10a7yvqMRmjqPNj\nl01oZEN/UY3b+stzZMPl8f7q62C26wfu9YzkOtXYDswbsKbts5A4NfUU1a0vQlG7zuDiTe/q\nSaeoy/PQONLma0vwuvDb0s9/q6A8vv7uPujaZ/u2NT0LiVNTX1GdyQhFdUfp172rJ52iLp3O\nmjibry0hSqtAZggSIECQAAGCBAgQJECAIAECBAkQIEiAAEECBAgSIECQAAGCBAgQJECAIAEC\nBAkQIEiAAEECBAgSIECQAAGCBAgQJECAIAECBAkQIEiAAEECBAgSIECQAAGCBAgQJECAIAEC\nBAkQIEiAAEECBAjSEpiO169ARyLYKEtAkJLHRlkMApQyNs5iEKSUsXEW4x6k5t+//+1Nsb9c\nKmOu3+J92JjiELG6tSNIi/EapH1zvnTcNv/fJKlsz5+2UQtcNYK0GK9B2taXw+3/i8vl2EzV\nW3OMW+KKEaTFeA3Sbzt1vv1cmvpvqjZlxPrWjSAtxts50qX7/8/OccTBml8MgpQy1vxijAcp\nXl1osAEWYyxIJd0MkRGkxRgL0o8pTpfLgc6GaAjSYowF6dJeUDLFOVp1a0eQFmM0SM3IBrMj\nR9EQJECAIAECBAkQIEiAAEECBAgSIECQAAGCBAgQJECAIAECBAkQIEiAAEECBAgSIECQAAGC\nBAgQJECAIAECBAkQIEiAAEECBAgSIECQAAGCBAgQJECAIAECBAkQIEiAAEECBAgSIECQAIH/\nAaE1mSWOY0vrAAAAAElFTkSuQmCC",
      "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": 53,
   "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": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PJEFOlLHe5//33d/pz6mu+cKEikOn+X\nJs07RJLHV6ST/eInqukqV11/+V1VwlEFScOexDLNq4RJ4Ym4+Em7S6V6LySjCpCEI7HWbDUw\n69LIJEQSJ+LiJ1/q2ph3bbbv8zdJRYrUkFSmOZWGFyVEEifi4idXVR2v9aH6M+myUxfhqAIk\nsZI8XRqYhEjieIp0cFn85FK9G4In6ajEU/AinUxWKiGSON6PUTgtfvL91fZNHE4LD10UL1Kd\nspVncgmTAuLdtMt0zYY8zpT/esTMd1GlPIrnk0Ck0Pw3IlK2epWeJuVTPJ/Ch67ZkN+JEt+m\nOZXyK5/SQaTIxLRJ28LDpCAgUgqi2WRUKfMCKg8PkVwXiHTY/9NFaonU1tOo5PIt6GBHRJHO\n8UQqwaOOGF0RqBSBmE27a2X7HO12RHoQ2KapSpgkTNR7pKvtyg6bE+lJMJsmJilMEiVuZ8O5\nm7KqT1ZuRf5iPeoIY9NIJcVFSRRvkS6H5rQ/iC606mtC4SK1yNs0at9hkigiqwj9vZfVksWf\nIFKLsE0Dk5pCQiUxPEU6q327NuRZfYmFVCPSAEmbeiq1hYRJUng/2HevH8voS0U0ziL20Xky\n7iZfq9W7ffcoJZ1JaZdkLhOBB/tWiRRyHOkTRWrR2eTsk8akf/qEgw5ufRqeIu0eV6Sr2jkm\ngki+rD7dHyopk5n4tAKZe6SLy9oNjlnEPro0Vpzuv7/jg2ZvlPDJAt9eu0N+a39vy6MXVkIZ\ndrLqckCmOUTGkdRhYQlivyxiHlw8ls20pnnXKyiHzjtc0hJ1ZsPPqbuAHY4LCz0gkjcGgd47\nDIdn3brBkWlCRJHuu94coHBrf+ORJT4mJV8OMzsiinRU1Xc31e52qcKt/Y1I1gxUWjE0yz3T\nG1+RHNb+rnozVq/h1v5GJGuUr0l13Efnc8ZTJOe1v7UvJKISOXZjqOFFaf1sIWyKufZ3nCsS\nHjkgZlLDtmUSmCJky9890qWbIx7yHgmRHGgL662SwAzWzbrkKZLT2t/7Xq/dbvY4RIpDV1jv\npYZk5oJvUiZPkdzW/v45tuNI1eEUbhwJkRx4Ftbv0yWppyq218rzbtpltmQxHjnxLq6HS4LP\nJ21LJUTaNIPierokmP52XIq7+En4LBDJiXFx/b7vl4TYikofJhIeOTItMFxahbdI3/ucZn8j\nkiO6Avsn7tIGVBJZRSif55EQyRFtgf37h0uueD8hWzXfqpzNE7KI5IrJJPk23md3iXuv2dBN\n+3Fes8E+ixjHbRh9kT16wYO59Hk2SU0RyqP7G5GcMRTZazhJvOvhQ20SuyLNTkL1ySLGcRtG\nV2TNe72BWXGV6g+c4fpZ90iI5M60zFTz3mCKQwiV6s+alPdRvXZ4tIJJoannJak/xSGQSp8j\nk/84UkarCCHSCsaFpp7/jabdBVPpM7rzPmpmAyKtQeleaUwKqNIHyPRJIuHRKpTuRftzMhU8\npEqFt/IQafMo7bbepAC94UOKlemDRMKjdSjt5tOkye7yk1pHlNnKQyRQk433C+1zfsFdKlAm\nRAK9SKbGXUd4lwpr5X2OSHi0FjX6OXjf/Ox5BJcKkgmR4FF0upHZen49lAgqldLKQyR4j8Hq\n3l9YWSiGSyVcmD5GJDxaj6rNk1frxa8+j6JS9osS5XnK5hnVx6JMxWdnUmSXgmezijxPWfcs\n8MgH49Nkz/dzUSlflxAJZkrv9YtFk7bexEMkmONdrhZrsP7+xugSz7Mj70NEwqNQDExaXoT1\n9zeOTdk9r45IMEuvZK1MatiiTZ8hEh6Fo1+21irVm7MJkWCBtSY1bMcmRIIFhoW74ntfNmHT\nR4iER0HxNqkhkkzJ1vlKItLiapKIlBXD+vL4LrJI3eMp5uYhEligfG6UhnyoTBFFUkPkssCj\nCPQrzM+kzxy4jSjST4VIJTNSyTO1SNcl7xlFtidXzKbd/aD2tzYF0aYdHsWiV28iX9pcwkxX\n22+HiHuP9K1UsyYrIpXKu+Zkvv48e5WU7ekVubPhtleHOyIVzKvuZEzK3SXjo1qaHVek7cFJ\nVRdRkfAoLi5PVtiRsUqmh/ANe7rheeZedws9DY5ZIFJk5E3K1yXV+99mTye8z9wvSZHwKDai\nnXcv8hxeylsk0SwQKTajsVm5hPNzSY1+WuzqACJtnGAm1bnJpCYby7vak5VIeBQfqZl3BjKa\n96A0W4u7WoNIW2dskrRKucx7UNrNxX1tyWquHSIlYPJchbxJdQ49ecqwvbSvJWvP3XMAkfAo\nBeNSD2RSapXUzKuFna1YffJeK9vvPkekvIllktkl6YrXuaRmX/oHtP4zXNVRNgs8SsOk3F/N\nO/ka0atkO5fUnqlK+Yr017q7mpO1bffJhAIeTMv9YZL8+d0wdcl6LqkTQ5cmOQg++bP2kFBZ\nIFIiNAXfmhTmBJ+qZD2X1JG+StMM5rIsWiQ8SoWu5P9MCnV+NwxMUoYYBHiqpEt+JktEgjUY\nTPoXsk7eKtlPyl5DpxIiQQS0Rf+vuygF49W+s59Luo7GJG3q5ixTiSQxjoRH6TCcZoFNeqqk\nZoKQYd2TS24gEmgLv3kr3IjSg8Ykh0nZq3F9CLDkph0iJcQgUrAJQ296vQ4BzwDlqBIiwSqM\nncNxTOr3OoSgTdhFpYJFwqOkGMcrVWiT1LjXwTc585v2KkUV6ed0aOctHI4/AlkgUlLGxa/e\nG4FNanJ6quR/EmjHkN9v2ZoUUaT7rjcHaH76KiIVgGkqmgrcvHtkNOzA80ptfg6D21Ppjlmv\n4aiq726q3e1SzU9ftckCjxKjTK8Cm/TKScAkZRiRGrxh1b6LKFLVm7F6VZVvFoiUmDmRQvaD\n93Lq94V7JWW6uj5wWd5hRe6uxynTi1VZIFJq1Ox2KJMG9e735N9Yft2rx4dYNKnYKxIeJWde\npFDNu1HFe6ikTC8eI8sDllSKe490ab+MQuQeCZGSs/QHPYxJk4pfq5KhOTc26MV8+y5m9/e+\n12u3u3tmgUjpUZqtwasAKunqfdWaQ5oOhuElaMBTJae4XCOw5ufYjiNVh5P3OBIeZcDyvbrm\nlJTKc/Cm+1J4o3T6Bq06t0qd2YBIOaBGPzUvpVUyz0NwkWn8RZ6DqxAiQWTU4Mfk/Q5dS8k3\nR8Pbtou0ji6Zo6VbEAkiYyWSqEqmen+//7ts0+AebhCYfp6DT2Cyh4hngUd5oF7/Td8eIKWS\nseKnw0tGm3qdipOYFCJBAgwi6d4SUWmm3idD/CabXuPF2njWLiiGSOCBqSmkrR+BFp7zfJip\nTe/R1hVZrIxMNCfJLPAoFwxNIUMFaYdoHLNb8du+TUq498MuMqlDpLNApFwwrAhpriAvl3zO\ni55NUSZcBDlEOgtEygbDPcVcDa13ybfJ/2++F8KHIkXCo3yYH9gxsc4lv5ljrzyD2IRIEILl\nKtLPafNM1P7uTNomRIIQWFWRq0tWiWramsZJdL+/Yj4hEgTBto4cZLJNcjRx4ZW+4fjfeVwz\ntSe5SHhUAA6V9M9SJusk1b8RNod72oRIEATHSpqc9gtJ6nfSCrQuoCe2MiEShMG9luY06CU4\n3k3H9E7J66SxsKlAkfCoCFZXk8knpfndjHujL1D1PmsQCZLgXU02tiyE8I4h+EmDSBAGqWpa\nKdEjCNMKkOKUJxIeFYJwRa1NThnm1QqDSBAI0Ypa+5iQ77H2mUQ5RDILRCoEwYqKooIfxYmU\nf5FCh1hNFaARIkE4ZKqqCI0QCcIhUVWFaIRIEA7/qipGo/JEKqdkwbeyCtIIkSAgfpVVVlUj\nEgQDkaQPEcuirMLdOl61VVhVIxKEw6e6CqvqwkSCokAk4UMyzAIi4DNBTi6KKCAShAORhA/p\nuH8ptb88EuGRoy2wviJLOwUiinSv2q9hPnSJINIWWF2RxZ0BEUU6qvOfTedq3yaCSJtg9eN4\nolFEIKJIVXfgrdrdEGkzrKzK4s6AiCI93bnv94i0HVbNmCvvBIgo0k7dn1t7RNoQefYMCxPx\nQ57V12PrpvaItCHcq7O8EyDmX4vjy56LQqQt4VqfBdZ/1Mvu9fDcun0h0pZwrNAC6z/P9muB\nBQmzuNVogfWPSBAFlyotsfoRCeLg0A1eYvUjEsTCulZLrH5EgmhYVmuRtR91ZsOAEFlA3tjV\na5G1H3VAFpG2jlXFFln7MZt2127id8gsIG9sKrbMyo87IKuOobOAvLGo2TIrP25nw1ldzcna\ntvugZJartszKp9cOorJYtYXWPSJBXJbqttC6RySIzELlFlr3iASRma/cUqs+lUiMI22Xj6x6\nRILozFVvqVVP0w6iM1O9xdY8IkF8zPVbbM0jEiTAWMHF1nxUkX5Oh27V4uNPqCygDAw1XG7F\nRxTpvuvNAZqfvlpueYIdiLT+wx5V9d1Ntbtdqvnpq+WWJ1iir+JyKz6iSFVvxupVVSGygHLQ\n1XHB9R71CVnTC7EsoBwQiSsSSKCp5ILrPe490uXWbnGPBLWmlkuu9pjd3/ter93uPrdnySUK\ntiDSWn6O7ThSdTgxjgTdQ9GD16kCEYCZDZCU3uICRdc6IkEWlL5SByIBCIBIAAIgEoAAiAQg\nACIBCIBIAAIgEoAAiAQgACIBCIBIAAIgEoAAiAQgACIBCJCpSACFseIslxcnm9zsyDEmgrIm\nl6AQKceYCMqaXIJCpBxjIihrcgkKkXKMiaCsySUoRMoxJoKyJpegECnHmAjKmlyCQqQcYyIo\na3IJCpFyjImgrMklKETKMSaCsiaXoBApx5gIyppcgkKkHGMiKGtyCQqRcoyJoKzJJahc4gAo\nGkQCEACRAARAJAABEAlAAEQCEACRAARAJAABEAlAAEQCEACRAARAJAABEAlAAEQCEACRAARA\nJAABgop0fqZ+rNT+0mbXX6X8793qeA8ZgFVM9b0XSJKYdEGNNqMHdd7pyiRxSemDGpZfgupr\nCCnS9bmq/75151S/Rape7+4CBmAX063qQrqlikkX1HQzblDHrkzuw+z1m4mDmpRf7OprCSjS\ntXp8vrPa3+v7l7o+f3NRP3X9o6prs89PuAjsYvpSx7qppK9EMWmD6m2mCOqqvu5NEMMy0W8m\nDqpXfmmqryOcSH+nwuPz7dtPdmvP14Z7daibM7dpuHx3f3QjoY3p8U7zI0VM+qB6mymCOmjL\nRL+ZOKhe+SWpvgfhRPo7B3qnaPNj//jNQd3b/5u21FUdgkVgGVP12KzSxKQPqreZJKj6FVAv\ne/1m4qB65ZcwqIAiXevx+fHI69pdmkbvRkEb0+nRtDuliUkflH4zMve+0rkGZT7RohI0z8cn\n2rV/KH6en6+7IKX61JqYzk1vQ3VOF5MmqN5mutPj3DSVchNpHFTv56eLdFKHe319NmSvzc1i\nnVqkfkynXqdikpg0QfU2k50et/ZeNjORJkHVk5cfK1Ld9i8fBneEqUXqxXRumnb3L3VOLlK/\noN6bqYK6V/tevpmINA1q+vJzRfo7S6vT81X1OmkG+8RiGtOubWrem+GHRDHpCuq9mSqofTce\n08tev5k4qN7PVEG1mQZNvJ/6tRsoe/WpdF0st9hdLNOYen/HEsWkLajXZpqgbrv9rd3oZa/f\nTBxUw6DXLn71tSEETfz5h6L5k3/uPt+5aUI1nNom3uU1uhSJaUzd37F70/2dKCZtUK/NJEFd\nXoMVvez1m4mDanjdY6apvjaEoIk/74qaseid+m5eHJ4THBINQ09jOqpmdtaxKf5UQ+O6oF6b\nKYK6vU7ZjGY26INqeJTfZ85sqF+f795NZusuuN0dSbfVsDccGzGm/TuQNDFpgurHlyCor97k\n4p22ePIJqn43jRNVXxtC0MQfqd/+iuBwGb73nHQdMn/bmN6BpIlJF1RvM0FQ/Vn6d23x5BNU\n3eusSVN9bQhJcgX4MBAJQABEAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAARAJQABEAhAAkQAE\nQCQAARAJQABEAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAARAJQABE\nAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAARCpBFSP4VegQyZQKSWASNlDpRQDAuUMlVMMiJQz\nVE4xPEVqfv79O6nqVNdHpbpv8T7vVHVOGN3WQaRiGIp0au6XLvvm/8akQ3v/tE8a4KZBpGIY\nirS/1+fH/1VdX5qt+15d0oa4YRCpGIYi/bRbt8frg7r/bd3VIWF82waRimF0j1T3/393jkMa\nKPliQKScoQ87nGMAAAC3SURBVOSLYV6kdHFBAxVQDHMiHehmSAwiFcOcSN+qutb1mc6GZCBS\nMcyJVLcDSqq6JYtu6yBSMcyK1MxsUF94lAxEAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAARAJ\nQABEAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAARAJQABEAhAAkQAE\nQCQAARAJQABEAhAAkQAEQCQAARAJQABEAhAAkQAEQCQAAf4Hua/z7xqt1cgAAAAASUVORK5C\nYII=",
      "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": 55,
   "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": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O5eSLH/lslHGyJZITFd6jAJNTXJrJKx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\nK0IdAAAAAElFTkSuQmCC",
      "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": 57,
   "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": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAJYCAMAAABvmDbGAAAAOVBMVEUAAAAAAP8AzQBNTU1o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\nACIBCIBIAAL8D/Rx5CEdVTajAAAAAElFTkSuQmCC",
      "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": []
  }
 ],
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