r_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T08:52:31.271859Z",
     "start_time": "2019-12-19T08:52:31.225Z"
    }
   },
   "outputs": [],
   "source": [
    "suppressMessages(library(gglasso))\n",
    "suppressMessages(library(bst))\n",
    "suppressMessages(library(IRdisplay))\n",
    "suppressMessages(library(caret))\n",
    "suppressMessages(library(earth))\n",
    "suppressMessages(library(h2o))\n",
    "suppressMessages(library(xgboost))\n",
    "suppressMessages(library(REEMtree))\n",
    "suppressMessages(library(fastDummies))\n",
    "library(data.table)\n",
    "library(desirability)\n",
    "library(doParallel)\n",
    "library(MASS)\n",
    "library(devtools)\n",
    "library(ILAMM)\n",
    "library(devtools)\n",
    "library(tfHuber)\n",
    "library(randomGLM)\n",
    "library(plyr)\n",
    "set.seed(18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in data and create train-test-split and dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T08:52:33.633295Z",
     "start_time": "2019-12-19T08:52:32.369Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.table: 6 × 90</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>V.Pop</th><th scope=col>H.Pop</th><th scope=col>Curr.Win..</th><th scope=col>LS.Win..</th><th scope=col>Last.Game</th><th scope=col>Last.Attendance.vs.Opp</th><th scope=col>Capacity</th><th scope=col>Playoffs.</th><th scope=col>Home_Atlanta Hawks</th><th scope=col>Home_Boston Celtics</th><th scope=col>⋯</th><th scope=col>Day.of.Week_Wednesday</th><th scope=col>Month_April</th><th scope=col>Month_December</th><th scope=col>Month_February</th><th scope=col>Month_January</th><th scope=col>Month_June</th><th scope=col>Month_March</th><th scope=col>Month_May</th><th scope=col>Month_November</th><th scope=col>Month_October</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>-0.53671550</td><td>-0.592292553</td><td>-0.8984323</td><td>-0.57431379</td><td>-1.6754137</td><td>-0.41414101</td><td> 0.4664760</td><td>-0.2788241</td><td>-0.2179768</td><td>-0.2219862</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>-0.44269478</td><td>-0.003514696</td><td> 0.9675251</td><td>-0.01024471</td><td> 0.2845056</td><td> 0.45442179</td><td>-0.8678965</td><td>-0.2788241</td><td>-0.2179768</td><td> 4.5044037</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td> 0.96761600</td><td>-0.690422196</td><td>-1.1993932</td><td>-1.36421592</td><td>-0.5961520</td><td> 0.69552455</td><td> 0.5009336</td><td>-0.2788241</td><td>-0.2179768</td><td>-0.2219862</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>-0.34867406</td><td>-0.690422196</td><td> 1.0965083</td><td> 0.41052831</td><td> 0.8931711</td><td> 0.07621655</td><td> 0.6542702</td><td>-0.2788241</td><td>-0.2179768</td><td>-0.2219862</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>-0.44269478</td><td>-0.690422196</td><td>-0.1226221</td><td> 0.13375800</td><td>-0.2905754</td><td> 0.63920136</td><td> 0.8601546</td><td>-0.2788241</td><td>-0.2179768</td><td>-0.2219862</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>-0.06661191</td><td>-0.494162910</td><td> 0.7345231</td><td> 0.98191169</td><td> 0.3562353</td><td>-0.17970810</td><td>-0.5147056</td><td>-0.2788241</td><td>-0.2179768</td><td>-0.2219862</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 90\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " V.Pop & H.Pop & Curr.Win.. & LS.Win.. & Last.Game & Last.Attendance.vs.Opp & Capacity & Playoffs. & Home\\_Atlanta Hawks & Home\\_Boston Celtics & ⋯ & Day.of.Week\\_Wednesday & Month\\_April & Month\\_December & Month\\_February & Month\\_January & Month\\_June & Month\\_March & Month\\_May & Month\\_November & Month\\_October\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t -0.53671550 & -0.592292553 & -0.8984323 & -0.57431379 & -1.6754137 & -0.41414101 &  0.4664760 & -0.2788241 & -0.2179768 & -0.2219862 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t -0.44269478 & -0.003514696 &  0.9675251 & -0.01024471 &  0.2845056 &  0.45442179 & -0.8678965 & -0.2788241 & -0.2179768 &  4.5044037 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t  0.96761600 & -0.690422196 & -1.1993932 & -1.36421592 & -0.5961520 &  0.69552455 &  0.5009336 & -0.2788241 & -0.2179768 & -0.2219862 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t -0.34867406 & -0.690422196 &  1.0965083 &  0.41052831 &  0.8931711 &  0.07621655 &  0.6542702 & -0.2788241 & -0.2179768 & -0.2219862 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t -0.44269478 & -0.690422196 & -0.1226221 &  0.13375800 & -0.2905754 &  0.63920136 &  0.8601546 & -0.2788241 & -0.2179768 & -0.2219862 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t -0.06661191 & -0.494162910 &  0.7345231 &  0.98191169 &  0.3562353 & -0.17970810 & -0.5147056 & -0.2788241 & -0.2179768 & -0.2219862 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 90\n",
       "\n",
       "| V.Pop &lt;dbl&gt; | H.Pop &lt;dbl&gt; | Curr.Win.. &lt;dbl&gt; | LS.Win.. &lt;dbl&gt; | Last.Game &lt;dbl&gt; | Last.Attendance.vs.Opp &lt;dbl&gt; | Capacity &lt;dbl&gt; | Playoffs. &lt;dbl&gt; | Home_Atlanta Hawks &lt;dbl&gt; | Home_Boston Celtics &lt;dbl&gt; | ⋯ ⋯ | Day.of.Week_Wednesday &lt;dbl&gt; | Month_April &lt;dbl&gt; | Month_December &lt;dbl&gt; | Month_February &lt;dbl&gt; | Month_January &lt;dbl&gt; | Month_June &lt;dbl&gt; | Month_March &lt;dbl&gt; | Month_May &lt;dbl&gt; | Month_November &lt;dbl&gt; | Month_October &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| -0.53671550 | -0.592292553 | -0.8984323 | -0.57431379 | -1.6754137 | -0.41414101 |  0.4664760 | -0.2788241 | -0.2179768 | -0.2219862 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| -0.44269478 | -0.003514696 |  0.9675251 | -0.01024471 |  0.2845056 |  0.45442179 | -0.8678965 | -0.2788241 | -0.2179768 |  4.5044037 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "|  0.96761600 | -0.690422196 | -1.1993932 | -1.36421592 | -0.5961520 |  0.69552455 |  0.5009336 | -0.2788241 | -0.2179768 | -0.2219862 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| -0.34867406 | -0.690422196 |  1.0965083 |  0.41052831 |  0.8931711 |  0.07621655 |  0.6542702 | -0.2788241 | -0.2179768 | -0.2219862 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| -0.44269478 | -0.690422196 | -0.1226221 |  0.13375800 | -0.2905754 |  0.63920136 |  0.8601546 | -0.2788241 | -0.2179768 | -0.2219862 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| -0.06661191 | -0.494162910 |  0.7345231 |  0.98191169 |  0.3562353 | -0.17970810 | -0.5147056 | -0.2788241 | -0.2179768 | -0.2219862 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "\n"
      ],
      "text/plain": [
       "  V.Pop       H.Pop        Curr.Win.. LS.Win..    Last.Game \n",
       "1 -0.53671550 -0.592292553 -0.8984323 -0.57431379 -1.6754137\n",
       "2 -0.44269478 -0.003514696  0.9675251 -0.01024471  0.2845056\n",
       "3  0.96761600 -0.690422196 -1.1993932 -1.36421592 -0.5961520\n",
       "4 -0.34867406 -0.690422196  1.0965083  0.41052831  0.8931711\n",
       "5 -0.44269478 -0.690422196 -0.1226221  0.13375800 -0.2905754\n",
       "6 -0.06661191 -0.494162910  0.7345231  0.98191169  0.3562353\n",
       "  Last.Attendance.vs.Opp Capacity   Playoffs.  Home_Atlanta Hawks\n",
       "1 -0.41414101             0.4664760 -0.2788241 -0.2179768        \n",
       "2  0.45442179            -0.8678965 -0.2788241 -0.2179768        \n",
       "3  0.69552455             0.5009336 -0.2788241 -0.2179768        \n",
       "4  0.07621655             0.6542702 -0.2788241 -0.2179768        \n",
       "5  0.63920136             0.8601546 -0.2788241 -0.2179768        \n",
       "6 -0.17970810            -0.5147056 -0.2788241 -0.2179768        \n",
       "  Home_Boston Celtics ⋯ Day.of.Week_Wednesday Month_April Month_December\n",
       "1 -0.2219862          ⋯ -0.5105691            -0.3769627  -0.4300806    \n",
       "2  4.5044037          ⋯ -0.5105691            -0.3769627  -0.4300806    \n",
       "3 -0.2219862          ⋯ -0.5105691            -0.3769627  -0.4300806    \n",
       "4 -0.2219862          ⋯ -0.5105691            -0.3769627  -0.4300806    \n",
       "5 -0.2219862          ⋯ -0.5105691            -0.3769627  -0.4300806    \n",
       "6 -0.2219862          ⋯ -0.5105691            -0.3769627  -0.4300806    \n",
       "  Month_February Month_January Month_June  Month_March Month_May \n",
       "1 -0.4063786     2.1006        -0.08037997 -0.4729413  -0.1927284\n",
       "2 -0.4063786     2.1006        -0.08037997 -0.4729413  -0.1927284\n",
       "3 -0.4063786     2.1006        -0.08037997 -0.4729413  -0.1927284\n",
       "4 -0.4063786     2.1006        -0.08037997 -0.4729413  -0.1927284\n",
       "5 -0.4063786     2.1006        -0.08037997 -0.4729413  -0.1927284\n",
       "6 -0.4063786     2.1006        -0.08037997 -0.4729413  -0.1927284\n",
       "  Month_November Month_October\n",
       "1 -0.4224709     -0.1294096   \n",
       "2 -0.4224709     -0.1294096   \n",
       "3 -0.4224709     -0.1294096   \n",
       "4 -0.4224709     -0.1294096   \n",
       "5 -0.4224709     -0.1294096   \n",
       "6 -0.4224709     -0.1294096   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "load_dummy_std <- function(){\n",
    "\n",
    "    dataset_3 <- data.table::fread('./data/processed/dataset_3.csv', drop = 1, check.names = TRUE)\n",
    "    cat_columns <- c(\"Home\", \"Visitor\", \"Last.Five\", \"Day.of.Week\", \"Month\")\n",
    "    dataset_3[ , (cat_columns) := lapply(.SD,as.factor), .SDcols = cat_columns]\n",
    "    X <- dataset_3[,1:(ncol(dataset_3)-1)]\n",
    "    y <- dataset_3[,'Attendance']\n",
    "    X <- dummy_cols(X, select_columns = cat_columns, ignore_na = TRUE, remove_selected_columns = TRUE)\n",
    "    training_indices <- 1:(nrow(X)-2960)\n",
    "    X_train <- X[training_indices,]\n",
    "    X_test <- X[-training_indices,]\n",
    "    suppressWarnings(preProcValues <- preProcess(X_train, method = c(\"center\", \"scale\")))\n",
    "    X_train <- predict(preProcValues, X_train)\n",
    "    X_test <- predict(preProcValues, X_test)\n",
    "    y_train <- y[training_indices]\n",
    "    y_test <- y[-training_indices]\n",
    "    \n",
    "    return(list(X.train = X_train, X.test = X_test, y.train =  y_train, y.test = y_test))\n",
    "}\n",
    "data <- load_dummy_std()\n",
    "head(data$X.train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T08:52:33.647463Z",
     "start_time": "2019-12-19T08:52:33.541Z"
    }
   },
   "outputs": [],
   "source": [
    "metrics <- function(preds)\n",
    "{\n",
    "    r2 <- function(preds){\n",
    "        rss <- sum((preds - (as.matrix(data$y.test[,'Attendance']))) ^ 2)\n",
    "        tss <- sum((as.matrix((data$y.test[,'Attendance']) - mean(as.matrix(data$y.test[,'Attendance']))) ^ 2))\n",
    "        rsq <- 1 - rss/tss\n",
    "        return(rsq)\n",
    "    }\n",
    "    \n",
    "    mae <- function(preds)\n",
    "        {\n",
    "        error <- as.matrix(data$y.test[,'Attendance']) - preds  \n",
    "        mae <- mean(abs(error))\n",
    "        return(mae)\n",
    "        }\n",
    "    \n",
    "    rmse <- function(preds)\n",
    "        {\n",
    "        error <- as.matrix(data$y.test[,'Attendance']) - preds\n",
    "        rmse = sqrt(mean(error^2))\n",
    "        return(rmse)\n",
    "        }\n",
    "    colnames = c(\"$R^2$\",\"MAE\", \"RMSE\")\n",
    "    mat = matrix(c(r2(preds),mae(preds),rmse(preds)), nrow = 1, ncol = 3)\n",
    "    colnames(mat) = colnames\n",
    "    return(mat)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouped Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 15 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Last.Game</th><th scope=col>Capacity</th><th scope=col>Home_Los.Angeles.Lakers</th><th scope=col>LS.Win..</th><th scope=col>Home_Utah.Jazz</th><th scope=col>Home_Dallas.Mavericks</th><th scope=col>Curr.Win..</th><th scope=col>Home_Miami.Heat</th><th scope=col>Home_New.York.Knicks</th><th scope=col>Visitor_Los.Angeles.Lakers</th><th scope=col>Home_Cleveland.Cavaliers</th><th scope=col>H.Pop</th><th scope=col>Home_Chicago.Bulls</th><th scope=col>V.Pop</th><th scope=col>Home_Portland.Trail.Blazers</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>928.3619</td><td>510.0042</td><td>223.7919</td><td>222.3718</td><td>209.999</td><td>209.0234</td><td>201.4591</td><td>193.2703</td><td>174.9298</td><td>174.811</td><td>170.5715</td><td>166.2883</td><td>138.0806</td><td>134.2895</td><td>132.7285</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 15 of type dbl\n",
       "\\begin{tabular}{lllllllllllllll}\n",
       " Last.Game & Capacity & Home\\_Los.Angeles.Lakers & LS.Win.. & Home\\_Utah.Jazz & Home\\_Dallas.Mavericks & Curr.Win.. & Home\\_Miami.Heat & Home\\_New.York.Knicks & Visitor\\_Los.Angeles.Lakers & Home\\_Cleveland.Cavaliers & H.Pop & Home\\_Chicago.Bulls & V.Pop & Home\\_Portland.Trail.Blazers\\\\\n",
       "\\hline\n",
       "\t 928.3619 & 510.0042 & 223.7919 & 222.3718 & 209.999 & 209.0234 & 201.4591 & 193.2703 & 174.9298 & 174.811 & 170.5715 & 166.2883 & 138.0806 & 134.2895 & 132.7285\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 15 of type dbl\n",
       "\n",
       "| Last.Game | Capacity | Home_Los.Angeles.Lakers | LS.Win.. | Home_Utah.Jazz | Home_Dallas.Mavericks | Curr.Win.. | Home_Miami.Heat | Home_New.York.Knicks | Visitor_Los.Angeles.Lakers | Home_Cleveland.Cavaliers | H.Pop | Home_Chicago.Bulls | V.Pop | Home_Portland.Trail.Blazers |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 928.3619 | 510.0042 | 223.7919 | 222.3718 | 209.999 | 209.0234 | 201.4591 | 193.2703 | 174.9298 | 174.811 | 170.5715 | 166.2883 | 138.0806 | 134.2895 | 132.7285 |\n",
       "\n"
      ],
      "text/plain": [
       "     Last.Game Capacity Home_Los.Angeles.Lakers LS.Win.. Home_Utah.Jazz\n",
       "[1,] 928.3619  510.0042 223.7919                222.3718 209.999       \n",
       "     Home_Dallas.Mavericks Curr.Win.. Home_Miami.Heat Home_New.York.Knicks\n",
       "[1,] 209.0234              201.4591   193.2703        174.9298            \n",
       "     Visitor_Los.Angeles.Lakers Home_Cleveland.Cavaliers H.Pop   \n",
       "[1,] 174.811                    170.5715                 166.2883\n",
       "     Home_Chicago.Bulls V.Pop    Home_Portland.Trail.Blazers\n",
       "[1,] 138.0806           134.2895 132.7285                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>$R^2$</th><th scope=col>MAE</th><th scope=col>RMSE</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.585459</td><td>939.0322</td><td>1278.388</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 3 of type dbl\n",
       "\\begin{tabular}{lll}\n",
       " \\$R\\textasciicircum{}2\\$ & MAE & RMSE\\\\\n",
       "\\hline\n",
       "\t 0.585459 & 939.0322 & 1278.388\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 3 of type dbl\n",
       "\n",
       "| $R^2$ | MAE | RMSE |\n",
       "|---|---|---|\n",
       "| 0.585459 | 939.0322 | 1278.388 |\n",
       "\n"
      ],
      "text/plain": [
       "     $R^2$    MAE      RMSE    \n",
       "[1,] 0.585459 939.0322 1278.388"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NFnEEdHxflj2lDYrJSWnZmyuwjeGoZw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NOWit7gqs4c4SO2Bm7hbyy43M/i5DKEOj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\nmyuHbZP7Tm5TU74mWGo/IU5tqfDDMYWTjBfmA5wAL4SFuGE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47GgTs0wxBjAEcvcdMgVee/J6ZxJMgad7c8KNYB1z8ZFGeQvpayFrI9Xl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\noPyZZrsE9207Qg6dfzlEfSqIoGq0wd8LvdmSKMMVWnR55syIbbwE8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qsk2gae2xLWOavKvrx1KOsq4TqpKaDvEt/wkoB0Uo/iCdb4c2jP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\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lgOWA5YDrSSA2oqzQZ8zNlkN0DQSADx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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups <- rep(1:13, c(rep(1,7),1,26,29,5,6,8))\n",
    "\n",
    "g_lass <- cv.gglasso(x=as.matrix(X.train), y=as.matrix(y.train), group=groups, loss=\"ls\", pred.loss= c(\"L1\"), nlambda = 250,lambda.factor=1e-8, nfolds=5, intercept = TRUE)\n",
    "g_lass_coefficients <- coef(g_lass$gglasso.fit, s = g_lass$lambda.min)\n",
    "display(t(g_lass_coefficients[order(g_lass_coefficients[,c(1)], decreasing = TRUE),][2:16]))\n",
    "preds <- predict(g_lass$gglasso.fit, newx = as.matrix(X.test), s = g_lass$lambda.min)\n",
    "plot(g_lass$gglasso.fit, group = FALSE, log.l = TRUE)\n",
    "metrics(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T16:16:47.127187Z",
     "start_time": "2019-12-19T16:16:46.733Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>$xgbLinear</strong> = <dl>\n",
       "\t<dt>$label</dt>\n",
       "\t\t<dd>'eXtreme Gradient Boosting'</dd>\n",
       "\t<dt>$library</dt>\n",
       "\t\t<dd>'xgboost'</dd>\n",
       "\t<dt>$type</dt>\n",
       "\t\t<dd><ol class=list-inline>\n",
       "\t<li>'Regression'</li>\n",
       "\t<li>'Classification'</li>\n",
       "</ol>\n",
       "</dd>\n",
       "\t<dt>$parameters</dt>\n",
       "\t\t<dd><table>\n",
       "<caption>A data.frame: 4 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>parameter</th><th scope=col>class</th><th scope=col>label</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>nrounds</td><td>numeric</td><td># Boosting Iterations</td></tr>\n",
       "\t<tr><td>lambda </td><td>numeric</td><td>L2 Regularization    </td></tr>\n",
       "\t<tr><td>alpha  </td><td>numeric</td><td>L1 Regularization    </td></tr>\n",
       "\t<tr><td>eta    </td><td>numeric</td><td>Learning Rate        </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$grid</dt>\n",
       "\t\t<dd><pre class=language-r><code>function (x, y, len = NULL, search = \"grid\") \n",
       "{\n",
       "<span style=white-space:pre-wrap>    if (search == \"grid\") {</span>\n",
       "<span style=white-space:pre-wrap>        out &lt;- expand.grid(lambda = c(0, 10^seq(-1, -4, length = len - </span>\n",
       "<span style=white-space:pre-wrap>            1)), alpha = c(0, 10^seq(-1, -4, length = len - 1)), </span>\n",
       "<span style=white-space:pre-wrap>            nrounds = floor((1:len) * 50), eta = 0.3)</span>\n",
       "<span style=white-space:pre-wrap>    }</span>\n",
       "<span style=white-space:pre-wrap>    else {</span>\n",
       "<span style=white-space:pre-wrap>        out &lt;- data.frame(lambda = 10^runif(len, min = -5, 0), </span>\n",
       "<span style=white-space:pre-wrap>            alpha = 10^runif(len, min = -5, 0), nrounds = sample(1:100, </span>\n",
       "<span style=white-space:pre-wrap>                size = len, replace = TRUE), eta = runif(len, </span>\n",
       "<span style=white-space:pre-wrap>                max = 3))</span>\n",
       "<span style=white-space:pre-wrap>    }</span>\n",
       "<span style=white-space:pre-wrap>    out</span>\n",
       "}</code></pre></dd>\n",
       "\t<dt>$loop</dt>\n",
       "\t\t<dd>NULL</dd>\n",
       "\t<dt>$fit</dt>\n",
       "\t\t<dd><pre class=language-r><code>function (x, y, wts, param, lev, last, classProbs, ...) \n",
       "{\n",
       "<span style=white-space:pre-wrap>    if (!inherits(x, \"xgb.DMatrix\")) </span>\n",
       "<span style=white-space:pre-wrap>        x &lt;- as.matrix(x)</span>\n",
       "<span style=white-space:pre-wrap>    if (is.factor(y)) {</span>\n",
       "<span style=white-space:pre-wrap>        if (length(lev) == 2) {</span>\n",
       "<span style=white-space:pre-wrap>            y &lt;- ifelse(y == lev[1], 1, 0)</span>\n",
       "<span style=white-space:pre-wrap>            if (!inherits(x, \"xgb.DMatrix\")) </span>\n",
       "<span style=white-space:pre-wrap>                x &lt;- xgboost::xgb.DMatrix(x, label = y, missing = NA)</span>\n",
       "<span style=white-space:pre-wrap>            else xgboost::setinfo(x, \"label\", y)</span>\n",
       "<span style=white-space:pre-wrap>            if (!is.null(wts)) </span>\n",
       "<span style=white-space:pre-wrap>                xgboost::setinfo(x, \"weight\", wts)</span>\n",
       "<span style=white-space:pre-wrap>            out &lt;- xgboost::xgb.train(list(lambda = param$lambda, </span>\n",
       "<span style=white-space:pre-wrap>                alpha = param$alpha), data = x, nrounds = param$nrounds, </span>\n",
       "<span style=white-space:pre-wrap>                objective = \"binary:logistic\", ...)</span>\n",
       "<span style=white-space:pre-wrap>        }</span>\n",
       "<span style=white-space:pre-wrap>        else {</span>\n",
       "<span style=white-space:pre-wrap>            y &lt;- as.numeric(y) - 1</span>\n",
       "<span style=white-space:pre-wrap>            if (!inherits(x, \"xgb.DMatrix\")) </span>\n",
       "<span style=white-space:pre-wrap>                x &lt;- xgboost::xgb.DMatrix(x, label = y, missing = NA)</span>\n",
       "<span style=white-space:pre-wrap>            else xgboost::setinfo(x, \"label\", y)</span>\n",
       "<span style=white-space:pre-wrap>            if (!is.null(wts)) </span>\n",
       "<span style=white-space:pre-wrap>                xgboost::setinfo(x, \"weight\", wts)</span>\n",
       "<span style=white-space:pre-wrap>            out &lt;- xgboost::xgb.train(list(lambda = param$lambda, </span>\n",
       "<span style=white-space:pre-wrap>                alpha = param$alpha), data = x, num_class = length(lev), </span>\n",
       "<span style=white-space:pre-wrap>                nrounds = param$nrounds, objective = \"multi:softprob\", </span>\n",
       "<span style=white-space:pre-wrap>                ...)</span>\n",
       "<span style=white-space:pre-wrap>        }</span>\n",
       "<span style=white-space:pre-wrap>    }</span>\n",
       "<span style=white-space:pre-wrap>    else {</span>\n",
       "<span style=white-space:pre-wrap>        if (!inherits(x, \"xgb.DMatrix\")) </span>\n",
       "<span style=white-space:pre-wrap>            x &lt;- xgboost::xgb.DMatrix(x, label = y, missing = NA)</span>\n",
       "<span style=white-space:pre-wrap>        else xgboost::setinfo(x, \"label\", y)</span>\n",
       "<span style=white-space:pre-wrap>        if (!is.null(wts)) </span>\n",
       "<span style=white-space:pre-wrap>            xgboost::setinfo(x, \"weight\", wts)</span>\n",
       "<span style=white-space:pre-wrap>        out &lt;- xgboost::xgb.train(list(lambda = param$lambda, </span>\n",
       "<span style=white-space:pre-wrap>            alpha = param$alpha), data = x, nrounds = param$nrounds, </span>\n",
       "<span style=white-space:pre-wrap>            objective = \"reg:linear\", ...)</span>\n",
       "<span style=white-space:pre-wrap>    }</span>\n",
       "<span style=white-space:pre-wrap>    out</span>\n",
       "}</code></pre></dd>\n",
       "\t<dt>$predict</dt>\n",
       "\t\t<dd><pre class=language-r><code>function (modelFit, newdata, submodels = NULL) \n",
       "{\n",
       "<span style=white-space:pre-wrap>    if (!inherits(newdata, \"xgb.DMatrix\")) {</span>\n",
       "<span style=white-space:pre-wrap>        newdata &lt;- as.matrix(newdata)</span>\n",
       "<span style=white-space:pre-wrap>        newdata &lt;- xgboost::xgb.DMatrix(data = newdata, missing = NA)</span>\n",
       "<span style=white-space:pre-wrap>    }</span>\n",
       "<span style=white-space:pre-wrap>    out &lt;- predict(modelFit, newdata)</span>\n",
       "<span style=white-space:pre-wrap>    if (modelFit$problemType == \"Classification\") {</span>\n",
       "<span style=white-space:pre-wrap>        if (length(modelFit$obsLevels) == 2) {</span>\n",
       "<span style=white-space:pre-wrap>            out &lt;- ifelse(out &gt;= 0.5, modelFit$obsLevels[1], </span>\n",
       "<span style=white-space:pre-wrap>                modelFit$obsLevels[2])</span>\n",
       "<span style=white-space:pre-wrap>        }</span>\n",
       "<span style=white-space:pre-wrap>        else {</span>\n",
       "<span style=white-space:pre-wrap>            out &lt;- matrix(out, ncol = length(modelFit$obsLevels), </span>\n",
       "<span style=white-space:pre-wrap>                byrow = TRUE)</span>\n",
       "<span style=white-space:pre-wrap>            out &lt;- modelFit$obsLevels[apply(out, 1, which.max)]</span>\n",
       "<span style=white-space:pre-wrap>        }</span>\n",
       "<span style=white-space:pre-wrap>    }</span>\n",
       "<span style=white-space:pre-wrap>    out</span>\n",
       "}</code></pre></dd>\n",
       "\t<dt>$prob</dt>\n",
       "\t\t<dd><pre class=language-r><code>function (modelFit, newdata, submodels = NULL) \n",
       "{\n",
       "<span style=white-space:pre-wrap>    if (!inherits(newdata, \"xgb.DMatrix\")) {</span>\n",
       "<span style=white-space:pre-wrap>        newdata &lt;- as.matrix(newdata)</span>\n",
       "<span style=white-space:pre-wrap>        newdata &lt;- xgboost::xgb.DMatrix(data = newdata, missing = NA)</span>\n",
       "<span style=white-space:pre-wrap>    }</span>\n",
       "<span style=white-space:pre-wrap>    out &lt;- predict(modelFit, newdata)</span>\n",
       "<span style=white-space:pre-wrap>    if (length(modelFit$obsLevels) == 2) {</span>\n",
       "<span style=white-space:pre-wrap>        out &lt;- cbind(out, 1 - out)</span>\n",
       "<span style=white-space:pre-wrap>        colnames(out) &lt;- modelFit$obsLevels</span>\n",
       "<span style=white-space:pre-wrap>    }</span>\n",
       "<span style=white-space:pre-wrap>    else {</span>\n",
       "<span style=white-space:pre-wrap>        out &lt;- matrix(out, ncol = length(modelFit$obsLevels), </span>\n",
       "<span style=white-space:pre-wrap>            byrow = TRUE)</span>\n",
       "<span style=white-space:pre-wrap>        colnames(out) &lt;- modelFit$obsLevels</span>\n",
       "<span style=white-space:pre-wrap>    }</span>\n",
       "<span style=white-space:pre-wrap>    as.data.frame(out)</span>\n",
       "}</code></pre></dd>\n",
       "\t<dt>$predictors</dt>\n",
       "\t\t<dd><pre class=language-r><code>function (x, ...) \n",
       "{\n",
       "<span style=white-space:pre-wrap>    imp &lt;- xgboost::xgb.importance(x$xNames, model = x)</span>\n",
       "<span style=white-space:pre-wrap>    x$xNames[x$xNames %in% imp$Feature]</span>\n",
       "}</code></pre></dd>\n",
       "\t<dt>$varImp</dt>\n",
       "\t\t<dd><pre class=language-r><code>function (object, numTrees = NULL, ...) \n",
       "{\n",
       "<span style=white-space:pre-wrap>    imp &lt;- xgboost::xgb.importance(object$xNames, model = object)</span>\n",
       "<span style=white-space:pre-wrap>    imp &lt;- as.data.frame(imp)[, 1:2]</span>\n",
       "<span style=white-space:pre-wrap>    rownames(imp) &lt;- as.character(imp[, 1])</span>\n",
       "<span style=white-space:pre-wrap>    imp &lt;- imp[, 2, drop = FALSE]</span>\n",
       "<span style=white-space:pre-wrap>    colnames(imp) &lt;- \"Overall\"</span>\n",
       "<span style=white-space:pre-wrap>    missing &lt;- object$xNames[!(object$xNames %in% rownames(imp))]</span>\n",
       "<span style=white-space:pre-wrap>    missing_imp &lt;- data.frame(Overall = rep(0, times = length(missing)))</span>\n",
       "<span style=white-space:pre-wrap>    rownames(missing_imp) &lt;- missing</span>\n",
       "<span style=white-space:pre-wrap>    imp &lt;- rbind(imp, missing_imp)</span>\n",
       "<span style=white-space:pre-wrap>    imp</span>\n",
       "}</code></pre></dd>\n",
       "\t<dt>$levels</dt>\n",
       "\t\t<dd><pre class=language-r><code>function (x) \n",
       "x$obsLevels</code></pre></dd>\n",
       "\t<dt>$tags</dt>\n",
       "\t\t<dd><ol class=list-inline>\n",
       "\t<li>'Linear Classifier Models'</li>\n",
       "\t<li>'Linear Regression Models'</li>\n",
       "\t<li>'L1 Regularization Models'</li>\n",
       "\t<li>'L2 Regularization Models'</li>\n",
       "\t<li>'Boosting'</li>\n",
       "\t<li>'Ensemble Model'</li>\n",
       "\t<li>'Implicit Feature Selection'</li>\n",
       "</ol>\n",
       "</dd>\n",
       "\t<dt>$sort</dt>\n",
       "\t\t<dd><pre class=language-r><code>function (x) \n",
       "{\n",
       "<span style=white-space:pre-wrap>    x[order(x$nrounds, x$alpha, x$lambda), ]</span>\n",
       "}</code></pre></dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\textbf{\\$xgbLinear} = \\begin{description}\n",
       "\\item[\\$label] 'eXtreme Gradient Boosting'\n",
       "\\item[\\$library] 'xgboost'\n",
       "\\item[\\$type] \\begin{enumerate*}\n",
       "\\item 'Regression'\n",
       "\\item 'Classification'\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$parameters] A data.frame: 4 × 3\n",
       "\\begin{tabular}{lll}\n",
       " parameter & class & label\\\\\n",
       " <fct> & <fct> & <fct>\\\\\n",
       "\\hline\n",
       "\t nrounds & numeric & \\# Boosting Iterations\\\\\n",
       "\t lambda  & numeric & L2 Regularization    \\\\\n",
       "\t alpha   & numeric & L1 Regularization    \\\\\n",
       "\t eta     & numeric & Learning Rate        \\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$grid] \\begin{minted}{r}\n",
       "function (x, y, len = NULL, search = \"grid\") \n",
       "\\{\n",
       "    if (search == \"grid\") \\{\n",
       "        out <- expand.grid(lambda = c(0, 10\\textasciicircum{}seq(-1, -4, length = len - \n",
       "            1)), alpha = c(0, 10\\textasciicircum{}seq(-1, -4, length = len - 1)), \n",
       "            nrounds = floor((1:len) * 50), eta = 0.3)\n",
       "    \\}\n",
       "    else \\{\n",
       "        out <- data.frame(lambda = 10\\textasciicircum{}runif(len, min = -5, 0), \n",
       "            alpha = 10\\textasciicircum{}runif(len, min = -5, 0), nrounds = sample(1:100, \n",
       "                size = len, replace = TRUE), eta = runif(len, \n",
       "                max = 3))\n",
       "    \\}\n",
       "    out\n",
       "\\}\n",
       "\\end{minted}\n",
       "\\item[\\$loop] NULL\n",
       "\\item[\\$fit] \\begin{minted}{r}\n",
       "function (x, y, wts, param, lev, last, classProbs, ...) \n",
       "\\{\n",
       "    if (!inherits(x, \"xgb.DMatrix\")) \n",
       "        x <- as.matrix(x)\n",
       "    if (is.factor(y)) \\{\n",
       "        if (length(lev) == 2) \\{\n",
       "            y <- ifelse(y == lev{[}1{]}, 1, 0)\n",
       "            if (!inherits(x, \"xgb.DMatrix\")) \n",
       "                x <- xgboost::xgb.DMatrix(x, label = y, missing = NA)\n",
       "            else xgboost::setinfo(x, \"label\", y)\n",
       "            if (!is.null(wts)) \n",
       "                xgboost::setinfo(x, \"weight\", wts)\n",
       "            out <- xgboost::xgb.train(list(lambda = param\\$lambda, \n",
       "                alpha = param\\$alpha), data = x, nrounds = param\\$nrounds, \n",
       "                objective = \"binary:logistic\", ...)\n",
       "        \\}\n",
       "        else \\{\n",
       "            y <- as.numeric(y) - 1\n",
       "            if (!inherits(x, \"xgb.DMatrix\")) \n",
       "                x <- xgboost::xgb.DMatrix(x, label = y, missing = NA)\n",
       "            else xgboost::setinfo(x, \"label\", y)\n",
       "            if (!is.null(wts)) \n",
       "                xgboost::setinfo(x, \"weight\", wts)\n",
       "            out <- xgboost::xgb.train(list(lambda = param\\$lambda, \n",
       "                alpha = param\\$alpha), data = x, num\\_class = length(lev), \n",
       "                nrounds = param\\$nrounds, objective = \"multi:softprob\", \n",
       "                ...)\n",
       "        \\}\n",
       "    \\}\n",
       "    else \\{\n",
       "        if (!inherits(x, \"xgb.DMatrix\")) \n",
       "            x <- xgboost::xgb.DMatrix(x, label = y, missing = NA)\n",
       "        else xgboost::setinfo(x, \"label\", y)\n",
       "        if (!is.null(wts)) \n",
       "            xgboost::setinfo(x, \"weight\", wts)\n",
       "        out <- xgboost::xgb.train(list(lambda = param\\$lambda, \n",
       "            alpha = param\\$alpha), data = x, nrounds = param\\$nrounds, \n",
       "            objective = \"reg:linear\", ...)\n",
       "    \\}\n",
       "    out\n",
       "\\}\n",
       "\\end{minted}\n",
       "\\item[\\$predict] \\begin{minted}{r}\n",
       "function (modelFit, newdata, submodels = NULL) \n",
       "\\{\n",
       "    if (!inherits(newdata, \"xgb.DMatrix\")) \\{\n",
       "        newdata <- as.matrix(newdata)\n",
       "        newdata <- xgboost::xgb.DMatrix(data = newdata, missing = NA)\n",
       "    \\}\n",
       "    out <- predict(modelFit, newdata)\n",
       "    if (modelFit\\$problemType == \"Classification\") \\{\n",
       "        if (length(modelFit\\$obsLevels) == 2) \\{\n",
       "            out <- ifelse(out >= 0.5, modelFit\\$obsLevels{[}1{]}, \n",
       "                modelFit\\$obsLevels{[}2{]})\n",
       "        \\}\n",
       "        else \\{\n",
       "            out <- matrix(out, ncol = length(modelFit\\$obsLevels), \n",
       "                byrow = TRUE)\n",
       "            out <- modelFit\\$obsLevels{[}apply(out, 1, which.max){]}\n",
       "        \\}\n",
       "    \\}\n",
       "    out\n",
       "\\}\n",
       "\\end{minted}\n",
       "\\item[\\$prob] \\begin{minted}{r}\n",
       "function (modelFit, newdata, submodels = NULL) \n",
       "\\{\n",
       "    if (!inherits(newdata, \"xgb.DMatrix\")) \\{\n",
       "        newdata <- as.matrix(newdata)\n",
       "        newdata <- xgboost::xgb.DMatrix(data = newdata, missing = NA)\n",
       "    \\}\n",
       "    out <- predict(modelFit, newdata)\n",
       "    if (length(modelFit\\$obsLevels) == 2) \\{\n",
       "        out <- cbind(out, 1 - out)\n",
       "        colnames(out) <- modelFit\\$obsLevels\n",
       "    \\}\n",
       "    else \\{\n",
       "        out <- matrix(out, ncol = length(modelFit\\$obsLevels), \n",
       "            byrow = TRUE)\n",
       "        colnames(out) <- modelFit\\$obsLevels\n",
       "    \\}\n",
       "    as.data.frame(out)\n",
       "\\}\n",
       "\\end{minted}\n",
       "\\item[\\$predictors] \\begin{minted}{r}\n",
       "function (x, ...) \n",
       "\\{\n",
       "    imp <- xgboost::xgb.importance(x\\$xNames, model = x)\n",
       "    x\\$xNames{[}x\\$xNames \\%in\\% imp\\$Feature{]}\n",
       "\\}\n",
       "\\end{minted}\n",
       "\\item[\\$varImp] \\begin{minted}{r}\n",
       "function (object, numTrees = NULL, ...) \n",
       "\\{\n",
       "    imp <- xgboost::xgb.importance(object\\$xNames, model = object)\n",
       "    imp <- as.data.frame(imp){[}, 1:2{]}\n",
       "    rownames(imp) <- as.character(imp{[}, 1{]})\n",
       "    imp <- imp{[}, 2, drop = FALSE{]}\n",
       "    colnames(imp) <- \"Overall\"\n",
       "    missing <- object\\$xNames{[}!(object\\$xNames \\%in\\% rownames(imp)){]}\n",
       "    missing\\_imp <- data.frame(Overall = rep(0, times = length(missing)))\n",
       "    rownames(missing\\_imp) <- missing\n",
       "    imp <- rbind(imp, missing\\_imp)\n",
       "    imp\n",
       "\\}\n",
       "\\end{minted}\n",
       "\\item[\\$levels] \\begin{minted}{r}\n",
       "function (x) \n",
       "x\\$obsLevels\n",
       "\\end{minted}\n",
       "\\item[\\$tags] \\begin{enumerate*}\n",
       "\\item 'Linear Classifier Models'\n",
       "\\item 'Linear Regression Models'\n",
       "\\item 'L1 Regularization Models'\n",
       "\\item 'L2 Regularization Models'\n",
       "\\item 'Boosting'\n",
       "\\item 'Ensemble Model'\n",
       "\\item 'Implicit Feature Selection'\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$sort] \\begin{minted}{r}\n",
       "function (x) \n",
       "\\{\n",
       "    x{[}order(x\\$nrounds, x\\$alpha, x\\$lambda), {]}\n",
       "\\}\n",
       "\\end{minted}\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "**$xgbLinear** = $label\n",
       ":   'eXtreme Gradient Boosting'\n",
       "$library\n",
       ":   'xgboost'\n",
       "$type\n",
       ":   1. 'Regression'\n",
       "2. 'Classification'\n",
       "\n",
       "\n",
       "\n",
       "$parameters\n",
       ":   \n",
       "A data.frame: 4 × 3\n",
       "\n",
       "| parameter &lt;fct&gt; | class &lt;fct&gt; | label &lt;fct&gt; |\n",
       "|---|---|---|\n",
       "| nrounds | numeric | # Boosting Iterations |\n",
       "| lambda  | numeric | L2 Regularization     |\n",
       "| alpha   | numeric | L1 Regularization     |\n",
       "| eta     | numeric | Learning Rate         |\n",
       "\n",
       "\n",
       "$grid\n",
       ":   ```r\n",
       "function (x, y, len = NULL, search = \"grid\") \n",
       "{\n",
       "    if (search == \"grid\") {\n",
       "        out <- expand.grid(lambda = c(0, 10^seq(-1, -4, length = len - \n",
       "            1)), alpha = c(0, 10^seq(-1, -4, length = len - 1)), \n",
       "            nrounds = floor((1:len) * 50), eta = 0.3)\n",
       "    }\n",
       "    else {\n",
       "        out <- data.frame(lambda = 10^runif(len, min = -5, 0), \n",
       "            alpha = 10^runif(len, min = -5, 0), nrounds = sample(1:100, \n",
       "                size = len, replace = TRUE), eta = runif(len, \n",
       "                max = 3))\n",
       "    }\n",
       "    out\n",
       "}\n",
       "```\n",
       "$loop\n",
       ":   NULL\n",
       "$fit\n",
       ":   ```r\n",
       "function (x, y, wts, param, lev, last, classProbs, ...) \n",
       "{\n",
       "    if (!inherits(x, \"xgb.DMatrix\")) \n",
       "        x <- as.matrix(x)\n",
       "    if (is.factor(y)) {\n",
       "        if (length(lev) == 2) {\n",
       "            y <- ifelse(y == lev[1], 1, 0)\n",
       "            if (!inherits(x, \"xgb.DMatrix\")) \n",
       "                x <- xgboost::xgb.DMatrix(x, label = y, missing = NA)\n",
       "            else xgboost::setinfo(x, \"label\", y)\n",
       "            if (!is.null(wts)) \n",
       "                xgboost::setinfo(x, \"weight\", wts)\n",
       "            out <- xgboost::xgb.train(list(lambda = param$lambda, \n",
       "                alpha = param$alpha), data = x, nrounds = param$nrounds, \n",
       "                objective = \"binary:logistic\", ...)\n",
       "        }\n",
       "        else {\n",
       "            y <- as.numeric(y) - 1\n",
       "            if (!inherits(x, \"xgb.DMatrix\")) \n",
       "                x <- xgboost::xgb.DMatrix(x, label = y, missing = NA)\n",
       "            else xgboost::setinfo(x, \"label\", y)\n",
       "            if (!is.null(wts)) \n",
       "                xgboost::setinfo(x, \"weight\", wts)\n",
       "            out <- xgboost::xgb.train(list(lambda = param$lambda, \n",
       "                alpha = param$alpha), data = x, num_class = length(lev), \n",
       "                nrounds = param$nrounds, objective = \"multi:softprob\", \n",
       "                ...)\n",
       "        }\n",
       "    }\n",
       "    else {\n",
       "        if (!inherits(x, \"xgb.DMatrix\")) \n",
       "            x <- xgboost::xgb.DMatrix(x, label = y, missing = NA)\n",
       "        else xgboost::setinfo(x, \"label\", y)\n",
       "        if (!is.null(wts)) \n",
       "            xgboost::setinfo(x, \"weight\", wts)\n",
       "        out <- xgboost::xgb.train(list(lambda = param$lambda, \n",
       "            alpha = param$alpha), data = x, nrounds = param$nrounds, \n",
       "            objective = \"reg:linear\", ...)\n",
       "    }\n",
       "    out\n",
       "}\n",
       "```\n",
       "$predict\n",
       ":   ```r\n",
       "function (modelFit, newdata, submodels = NULL) \n",
       "{\n",
       "    if (!inherits(newdata, \"xgb.DMatrix\")) {\n",
       "        newdata <- as.matrix(newdata)\n",
       "        newdata <- xgboost::xgb.DMatrix(data = newdata, missing = NA)\n",
       "    }\n",
       "    out <- predict(modelFit, newdata)\n",
       "    if (modelFit$problemType == \"Classification\") {\n",
       "        if (length(modelFit$obsLevels) == 2) {\n",
       "            out <- ifelse(out >= 0.5, modelFit$obsLevels[1], \n",
       "                modelFit$obsLevels[2])\n",
       "        }\n",
       "        else {\n",
       "            out <- matrix(out, ncol = length(modelFit$obsLevels), \n",
       "                byrow = TRUE)\n",
       "            out <- modelFit$obsLevels[apply(out, 1, which.max)]\n",
       "        }\n",
       "    }\n",
       "    out\n",
       "}\n",
       "```\n",
       "$prob\n",
       ":   ```r\n",
       "function (modelFit, newdata, submodels = NULL) \n",
       "{\n",
       "    if (!inherits(newdata, \"xgb.DMatrix\")) {\n",
       "        newdata <- as.matrix(newdata)\n",
       "        newdata <- xgboost::xgb.DMatrix(data = newdata, missing = NA)\n",
       "    }\n",
       "    out <- predict(modelFit, newdata)\n",
       "    if (length(modelFit$obsLevels) == 2) {\n",
       "        out <- cbind(out, 1 - out)\n",
       "        colnames(out) <- modelFit$obsLevels\n",
       "    }\n",
       "    else {\n",
       "        out <- matrix(out, ncol = length(modelFit$obsLevels), \n",
       "            byrow = TRUE)\n",
       "        colnames(out) <- modelFit$obsLevels\n",
       "    }\n",
       "    as.data.frame(out)\n",
       "}\n",
       "```\n",
       "$predictors\n",
       ":   ```r\n",
       "function (x, ...) \n",
       "{\n",
       "    imp <- xgboost::xgb.importance(x$xNames, model = x)\n",
       "    x$xNames[x$xNames %in% imp$Feature]\n",
       "}\n",
       "```\n",
       "$varImp\n",
       ":   ```r\n",
       "function (object, numTrees = NULL, ...) \n",
       "{\n",
       "    imp <- xgboost::xgb.importance(object$xNames, model = object)\n",
       "    imp <- as.data.frame(imp)[, 1:2]\n",
       "    rownames(imp) <- as.character(imp[, 1])\n",
       "    imp <- imp[, 2, drop = FALSE]\n",
       "    colnames(imp) <- \"Overall\"\n",
       "    missing <- object$xNames[!(object$xNames %in% rownames(imp))]\n",
       "    missing_imp <- data.frame(Overall = rep(0, times = length(missing)))\n",
       "    rownames(missing_imp) <- missing\n",
       "    imp <- rbind(imp, missing_imp)\n",
       "    imp\n",
       "}\n",
       "```\n",
       "$levels\n",
       ":   ```r\n",
       "function (x) \n",
       "x$obsLevels\n",
       "```\n",
       "$tags\n",
       ":   1. 'Linear Classifier Models'\n",
       "2. 'Linear Regression Models'\n",
       "3. 'L1 Regularization Models'\n",
       "4. 'L2 Regularization Models'\n",
       "5. 'Boosting'\n",
       "6. 'Ensemble Model'\n",
       "7. 'Implicit Feature Selection'\n",
       "\n",
       "\n",
       "\n",
       "$sort\n",
       ":   ```r\n",
       "function (x) \n",
       "{\n",
       "    x[order(x$nrounds, x$alpha, x$lambda), ]\n",
       "}\n",
       "```\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$xgbLinear\n",
       "$xgbLinear$label\n",
       "[1] \"eXtreme Gradient Boosting\"\n",
       "\n",
       "$xgbLinear$library\n",
       "[1] \"xgboost\"\n",
       "\n",
       "$xgbLinear$type\n",
       "[1] \"Regression\"     \"Classification\"\n",
       "\n",
       "$xgbLinear$parameters\n",
       "  parameter   class                 label\n",
       "1   nrounds numeric # Boosting Iterations\n",
       "2    lambda numeric     L2 Regularization\n",
       "3     alpha numeric     L1 Regularization\n",
       "4       eta numeric         Learning Rate\n",
       "\n",
       "$xgbLinear$grid\n",
       "function(x, y, len = NULL, search = \"grid\")  {\n",
       "                    if(search == \"grid\") {\n",
       "                      out <- expand.grid(lambda = c(0, 10 ^ seq(-1, -4, length = len - 1)),\n",
       "                                         alpha = c(0, 10 ^ seq(-1, -4, length = len - 1)),\n",
       "                                         nrounds = floor((1:len) * 50),\n",
       "                                         eta = 0.3)\n",
       "                    } else {\n",
       "                      out <- data.frame(lambda = 10^runif(len, min = -5, 0),\n",
       "                                        alpha = 10^runif(len, min = -5, 0),\n",
       "                                        nrounds = sample(1:100, size = len, replace = TRUE),\n",
       "                                        eta = runif(len, max = 3))\n",
       "                    }\n",
       "                    out\n",
       "                  }\n",
       "\n",
       "$xgbLinear$loop\n",
       "NULL\n",
       "\n",
       "$xgbLinear$fit\n",
       "function(x, y, wts, param, lev, last, classProbs, ...) { \n",
       "                    if(!inherits(x, \"xgb.DMatrix\"))\n",
       "                     x <- as.matrix(x)\n",
       "                    \n",
       "                    if(is.factor(y)) {\n",
       "                      if(length(lev) == 2) {\n",
       "                        y <- ifelse(y == lev[1], 1, 0) \n",
       "                        \n",
       "                        if(!inherits(x, \"xgb.DMatrix\"))\n",
       "                          x <- xgboost::xgb.DMatrix(x, label = y, missing = NA) else\n",
       "                            xgboost::setinfo(x, \"label\", y)\n",
       "                        \n",
       "                        if (!is.null(wts))\n",
       "                          xgboost::setinfo(x, 'weight', wts)\n",
       "                        \n",
       "                        \n",
       "                        out <- xgboost::xgb.train(list(lambda = param$lambda, \n",
       "                                                       alpha = param$alpha), \n",
       "                                                  data = x,\n",
       "                                                  nrounds = param$nrounds,\n",
       "                                                  objective = \"binary:logistic\",\n",
       "                                                  ...)\n",
       "                      } else {\n",
       "                        y <- as.numeric(y) - 1\n",
       "\n",
       "                        if(!inherits(x, \"xgb.DMatrix\"))\n",
       "                          x <- xgboost::xgb.DMatrix(x, label = y, missing = NA) else\n",
       "                            xgboost::setinfo(x, \"label\", y)\n",
       "                        \n",
       "                        if (!is.null(wts))\n",
       "                          xgboost::setinfo(x, 'weight', wts)\n",
       "                        \n",
       "                        out <- xgboost::xgb.train(list(lambda = param$lambda, \n",
       "                                                       alpha = param$alpha), \n",
       "                                                  data = x,\n",
       "                                                  num_class = length(lev),\n",
       "                                                  nrounds = param$nrounds,\n",
       "                                                  objective = \"multi:softprob\",\n",
       "                                                  ...)\n",
       "                      }\n",
       "                    } else {\n",
       "                      if(!inherits(x, \"xgb.DMatrix\"))\n",
       "                        x <- xgboost::xgb.DMatrix(x, label = y, missing = NA) else\n",
       "                          xgboost::setinfo(x, \"label\", y)\n",
       "                      \n",
       "                      if (!is.null(wts))\n",
       "                        xgboost::setinfo(x, 'weight', wts)\n",
       "                      \n",
       "                      out <- xgboost::xgb.train(list(lambda = param$lambda, \n",
       "                                                     alpha = param$alpha), \n",
       "                                                data = x,\n",
       "                                                nrounds = param$nrounds,\n",
       "                                                objective = \"reg:linear\",\n",
       "                                                ...)\n",
       "                    }\n",
       "                    out\n",
       "                  }\n",
       "\n",
       "$xgbLinear$predict\n",
       "function(modelFit, newdata, submodels = NULL) {\n",
       "                    if(!inherits(newdata, \"xgb.DMatrix\")) {\n",
       "                      newdata <- as.matrix(newdata)\n",
       "                      newdata <- xgboost::xgb.DMatrix(data=newdata, missing = NA)\n",
       "                    }\n",
       "                    out <- predict(modelFit, newdata)\n",
       "                    if(modelFit$problemType == \"Classification\") {\n",
       "                      if(length(modelFit$obsLevels) == 2) {\n",
       "                        out <- ifelse(out >= .5,\n",
       "                                      modelFit$obsLevels[1],\n",
       "                                      modelFit$obsLevels[2])\n",
       "                      } else {\n",
       "                        out <- matrix(out, ncol = length(modelFit$obsLevels), byrow = TRUE)\n",
       "                        out <- modelFit$obsLevels[apply(out, 1, which.max)]\n",
       "                      }\n",
       "                    }\n",
       "                    out\n",
       "                  }\n",
       "\n",
       "$xgbLinear$prob\n",
       "function(modelFit, newdata, submodels = NULL) {\n",
       "                    if(!inherits(newdata, \"xgb.DMatrix\")) {\n",
       "                      newdata <- as.matrix(newdata)\n",
       "                      newdata <- xgboost::xgb.DMatrix(data=newdata, missing = NA)\n",
       "                    }\n",
       "                    out <- predict(modelFit, newdata)\n",
       "                    if(length(modelFit$obsLevels) == 2) {\n",
       "                      out <- cbind(out, 1 - out)\n",
       "                      colnames(out) <- modelFit$obsLevels\n",
       "                    } else {\n",
       "                      out <- matrix(out, ncol = length(modelFit$obsLevels), byrow = TRUE)\n",
       "                      colnames(out) <- modelFit$obsLevels\n",
       "                    }\n",
       "                    as.data.frame(out)\n",
       "                  }\n",
       "\n",
       "$xgbLinear$predictors\n",
       "function(x, ...) {\n",
       "                    imp <- xgboost::xgb.importance(x$xNames, model = x)\n",
       "                    x$xNames[x$xNames %in% imp$Feature]\n",
       "                  }\n",
       "\n",
       "$xgbLinear$varImp\n",
       "function(object, numTrees = NULL, ...) {\n",
       "                    imp <- xgboost::xgb.importance(object$xNames, model = object)\n",
       "                    imp <- as.data.frame(imp)[, 1:2]\n",
       "                    rownames(imp) <- as.character(imp[,1])\n",
       "                    imp <- imp[,2,drop = FALSE]\n",
       "                    colnames(imp) <- \"Overall\"\n",
       "\n",
       "                    missing <- object$xNames[!(object$xNames %in% rownames(imp))]\n",
       "                    missing_imp <- data.frame(Overall=rep(0, times=length(missing)))\n",
       "                    rownames(missing_imp) <- missing\n",
       "                    imp <- rbind(imp, missing_imp)\n",
       "\n",
       "                    imp\n",
       "                  }\n",
       "\n",
       "$xgbLinear$levels\n",
       "function(x) x$obsLevels\n",
       "\n",
       "$xgbLinear$tags\n",
       "[1] \"Linear Classifier Models\"   \"Linear Regression Models\"  \n",
       "[3] \"L1 Regularization Models\"   \"L2 Regularization Models\"  \n",
       "[5] \"Boosting\"                   \"Ensemble Model\"            \n",
       "[7] \"Implicit Feature Selection\"\n",
       "\n",
       "$xgbLinear$sort\n",
       "function(x) {\n",
       "                    # This is a toss-up, but the # trees probably adds\n",
       "                    # complexity faster than number of splits\n",
       "                    x[order(x$nrounds, x$alpha, x$lambda),]\n",
       "                  }\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getModelInfo(\"xgbLinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T08:52:40.951295Z",
     "start_time": "2019-12-19T08:52:40.896Z"
    }
   },
   "outputs": [],
   "source": [
    "X.train = data.frame(as.matrix(data$X.train))\n",
    "X.test = data.frame(as.matrix(data$X.test))\n",
    "y.train = c(as.matrix(data$y.train))\n",
    "y.test = c(as.matrix(data$y.test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "cl <- makePSOCKcluster(7)\n",
    "registerDoParallel(cl)\n",
    "\n",
    "ctrl <- gafsControl(allowParallel = TRUE, functions = rfGA)\n",
    "obj <- gafs(X.train, y.train, iters = 200, popSize = 50, pcrossover = 0.8, pmutation = 0.1, elite = 0, suggestions = NULL, differences = TRUE, gafsControl = ctrl)\n",
    "plot(obj) + theme_bw()\n",
    "\n",
    "obj$fit()\n",
    "\n",
    "obj$fitness_intern <- function (object, x, y, maximize, p) {\n",
    "  RMSE <- rfStats(object)[1]\n",
    "  d_RMSE <- dMin(0, 4)\n",
    "  d_Size <- dMin(1, p, 2)\n",
    "  overall <- dOverall(d_RMSE, d_Size)\n",
    "  D <- predict(overall, data.frame(RMSE, ncol(x)))\n",
    "  c(D = D, RMSE = as.vector(RMSE))\n",
    "  }\n",
    "\n",
    "ga_ctrl_d <- gafsControl(functions = rfGA2,\n",
    "                         method = \"repeatedcv\",\n",
    "                         repeats = 5,\n",
    "                         metric = c(internal = \"D\", external = \"RMSE\"),\n",
    "                         maximize = c(internal = TRUE, external = FALSE))\n",
    "\n",
    "set.seed(10)\n",
    "rf_ga_d <- gafs(x = x, y = y,\n",
    "                iters = 150,\n",
    "                gafsControl = ga_ctrl_d)\n",
    "\n",
    "rf_ga_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj <- safs(x = predictors, \n",
    "            y = outcome,\n",
    "            iters = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T03:03:41.926933Z",
     "start_time": "2019-12-19T03:03:25.960Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>$R^2$</th><th scope=col>MAE</th><th scope=col>RMSE</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.5761534</td><td>961.4317</td><td>1292.657</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 3 of type dbl\n",
       "\\begin{tabular}{lll}\n",
       " \\$R\\textasciicircum{}2\\$ & MAE & RMSE\\\\\n",
       "\\hline\n",
       "\t 0.5761534 & 961.4317 & 1292.657\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 3 of type dbl\n",
       "\n",
       "| $R^2$ | MAE | RMSE |\n",
       "|---|---|---|\n",
       "| 0.5761534 | 961.4317 | 1292.657 |\n",
       "\n"
      ],
      "text/plain": [
       "     $R^2$     MAE      RMSE    \n",
       "[1,] 0.5761534 961.4317 1292.657"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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4r133zzjcsKJMy2+vXre7ZZQZPL6o0x\nN4fV/PRhTaPtevfddz376Au9UaxuO3XqVIr1upCQkOCypht3WZMqmH10P72pqxXcpNhXy2YN\nu3OVKFHCs5/Vq+SyAjSXFVSl2FdvFKv5WPc4clnXLJnXekNbvTlt6pvKWtcquax7N3luljtn\nzpwUebGAAAIIIBA8gRA9lfUfOAkBBBBAAAFHCBw5ckSsAEXckya4C62TLOi9iEqVKmVuKKvX\nH2U16cx5u3fvNr1Pl112meh9izJK+/btMzel1VnxUk/OoMfotOFz584110iVLl3alE3v1ZTZ\nNVExMTFy5swZpv7OCJ31CCCAQBAECJCCgMwpEEAAAQTynkDyACkiIiLvAVBjBBBAwKECTNLg\n0Iaj2AgggAACCCCAAAIIIOB/AQIk/5uSIwIIIIAAAmZyCJ3EQWe+IyGAAAIIOEeAIXbOaStK\nigACCCCAAAIIIIAAAgEWoAcpwMBkjwACCCCAAAIIIIAAAs4RIEByTltRUgQQQAABBBBAAAEE\nEAiwAAFSgIHJHgEEEEAAAQQQQAABBJwjQIDknLaipAgggAACCCCAAAIIIBBggYzvgBfgE5M9\nAgjkrMCyZcvk7NmzmRZCb7jZvn37NPtcuHBBFi1aZNb36NFDdKau9NJvv/1mbo559dVXS5Uq\nVdLbJc26xMRE2bx5s2zatEn++OMP0Zt1NmrUSOrXr5/uebQcWp5LpSuvvNLkdan92I5AMAX0\nxrTz5s3z+pQtW7aUChUqyMKFCyUuLi7NcXpzXL0Rrd68tnr16mm2601w9bPVoEEDqV27dprt\nuiI+Pl4WLFhgbn7bqVMnzz581jwUvEAAgdwu4CIhgECeFKhVq5bL+v8t00fTpk3Ttfn44489\nx+nrjNIjjzxi9vvggw8y2iXF+kOHDrmuvfZaT97Jy2cFSK6dO3em2F8XypQpk+7+yY/V1xMn\nTkxzLCsQyGmB2NhYr96/7vfz3LlzTZFLly59yePatWvnOn78eIoqvvbaa+a4cePGpViffCE6\nOtrso5+55InPWnINXiOAQG4WoAfJ+qtDQiAvC0ydOjXdnhk1iYiISJdmxowZUrNmTbG+SMmb\nb74pAwYMSHe/rKzUXiDtaTp48KA8/PDD0qtXLylbtqwcO3ZM5s+fLy+99JI0b95c1q1bJ1Zw\nlyJr/dV8+vTpKdalXtBjg5GsDgH54Q+R7YdEihcSaXe5SNniwTgz53CiQP78+eXdd99NUfSf\nf/5ZrEBGtPdmyJAhKbY1adLEs6w9t+6eXPfKhIQE2b9/v7z//vvy7bffivWDg2zdulVCQ/0z\not5OnzWXhnFrrV8/NlvPRURCOliPim4JnhFAAAHfBQiQfLfjSARyhYB+AStYsKDXddm3b598\n/fXXcu+994r167e8/fbb5gtYvXr1vM4jvR1XrFghBw4ckH79+sn48eM9u+gQOx1WpIGSnmvW\nrFnyxBNPeLbrC/3Sdtttt6VYlxMLOw6L3DZVZOtBkcvKipw4J3ImVuTh7iKP9siJEnFOuwto\n4JL6vVuiRAkTINWtWzfNtuT10WOTD4FLvu0f//iHGWK3Y8cOWb9+vbRo0SL5Zp9f2+Wz5tor\nkqi/y6y3HpdZj1PW44QVID0kEjrWevZPPGhlSkIAgbwowH8hebHVqTMC2RB45513RK+b6Nat\nm6fnSHuRspt++eUXk0WHDtbPwOmkRx99VPQ6Ig2U7JiOnRHp/opItUiRnVZ898sYkb0viUwd\nKjJhicjLX9mx1JQptwpoz9TgwYNN9VavXp2rqumyPmuJ1qVRIcWsH0f2iIT9bj2OWIHR51Zv\nkvUDxcXHclV1qQwCCOSAAAFSDqBzSgScKmCNNzbDgazrH8wv1zqBQ8WKFc1wnvPnz2erWm3a\ntDHHW9cKiXWtUZq8tCdJLy7X7XZMry0TKV1Y5IO7RcqV+LuEISEifZqJvDpQZNxC60fuGDuW\nnDLlVoFff/3VVE0ndchNyTXFqk2iFRBZc1uEVP1fzUJ7Wuves4Kkl62H1YtLQgABBHwVYIid\nr3Ich0AuEdi4caOEh4enqU3RokXTzHKlw+D27t1rhte5j9EhejokzpqsQe688840+Xi74ppr\nrpFBgwbJhx9+KDq0SK8Z6tKli3Ts2NEMsXOfL738tEdr1apV6W0y6/RYfw0xyugkX28VubWl\nSP58affoawVJ938o8uMuka4N027Pzpp464vi+9+LxMZnJxd7H9vyMpGm1e1dRruU7tSpU7Jn\nzx554403ZPHixVKpUiXp2dOKHPyU7PBZc1k/RoTcaj2sHyRSJw2SLpa2AqSV1vZBqbdmb9ll\nXV/ommHlcS57+dj56JCmlltrO5eQsiEQHAECpOA4cxYEbCvQqlWrdMtmzYAl33zzTYpt7okQ\nhg4d6lmvrzVA0i9k2QmQ9NqGmTNnylVXXSWvvvqq/Pjjj+YxZswYKVy4sPmS99///jfd6cL1\nwnQtb0ZJe7n0+qZAppg4kRLpfGHTc4ZZQVORAtb3Kmsff6doq+NOA6QLCf7O2T75xVt1I0BK\n2x56DWCIdlNmkHRqfGsGSTNddwa7ZHm1HT5rLitACU3qpU23ArotEEGM9Vm7+E6A8k63IsFf\nGXLcGrZIgBR8eM5oOwECJNs1CQVCILgCOuGBXq+QOlWrVi3FKp2xzppiWHQyhmbNrC6RpKS9\nPTqJggY0ejF48m3ufbx91i97//nPf+Shhx4yw+n0Xk0rV640gdrs2bNFe7DmzJmTJhjS4Orl\nl61xNRmkIkWsKa4CnOpXEvl2u8idbdOe6DcrNjtu3XJK9/F3KlPMOu8of+dKfk4Q0Pe9ewZJ\nvSeSDqnbvn27VK1a1Qx7bd26tZnAxJ91scNnLeTKv3uI5NG0NXPts9b9YfWCWPv4O4UUtX7s\nsH6MICGAQO4XIEDK/W1MDRHIVODxxx/3aha7jz76yMxa9+eff0rq4OnkyZPmHNqLlJ0AyV1Q\nDZS0J0kfI0eONDe0feqpp0zP0u233y56s8vkSb+0/fvf/06+Kuiv/9VRpJsVo322QaT31f87\nvV53dN9MkU71RC4v/7/1vEIguwL6w4b2ECVP+jl59tlnZcSIEaKTM+hQ2eSpUKFCZlF7nzJK\nOkxPU3qzW9rhsxb6f9YlSNZn7OLbVk/SsP/VwqU9PHdYy/r7jX8m7ftf5rxCAIE8JUCAlKea\nm8oi4LuADq/TwEVnr0tvWM+CBQvMFNyvvPKKlCxZMksn0mE7Q62hesWKFTND9VIfrF/yNN/N\nmzebKca3bdsmV1xxRerdcnT52toiz/UWGWp9aZu1VqTVZX/3Gs1e9/fkDbP+laPF4+R5RECH\npOoPCHotn17Tp72+ye+BpBOsaHL/qJEei3XDZrO6VKlS6W3O8XUhjazA6E0rGLrH6kmaY/UW\nWRNfuqKtxyyraNZw1nxfW+syHnmY4+WnAAggYH+BUPsXkRIigEBOC+jscXrzyrZt25ohbp98\n8omkfvTu3VtiYmLkvfesaaSymMLCwszkD2+99Za5yWVGh7tvXJveL9sZHRPM9fd3Fln5iBUQ\nWSP65m8U2bJfZEQXkVXWtMPume2CWR7OlTcFJk+ebK7V0xssT5gwIQWC+35l33//veislOkl\nHS6rqUGDBulttsW60DutQMjqrRVrJPDFL6zn9VbQZP0Ikc/63IVUt5ZJCCCAQDYECJCygceh\nCOQVgRkzdOomEZ2xLqN0Z9IMdr7eE+mmm24yX9huueUW0WF8qZPenFZ7qfRLW40aNVJvts3y\n1VbR3hoq8o11fcT8ESLDrxMpZE3QQEIgWALagzt16lRzOh1yp7PauZNeM6ifoQ0bNsiTTz7p\nXu151s/eSy+9ZK5L7N+/v2e9HV9oT1I+q5phVjynvUah1o8Tem8kEgIIIJBdAYbYZVeQ4xHI\n5QIXLlww1zlor02fPn0yrK3OIqf3KtKLxHViheQ3fNXhcTq5Qnrpnnvukeuvv16GDx8uOuW4\nXuvUsGFDM/FD48aNRYff6cXny5cvFx0eNGXKlDTZxMfHS69evdKsT75Ch+S98MILyVfxGoFc\nK6CfqYEDB5rP09133y1Lly711HXhwoWi0+o///zz8sUXX4hO5qC9s3/88Ydor5P2BE+bNs18\nBj0HJb3gs5ZahGUEEMiNAgRIubFVqRMCfhSYN2+enDhxQvr16yclSpTIMGe9LkknUNBZ8bQX\nKXmApMPz9JFe0i9ymjQA0+smNCjSZ72vkc5ip0l/Ee/atavo0CENwlInHSqk5cwsRUVFZbaZ\nbQjkOgGdLv+rr74yn6P3339f/vGPf5g66ix3S5YskdGjR8u6detEh7Zq0s9ZR+u+Y9qLe8cd\nOttB2sRnLa0JaxBAIPcJhFj/2aU/CDn31ZUaIYCAgwQSExNl586dotcnaVCU3sQQDqoORUXA\ntgJHjx6VY8eOmYlPkk/oYNsCUzAEEEAgwAIESAEGJnsEEEAAAQQQQAABBBBwjgCTNDinrSgp\nAggggAACCCCAAAIIBFiAACnAwGSPAAIIIIAAAggggAACzhEgQHJOW1FSBBBAAAEEEEAAAQQQ\nCLAAAVKAgckeAQQQQAABBBBAAAEEnCNAgOSctqKkCCCAAAIIIIAAAgggEGABAqQAA5M9Aggg\ngAACCCCAAAIIOEeAAMk5bUVJEUAAAQQQQAABBBBAIMACBEgBBiZ7BBBAAAEEEEAAAQQQcI4A\nAZJz2oqSIoAAAggggAACCCCAQIAFCJACDEz2CCCAAAIIIIAAAggg4BwBAiTntBUlRQABBBBA\nAAEEEEAAgQALECAFGJjsEUAAAQQQQAABBBBAwDkCBEjOaStKigACCCCAAAIIIIAAAgEWIEAK\nMDDZI4AAA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JfEbl7NL5875reZokmbYM4xzRNs7126rna9qr3smWedostoj0Dm5DH6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\nhgACCCCAAAIIIIAAAhUs8P8B+ExL3uSEcKIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Robust Linear Model\n",
    "\n",
    "to.drop <- c('Home_Atlanta.Hawks','Visitor_Atlanta.Hawks', \"Last.Five_0\", \"Day.of.Week_Friday\", \"Month_April\", 'Home_Detroit.Pistons',\n",
    "            'Home_Golden.State.Warriors', 'Home_Milwaukee.Bucks')\n",
    "X.train <- X.train[ , !(names(X.train) %in% to.drop)]\n",
    "X.test <- X.test[, !(names(X.test) %in% to.drop)]\n",
    "cl <- makePSOCKcluster(7)\n",
    "registerDoParallel(cl)\n",
    "\n",
    "grid <- expand.grid(intercept = c(TRUE, FALSE), psi = c(\"psi.huber\",\"psi.hampel\", \"psi.bisquare\"))\n",
    "tc <- trainControl(method = 'repeatedcv', number = 5, search = 'grid', summaryFunction = defaultSummary,\n",
    "                  verboseIter = FALSE)\n",
    "\n",
    "rlm <- train(X.train, y.train, method = \"rlm\", metric = \"RMSE\", \n",
    "      maximize = FALSE, trControl = tc, tuneGrid = grid)\n",
    "\n",
    "plot(rlm)  \n",
    "\n",
    "preds = predict(rlm, newdata = X.test)\n",
    "\n",
    "\n",
    "metrics(preds)\n",
    "\n",
    "stopCluster(cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T09:26:37.410950Z",
     "start_time": "2019-12-19T09:22:05.908Z"
    }
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in plot.train(bagEarthgcv): There are no tuning parameters with more than 1 value.\n",
     "output_type": "error",
     "traceback": [
      "Error in plot.train(bagEarthgcv): There are no tuning parameters with more than 1 value.\nTraceback:\n",
      "1. plot(bagEarthgcv)",
      "2. plot.train(bagEarthgcv)",
      "3. stop(plotIt)"
     ]
    }
   ],
   "source": [
    "#Bagged MARS using gCV Pruning\n",
    "\n",
    "cl <- makePSOCKcluster(7)\n",
    "registerDoParallel(cl)\n",
    "\n",
    "grid <- expand.grid(degree = c(1))\n",
    "tc <- trainControl(method = 'none',\n",
    "                  verboseIter = FALSE)\n",
    "\n",
    "bagEarthgcv <- train(X.train, y.train, method = \"bagEarthGCV\", metric = \"RMSE\", \n",
    "      maximize = FALSE, trControl = tc, tuneGrid = grid)\n",
    "\n",
    "plot(bagEarthgcv)  \n",
    "\n",
    "preds = predict(bagEarthgcv, newdata = X.test)\n",
    "\n",
    "\n",
    "metrics(preds)\n",
    "\n",
    "stopCluster(cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in LiblineaR::LiblineaR(data = as.matrix(x), target = y, cost = param$cost, :\n",
      "“No value provided for svr_eps. Using default of 0.1”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>$R^2$</th><th scope=col>MAE</th><th scope=col>RMSE</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.545341</td><td>1014.685</td><td>1338.819</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 3 of type dbl\n",
       "\\begin{tabular}{lll}\n",
       " \\$R\\textasciicircum{}2\\$ & MAE & RMSE\\\\\n",
       "\\hline\n",
       "\t 0.545341 & 1014.685 & 1338.819\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 3 of type dbl\n",
       "\n",
       "| $R^2$ | MAE | RMSE |\n",
       "|---|---|---|\n",
       "| 0.545341 | 1014.685 | 1338.819 |\n",
       "\n"
      ],
      "text/plain": [
       "     $R^2$    MAE      RMSE    \n",
       "[1,] 0.545341 1014.685 1338.819"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BsabwQlpaWq4/p9c8jo40Af+DYsUcu0jrOtqLAAIIIIAAAggg4AiE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\nAggggAACCDgo8D8QiaiwDuI/HwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#L2 Regularized Support Vector Machine (dual) with Linear Kernel\n",
    "\n",
    "cl <- makePSOCKcluster(7)\n",
    "registerDoParallel(cl)\n",
    "\n",
    "grid <- expand.grid(cost = 2^runif(50, min = -10, max = 10), Loss = c(\"L1\", \"L2\"))\n",
    "tc <- trainControl(method = 'cv', number = 5, search = 'grid', summaryFunction = defaultSummary,\n",
    "                  verboseIter = FALSE)\n",
    "\n",
    "regSVM <- train(X.train, y.train, method = \"svmLinear3\", metric = \"RMSE\", \n",
    "      maximize = FALSE, trControl = tc, tuneGrid = grid)\n",
    "\n",
    "plot(regSVM)  \n",
    "\n",
    "preds = predict(regSVM, newdata = X.test)\n",
    "\n",
    "\n",
    "metrics(preds)\n",
    "\n",
    "stopCluster(cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T08:52:27.314070Z",
     "start_time": "2019-12-19T08:52:27.140Z"
    }
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in eval(expr, envir, enclos): object 'X.train' not found\n",
     "output_type": "error",
     "traceback": [
      "Error in eval(expr, envir, enclos): object 'X.train' not found\nTraceback:\n"
     ]
    }
   ],
   "source": [
    "X.train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T21:24:43.643287Z",
     "start_time": "2019-12-19T20:51:32.563Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eXtreme Gradient Boosting \n",
      "\n",
      "11838 samples\n",
      "   90 predictor\n",
      "\n",
      "No pre-processing\n",
      "Resampling: Cross-Validated (5 fold) \n",
      "Summary of sample sizes: 9470, 9471, 9470, 9471, 9470 \n",
      "Resampling results across tuning parameters:\n",
      "\n",
      "  lambda        alpha         nrounds  RMSE      Rsquared   MAE     \n",
      "  0.0000000000  0.0000000000   50      1345.939  0.6885505  880.1535\n",
      "  0.0000000000  0.0000000000  100      1333.322  0.6951104  872.2563\n",
      "  0.0000000000  0.0000000000  150      1333.093  0.6958215  871.8325\n",
      "  0.0000000000  0.0001000000   50      1345.939  0.6885505  880.1535\n",
      "  0.0000000000  0.0001000000  100      1333.322  0.6951104  872.2563\n",
      "  0.0000000000  0.0001000000  150      1333.093  0.6958215  871.8325\n",
      "  0.0000000000  0.0003162278   50      1345.939  0.6885505  880.1535\n",
      "  0.0000000000  0.0003162278  100      1333.322  0.6951104  872.2563\n",
      "  0.0000000000  0.0003162278  150      1333.093  0.6958216  871.8325\n",
      "  0.0000000000  0.0010000000   50      1345.939  0.6885505  880.1535\n",
      "  0.0000000000  0.0010000000  100      1333.322  0.6951104  872.2563\n",
      "  0.0000000000  0.0010000000  150      1333.093  0.6958215  871.8325\n",
      "  0.0000000000  0.0031622777   50      1345.939  0.6885505  880.1535\n",
      "  0.0000000000  0.0031622777  100      1333.322  0.6951104  872.2563\n",
      "  0.0000000000  0.0031622777  150      1333.093  0.6958215  871.8325\n",
      "  0.0000000000  0.0100000000   50      1345.940  0.6885504  880.1534\n",
      "  0.0000000000  0.0100000000  100      1333.322  0.6951104  872.2563\n",
      "  0.0000000000  0.0100000000  150      1333.094  0.6958214  871.8326\n",
      "  0.0000000000  0.0316227766   50      1342.162  0.6902954  877.2952\n",
      "  0.0000000000  0.0316227766  100      1333.962  0.6948803  872.0986\n",
      "  0.0000000000  0.0316227766  150      1334.979  0.6951522  873.9277\n",
      "  0.0000000000  0.1000000000   50      1344.177  0.6894034  880.0866\n",
      "  0.0000000000  0.1000000000  100      1333.712  0.6949710  874.1255\n",
      "  0.0000000000  0.1000000000  150      1333.350  0.6956737  874.7909\n",
      "  0.0001000000  0.0000000000   50      1340.977  0.6908509  878.2538\n",
      "  0.0001000000  0.0000000000  100      1333.421  0.6949734  873.0741\n",
      "  0.0001000000  0.0000000000  150      1334.195  0.6952410  873.3378\n",
      "  0.0001000000  0.0001000000   50      1340.977  0.6908509  878.2538\n",
      "  0.0001000000  0.0001000000  100      1333.421  0.6949734  873.0741\n",
      "  0.0001000000  0.0001000000  150      1334.739  0.6950189  873.7712\n",
      "  0.0001000000  0.0003162278   50      1340.977  0.6908509  878.2538\n",
      "  0.0001000000  0.0003162278  100      1333.421  0.6949734  873.0740\n",
      "  0.0001000000  0.0003162278  150      1334.739  0.6950188  873.7712\n",
      "  0.0001000000  0.0010000000   50      1340.977  0.6908509  878.2538\n",
      "  0.0001000000  0.0010000000  100      1333.421  0.6949735  873.0740\n",
      "  0.0001000000  0.0010000000  150      1334.739  0.6950189  873.7712\n",
      "  0.0001000000  0.0031622777   50      1340.977  0.6908509  878.2538\n",
      "  0.0001000000  0.0031622777  100      1333.421  0.6949735  873.0740\n",
      "  0.0001000000  0.0031622777  150      1334.195  0.6952410  873.3378\n",
      "  0.0001000000  0.0100000000   50      1340.977  0.6908510  878.2538\n",
      "  0.0001000000  0.0100000000  100      1333.421  0.6949736  873.0736\n",
      "  0.0001000000  0.0100000000  150      1334.739  0.6950191  873.7707\n",
      "  0.0001000000  0.0316227766   50      1341.794  0.6904691  876.7591\n",
      "  0.0001000000  0.0316227766  100      1335.280  0.6941886  873.3090\n",
      "  0.0001000000  0.0316227766  150      1339.191  0.6931595  874.9208\n",
      "  0.0001000000  0.1000000000   50      1342.833  0.6900425  878.6846\n",
      "  0.0001000000  0.1000000000  100      1333.516  0.6950354  870.4958\n",
      "  0.0001000000  0.1000000000  150      1337.197  0.6941567  872.2811\n",
      "  0.0003162278  0.0000000000   50      1343.971  0.6896556  878.8305\n",
      "  0.0003162278  0.0000000000  100      1337.085  0.6937546  873.4415\n",
      "  0.0003162278  0.0000000000  150      1341.779  0.6923503  875.5591\n",
      "  0.0003162278  0.0001000000   50      1343.971  0.6896556  878.8305\n",
      "  0.0003162278  0.0001000000  100      1337.085  0.6937546  873.4415\n",
      "  0.0003162278  0.0001000000  150      1341.779  0.6923503  875.5591\n",
      "  0.0003162278  0.0003162278   50      1343.971  0.6896556  878.8305\n",
      "  0.0003162278  0.0003162278  100      1337.085  0.6937546  873.4415\n",
      "  0.0003162278  0.0003162278  150      1341.779  0.6923503  875.5591\n",
      "  0.0003162278  0.0010000000   50      1343.971  0.6896556  878.8305\n",
      "  0.0003162278  0.0010000000  100      1337.085  0.6937546  873.4415\n",
      "  0.0003162278  0.0010000000  150      1341.779  0.6923503  875.5591\n",
      "  0.0003162278  0.0031622777   50      1343.971  0.6896556  878.8305\n",
      "  0.0003162278  0.0031622777  100      1337.085  0.6937546  873.4415\n",
      "  0.0003162278  0.0031622777  150      1341.779  0.6923503  875.5590\n",
      "  0.0003162278  0.0100000000   50      1344.374  0.6895533  878.8262\n",
      "  0.0003162278  0.0100000000  100      1338.216  0.6934561  873.9542\n",
      "  0.0003162278  0.0100000000  150      1345.339  0.6910255  877.4708\n",
      "  0.0003162278  0.0316227766   50      1344.374  0.6895535  878.8260\n",
      "  0.0003162278  0.0316227766  100      1338.215  0.6934563  873.9539\n",
      "  0.0003162278  0.0316227766  150      1345.338  0.6910257  877.4703\n",
      "  0.0003162278  0.1000000000   50      1343.972  0.6896549  878.8595\n",
      "  0.0003162278  0.1000000000  100      1338.497  0.6931927  874.6419\n",
      "  0.0003162278  0.1000000000  150      1341.122  0.6927579  877.0010\n",
      "  0.0010000000  0.0000000000   50      1340.075  0.6913990  881.0034\n",
      "  0.0010000000  0.0000000000  100      1336.862  0.6937316  876.4153\n",
      "  0.0010000000  0.0000000000  150      1337.924  0.6939063  876.1308\n",
      "  0.0010000000  0.0001000000   50      1340.075  0.6913990  881.0034\n",
      "  0.0010000000  0.0001000000  100      1336.862  0.6937316  876.4153\n",
      "  0.0010000000  0.0001000000  150      1337.924  0.6939064  876.1308\n",
      "  0.0010000000  0.0003162278   50      1340.075  0.6913990  881.0034\n",
      "  0.0010000000  0.0003162278  100      1336.862  0.6937316  876.4153\n",
      "  0.0010000000  0.0003162278  150      1337.924  0.6939064  876.1308\n",
      "  0.0010000000  0.0010000000   50      1340.075  0.6913990  881.0034\n",
      "  0.0010000000  0.0010000000  100      1336.862  0.6937316  876.4153\n",
      "  0.0010000000  0.0010000000  150      1337.749  0.6939726  876.1079\n",
      "  0.0010000000  0.0031622777   50      1340.075  0.6913990  881.0033\n",
      "  0.0010000000  0.0031622777  100      1336.862  0.6937316  876.4152\n",
      "  0.0010000000  0.0031622777  150      1337.924  0.6939065  876.1305\n",
      "  0.0010000000  0.0100000000   50      1340.075  0.6913991  881.0033\n",
      "  0.0010000000  0.0100000000  100      1336.862  0.6937317  876.4151\n",
      "  0.0010000000  0.0100000000  150      1337.749  0.6939728  876.1076\n",
      "  0.0010000000  0.0316227766   50      1336.147  0.6931450  878.4495\n",
      "  0.0010000000  0.0316227766  100      1331.296  0.6961086  872.9793\n",
      "  0.0010000000  0.0316227766  150      1334.901  0.6951296  873.4424\n",
      "  0.0010000000  0.1000000000   50      1340.525  0.6912145  879.0617\n",
      "  0.0010000000  0.1000000000  100      1333.842  0.6952041  872.7545\n",
      "  0.0010000000  0.1000000000  150      1334.359  0.6954736  872.7317\n",
      "  0.0031622777  0.0000000000   50      1341.001  0.6909579  879.3520\n",
      "  0.0031622777  0.0000000000  100      1339.109  0.6928398  876.3913\n",
      "  0.0031622777  0.0000000000  150      1341.829  0.6922937  877.8347\n",
      "  0.0031622777  0.0001000000   50      1341.001  0.6909579  879.3520\n",
      "  0.0031622777  0.0001000000  100      1339.109  0.6928398  876.3913\n",
      "  0.0031622777  0.0001000000  150      1341.829  0.6922937  877.8347\n",
      "  0.0031622777  0.0003162278   50      1341.001  0.6909579  879.3520\n",
      "  0.0031622777  0.0003162278  100      1339.109  0.6928398  876.3913\n",
      "  0.0031622777  0.0003162278  150      1341.829  0.6922937  877.8347\n",
      "  0.0031622777  0.0010000000   50      1341.001  0.6909579  879.3520\n",
      "  0.0031622777  0.0010000000  100      1339.109  0.6928398  876.3912\n",
      "  0.0031622777  0.0010000000  150      1341.829  0.6922937  877.8347\n",
      "  0.0031622777  0.0031622777   50      1341.001  0.6909579  879.3520\n",
      "  0.0031622777  0.0031622777  100      1339.109  0.6928398  876.3912\n",
      "  0.0031622777  0.0031622777  150      1341.829  0.6922935  877.8350\n",
      "  0.0031622777  0.0100000000   50      1341.001  0.6909579  879.3520\n",
      "  0.0031622777  0.0100000000  100      1339.029  0.6928698  876.0810\n",
      "  0.0031622777  0.0100000000  150      1342.581  0.6920016  877.9086\n",
      "  0.0031622777  0.0316227766   50      1341.000  0.6909580  879.3518\n",
      "  0.0031622777  0.0316227766  100      1339.108  0.6928401  876.3908\n",
      "  0.0031622777  0.0316227766  150      1341.788  0.6923245  877.6510\n",
      "  0.0031622777  0.1000000000   50      1343.704  0.6897430  882.5292\n",
      "  0.0031622777  0.1000000000  100      1338.153  0.6933212  877.2722\n",
      "  0.0031622777  0.1000000000  150      1340.273  0.6930995  878.3241\n",
      "  0.0100000000  0.0000000000   50      1347.751  0.6878350  881.3774\n",
      "  0.0100000000  0.0000000000  100      1345.055  0.6899837  878.0225\n",
      "  0.0100000000  0.0000000000  150      1345.221  0.6904953  876.3045\n",
      "  0.0100000000  0.0001000000   50      1347.751  0.6878350  881.3774\n",
      "  0.0100000000  0.0001000000  100      1345.055  0.6899837  878.0225\n",
      "  0.0100000000  0.0001000000  150      1345.221  0.6904953  876.3045\n",
      "  0.0100000000  0.0003162278   50      1347.751  0.6878350  881.3774\n",
      "  0.0100000000  0.0003162278  100      1345.055  0.6899837  878.0225\n",
      "  0.0100000000  0.0003162278  150      1346.077  0.6901089  877.5874\n",
      "  0.0100000000  0.0010000000   50      1349.791  0.6868985  882.5973\n",
      "  0.0100000000  0.0010000000  100      1347.258  0.6890049  878.3677\n",
      "  0.0100000000  0.0010000000  150      1345.720  0.6903136  876.9216\n",
      "  0.0100000000  0.0031622777   50      1349.791  0.6868986  882.5973\n",
      "  0.0100000000  0.0031622777  100      1347.257  0.6890049  878.3677\n",
      "  0.0100000000  0.0031622777  150      1345.720  0.6903136  876.9216\n",
      "  0.0100000000  0.0100000000   50      1347.751  0.6878350  881.3773\n",
      "  0.0100000000  0.0100000000  100      1342.381  0.6911598  877.1437\n",
      "  0.0100000000  0.0100000000  150      1342.676  0.6916185  876.4730\n",
      "  0.0100000000  0.0316227766   50      1346.097  0.6885001  881.2728\n",
      "  0.0100000000  0.0316227766  100      1342.443  0.6910768  876.7153\n",
      "  0.0100000000  0.0316227766  150      1341.981  0.6919625  877.8887\n",
      "  0.0100000000  0.1000000000   50      1349.785  0.6868974  882.5581\n",
      "  0.0100000000  0.1000000000  100      1342.351  0.6912344  876.5873\n",
      "  0.0100000000  0.1000000000  150      1341.154  0.6922900  875.3400\n",
      "  0.0316227766  0.0000000000   50      1320.596  0.6999464  870.1760\n",
      "  0.0316227766  0.0000000000  100      1315.765  0.7026719  863.8663\n",
      "  0.0316227766  0.0000000000  150      1320.229  0.7012439  867.6093\n",
      "  0.0316227766  0.0001000000   50      1320.596  0.6999464  870.1760\n",
      "  0.0316227766  0.0001000000  100      1315.765  0.7026719  863.8663\n",
      "  0.0316227766  0.0001000000  150      1320.229  0.7012439  867.6093\n",
      "  0.0316227766  0.0003162278   50      1320.596  0.6999464  870.1760\n",
      "  0.0316227766  0.0003162278  100      1315.765  0.7026719  863.8663\n",
      "  0.0316227766  0.0003162278  150      1320.229  0.7012439  867.6093\n",
      "  0.0316227766  0.0010000000   50      1320.596  0.6999464  870.1760\n",
      "  0.0316227766  0.0010000000  100      1315.765  0.7026719  863.8663\n",
      "  0.0316227766  0.0010000000  150      1319.340  0.7016287  867.2909\n",
      "  0.0316227766  0.0031622777   50      1320.596  0.6999464  870.1759\n",
      "  0.0316227766  0.0031622777  100      1315.765  0.7026720  863.8662\n",
      "  0.0316227766  0.0031622777  150      1319.340  0.7016287  867.2909\n",
      "  0.0316227766  0.0100000000   50      1320.596  0.6999465  870.1759\n",
      "  0.0316227766  0.0100000000  100      1315.765  0.7026720  863.8661\n",
      "  0.0316227766  0.0100000000  150      1320.229  0.7012440  867.6091\n",
      "  0.0316227766  0.0316227766   50      1320.596  0.6999465  870.1758\n",
      "  0.0316227766  0.0316227766  100      1315.764  0.7026722  863.8658\n",
      "  0.0316227766  0.0316227766  150      1320.229  0.7012442  867.6086\n",
      "  0.0316227766  0.1000000000   50      1320.602  0.6999441  870.1784\n",
      "  0.0316227766  0.1000000000  100      1317.443  0.7021043  865.3276\n",
      "  0.0316227766  0.1000000000  150      1321.428  0.7008941  868.4416\n",
      "  0.1000000000  0.0000000000   50      1328.059  0.6967242  867.8335\n",
      "  0.1000000000  0.0000000000  100      1321.188  0.7006517  862.1207\n",
      "  0.1000000000  0.0000000000  150      1320.920  0.7013040  863.2441\n",
      "  0.1000000000  0.0001000000   50      1328.059  0.6967242  867.8335\n",
      "  0.1000000000  0.0001000000  100      1321.188  0.7006517  862.1207\n",
      "  0.1000000000  0.0001000000  150      1320.916  0.7013041  863.2173\n",
      "  0.1000000000  0.0003162278   50      1328.059  0.6967242  867.8335\n",
      "  0.1000000000  0.0003162278  100      1321.188  0.7006517  862.1207\n",
      "  0.1000000000  0.0003162278  150      1320.916  0.7013041  863.2173\n",
      "  0.1000000000  0.0010000000   50      1328.059  0.6967242  867.8335\n",
      "  0.1000000000  0.0010000000  100      1321.188  0.7006517  862.1207\n",
      "  0.1000000000  0.0010000000  150      1320.742  0.7013861  862.4519\n",
      "  0.1000000000  0.0031622777   50      1328.059  0.6967242  867.8335\n",
      "  0.1000000000  0.0031622777  100      1321.188  0.7006517  862.1206\n",
      "  0.1000000000  0.0031622777  150      1320.916  0.7013042  863.2172\n",
      "  0.1000000000  0.0100000000   50      1328.059  0.6967242  867.8335\n",
      "  0.1000000000  0.0100000000  100      1321.188  0.7006518  862.1206\n",
      "  0.1000000000  0.0100000000  150      1320.916  0.7013042  863.2172\n",
      "  0.1000000000  0.0316227766   50      1328.059  0.6967243  867.8334\n",
      "  0.1000000000  0.0316227766  100      1320.362  0.7010063  861.4861\n",
      "  0.1000000000  0.0316227766  150      1318.284  0.7024791  861.2135\n",
      "  0.1000000000  0.1000000000   50      1330.092  0.6958482  871.4954\n",
      "  0.1000000000  0.1000000000  100      1325.851  0.6984742  865.6783\n",
      "  0.1000000000  0.1000000000  150      1327.572  0.6983217  866.2390\n",
      "\n",
      "Tuning parameter 'eta' was held constant at a value of 0.3\n",
      "RMSE was used to select the optimal model using  the one SE rule.\n",
      "The final values used for the model were nrounds = 50, lambda =\n",
      " 0.03162278, alpha = 0 and eta = 0.3.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>$R^2$</th><th scope=col>MAE</th><th scope=col>RMSE</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.4636894</td><td>991.6014</td><td>1454.076</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 3 of type dbl\n",
       "\\begin{tabular}{lll}\n",
       " \\$R\\textasciicircum{}2\\$ & MAE & RMSE\\\\\n",
       "\\hline\n",
       "\t 0.4636894 & 991.6014 & 1454.076\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 3 of type dbl\n",
       "\n",
       "| $R^2$ | MAE | RMSE |\n",
       "|---|---|---|\n",
       "| 0.4636894 | 991.6014 | 1454.076 |\n",
       "\n"
      ],
      "text/plain": [
       "     $R^2$     MAE      RMSE    \n",
       "[1,] 0.4636894 991.6014 1454.076"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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86dWlQMKE/BHKmQF3y1DkY7CnCVTc9m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\nzfbIiPyp8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QzzZiBMiXgvzJi6WaYuX0\nGfAEWITGjx9vyMuimV7Oletop/Tmha/5ibwVWTHuZJdzswxylEWYaY2gSB5pNZzclyAvvObL\nr+iVhfjKdbR952uRkdEyx7zdnZve7KzIim4J5qieJ695yVLJ/8uDExUVZZhe6BzvuZ6LNzwx\n+MSQlDKrIU+jcePGqYxbSWdF1krbuZYprS9WkfWlPUROvMuJEWvWTeonIz9iDMoop2OQEdCe\nPXtqw95sO/FI98477xjiPdA1yJerLJ42ZeUom/HKZokMJBDsBHzts90ZSFI3q8+3r/lZfQ4d\nOQei3zA3OHd8rt2dq/VahhVZs5xW+mYrsqLf137RqqyvbWfW0Tx6aw8r3ydW+lsr7wJW9Jr1\n4pEEgoWAr/1wIN7f/embvb1n+9IXWOlvfZENk0ZUnbtPQTYN3bFjh97zRU2Z8ymNo5BkJXvo\nqCF8vW7FcVNSRznz3Ep+VmRlj4UjR47o/SrScjBhlsPKUbkPh3qxhjI8rCS7JmRl7cy2bdv0\nJqrKjXiaZbYia6Xt0szUzU1f20ONFEE22FW/gujPRVr7FCl37npdljgokX2NZK56WkF5stMO\nOWTDXTWCmZYo75FAUBGw2me7K7yV59tKflafQ3dl8xTna7/hKX2wx1upn6+yVtrOKh8r3ydW\n+lsr7wJW9FqtH+VJIKMJWOmH3ZXFyvOdkX2zlb7AXT2sxFkykKwopiwJkAAJkAAJkAAJkAAJ\nkAAJXGsEvHqxu9YqxPKSAAmQAAmQAAmQAAmQAAmQQHoJ0EBKLzmmIwESIAESIAESIAESIAES\nCDkCEW+q4Eut1GJQvXmdbIAp6zMKFizoSzInGeXiWO+HJPN+ZQNatTDe6b7jRXrzUwuv9N41\nop+BBEiABEgg/QSs9NnucklvP75v3z69T5zyiOpOLeNIgARIgAR8JJDeftid+izVN6uFV16D\ncsygvZ0pWHaPXLI/wIEDB7ymNQXeeOMN7f3M1CEuvl33VjJl05ufuJ8W/e7cBJq6eSQBEiAB\nEvBOwEqf7U5bevtx8YQkWwuojZfdqWUcCZAACZCAjwTS2w+7U5/V+mavU+wUJKh9YSCjPuPG\njYPajBTKEMHevXvRpEkTXLp0yZ2R6RSnXGri7bffhtprBrJT+YoVK9CiRQu88sor+OKLL5xk\n05ufchGI7t27O+niBQmQgPrF4N9/YftrNoyFi2BcvkwkJOCVgJU+252y9PbjZ86cwf3334+t\nW7e6U8s4EsiyBOIRjzVYioWYhcPYn2U5sOK+E0hvP+wuhyzZNyuAaQZzp9kRI0Y4yZmjNa7x\nTkLqQhlQRrly5YySJUvaN90UGdmtW+JLlSrlFG81P9nsydx1XLkN5wiSawPwOssSsKlf4hM7\nP24khOUwEnLGGAkRUUZCnoJG0kefZFkmrLh3Alb7bHcarfbjokM2mpX949SXs96XjCNI7sgy\nLisSmGSMMeobxYyKRg6juhFjVDCyG12NdsYJ42hWxME6+0ggPf2wO9VZtW/2ulFsw4YNDTE8\nlPXoxE2G2nLmzGnccMMNTvGuF3/88Yf+wlOjRa63jAEDBuh7M2bMsN+zmp/Iyxdqp06djB9+\n+EGfc4qdHSdPsigBW0KCkdC4mZFQrbZhW7hIb1hsu3LFSBo52kjIld9IeuvdLEqG1fZGwGqf\n7U6f1X7czFOtbdUbc3vbQNRdnowjgVAk8JMxyqhiRBsjjWHGReOCruImY43R3mhstDJq2+NC\nse6sk38ErPbD7nLLyn1zmlPsEhISsH79elSuXBmuG6rGxMTozVDV7rsQOU9BnDpIUA2VSsSM\nW716tb6Xnvzq168PmQ7yyy+/pCpjqgwZQQJZhIAxfgLwzxZEzJ2FsKa3ICwsDGE5cyK8WxeE\njxkF23vvw1DTZhlIwJWAlT7bNa1cp6cfj4yMxMCBA/VG5Pfcc487tYwjgSxH4DIu4X28iv74\nAN3QB7mQWzOoiXr4AX9C7o/FV1mOCyvsnUB6+mF3WrNy3xzpDogZJ3MOZddaTx7rChQooL8M\nT5w4gRIlSpjJnI7Hjh3T1+50SHoJsr5JQnryU0OIOi3/IwES+I+A8ftMhD3QCWHFi/8XmXIW\nfl8H2Hr3hTF7DsKefDzVfX8jbPPmA+fO+6smaNOHVa6EsBrVg7Z8/hbMSp/tLq/09OMtW7aE\n/DGQAAn8R2AVFiNBrT16UJlHriEPYnAfHsc8zMRzeMX1tt/XB7AH27DRbz3BqiAHcqIpWkP9\ndBisRfSrXOnph91lmJX75jQNpPPnk19yChUq5I4bTAMnLUcNaelwTZ+WrBTAVd5toRhJAiSg\nfm04C9Ss4ZlEUeUG//QZz/fTecdQP6jYujwDpPQd6VQT1MnC2rZBxNjvgrqM/hQurX7Ylz44\nrfRSLl90+FN+piWBUCFwHmeVGZQPOdQ/d6EwiuKcksmI8BNGYSJGZ4TqoNCZXTH9HStRGMWC\nojyBLgT7Yf+JpmkgqTVGOgebzeY2J/GtLkG57HZ7XyLT0uGaPi1Z0eUqL3EMJEACbghUqQws\nT57e6nrXEONl6zZAZAIcwtTeZpH7dgRYK9VlJoG0+mFf+uC00ks9fNGRmfVlXiQQrATKozJO\n4CiO4ABKoEyqYq5XL/jXKZmMCK9gsBqXGpwRqqkzEwiwH/YfcpprkIoVK6bXLpw+fdptTmZ8\n3rx53d6XSHPqnSnrKGjGmekDkZ+jfp6TQFYlEN7tSRhz5sI2aYoTArUIE7Y+LwNFiyKsVQun\ne7wgASFgpc92R4z9uDsqjCMB6wRqoC5kvdEgvKCm2jmv9V6uHH5Px09q+l1X64qZIuQJsB/2\nv4nTHEGSxVlFihSBaci4Zifx0dHRaTpH8OXLVrkA16oDkZ9rGXlNAlmRQFj9egj/YAhsDz0K\nY8YfycbQxYuwjf0x2XnDXzMgoz0MJOBKwEqf7ZpWrtmPu6PCOBKwTkDWxwzDGHRGK7THTeiE\nJ1EAhSBrkybhezyBnrgVd1pXzBQhT4D9sP9NnOYIkqhXO5pjy5YtUPsNOeUmjhlkMz/xIpfW\nFDtJL2HhwoVO6R3jTG92EudvfqkyYQQJZFEC4S/1RrgyhNTDC1u/AbB9+Il2LhCxfiXCGqX2\nKplFMbHaLgSs9tkuyfUl+3F3VBhHAtYJXIcqeq1MIzTDeIxQk976YS924jN19aryccdAAp4I\nsB/2RMa3eK8GUs+ePZGYmIjvvnNelDx69Ggd36tXrzRzatasGWrVqoWJEyeqddv/ebZS+yjp\nuLp166Jp06Z2Hf7mZ1fEExIgAYTffhsiZv6GyMN7EblLuf3+djjCypUjGRLwSMBqn+1OEftx\nd1QYRwLpI1BIOWN4HR/jb2zCMuzHOMxSY0r3pk8ZU2UZAuyH/WxqdxtDOcapBbWGskKN8PBw\nQ+1TYag9h4zXXntNX7dv395R1FB7IumNWmvXru0UP2HCBB1fr149Y9KkSYbas8iQjQDVyJOx\nZs0aJ1kr+TklVBe//fabzocbxbqS4TUJkAAJ+E7ASp8t3wPqa8iQ3dbN4E8/Ljq4UaxJkkcS\nIAESSB8BK/2wp/d315yzUt8M18q7u1bT6Yw77rjDUJtN6i9C+TJs1aqV8e+//zqJpwV4/Pjx\nRv78+e3p5XzUqFFO6c0LX/Mz5c0jDSSTBI8kQAIk4B8BX/tsdwaS5JzeflzSZqUvYakvAwmQ\nAAlkBAFf++G03t8dy5WV+uYwqbivg1AXLlzQO52LUwXxkGE1SFa7d+9GXFwcKlasiBw53Pv2\nN/X6m5+ph0cSIAESIAHrBKz22e5yYD/ujgrjSIAESCDzCLAfts7akoFkXT1TkAAJkAAJkAAJ\nkAAJkAAJkMC1Q8Crk4ZrpyosKQmQAAmQAAmQAAmQAAmQAAn4R4AGkn/8mJoESIAESIAESIAE\nSIAESCCECNBACqHGZFVIgARIgARIgARIgARIgAT8I0ADyT9+TE0CJEACJEACJEACJEACJBBC\nBGgghVBjsiokQAIkQAIkQAIkQAIkQAL+EaCB5B8/piYBEiABEiABEiABEiABEgghAjSQQqgx\nWRUSIAESIAESIAESIAESIAH/CNBA8o8fU5MACZAACZAACZAACZAACYQQARpIIdSYrAoJkAAJ\nkAAJkAAJkAAJkIB/BGgg+cePqUmABEiABEiABEiABEiABEKIAA2kEGpMVoUESIAESIAESIAE\nSIAESMA/AjSQ/OPH1CRAAiRAAiRAAiRAAiRAAiFEgAZSCDUmq0ICJEACJEACJEACJEACJOAf\nARpI/vFjahIgARIgARIgARIgARIggRAiQAMphBqTVSEBEiABEiABEiABEiABEvCPAA0k//gx\nNQmQAAmQAAmQAAmQAAmQQAgRoIEUQo3JqpAACZAACZAACZAACZAACfhHgAaSf/yYmgRIgARI\ngARIgARIgARIIIQI0EAKocZkVUiABEiABEiABEiABEiABPwjQAPJP35MTQIkQAIkQAIkQAIk\nQAIkEEIEaCCFUGOyKiRAAiRAAiRAAiRAAiRAAv4RoIHkHz+mJgESIAESIAESIAESIAESCCEC\nNJBCqDFZFRIgARIgARIgARIgARIgAf8I0EDyjx9TkwAJkAAJkAAJkAAJkAAJhBABGkgh1Jis\nCgmQAAmQAAmQAAmQAAmQgH8EaCD5x4+pSYAESIAESIAESIAESIAEQogADaQQakxWhQRIgARI\ngARIgARIgARIwD8CNJD848fUJEACJEACJEACJEACJEACIUSABlIINSarQgIkQAIkQAIkQAIk\nQAIk4B8BGkj+8WNqEiABEiABEiABEiABEiCBECJAAymEGpNVIQESIAESIAESIAESIAES8I8A\nDST/+DE1CZAACZAACZAACZAACZBACBGggRRCjcmqkAAJkAAJkAAJkAAJkAAJ+EeABpJ//Jia\nBEiABEiABEiABEiABEgghAjQQAqhxmRVSIAESIAESIAESIAESIAE/CNAA8k/fkxNAiRAAiRA\nAiRAAiRAAiQQQgRoIIVQY7IqJEACJEACJEACJEACJEAC/hGggeQfP6YmARIgARIgARIgARIg\nARIIIQI0kEKoMVkVEiABEiABEiABEiABEiAB/wjQQPKPX1CmTkpKwq+//hqUZQtkoQ4fPoyl\nS5cGUmVQ6lq2bBkOHToUlGULZKGmTp2KxMTEQKoMOl1nzpzBnDlzgq5cgS7Qhg0bsH379kCr\npT4SIAESIAESyBQCNJAyBXPmZrJt2zZ07NgRp0+fztyMMzm38ePHo2/fvpmca+Zn169fP4wd\nOzbzM87EHM+dO4cOHTpg69atmZhr5mc1c+ZMdO3aNfMzzuQchwwZgs8//zyTc2V2JEACJEAC\nJBAYAjSQAsMxqLQYhqHLYx6DqnABLIzUL9TrKLiyQj1tNpv+ZIR6e2aFtswqn1n9geV/JEAC\nJEACIUmABlJINisrRQIkQAIkQAIkQAIkQAIkkB4CNJDSQ41pSIAESIAESIAESIAESIAEQpIA\nDaSQbFZWigRIgARIgARIgARIgARIID0EaCClhxrTkAAJkAAJkAAJkAAJkAAJhCQBGkgh2ays\nFAmQAAmQAAmQAAmQAAmQQHoI0EBKDzWmIQESIAESIAESIAESIAESCEkCNJBCsllZKRIgARIg\nARIgARIgARIggfQQCFP7ciRvmpOe1EwTEAJz5sxBu3btArenj2rS+IR4ZM+eXZUvLCBlDEYl\ntqQkyP45kdmyBWPxAlamxIQEhIeHIzwiImA6g0+R+szGxyObasuwsND93cZmS0KS+txmyybP\nZuiGpMRE3fVEREQGrJJ33303Jk6cGDB9VEQCJEACJEACnggE7tvLUw6M90rgyJEjiM6ZHWM+\netWrLAVIgARIIKsRmDRzAbbu3ZPVqs36kgAJkAAJXCUCNJCuEnjXbKNy5kCb5je5RvOaBEiA\nBLI8gU3b9mDrgZNZngMBkAAJkAAJZA6B0J3Lkjn8mAsJkAAJkAAJkAAJkAAJkEAIEaCBFEKN\nyaqQAAmQAAmQAAmQAAmQAAn4R4BT7Pzjx9QkEJIEEhISMWPeUpQsWhgN61bL1DruO/Qvlqze\njM73tnSb74HDxzw6NClZrBAiI527tUP/Hse6f3YiV3QUGqm6yJGBBEiABEiABEiABDwRcH6T\n8CTFeBIggSxF4OLlK+j47Oto37oppox4J9Pqfv7CJbR54hUcVEaNOwPp+MkzKNfkfo/l2TZ3\nPCpfV9p+f9An32HwV+O15ziJFG+Ag19+Cv2efdguwxMSIAESIAESIAEScCRAA8mRBs9JgASu\nGoEz5y7goV5vYdvu/R5HedZv2aXLd/vN9VGzcvlUZc2fN4897u//rcI7n/+Ae1vdgtd7PYaE\nhCS88clovDr0G4hTlJ5PdLTL8oQESIAESIAESIAETAI0kEwSPJIACVw1AlP/WoTur3+CoydO\nqz2CPHdL6/7Zocs4sMdjaHZjXY/lvXwlFk/3/wglihbCpOFvISJlD6npI4egym2P4INvfsLz\nj95rj/eoiDdIgARIgARIgASyHAHPbyJZDgUrTAK+EViwfB1OnDqLTnc1x9rNOyDXx9TUr7rV\nK6KDmpKWI8d/m4DOW7oWMjJy9+2N8d2kP3DqzDm0a9kENatcZ8/snx178ffi1dh/6CjKly6O\npo3qKF2V7PflZI66f/joSTzSvqXTS318fAJ+mj4XZUoWQfOb6uk0VspnZrJjz0H8sWA5zp6/\niNuUnlpV/yufKSNH2Vd61sIVWL5uCy5euoKqFcqgWaO6TtPaRG7+srU4cPg4bmtcD6VLFJEo\nj+FPla9M5yuQLwbTvn0Pb346Bjv3HXIrb44g1atZ2e19M3LhivXYf/ionkpnGkdyL3v2bHi4\nXQsMGT5e1WMl7rqNrvVNZjySAAmQAAmQAAkkE6CBxE8CCVgk8Pn3U5SB8I9eJ9P3veHaYElK\nStJaxLCZ8+Mw/bIvEZ+M/gUbt+3WBsPwcdO0zILl6/H3+GH6/MV3vsTnY6bAZrOhWOECegQl\nLCwMLzx5Hz7o/6zd4cBHI3/G7EWrcH/b5ohKGQ0RBZfUSMmTLw/RRpdpIFkpn+iQkZuvx09T\neUUgb57cePuzMehyfxu55RTEccM93frjr0Ur9VqegvljtKEoIz4fv9YdPR7vYJf/9LvJ+H3O\nEm3weDOQIlV9XuvxKPp0vV9zEwPJUxADSdYYxSeIYThH86pesZw2KmXanBlWrt+qTxvWqWpG\n2Y8N6yQ7nVi9cRsNJDsVnpAACZAACZAACZgEaCCZJHgkAQsEZCrY+1//iOHvvogH2t6GuPh4\ndHvlA/wxfzm+mTAd/Z9/xK5NRn5+nDYH3w55Wa2tyak9w8nN736ZiU+/m4RWTRvgh48GoKgy\nkE6ePosu/YbqePHI9tJTD9r1WDnxtXyjfp6hjSNxiPDN4L6IjsqJuUvW4P7ug1JlJwaJGEd9\nn34Qr/d8HHlyR2PLzn1o0bkPXh78tR7dyheTvAbotpuuR76Y3F5HjySTlrc00H+pMnSJkGlz\nO/YeROGC+VD+lgf0CJYpUrFcKYz/ZKDd456M6EkomC+vKWI/FsiXXMbDx7jxqB0KT0iABEiA\nBEiABOwEaCDZUfCEBKwReOfFbnim8z32RB/0f04bSKs2bLPHyYmMDr3zUld0e7CtPV6mqr30\n7nCIU4GJX7yJvMqYkFCoQD789PkbqNjsYT3V7NnO7Tw6LLAr83DiS/neUqNFRQrl18abOQIj\nDhAGvfAEer/9hZPm7WoanoQ7b22kjSM5r16pHMYo406mxMXFJUiUDi906WSeBuwoI3HCTaYs\nvvtSN7S9/SZ1DYyfOluvKbq726vYOmecHoU6f/GSzrdQAXcGUoy+d+lybMDKRkUkQAIkQAIk\nQAKhQ4AbxYZOW7ImmUygcf0aTjmWK1VMX5sv5443Zf8dxyDrY85duKineJnGkXlf9ulp3/oW\nXFKutrftPmBGWz56K5+shzp89ARaqREc0zgyM5FRMdcghpOEe7oNwPMDh+kpdFJGGQHr/lh7\nPQLmmiaQ19eVLoEJynicN+FTvPzMQ6imptaJgTa439NqpO0BPd1v2KhfdJY5U9aBiXHqGpKS\nkuMiItj9ubLhNQmQAAmQAAmQgNoWhBBIgATSR6BY4YJOCf97KVfDGi5BnC84hu27k0djypYs\n6hhtPzfjd+07bI+zeuKtfJu279EqSypPb65BRpXEoYFjEIcLI9/vp9ZchWPEj7+h3VMDUPD6\nu9G2yytYoZw2ZHSQMj149+24+YZaqbJ6rENrHScbwkoQ73USTp+9oI+O/50+d15f5s2TyzGa\n5yRAAiRAAiRAAiSgCdBA4geBBNJJIDw8zOeU2bM5GxuyFkmCp2leF5SHOAmm0aUv1H8yxcwx\nXLh42fHS6dxb+Qoqr3ESxNGDa5B83I2+dH3gLhxdNRXTRw3Ro0alihXW0wqbPtBTe/Nz1ZNZ\n14XV1EQJ5uid3UBKMYYcy3H6bLKBVFKVnYEESIAESIAESIAEXAnQQHIlwmsSyAQClZRTAQlb\ndu3TR9f/xPmBhLKlkkeYTEMpNi5ex5v/7T6Q/hEmmZ4met1N49t/+BgSE5M985l57VLrjP6Y\nv0ylyaHW/zTGF2/1xq6FP2GImuImHu7E3XhGBvEIWOW2ztp7nWs+Zh2qKA93EqpVKKuPC1ds\n0EfH/8w405ud4z2ekwAJkAAJkAAJkAANJH4GSOAqEBCPdY3qVteuu8XdtGPYrKa+TVcusmVN\nU51qFfWtQvmTnQ3IvkqOYczkWfrSgPPIkqOMp3PZH0imq8keS7Kfk2P4/PvJjpf6XDzVte3y\nKmbOWwHHQlIAAEAASURBVOZ0z9yTKNrBzbaTQIAuypQoip17D+G9L8c5jaTJaNfg4eN0Lo91\nuEMfZRNZ2Wtq4u/zcP5CssMGuXFO7fM0ccY8zbVpw9palv+RAAmQAAmQAAmQgCMBGkiONHhO\nAplI4Mu3e+u9h1o88iI+VvsciXvtz5Tb72YP9FL7EeXC1G/eg+yJJKFTm+b62P2NT5SBMBZj\nJv+Je58eoA2s3Lmi9L30/PfdB6+iaKECaKnKMHzcVK1P9kUSV+Xh4c7dQ68nO+ryPDPgI7z2\n4Ui9YaxsuPqccthgbsBqlkHWJ4WXb4bpfy82o/w+3tuqCW698XrtWvy2h3pj3K9/Yepfi9D6\nsb663DL9TzbZNUP/5zurDXxPo/lDL2DyHwswaeZ8dd5buVI/h9EfvGLfY8qU55EESIAESIAE\nSIAEhEAkMZAACVwdAvVrVcGKaSPw1Ksf6n2EpBQy5a1x/Zpqk9jnUKd68uiRxLdu1hCfDeqF\n1z4aidc/Hi1RkE1pF0/+UhsAYfB9PZROnPKfbOL6++gheOX9Edqtt0yrE2cIf475EG27vuoo\nCtmIdsJnb+CVoSMghtGQ4cm3q1Usi4U/f44GKRuwOiUK4IWMeE0Z8Q4GfPAtRqr9mxauWK+1\nF1BrqYa++qz2bOeY3UP3tFDrqAz0HPSpfV8n2ZtJ9nsyR70c5XlOAiRAAiRAAiRAAkIgTE1P\nsT43h+wCSmDs2LEY2L8f9i9JdlEcUOVUdk0QkGlg+w4dRdUKZVJ5j3OsgDhO2L7nAMQpgeyZ\nFMgg08+OnzqDSuWT1/F40i1lOKI2WT1+6iwqli2JmKvgDS42Lk5Pt5PNasuVcvYQ6Fpu6eJ2\n7z+sNvNN0OXNkeIC3FWO18FLYKjalHnq/LVYsXJV8BaSJSMBEiABEggZAhxBCpmmZEWuZQJi\nZNSuVsFrFWTam+z/kxFB9mNy3ZPJXT5ShlLFi+g/d/czI04cRdSq6p2XlEWmKVZMcYqRGWVj\nHiRAAiRAAiRAAtc2AedFBtd2XVh6EiABEiABEiABEiABEiABEvCLAA0kv/AxMQmQAAmQAAmQ\nAAmQAAmQQCgR4BS7IGjNrVu34uCRY4iu2jIgpXFcVZbiBC0geoNNiVnPUK6jMM8K9TTrKPUN\n5fY06xnKdcyIz6w4DylQsICoZiABEiABEiCBDCdAAynDEXvPoEKFCsibNy/69OnjXdgHicTE\nRFy8eBExMTGpXDX7kPyaEYmNjVUblCYgT54810yZ01PQCxcuIFu2bMiZM2d6kl8TacTxw/nz\n55E7d+6Qdr8dHx8P+dzKsxnK4dKlS3rtV3R0dECquXjxYhw/fjwguqiEBEiABEiABLwRoIHk\njVAm3M+ePbs2kAYNGhSQ3OQl7N9//0WpUqUgrpFDNZw7dw6XL19G8eJpezG71ut/9OhRREVF\n6c/ItV4XT+VPSkrCoUOHdFvK8xCqQX64OHv2rH42Q7WOUq8TJ07oH2cKFiwYkGoOHToUU6dO\nDYguKiEBEiABEiABbwS4BskbId4nARIgARIgARIgARIgARLIMgRoIGWZpmZFSYAESIAESIAE\nSIAESIAEvBGggeSNEO+TAAmQAAmQAAmQAAmQAAlkGQI0kLJMU7OiJEACJEACJEACJEACJEAC\n3gjQQPJGiPdJgARIgARIgARIgARIgASyDAEaSFmmqVlREiABEiABEiABEiABEiABbwRoIHkj\nxPskQAIkQAIkQAIkQAIkQAJZhgANpCzT1KwoCZAACZAACZAACZAACZCANwLcKNYbId4nARIg\nARIIaQKnTp3CN998gwEDBqSqp2zuO336dMhmxs2aNUOZMmWcZGRT7jVr1jjFyUWdOnVQunRp\ne/yVK1cwa9YsvYnurbfeisqVK9vv8YQESIAESCC4CNBACq72YGlIgARIgAQykUBiYiI6deqE\n5cuXpzKQvv76a7z00ku47rrrEBcXhwMHDmD8+PFa3iyiXPfr18+8tB9Hjx6NLl266OslS5ag\ndevWqFKlCrJly4bu3btj1KhRePzxx+3yPCEBEiABEggeApxiFzxtwZKQAAmQAAlkIoEdO3ag\nefPmmD9/fqpcz5w5ow2mN954A5s3b8bOnTvRrVs3bdTIaJAZ1q1bhxYtWuDChQtOf4899pgW\nEcPKTCcjTWKIvffee3j66adx/PhxUw2PJEACJEACQUSABlIQNQaLQgIkQAIkkDkENm7cqKfB\nxcfH6xEd11x37dqFu+++Gz169LDfatu2LcQ4+ueff+xx69evR4MGDZA7d26nv8jI5Akas2fP\nxrZt2/RIlJmoZ8+eyJEjB3744QczikcSIAESIIEgIkADKYgag0UhARIgARLIHALh4eEYOXIk\nli5digoVKiAsLMwpYzF6xo4dq40euWGz2TB8+HDky5cPtWvX1rJiLMkoVM2aNfU6pVdffRXj\nxo2DTNszw969e1G4cGE9Tc+Mi4qK0mm2bNliRvFIAiRAAiQQRAS4BimDG6Njx456YW5a2ciX\naUxMjJ7fnpacr/cMw9Cihw4dSvWl76uOa0HOrKesCwjlIPWUaTrnzp0L2WqabSkL3l1fVEOp\n0mY9s8JnVtrt0qVLAWk+caIgU9ly5cqVpr7s2bNj9erV2uBJU1DdFKNG/nwJYvjImiFx1LBg\nwQJIPhI2bdqk45566inUrVsXUs6hQ4fis88+03IyqiQGUsGCBVNlU6BAARw9ejRVPCNIgARI\ngASuPgEaSBncBu+++y66du2aZi5z587FhAkT9K+MaQr6eFMMrtOnT6NQoUKQX0lDNcjLlxgO\n8qIRykHWQsgLmbeXw2uZgfw6f/LkSd2W5tSka7k+nsouIw7yuZVnM5SDGPPS9+TJkycg1cyZ\nMyfKly+PYcOGpalPHCCIXKBDiRIl8OCDD+LHH3/U65LEAUOxYsW0cSTT7l555RU0adJEZztt\n2jS0b98eb775Jj766CP9uRZDyTVER0fjyJEjrtG8JgESIAESCAICNJAyuBGqVasG+UsryIvh\n5MmTIdMuAhFkTr0EeamIiIgIhMqg1CH1TEhICBi3oKykKpS8bMqLX6A+H8FYT/llXoKsyzB/\nnQ/GcvpbJqnn5cuXQ7othZG4xhYDKVCfWflxQKa2tWnTxt8mSFf6Xr166XQvv/yynionLsEH\nDRqEm266Cb///ruTznvvvRfVq1fH33//reOLFCmiHTM4CamLs2fPOrkBd73PaxIgARIggatH\nIHSHF64eU+ZMAiRAAiRwjROQkT6ZHucYypYti5tvvtlu/MhovUyrcw1Vq1a1Ty+U0Sf5Ecw1\nnDhxIkNGu1zz4TUJkAAJkIB1AjSQrDNjChIgARIggRAnMGTIEIihI2s5zSAjY+K1ToweCQMH\nDtRT7RzXlIlhJRvCXn/99VpGpt7JaNGyZcv0tfwnU+vEi16NGjXscTwhARIgARIIHgI0kIKn\nLVgSEiABEiCBICHw6KOPQtbDyfQ62QNJ9kKSvYtkimTv3r11KR955BE9jVncdm/fvl0bPU88\n8YSe+jtgwAAt06hRIz3qJGnEkJLRpGeffRb16tXT65qCpLosBgmQAAmQgAMBGkgOMHhKAiRA\nAiRAAkKgSpUq+PXXX7F27VpUrlwZtWrVwuLFizF16lQ0btxYQxIveFOmTNEe9mS0qU6dOpDN\nYMXxjjmCJILihEfWZMkUPRl9io2N1XsghfIaUQ2I/5EACZDANUqAThqu0YZjsUmABEiABAJD\noE+fPpA/19C6dWu9DklGfsTAKV26tKsI7rrrLuzfv19PxRNnKuLdzjWUKVNGT7GTdUdiFIW6\n503X+vOaBEiABK41AjSQrrUWY3lJgARIgAQyjYDsyyUjP2kFkXFnPLmmkQ1jGUiABEiABIKf\nAKfYBX8bsYQkQAIkQAIkQAIkQAIkQAKZRIAGUiaBZjYkQAIkQAIkQAIkQAIkQALBT4AGUvC3\nEUtIAiRAAiRAAiRAAiRAAiSQSQRoIGUSaGZDAiRAAiRAAiRAAiRAAiQQ/ARoIAV/G7GEJEAC\nJEACJEACJEACJEACmUSABlImgWY2JEACJEACJEACJEACJEACwU+ABlLwtxFLSAIkQAIkQAIk\nQAIkQAIkkEkEaCBlEmhmQwIkQAIkQAIkQAIkQAIkEPwEaCAFfxuxhCRAAiRAAiRAAiRAAiRA\nAplEgAZSJoFmNiRAAiRAAiRAAiRAAiRAAsFPgAZS8LcRS0gCJEACJEACJEACJEACJJBJBGgg\nZRJoZkMCJEACJEACJEACJEACJBD8BGggBX8bsYQkQAIkQAIkQAIkQAIkQAKZRIAGUiaBZjYk\nQAIkQAIkQAIkQAIkQALBT4AGUvC3EUtIAiRAAiRAAiRAAiRAAiSQSQRoIGUS6FDPxmaz4VLs\npVCvJutHAiRAAiRAAiRAAiQQ4gRoIIV4A2d09XYe2YGHPuyAQp2jUfTRPKj0TCkMmfQ2EhIT\nMjpr6icBEiABEiABEiABEiCBgBOggRRwpFlH4Ya969D01QZ65GhC3ylY9P5K9G3fHyNnf42O\nQ9oiMSkx68BgTUmABEiABEiABEiABEKCAA2kkGjGq1OJZ756Eq3rtcG0x4ah1aa/UXfWJ3gq\nLhbzB83Huj2rMWr2iKtTMOZKAiRAAiRAAiRAAiRAAukkQAMpneCyerLN+zdh8/6N+Cr2FIyp\nDWAcHwnj9EwY+15HqTGN8EnV5vh50fisjon1JwESIAESIAESIAESuMYI0EC6xhosWIp76OQB\n/BSVEzlzLkZY3m4YW3cYKh2IQlKHtUBicbQP/wP5juwOluKyHCRAAiRAAiRAAiRAAiTgE4FI\nn6QodE0RWLx+AlbOVKM3l2KRp1wptG7/EiqWbBDQOpTIngs16hiIDWuHi/cOwGu9q+L85fOY\nc2wn7nh9M+LfLIiPctNRQ0ChUxkJkAAJkAAJkAAJkECGE+AIUoYjzrwMEpLi8X7fJtjc4U0k\nbDwIIzYB5yavwowmnTB2Yu+AFqTmgU1AduDt8Fx44sVeqHu6DL6Jr4Jv3p6MldPOYvYhoGwx\nOmkIKHQqIwESIAESIAESIAESyHACHEHKcMSZl8HXnz6AyD8PoeaUN1GhWHOUKlUKERERGD3m\nGZzpOw1zytVEi0bdAlOg2AtqKl0Yvl48FrYwGw6VLYI8Vc5ixLyDeHrkMgzInwvh2c4HJi9q\nIQESIAESIAESIAESIIFMIsARpEwCndHZXLxyBolfr0PxtzqhyfUPO2XX9YlvcLl9MSz//DOn\neL8u6rRGmBpBejYpD0ZE5UaeSmdhXAF6RMWjUMlK6BCZH4ln8vuVBROTAAmQAAmQAAmQAAmQ\nQGYToIGU2cQzKL9122Yh25Uw3HXnyzqHo2d24IuP29tzq3xnG0StUaM+AQonEurj7KZceKt2\nLB6qm4CEuOZIuFQJd5TKib4FjiKiwV4cXdEzQLlRDQmQAAmQAAmQAAmQAAlkDgEaSJnDOcNz\nCQsL03kYhk0fd+1fgojPNiHRSF4HZMYHqiBzf1+DpsfOIHtepTEbkHT8BBIPG8hdJgGty2zB\nma23IWJNv0BlRz0kQAIkQAIkQAIkQAIkkCkEaCBlCuaMz+T6qnciIdrAjD+G6sxsSUn6eCn+\nnD7unPkHYhvEBKQgSUr3h9ufwuiwMoCyy+LPNEP2y8cQnfdfZSmFYekaGyaE9Ua8MpwYSIAE\nSIAESIAESIAESOBaIkAnDddSa6VR1lw58yJ79wY49uZULChVFYYtxUCKO4ufx76I6N+Po86U\nt9PQ4PutL2d+iuqXd+GGWxJxfMuDOD77O7GToHw1oHybWqhe8ABGrJ6MunVb4Drf1VKSBEiA\nBEiABEiABEiABK46AY4gXfUmCFwBnuv1E2ztymFbpyE4+NlPWvEPrVvg8vsLUVh5uLu1wWN+\nZ3bgxH58+PPr+K5SGBIOFkK+0d+h1FGg6CzgTDsgYfX9yFcqAVvzTEP5Dw2/86MCEiABEiAB\nEiABEiABEshMAjSQMpN2BucVER6JfkMWoN6MoUiqJIuDgBwdaqDDkul4uEPy1Dt/i9B75PP4\nPV9uROZJxJbwmZAPUC61H2xCQ6DIJCD3pu6AsouezHUae3Mu9Dc7picBEiABEiABEiABEiCB\nTCVAAylTcWdOZg1rtEep5s11Zk3v76E2bK0dkIwnL5mIQuvnoHbVizh9pQcqf1oLK5Q9dFbt\nGbuvB3DsXmBPvgKI21kKnUpEY9qyyQHJl0pIgARIgARIgARIgARIILMI0EDKLNKZnI9hS/Zm\ndyXhYkByPnvpLN4e1RNf1swG2/EyuPLxUGxoADT9AihWA2gyBGj2jTKSOqvRpLX3onDJeMxd\nMRWGwWl2AWkAKiEBEiABEiABEiABEsgUAjSQMgVz5mdipHixi4u7FJDMB47rh59zxiJCeabb\nMvUvXIoG6ql1R66hyrNA1IY+2vX3/WePY9m2Ja4ivCYBEiABEiABEiABEiCBoCVAAylom8a/\ngtkNpAT/DaQlW/+HXPO+R5XqCdi7ehBK7i6L3DOUIZS8zMmpoEWrAJvKFEf8riJ4rHhuTFvO\naXZOgHhBAiRAAiRAAiRAAiQQ1ARoIAV186S/cKaBFBt/Of1KVMr4hHi8+2VXvFsnGy7vrInS\nU1/G/s+A0vU9q72s1iIlrr0LJUrGYZaaZsdAAiRAAiRAAiRAAiRAAtcKARpI10pLWSynLWUN\nkr9T7D6e9j6G4zDCkiJhG/Unlj8K1H8m7cJUVfezb+gNqGl4bU8exqqdK9JOwLskQAIkQAIk\nQAIkQAIkECQEaCAFSUMEvBgpa5ASEq6kW/WOw9uRc8pglKliw4mJX2Br5UK45Xvv6gpXADaW\nroTEPfnRtVgeTKU3O+/QKEECJEACJEACJEACJBAUBGggBUUzBL4Q5ghSfDqn2In3uQ8/exQ9\nrw/HhSWNcenY46j+FxDu4ycmrr2aZreuBcqWiMeMVdMCX0FqJAESIAESIAESIAESIIEMIODj\n624G5EyVGUsgMUnrj49P3wjS2Hnf4e24TbCdzwFM+x1hvwIxRX0vcnU1zS5ifS+Exdhwy797\nsW73Gt8TU5IESIAESIAESIAESIAErhIBGkhXCXxGZ2vug5QQH2s5q+PnjiP7mBdQpHwYjO/G\nY9u70ajQ1LuaJCQbZSJZoCywoVR9JO2PQfciMZhKb3beAVKCBEiABEiABEiABEjgqhOggXTV\nmyBjCmB6sUtMiHObwXZsxhz8jg1YBZv65xhGfvII7q9rQ9yMO7Hsprtw48uOd53PD2EfXsTj\nqIvCqKK8MtyOGhiJYcpUUv86AknrmqJScTXNbiWn2TmT4xUJkAAJkAAJkAAJkEAwEqCBFIyt\nEoAy2UeQ4pxHkLZhE+5GA9yF+uiLJ9ERTdAcVbFYmUsS5qz9A89d+h8SDuXClgOT0GSi58Ls\nwBa0w404goMYjBGYoHQ8iC74Bh/iedyPqs8ow2tjd0QUsKHm4Z3YtG+DZ2W8QwIkQAIkQAIk\nQAIkQAJBQIAGUhA0QoYUITF5VMhxBGk/duMhNcZTHpWwEDuwHiexUrnwbqXMnK64B4vi/0bY\nVw8jb7EwnJ38G677OxyR2TyXrp9K1RBNtWHURplaDXELnsJLmIxFWKH+/ioxGhtK3Iqkw1Ho\nXSiv2jR2imdlvEMCJEACJEACJEACJEACQUCABlIQNEJGFMEcQUqKj7erH4Y31AS4uvgMP6Ik\n1CIhFQqgEF5TIz6d8AT6n3kYt9aKQ+yvD+HsiMZ6HZE9scvJTjV6tBlr0R/vI1z9k2l65lS9\ncsoAewTPYirGw9ZJptndiJrF4vH7Sm4a64KRlyRAAiRAAiRAAiRAAkFGIDLIysPiBIiArEEK\nU7qS4v4zkObhD3yM71W83AGWYT6OqglycbiCuJNXcKzoebxdMwdO31AYBZu8gFgVL3/KZLKf\nJ8ddxhmc1jpkip2kj1f/iqM05mErsql/VVBTjST9gFpPA7avn0b2tvNRYNMWbD24BdVKV9dp\n+R8JkAAJkAAJkAAJkAAJBBsBGkjB1iIBKo+RZGhNSQnJBlICEpQZcxkFkeyr+4QyjR7FHSii\nzJpoIxcS4/do+eWlKqJU1a1qNCgKOdW/3Mij0hRRZ8nXcsyh/k7hOL7CELx27j0kfb0OF+Zs\nwolz+zG2Qls07dwLh+/er9w2FEWewsoQK9oODY5lwxsFc6ppdpOVgfRGgGpJNSRAAiRAAiRA\nAiRAAiQQWAI0kALLMyi0HVm9CXF7tiufckDs8RO4fOocogvmVeM75bEOy3Gl+7c4mDQDDavn\nRo85lxGRx4YLDxroWSgcv5T7H/Iil9d6yHS62Ucm4HCHT5E/sgiuf/gR/FZkKg6uPYXZvfpg\nx7IE3DH4Ka0n7H41krWkPurVWY0+ykDq34kGklfAFCABEiABEiABEiABErgqBGggXRXsGZfp\nmieaoG6tNcgTlUc52s6FSFxCxFfFsTypLx56qxt+n/YmEv/KpybERaPKniRs3hWBRFsY1t8U\nhdvO3IO8Rb0bR1J6WXf0wIsVsa3EBpz8sSlujmqmXDQUR/f7HkDdDuXR+P4ENLuxCpTvB9RS\ndlKimmaXs9UyRM7fjL3H9qB80esyDgI1kwAJkAAJkAAJkAAJkEA6CdBJQzrBBWOylV3vQN16\na7BhYx1ciamti5ikxoOOH8qNBjEfolr7kbi9V36s6BqLv7YUwObZFXDs4zNY+sY51PggL9o9\nPd/nap3bfwAX/rcVrd4dirVRK9EadfCccvUgI0vxN8SgxsMPYse4yVpfrgJq09giD8I4G4E3\n8+XF5CU/+5wPBUmABEiABEiABEiABEggMwlwBCkzaWdgXnEXr+D6iguxdc11qDlyERa8fJfO\nLUwtRSr+1b+4+Go+nDx2AcUK50Tr/u+pfYv6wYiNQ/zNiai5sAlKVDyHIzuO4fzhE4gpWRiG\nYSD+/AXEnj2L2DNnUo7qXK5Pn8GJTf8gLDwcF4fMxeNnyuDS2WhEFMyNf3+tgwURf6FM/Rux\neOZce40jHlLOGhbXws1VtmLQssl4ucMA+z2ekAAJkAAJkAAJkAAJkECwEKCBFCwt4Wc5Nn01\nHPViwhD1yDdaU+LlWOVLDjDiEtVeRhHYekpt6HpwJyrc2ljte9QF7xgvYVDfnIhekwfZG9TB\nibBtuHD+FH5u2VIbPnHnzsF0FS4Kw7NnR041+pMzXz79J47w5H6+8uUQ3agIcsTEYNGgt3Hr\nvM4Y2fIT7D65Hjny5pWkOtTuAsR//QSibu2DuAXrsf/4PpQtUi7lLg8kQAIkQAIkQAIkQAIk\nEBwEaCAFRzv4XYqkC8rtdm6gaO3q2HIkDAuvdERi9RhllezF8fNq9CZHYdhsOxGecAH7sQvZ\n/o3HsakFULh2HRSKjECO3CqxCsVvaIiqndohZ/78ycZQfmUQqb9s0eLy4b+QlJCA7+vfpEaN\nimFFtWcwayOwv9UtmD7xX1xX/DlsHz8ZDVs+bE8QpWylJQW64saLL2JQ3vyYumwSerd72X6f\nJyRAAiRAAiRAAiRAAiQQDARoIAVDKwSgDCXueQC2ZcPw/FtrMNXWAkWzt0D+6CM4iJaoNRB4\nIzEfChRKRPjxldiutngttEfGl4D7pk5EZM4cmN/6OpzMZkObUV8iQo0WeQsR2bKh1mtv45Fp\n5RG74zI6N4lEg+gk/DHzNP75fBgiYj5Dl+fVvDqHkL1zJGxLq6B5mX34WHmzo4HkAIenJEAC\nJEACJEACJEACQUGABlJQNIP/hSjdsDZeH/UaZoc3wpcNVuHI9EeQe3UC9jWNQ2L0CAyI/ByD\nS5/AuY2rcPqDZ1AhRo0a5bAhMTYBKx9phN17ElGoXCmfjCOztJ+ebqPWK51F97l3IXL6AURE\nRaHzpUvYX70jRlR4H58fXo7XCxQzxVH7STWgNeIhRN84CEcWqTKeOowSBUva7/OEBEiABEiA\nBEiABEiABK42AXqxu9otEKD84xKAkdkH4K2cr6Lz6WbIYVzQmiNV/OAq9+N22x+Yf8ckFKpQ\nDjGf50KVMbkQEWlgQYeKWLfyLKJy50GHOYt8Ls0hNaPv9/XAiO750HPFX2g/+Se0/uIT1On6\nJOrErUO1m5dizDznaXk51Cy+9fm7Q+1Zizfz5MWUZb/4nB8FSYAESIAESIAESIAESCAzCNBA\nygzKmZDHP4eBiwnhuPflIVj/zw24HF4QCys8g4j4MCw99CAee+4eLNsF3D9/ARaMD0P2xDAk\nKvnTF4qjQb+B6LzuH4RH+j6guOUIkFPN0ru+DLBwd3YM3dkQr+9tibpPd8PZPXtxR1giThwp\nimNQgg4h56PRMDaUR5vCEZimvNkxkAAJkAAJkAAJkAAJkEAwEaCBFEyt4UdZbMqdt4TchfKi\n9rdzsT+sPmZVe1XNaQtHrcHvIUK1tMjE4go2NT2CPDFJqPbwE3h41To07NEtObGP/yckAVuU\nQaZm56F8X6DDF8pBwyngD+WoYemZEih32624sOwMsmWPx1SMd9Ja+3GVbnMnxBS/jB07luPY\nmaNO93lBAiRAAiRAAiRAAiRAAleTAA2kq0k/gHlXLwlEKd8KMzckK7UZyaNBRkK0NopmqOlw\nDcpD+a/bCkO1+oHztbCx4K1YuQdIsnkvSHwiMGsT8MyYZKPord+ASKWnVU1g74fAby8AHW8A\nhs8Dqjz0IP48XRX1Kh3Hry4GUracwMZ8PSBuwl+PzoNflTc7BhIgARIgARIgARIgARIIFgI0\nkIKlJfwsR7QyjnrcDvSbCGw4GIakFAPJlhSNnxdH4aflQN87gdkHD+PsoNUYWv8vvLfnZtz+\nAVDzNWDOP6kLIOuaxODq9h1QTo0UPTwCOHMJ+PABYN/HwAfqOG1t8lokMbKebQ7M26Ly2dYC\nZ6NK4PnYTdijfOathcrcIUQ9WgjG5hJoXzA7flsxxeEOT0mABEiABEiABEiABEjg6hKggXR1\n+Qc094H3AC1qALd/GIlF+dWQjgo/l5mIIROK41O1JVHJ/MB7H7VAkehteGVOY/zTax/2K0On\nfX01+vMlsGg7cCVejQYpo+fJUUDZl4DHRwKX4qDTi+wvysfCQzcCeaOAp29VzhbaAy/9DJR5\nEegyWmeJdQfC8XGpXxE3ZRpuQnNMwQ/JN1L+r9VZptndgwIlLmLt1sU4ef6k031ekAAJkEBm\nEjh16hQGDx7sNsuLFy9iwoQJGDduHA4cOOBWxoy8cuUKhg0bhkOHDplR9qPcmzp1Kr799ls1\nvXiHPZ4nJEACJEACwUeABlLwtUm6SyTrjEZ1USM6vf7P3nnAR1Vlf/w3M5n0HhIChIQAofci\nvUoVEEUUBBULIhZYFAtrwa4r639d3bULigos0hERASlK79J7CxBIgfRMysz8z32TDEloIQzJ\nTPhdPo/X7rvv3O9LMvN759xz8+CXd05rp0aKZJN7cwse7Qy8OR/wrrITI6o/ikBTHHyrVUWw\nD/DKAKBzHWDo5zZR9Pi3QJ54hD59SMYWiSia8aQkd7gN8JPwuOJlTA/gwD9sdZ/rA/y9nwx7\nkjFKnYZ0Q8LuPbhzby/8gtlamF/BtW4ewG7/ZwFJ8jBBUtvNlzmRWEiABEigPAjk5eXh3nvv\nxTvvvHPJ7T///HOEhYVp4umtt95CTEwMZs2adUm9ggMvvvgixo8fj6NHjxYc0tZr165FaGio\ndo8pU6agYcOGmDq16IujIhdwhwRIgARIoFwJUCCVK/6bc/N2ta1okLpYa7x53BwEh17QttUY\nIq9OH6PaAQM8g4Kw5KCPFjanPEUbjgCpWcAH99pE0Q+jbGOKfETMXKsokXVXC+DhjhLiJwLJ\nV4TUwriaqHpbawT9mCCXW/EbRJ0VKl4jqsO6LxT3h3gwm10hLtwkARIoOwLKk9OtWzesXLny\nkpteuHABL7/8MiZOnIjdu3fj0KFDGDlyJEaMGAHlDSpelixZgi++kDjkYiU7O9t+3datW7Fh\nwwa8++67GDVqFOLj44vV5i4JkAAJkIAzEKBAcoancBNssFjFPSPFbPFCtmSuU5nnMiV8zlT5\nDHz2GGGJbKiJI+V1+m4ksP1tmxFtatuSPdj2rv9/owF4vAvw1Sqg3v1DcHT+YvQzDZIwu++L\nNNZQxi+Z9vRBaHg61u5ZjQvpNhFXpBJ3SIAESOAmEdi5cyeaNm2KnJwcPP3005fc5fDhwxgw\nYACeeUaSyuSX/v37a+Joz56igzYTExPxyCOP4KWXXiqoal8vXboU+/fv1zxLBQfHjBkjE3V7\n0ItUAIRrEiABEnAyAm5OZk+FM2fHjh1QH7RXK5s2bdI+pDMyJAOCA4oKGTHrbI/WbPVGiilF\nwt4yEB6iQ+aRtrBKHH1Ko8ZyJys+ujdTy3732x4D3A0eqOSZiRs1Y4iMafrgF2/srdIDlryJ\naDsvCj8N/R6Hsw6gijXC3sPd3mPR2ucHjDV646c/puOBLg/bz5VkQ32xsVgsYq9juJXknuVR\nx2w2O/Tnozz6cK17queoinozn5sr2UEqaFHeBKvVWuF/ZtXfIL1e77B+pqWl4fz585g9++rh\nuEajEUrEGAzypuYaRdn39ddf4/7778cnn3wCnU5SaxYqrVu3xvffX3yxo35GP/vsMwQGBqJJ\nkyaFaspLoccfR+fOnTF8+HDNO1T45LFjx7Twupo1a9oPe3l5oVGjRti7V7LasJAACZAACTgd\nAQqkm/xIJk2ahGXLll31LiaTCZ6entoXgKtWLOFJ9QXMnO9BytN5IzEjAeczz6NB0934Y8kL\nOKLbhwS/aFRxNyMr/TzOZOrx+vww3Nk4A9kZF2Qp4Y2uUE39UN3RQLxIaz0wtldPpE3bjGqD\nozAzdwoeSr34Njbn7ioyoVIAHg62YvS6WbijsWSZuI6i+qkW9cWpIhf1xUyJJPVzUlGLeo6q\npKSkXPJFtSL1+Vb6mVXPTb3EcERRwjI2NhZPPikDIq9SlEBq3LgxCouRK1VXAkUtJSkTJkzA\nN998o/0erlq1Cu7u7vbL1HH1kmvXrl04d8429tN+UjaUQAoJCSl8SNsODg7G2bNnLznOAyRA\nAiRAAuVPgALpJj8Dlf3oWkW9pXzttddQvXr1a1Ut0fm4v3bCmmsLscs1eMHb1x3VQ6ojqNYQ\nVDkxAR80no6ahgwE+Ljhx7+q45s/gCoBwCcPG+Hv5Vuie1yr0vj+kvjhfeCtYU/h+LDeGHJm\nOGZFzcErAR/YL606UjwGvbqjevcF2LRnPfyD/cUmMaSERX2ZzszMRJUqIrQqcFFfotQb54CA\nkrNxNRxKAKrMX+Hh4UW+fLpaP65lr8qIlpycjIiIi57Ua13jiucTEhI0D9LlhEFp+lOpUiUt\nHE6N3ymPUrVqVQwdOhTTpk3TxiVNnjxZ+1lV0QHPPvss5syRsZ4ieC4nkFT4na/vpX9Xvb29\ncebMmfLoDu9JAiRAAiRwDQL6a5znaRcikBp7CrMHDsbsOwYC2TaBlGPwRNLHq7RenAveh1HN\nhmDAoUk4kxuIk0nArztt8yctewEijhzX2RY1gDYSUTLzdB2E1KuL2tN0iMUxbMKf9psYRJ7v\n934W+iALHtV7YNHmookc7BW5QQIkQALlSGDs2LH473//CxUyrZIxfPnll1BhhA888IAWoqfC\n65SHV3m6VFGeswLvmcqCp0Rx8aKOOeqlWPG2uU8CJEACJHBjBCiQboyf01xtSk7B3HuGwuDp\ngaGrliHHz+YczNV7we3T7fjr2+9wumomIra7o8Xh71HF34JXJKLtj5eBZ3vfWGKGK0F4sruk\nCJcXvlFDH0Ls7KXoZO4hCb+Lprb1fqwVrEd9MFq8R/PWz75SUzxOAiRAAmVKQI1tVOFxhUtU\nVBQ6dOighU2rkL+NGzdq45iUh1ctzZs316r37NkTHTt21LaV90l5kYoX5WWLjo4ufpj7JEAC\nJEACTkCAAskJHoIjTNjx1TcweLhjwHeTERAVKWOQbDHyKsTu/NAQrHt/EvLMOvjsrqrdLjbN\nA7VCHXHnK7cxUFJ/q7mTNla+G1kXktFneWsswVxIGgj7RfXE2WXa0wk1QlKwctdypGel289x\ngwRIgATKi8D777+PevXqFZn0VYVIKi+SEj0qtHfFihVFlm+//VYz96OPPtISOqgdJZSUt2j9\n+vX2rqjQOpVFT82HxEICJEACJOB8BCiQnO+ZlMqi4ytWof59g+HmJYpEigX5HiQlkFqakSch\nH1Hr3eFlbQ1TcA2kZ+tQK6xUtyrxRSrl96iuwJSNXqh5xx3wmHFCrDLiV0n6XVAkkRQOejwN\nQ6gZA60GCbNbUHCKaxIgARIoNwIPPvgg3NzcoMLr1BxIai4kNXeRGvc4btw4LbGOmkOp8NKm\nTRvN3hYtWqBVq1batjqmvE7qmpOSQVR5k0aPHg1VR41rYiEBEiABEnA+AhRIzvdMSmVRjrzZ\n9AwOsl9r1l0cg2S2ZsDsYUFUnA7+DRohs4btgzv6JnuQlDFq8tgzEn4f3+kJxP6+GnfGD7xk\nTiSvUbfDesoDz8kg5/kbGGZnf4jcIAESKDcCdevWxdy5c7Ft2zbUqVNHy463Zs0azJs3D+3b\nt78uu1SyHpVWXIXoKe+TGq80derUEqUjv64bsTIJkAAJkIBDCFAgOQRj+TcSHBODMxs32w2x\n5AskFWJnOJsJQ7oVIZLWO0PmmUkLa4CIYMDTpqHs19yMjTB/4N7WwE9n6sKvWlW0+l8lLVHD\nSRy1365ePwmz290WMcHJ+P2vpcjMzrSf4wYJkAAJ3GwCKhPd5eZT6927tzYO6fjx45r3R3mA\n1OSxVyr169fXph5QSRsKl8jISC3ELj4+XkvtrSaPbdBA5kJgIQESIAEScEoCFEhO+Viu36gm\nDz+Ag/MXQoXaqWL3ILl5o8psHRJbZKOyyYjUU6dx3r82at/k8DrNiPz/RncDVh+Q8U/3PIXz\nP61HDOqLF+mHwlVwxP0pGMPz0E7mC1285eci57hDAiRAAuVFQE0gqzw/jsg4FxoaqqUDL6++\n8L4kQAIkQAIlI0CBVDJOTl8rsktntP7bM1g04jGsePYFWCwX50EyJuuw6LNkhMXWRJrMNRPv\nHoGaZRBeVwCteRTQrjbwe8BApJ44ibvWdsc8/AiZ5rWgCjxHD4A1wQ2vB4YyzM5OhRskQAIk\nQAIkQAIkQAJlTYACqayJ38T7tRk/DndOm4pcGURskWQIqqh5kP58Nxkp1c1oUnW4eJBOIc4S\nglqVb6Ihl2lapfyes8sblW6/A1WmZ+AcTmM9Vtpr1ukpUzftao4GgSlYtmMJTDkm+zlukAAJ\nkAAJkAAJkAAJkEBZEaBAKivSZXSfyM4d0fvLT5En2ZdUyXXzQrpM4h4sw3qOb/sZOalpiM3y\nKdMQO2XHnc2AAG9gb8sxOLVkNbql9rxkTqTjhpFwr2ZC3dw8/LZ9sbqMhQRIgARIgARIgARI\ngATKlAAFUpniLpubbVqwLH8MkgU5bp4w5OhgzAXcI7cg1SMUmbmGMg2xU712k5TfT3QVL1Jc\nDNx8/dFtTm0sxQKkIdUOxThyOJBiwPsBVTCfk8b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PAQbg3YwozGeYXenA8ioSIAESIAESIAESIIEiBCiQiuBwzZ3TW2ZC55EOn5NvaB3w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6FTWCGK2pLRo5VyIksdxR9RMcg2/UaDjDcr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\nFI310IGbMAEmwATSMwHVg/+Ee90LKEr0eWsYCvtKUIeffGs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/R8eOHbF48WJQPJJKpZLZ6xLOLGjTfr98+RJdunRJ29FlsqsfvwXMtxSZ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\nAAmQQIUR+H8+B+wXlV946QAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl <- makePSOCKcluster(7)\n",
    "registerDoParallel(cl)\n",
    "\n",
    "grid <- expand.grid(lambda = c(0, 10^seq(-1, -4, length = 8 - \n",
    "            1)), alpha = c(0, 10^seq(-1, -4, length = 8 - 1)), \n",
    "            nrounds = floor((1:3) * 50), eta = 0.3)\n",
    "tc <- trainControl(method = 'cv', number = 5, search = 'grid', summaryFunction = defaultSummary, selectionFunction = \"oneSE\",\n",
    "                  verboseIter = FALSE)\n",
    "\n",
    "xgbLinear <- train(X.train, y.train, method = \"xgbLinear\", metric = \"RMSE\", \n",
    "      maximize = FALSE, trControl = tc, tuneGrid = grid)\n",
    "\n",
    "plot(xgbLinear)  \n",
    "\n",
    "preds = predict(xgbLinear, newdata = X.test)\n",
    "\n",
    "print(xgbLinear)\n",
    "metrics(preds)\n",
    "\n",
    "stopCluster(cl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2019-12-19T08:10:36.514Z"
    }
   },
   "outputs": [],
   "source": [
    "to.drop <- c('Home_Atlanta.Hawks','Visitor_Atlanta.Hawks', \"Last.Five_0\", \"Day.of.Week_Friday\", \"Month_April\", 'Home_Detroit.Pistons',\n",
    "            'Home_Golden.State.Warriors', 'Home_Milwaukee.Bucks')\n",
    "X.train <- X.train[ , !(names(X.train) %in% to.drop)]\n",
    "X.test <- X.test[, !(names(X.test) %in% to.drop)]\n",
    "\n",
    "grid <- expand.grid(maxInteractionOrder = 1:3)\n",
    "            \n",
    "tc <- trainControl(method = 'repeatedcv', number = 5, search = 'grid', summaryFunction = defaultSummary, verboseIter = FALSE)\n",
    "\n",
    "ensembleGLM <- train(X.train, y.train, method = \"randomGLM\", metric = \"RMSE\", maximize = FALSE, trControl = tc, tuneGrid = grid)\n",
    "\n",
    "plot(ensembleGLM)  \n",
    "\n",
    "preds = predict(ensembleGLM, newdata = X.test)\n",
    "\n",
    "metrics(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T04:41:02.552235Z",
     "start_time": "2019-12-19T04:41:02.036Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 11838 × 91</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>intercept</th><th scope=col>V.Pop</th><th scope=col>H.Pop</th><th scope=col>Curr.Win..</th><th scope=col>LS.Win..</th><th scope=col>Last.Game</th><th scope=col>Last.Attendance.vs.Opp</th><th scope=col>Capacity</th><th scope=col>Playoffs.</th><th scope=col>Home_Atlanta.Hawks</th><th scope=col>⋯</th><th scope=col>Day.of.Week_Wednesday</th><th scope=col>Month_April</th><th scope=col>Month_December</th><th scope=col>Month_February</th><th scope=col>Month_January</th><th scope=col>Month_June</th><th scope=col>Month_March</th><th scope=col>Month_May</th><th scope=col>Month_November</th><th scope=col>Month_October</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.592292553</td><td>-0.89843234</td><td>-0.57431379</td><td>-1.6754137</td><td>-0.41414101</td><td> 0.46647597</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.003514696</td><td> 0.96752507</td><td>-0.01024471</td><td> 0.2845056</td><td> 0.45442179</td><td>-0.86789653</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.96761600</td><td>-0.690422196</td><td>-1.19939321</td><td>-1.36421592</td><td>-0.5961520</td><td> 0.69552455</td><td> 0.50093362</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.34867406</td><td>-0.690422196</td><td> 1.09650830</td><td> 0.41052831</td><td> 0.8931711</td><td> 0.07621655</td><td> 0.65427016</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.690422196</td><td>-0.12262209</td><td> 0.13375800</td><td>-0.2905754</td><td> 0.63920136</td><td> 0.86015462</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.06661191</td><td>-0.494162910</td><td> 0.73452311</td><td> 0.98191169</td><td> 0.3562353</td><td>-0.17970810</td><td>-0.51470562</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.16063263</td><td>-0.788551839</td><td> 1.09650830</td><td> 0.22248958</td><td>-1.4875898</td><td>-0.05520422</td><td> 0.03144814</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.02740881</td><td> 0.781522447</td><td>-1.01881668</td><td> 0.15284561</td><td> 1.0507276</td><td> 0.39958080</td><td> 2.95948695</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.396033267</td><td> 0.96752507</td><td> 0.98191169</td><td> 0.8944150</td><td>-0.07694299</td><td> 0.90064236</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.101644339</td><td> 0.06464245</td><td>-0.19537426</td><td>-0.3610612</td><td>-0.04285264</td><td>-1.21075015</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.34867406</td><td>-0.494162910</td><td> 0.39667026</td><td>-0.19537426</td><td> 0.4814512</td><td> 0.67403281</td><td> 0.15549568</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.40349169</td><td>-0.396033267</td><td>-0.02564581</td><td>-1.39325059</td><td> 0.7721014</td><td> 0.83262704</td><td> 0.41737382</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.592292553</td><td>-0.96077423</td><td>-0.57431379</td><td>-2.2687382</td><td> 0.11302424</td><td> 0.46647597</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.34867406</td><td>-0.788551839</td><td> 1.28353399</td><td> 0.22248958</td><td> 0.1302662</td><td>-4.14629311</td><td> 0.03144814</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.101644339</td><td>-0.26367850</td><td> 0.06803864</td><td> 0.3686740</td><td>-0.11572694</td><td> 1.14873744</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.003514696</td><td> 0.84714072</td><td>-0.01024471</td><td> 0.2845056</td><td>-0.04507593</td><td>-0.86789653</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.690422196</td><td>-0.68023570</td><td>-1.36421592</td><td>-0.8598518</td><td> 0.80742983</td><td> 0.50093362</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.40349169</td><td> 0.781522447</td><td>-0.78581472</td><td> 0.15284561</td><td> 1.8148764</td><td> 0.46726743</td><td> 2.95948695</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.101644339</td><td> 0.42579550</td><td>-0.19537426</td><td>-1.0182375</td><td>-0.04285264</td><td>-1.21075015</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.34867406</td><td>-0.494162910</td><td> 0.14891150</td><td>-0.01024471</td><td> 0.8181245</td><td> 0.77235135</td><td> 0.24077836</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.494162910</td><td> 0.79259156</td><td>-0.19537426</td><td>-0.2934778</td><td> 0.68712548</td><td> 0.15549568</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.690422196</td><td>-0.51658823</td><td>-1.39325059</td><td> 0.2944566</td><td> 0.93316887</td><td> 0.80157661</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.96761600</td><td>-0.494162910</td><td>-1.29259399</td><td>-0.01024471</td><td> 0.1688260</td><td>-0.74664542</td><td>-1.10823864</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.34867406</td><td>-0.690422196</td><td> 1.02169803</td><td> 0.41052831</td><td>-0.3581588</td><td>-0.87856025</td><td> 0.65427016</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.494162910</td><td>-1.01881668</td><td> 0.98191169</td><td> 0.3562353</td><td>-0.17970810</td><td>-0.51470562</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.396033267</td><td> 0.06464245</td><td>-1.39325059</td><td> 0.4524276</td><td> 0.74493085</td><td> 0.41737382</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.96761600</td><td>-0.003514696</td><td>-1.33482560</td><td>-0.01024471</td><td>-2.0411105</td><td> 0.45442179</td><td>-0.86789653</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.72475694</td><td>-0.101644339</td><td>-0.17966696</td><td> 0.06803864</td><td> 0.1655091</td><td> 0.94799076</td><td> 1.14873744</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.77957456</td><td>-0.690422196</td><td>-0.58105542</td><td>-1.36421592</td><td>-1.4448838</td><td> 0.81360561</td><td> 0.50093362</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.396033267</td><td>-0.35294076</td><td> 0.98191169</td><td> 0.8284900</td><td>-0.40623600</td><td> 0.90064236</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>2.1006</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋱</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>1</td><td> 0.02740881</td><td>-0.3960333</td><td>-0.32488690</td><td> 0.15863623</td><td> 0.59008223</td><td> 1.79283853</td><td> 0.00646634</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.16063263</td><td>-0.5922926</td><td> 0.52752875</td><td> 0.69163671</td><td>-1.56263647</td><td>-1.42375880</td><td>-1.47176685</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.34867406</td><td> 1.0759114</td><td> 0.49802611</td><td> 0.02844706</td><td> 0.28450564</td><td> 0.05373668</td><td>-0.86789653</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td> 0.5852632</td><td> 0.97006840</td><td>-0.46914712</td><td> 0.04858558</td><td>-0.88671229</td><td>-1.24434636</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 1.71978175</td><td> 0.9777817</td><td> 0.34840556</td><td> 0.62698178</td><td> 0.10994967</td><td> 0.35042153</td><td>-1.23056330</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.6904222</td><td>-0.62412800</td><td>-0.08141139</td><td> 0.59754544</td><td> 0.56212753</td><td>-0.21233974</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 1.53174031</td><td>-0.1016443</td><td> 0.45377215</td><td> 0.52533743</td><td> 0.77210140</td><td> 0.51741483</td><td> 1.22195994</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.1997740</td><td>-0.13628072</td><td> 0.74456144</td><td>-1.61404963</td><td>-0.11918538</td><td>-1.63630213</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 1.24967816</td><td> 0.2908742</td><td>-1.05022130</td><td> 0.64651141</td><td> 0.68917695</td><td>-0.39141411</td><td> 0.29763348</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.6904222</td><td>-0.43130715</td><td>-1.19213239</td><td>-0.75744007</td><td> 0.05546590</td><td>-1.00055848</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 1.71978175</td><td>-0.3960333</td><td> 1.56012127</td><td> 0.73929589</td><td> 0.98936346</td><td> 0.67798531</td><td> 0.73438420</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.21545025</td><td>-0.6904222</td><td> 1.45429657</td><td>-1.19213239</td><td>-0.90836256</td><td>-1.38670407</td><td> 0.67236043</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td> 0.8796521</td><td>-0.25007663</td><td>-0.19627289</td><td> 0.64025153</td><td> 0.95219029</td><td> 0.46992173</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.3960333</td><td>-1.16887323</td><td>-0.43793912</td><td> 0.43915974</td><td> 0.54656455</td><td> 0.31486231</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.34867406</td><td> 1.6646892</td><td>-0.75296791</td><td>-0.78244715</td><td>-0.70561229</td><td> 0.08807406</td><td>-0.14945453</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td> 1.0759114</td><td> 0.04916447</td><td> 0.02844706</td><td> 0.28450564</td><td> 0.45442179</td><td>-0.86789653</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td> 1.5665596</td><td>-0.17111023</td><td>-2.70051892</td><td> 1.66478310</td><td> 1.08953981</td><td> 2.73551222</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.02740881</td><td> 0.9777817</td><td>-0.72633358</td><td> 0.62698178</td><td> 0.10994967</td><td> 0.35042153</td><td>-1.23056330</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.02740881</td><td>-0.3960333</td><td> 0.52752875</td><td>-0.36251979</td><td> 1.00263137</td><td> 0.96083640</td><td> 0.63187269</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.77957456</td><td>-0.5922926</td><td>-0.17526635</td><td>-0.13626181</td><td>-0.17655430</td><td>-0.08361284</td><td> 0.26662160</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.4941629</td><td>-0.65257698</td><td>-0.93514801</td><td>-0.13675056</td><td> 0.13155161</td><td>-1.74312084</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.1997740</td><td> 0.55621188</td><td>-0.43793912</td><td> 0.81812447</td><td> 0.51790889</td><td> 0.24077836</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.06661191</td><td> 0.2908742</td><td> 0.48347967</td><td> 0.64651141</td><td> 0.68917695</td><td>-0.39141411</td><td> 0.29763348</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 1.24967816</td><td>-0.1997740</td><td>-1.07298964</td><td> 0.74456144</td><td>-1.49049220</td><td>-0.55149053</td><td>-1.63630213</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.3960333</td><td> 0.44063741</td><td> 0.73929589</td><td> 1.04284973</td><td> 0.70861722</td><td> 0.73438420</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td> 0.1927446</td><td> 1.47055967</td><td> 1.45246270</td><td> 0.19909346</td><td>-0.53642161</td><td>-0.72317440</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td> 0.8796521</td><td>-0.21003733</td><td>-0.19627289</td><td> 0.65808028</td><td> 0.88400959</td><td> 0.46992173</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.16063263</td><td>-0.3960333</td><td> 0.55621188</td><td>-0.36251979</td><td> 0.08258460</td><td> 0.83707361</td><td> 0.63187269</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td> 1.6646892</td><td>-0.70713666</td><td>-0.78244715</td><td>-0.78936598</td><td> 0.13426895</td><td>-0.14945453</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.77957456</td><td>-0.3960333</td><td> 0.82255519</td><td> 0.15863623</td><td>-1.51744265</td><td> 1.79283853</td><td> 0.00646634</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 11838 × 91\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " intercept & V.Pop & H.Pop & Curr.Win.. & LS.Win.. & Last.Game & Last.Attendance.vs.Opp & Capacity & Playoffs. & Home\\_Atlanta.Hawks & ⋯ & Day.of.Week\\_Wednesday & Month\\_April & Month\\_December & Month\\_February & Month\\_January & Month\\_June & Month\\_March & Month\\_May & Month\\_November & Month\\_October\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & -0.53671550 & -0.592292553 & -0.89843234 & -0.57431379 & -1.6754137 & -0.41414101 &  0.46647597 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.003514696 &  0.96752507 & -0.01024471 &  0.2845056 &  0.45442179 & -0.86789653 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.96761600 & -0.690422196 & -1.19939321 & -1.36421592 & -0.5961520 &  0.69552455 &  0.50093362 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.34867406 & -0.690422196 &  1.09650830 &  0.41052831 &  0.8931711 &  0.07621655 &  0.65427016 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.690422196 & -0.12262209 &  0.13375800 & -0.2905754 &  0.63920136 &  0.86015462 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.06661191 & -0.494162910 &  0.73452311 &  0.98191169 &  0.3562353 & -0.17970810 & -0.51470562 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.16063263 & -0.788551839 &  1.09650830 &  0.22248958 & -1.4875898 & -0.05520422 &  0.03144814 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.02740881 &  0.781522447 & -1.01881668 &  0.15284561 &  1.0507276 &  0.39958080 &  2.95948695 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.396033267 &  0.96752507 &  0.98191169 &  0.8944150 & -0.07694299 &  0.90064236 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.101644339 &  0.06464245 & -0.19537426 & -0.3610612 & -0.04285264 & -1.21075015 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.34867406 & -0.494162910 &  0.39667026 & -0.19537426 &  0.4814512 &  0.67403281 &  0.15549568 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.40349169 & -0.396033267 & -0.02564581 & -1.39325059 &  0.7721014 &  0.83262704 &  0.41737382 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.592292553 & -0.96077423 & -0.57431379 & -2.2687382 &  0.11302424 &  0.46647597 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.34867406 & -0.788551839 &  1.28353399 &  0.22248958 &  0.1302662 & -4.14629311 &  0.03144814 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.101644339 & -0.26367850 &  0.06803864 &  0.3686740 & -0.11572694 &  1.14873744 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 & -0.003514696 &  0.84714072 & -0.01024471 &  0.2845056 & -0.04507593 & -0.86789653 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.690422196 & -0.68023570 & -1.36421592 & -0.8598518 &  0.80742983 &  0.50093362 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.40349169 &  0.781522447 & -0.78581472 &  0.15284561 &  1.8148764 &  0.46726743 &  2.95948695 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 & -0.101644339 &  0.42579550 & -0.19537426 & -1.0182375 & -0.04285264 & -1.21075015 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.34867406 & -0.494162910 &  0.14891150 & -0.01024471 &  0.8181245 &  0.77235135 &  0.24077836 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.494162910 &  0.79259156 & -0.19537426 & -0.2934778 &  0.68712548 &  0.15549568 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.690422196 & -0.51658823 & -1.39325059 &  0.2944566 &  0.93316887 &  0.80157661 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.96761600 & -0.494162910 & -1.29259399 & -0.01024471 &  0.1688260 & -0.74664542 & -1.10823864 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.34867406 & -0.690422196 &  1.02169803 &  0.41052831 & -0.3581588 & -0.87856025 &  0.65427016 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 & -0.494162910 & -1.01881668 &  0.98191169 &  0.3562353 & -0.17970810 & -0.51470562 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.396033267 &  0.06464245 & -1.39325059 &  0.4524276 &  0.74493085 &  0.41737382 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.96761600 & -0.003514696 & -1.33482560 & -0.01024471 & -2.0411105 &  0.45442179 & -0.86789653 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.72475694 & -0.101644339 & -0.17966696 &  0.06803864 &  0.1655091 &  0.94799076 &  1.14873744 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.77957456 & -0.690422196 & -0.58105542 & -1.36421592 & -1.4448838 &  0.81360561 &  0.50093362 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 & -0.396033267 & -0.35294076 &  0.98191169 &  0.8284900 & -0.40623600 &  0.90064236 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & 2.1006 & -0.08037997 & -0.4729413 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 1 &  0.02740881 & -0.3960333 & -0.32488690 &  0.15863623 &  0.59008223 &  1.79283853 &  0.00646634 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.16063263 & -0.5922926 &  0.52752875 &  0.69163671 & -1.56263647 & -1.42375880 & -1.47176685 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.34867406 &  1.0759114 &  0.49802611 &  0.02844706 &  0.28450564 &  0.05373668 & -0.86789653 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 &  0.5852632 &  0.97006840 & -0.46914712 &  0.04858558 & -0.88671229 & -1.24434636 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  1.71978175 &  0.9777817 &  0.34840556 &  0.62698178 &  0.10994967 &  0.35042153 & -1.23056330 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 & -0.6904222 & -0.62412800 & -0.08141139 &  0.59754544 &  0.56212753 & -0.21233974 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  1.53174031 & -0.1016443 &  0.45377215 &  0.52533743 &  0.77210140 &  0.51741483 &  1.22195994 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 & -0.1997740 & -0.13628072 &  0.74456144 & -1.61404963 & -0.11918538 & -1.63630213 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  1.24967816 &  0.2908742 & -1.05022130 &  0.64651141 &  0.68917695 & -0.39141411 &  0.29763348 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.6904222 & -0.43130715 & -1.19213239 & -0.75744007 &  0.05546590 & -1.00055848 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  1.71978175 & -0.3960333 &  1.56012127 &  0.73929589 &  0.98936346 &  0.67798531 &  0.73438420 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.21545025 & -0.6904222 &  1.45429657 & -1.19213239 & -0.90836256 & -1.38670407 &  0.67236043 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 &  0.8796521 & -0.25007663 & -0.19627289 &  0.64025153 &  0.95219029 &  0.46992173 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.3960333 & -1.16887323 & -0.43793912 &  0.43915974 &  0.54656455 &  0.31486231 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.34867406 &  1.6646892 & -0.75296791 & -0.78244715 & -0.70561229 &  0.08807406 & -0.14945453 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 &  1.0759114 &  0.04916447 &  0.02844706 &  0.28450564 &  0.45442179 & -0.86789653 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 &  1.5665596 & -0.17111023 & -2.70051892 &  1.66478310 &  1.08953981 &  2.73551222 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.02740881 &  0.9777817 & -0.72633358 &  0.62698178 &  0.10994967 &  0.35042153 & -1.23056330 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.02740881 & -0.3960333 &  0.52752875 & -0.36251979 &  1.00263137 &  0.96083640 &  0.63187269 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.77957456 & -0.5922926 & -0.17526635 & -0.13626181 & -0.17655430 & -0.08361284 &  0.26662160 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 & -0.4941629 & -0.65257698 & -0.93514801 & -0.13675056 &  0.13155161 & -1.74312084 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 & -0.1997740 &  0.55621188 & -0.43793912 &  0.81812447 &  0.51790889 &  0.24077836 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.06661191 &  0.2908742 &  0.48347967 &  0.64651141 &  0.68917695 & -0.39141411 &  0.29763348 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  1.24967816 & -0.1997740 & -1.07298964 &  0.74456144 & -1.49049220 & -0.55149053 & -1.63630213 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.3960333 &  0.44063741 &  0.73929589 &  1.04284973 &  0.70861722 &  0.73438420 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 &  0.1927446 &  1.47055967 &  1.45246270 &  0.19909346 & -0.53642161 & -0.72317440 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 &  0.8796521 & -0.21003733 & -0.19627289 &  0.65808028 &  0.88400959 &  0.46992173 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.16063263 & -0.3960333 &  0.55621188 & -0.36251979 &  0.08258460 &  0.83707361 &  0.63187269 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 &  1.6646892 & -0.70713666 & -0.78244715 & -0.78936598 &  0.13426895 & -0.14945453 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.77957456 & -0.3960333 &  0.82255519 &  0.15863623 & -1.51744265 &  1.79283853 &  0.00646634 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 11838 × 91\n",
       "\n",
       "| intercept &lt;dbl&gt; | V.Pop &lt;dbl&gt; | H.Pop &lt;dbl&gt; | Curr.Win.. &lt;dbl&gt; | LS.Win.. &lt;dbl&gt; | Last.Game &lt;dbl&gt; | Last.Attendance.vs.Opp &lt;dbl&gt; | Capacity &lt;dbl&gt; | Playoffs. &lt;dbl&gt; | Home_Atlanta.Hawks &lt;dbl&gt; | ⋯ ⋯ | Day.of.Week_Wednesday &lt;dbl&gt; | Month_April &lt;dbl&gt; | Month_December &lt;dbl&gt; | Month_February &lt;dbl&gt; | Month_January &lt;dbl&gt; | Month_June &lt;dbl&gt; | Month_March &lt;dbl&gt; | Month_May &lt;dbl&gt; | Month_November &lt;dbl&gt; | Month_October &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | -0.53671550 | -0.592292553 | -0.89843234 | -0.57431379 | -1.6754137 | -0.41414101 |  0.46647597 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.003514696 |  0.96752507 | -0.01024471 |  0.2845056 |  0.45442179 | -0.86789653 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.96761600 | -0.690422196 | -1.19939321 | -1.36421592 | -0.5961520 |  0.69552455 |  0.50093362 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.34867406 | -0.690422196 |  1.09650830 |  0.41052831 |  0.8931711 |  0.07621655 |  0.65427016 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.690422196 | -0.12262209 |  0.13375800 | -0.2905754 |  0.63920136 |  0.86015462 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.06661191 | -0.494162910 |  0.73452311 |  0.98191169 |  0.3562353 | -0.17970810 | -0.51470562 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.16063263 | -0.788551839 |  1.09650830 |  0.22248958 | -1.4875898 | -0.05520422 |  0.03144814 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.02740881 |  0.781522447 | -1.01881668 |  0.15284561 |  1.0507276 |  0.39958080 |  2.95948695 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.396033267 |  0.96752507 |  0.98191169 |  0.8944150 | -0.07694299 |  0.90064236 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.101644339 |  0.06464245 | -0.19537426 | -0.3610612 | -0.04285264 | -1.21075015 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.34867406 | -0.494162910 |  0.39667026 | -0.19537426 |  0.4814512 |  0.67403281 |  0.15549568 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.40349169 | -0.396033267 | -0.02564581 | -1.39325059 |  0.7721014 |  0.83262704 |  0.41737382 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.592292553 | -0.96077423 | -0.57431379 | -2.2687382 |  0.11302424 |  0.46647597 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.34867406 | -0.788551839 |  1.28353399 |  0.22248958 |  0.1302662 | -4.14629311 |  0.03144814 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.101644339 | -0.26367850 |  0.06803864 |  0.3686740 | -0.11572694 |  1.14873744 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 | -0.003514696 |  0.84714072 | -0.01024471 |  0.2845056 | -0.04507593 | -0.86789653 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.690422196 | -0.68023570 | -1.36421592 | -0.8598518 |  0.80742983 |  0.50093362 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.40349169 |  0.781522447 | -0.78581472 |  0.15284561 |  1.8148764 |  0.46726743 |  2.95948695 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 | -0.101644339 |  0.42579550 | -0.19537426 | -1.0182375 | -0.04285264 | -1.21075015 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.34867406 | -0.494162910 |  0.14891150 | -0.01024471 |  0.8181245 |  0.77235135 |  0.24077836 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.494162910 |  0.79259156 | -0.19537426 | -0.2934778 |  0.68712548 |  0.15549568 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.690422196 | -0.51658823 | -1.39325059 |  0.2944566 |  0.93316887 |  0.80157661 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.96761600 | -0.494162910 | -1.29259399 | -0.01024471 |  0.1688260 | -0.74664542 | -1.10823864 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.34867406 | -0.690422196 |  1.02169803 |  0.41052831 | -0.3581588 | -0.87856025 |  0.65427016 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 | -0.494162910 | -1.01881668 |  0.98191169 |  0.3562353 | -0.17970810 | -0.51470562 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.396033267 |  0.06464245 | -1.39325059 |  0.4524276 |  0.74493085 |  0.41737382 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.96761600 | -0.003514696 | -1.33482560 | -0.01024471 | -2.0411105 |  0.45442179 | -0.86789653 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.72475694 | -0.101644339 | -0.17966696 |  0.06803864 |  0.1655091 |  0.94799076 |  1.14873744 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.77957456 | -0.690422196 | -0.58105542 | -1.36421592 | -1.4448838 |  0.81360561 |  0.50093362 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 | -0.396033267 | -0.35294076 |  0.98191169 |  0.8284900 | -0.40623600 |  0.90064236 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | 2.1006 | -0.08037997 | -0.4729413 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 1 |  0.02740881 | -0.3960333 | -0.32488690 |  0.15863623 |  0.59008223 |  1.79283853 |  0.00646634 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.16063263 | -0.5922926 |  0.52752875 |  0.69163671 | -1.56263647 | -1.42375880 | -1.47176685 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.34867406 |  1.0759114 |  0.49802611 |  0.02844706 |  0.28450564 |  0.05373668 | -0.86789653 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 |  0.5852632 |  0.97006840 | -0.46914712 |  0.04858558 | -0.88671229 | -1.24434636 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  1.71978175 |  0.9777817 |  0.34840556 |  0.62698178 |  0.10994967 |  0.35042153 | -1.23056330 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 | -0.6904222 | -0.62412800 | -0.08141139 |  0.59754544 |  0.56212753 | -0.21233974 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  1.53174031 | -0.1016443 |  0.45377215 |  0.52533743 |  0.77210140 |  0.51741483 |  1.22195994 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 | -0.1997740 | -0.13628072 |  0.74456144 | -1.61404963 | -0.11918538 | -1.63630213 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  1.24967816 |  0.2908742 | -1.05022130 |  0.64651141 |  0.68917695 | -0.39141411 |  0.29763348 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.6904222 | -0.43130715 | -1.19213239 | -0.75744007 |  0.05546590 | -1.00055848 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  1.71978175 | -0.3960333 |  1.56012127 |  0.73929589 |  0.98936346 |  0.67798531 |  0.73438420 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.21545025 | -0.6904222 |  1.45429657 | -1.19213239 | -0.90836256 | -1.38670407 |  0.67236043 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 |  0.8796521 | -0.25007663 | -0.19627289 |  0.64025153 |  0.95219029 |  0.46992173 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.3960333 | -1.16887323 | -0.43793912 |  0.43915974 |  0.54656455 |  0.31486231 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.34867406 |  1.6646892 | -0.75296791 | -0.78244715 | -0.70561229 |  0.08807406 | -0.14945453 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 |  1.0759114 |  0.04916447 |  0.02844706 |  0.28450564 |  0.45442179 | -0.86789653 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 |  1.5665596 | -0.17111023 | -2.70051892 |  1.66478310 |  1.08953981 |  2.73551222 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.02740881 |  0.9777817 | -0.72633358 |  0.62698178 |  0.10994967 |  0.35042153 | -1.23056330 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.02740881 | -0.3960333 |  0.52752875 | -0.36251979 |  1.00263137 |  0.96083640 |  0.63187269 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.77957456 | -0.5922926 | -0.17526635 | -0.13626181 | -0.17655430 | -0.08361284 |  0.26662160 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 | -0.4941629 | -0.65257698 | -0.93514801 | -0.13675056 |  0.13155161 | -1.74312084 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 | -0.1997740 |  0.55621188 | -0.43793912 |  0.81812447 |  0.51790889 |  0.24077836 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.06661191 |  0.2908742 |  0.48347967 |  0.64651141 |  0.68917695 | -0.39141411 |  0.29763348 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  1.24967816 | -0.1997740 | -1.07298964 |  0.74456144 | -1.49049220 | -0.55149053 | -1.63630213 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.3960333 |  0.44063741 |  0.73929589 |  1.04284973 |  0.70861722 |  0.73438420 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.44269478 |  0.1927446 |  1.47055967 |  1.45246270 |  0.19909346 | -0.53642161 | -0.72317440 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 |  0.8796521 | -0.21003733 | -0.19627289 |  0.65808028 |  0.88400959 |  0.46992173 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.16063263 | -0.3960333 |  0.55621188 | -0.36251979 |  0.08258460 |  0.83707361 |  0.63187269 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 |  1.6646892 | -0.70713666 | -0.78244715 | -0.78936598 |  0.13426895 | -0.14945453 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.77957456 | -0.3960333 |  0.82255519 |  0.15863623 | -1.51744265 |  1.79283853 |  0.00646634 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "\n"
      ],
      "text/plain": [
       "      intercept V.Pop       H.Pop        Curr.Win..  LS.Win..    Last.Game  \n",
       "1     1         -0.53671550 -0.592292553 -0.89843234 -0.57431379 -1.6754137 \n",
       "2     1         -0.44269478 -0.003514696  0.96752507 -0.01024471  0.2845056 \n",
       "3     1          0.96761600 -0.690422196 -1.19939321 -1.36421592 -0.5961520 \n",
       "4     1         -0.34867406 -0.690422196  1.09650830  0.41052831  0.8931711 \n",
       "5     1         -0.44269478 -0.690422196 -0.12262209  0.13375800 -0.2905754 \n",
       "6     1         -0.06661191 -0.494162910  0.73452311  0.98191169  0.3562353 \n",
       "7     1         -0.16063263 -0.788551839  1.09650830  0.22248958 -1.4875898 \n",
       "8     1          0.02740881  0.781522447 -1.01881668  0.15284561  1.0507276 \n",
       "9     1         -0.63073622 -0.396033267  0.96752507  0.98191169  0.8944150 \n",
       "10    1         -0.44269478 -0.101644339  0.06464245 -0.19537426 -0.3610612 \n",
       "11    1         -0.34867406 -0.494162910  0.39667026 -0.19537426  0.4814512 \n",
       "12    1          0.40349169 -0.396033267 -0.02564581 -1.39325059  0.7721014 \n",
       "13    1         -0.63073622 -0.592292553 -0.96077423 -0.57431379 -2.2687382 \n",
       "14    1         -0.34867406 -0.788551839  1.28353399  0.22248958  0.1302662 \n",
       "15    1         -0.63073622 -0.101644339 -0.26367850  0.06803864  0.3686740 \n",
       "16    1         -0.53671550 -0.003514696  0.84714072 -0.01024471  0.2845056 \n",
       "17    1         -0.44269478 -0.690422196 -0.68023570 -1.36421592 -0.8598518 \n",
       "18    1          0.40349169  0.781522447 -0.78581472  0.15284561  1.8148764 \n",
       "19    1         -0.53671550 -0.101644339  0.42579550 -0.19537426 -1.0182375 \n",
       "20    1         -0.34867406 -0.494162910  0.14891150 -0.01024471  0.8181245 \n",
       "21    1         -0.44269478 -0.494162910  0.79259156 -0.19537426 -0.2934778 \n",
       "22    1         -0.44269478 -0.690422196 -0.51658823 -1.39325059  0.2944566 \n",
       "23    1          0.96761600 -0.494162910 -1.29259399 -0.01024471  0.1688260 \n",
       "24    1         -0.34867406 -0.690422196  1.02169803  0.41052831 -0.3581588 \n",
       "25    1         -0.53671550 -0.494162910 -1.01881668  0.98191169  0.3562353 \n",
       "26    1         -0.63073622 -0.396033267  0.06464245 -1.39325059  0.4524276 \n",
       "27    1          0.96761600 -0.003514696 -1.33482560 -0.01024471 -2.0411105 \n",
       "28    1         -0.72475694 -0.101644339 -0.17966696  0.06803864  0.1655091 \n",
       "29    1          0.77957456 -0.690422196 -0.58105542 -1.36421592 -1.4448838 \n",
       "30    1         -0.53671550 -0.396033267 -0.35294076  0.98191169  0.8284900 \n",
       "⋮     ⋮         ⋮           ⋮            ⋮           ⋮           ⋮          \n",
       "11809 1          0.02740881 -0.3960333   -0.32488690  0.15863623  0.59008223\n",
       "11810 1         -0.16063263 -0.5922926    0.52752875  0.69163671 -1.56263647\n",
       "11811 1         -0.34867406  1.0759114    0.49802611  0.02844706  0.28450564\n",
       "11812 1         -0.53671550  0.5852632    0.97006840 -0.46914712  0.04858558\n",
       "11813 1          1.71978175  0.9777817    0.34840556  0.62698178  0.10994967\n",
       "11814 1         -0.53671550 -0.6904222   -0.62412800 -0.08141139  0.59754544\n",
       "11815 1          1.53174031 -0.1016443    0.45377215  0.52533743  0.77210140\n",
       "11816 1         -0.53671550 -0.1997740   -0.13628072  0.74456144 -1.61404963\n",
       "11817 1          1.24967816  0.2908742   -1.05022130  0.64651141  0.68917695\n",
       "11818 1         -0.44269478 -0.6904222   -0.43130715 -1.19213239 -0.75744007\n",
       "11819 1          1.71978175 -0.3960333    1.56012127  0.73929589  0.98936346\n",
       "11820 1          0.21545025 -0.6904222    1.45429657 -1.19213239 -0.90836256\n",
       "11821 1         -0.53671550  0.8796521   -0.25007663 -0.19627289  0.64025153\n",
       "11822 1         -0.63073622 -0.3960333   -1.16887323 -0.43793912  0.43915974\n",
       "11823 1         -0.34867406  1.6646892   -0.75296791 -0.78244715 -0.70561229\n",
       "11824 1         -0.53671550  1.0759114    0.04916447  0.02844706  0.28450564\n",
       "11825 1         -0.53671550  1.5665596   -0.17111023 -2.70051892  1.66478310\n",
       "11826 1          0.02740881  0.9777817   -0.72633358  0.62698178  0.10994967\n",
       "11827 1          0.02740881 -0.3960333    0.52752875 -0.36251979  1.00263137\n",
       "11828 1          0.77957456 -0.5922926   -0.17526635 -0.13626181 -0.17655430\n",
       "11829 1         -0.53671550 -0.4941629   -0.65257698 -0.93514801 -0.13675056\n",
       "11830 1         -0.53671550 -0.1997740    0.55621188 -0.43793912  0.81812447\n",
       "11831 1         -0.06661191  0.2908742    0.48347967  0.64651141  0.68917695\n",
       "11832 1          1.24967816 -0.1997740   -1.07298964  0.74456144 -1.49049220\n",
       "11833 1         -0.63073622 -0.3960333    0.44063741  0.73929589  1.04284973\n",
       "11834 1         -0.44269478  0.1927446    1.47055967  1.45246270  0.19909346\n",
       "11835 1         -0.53671550  0.8796521   -0.21003733 -0.19627289  0.65808028\n",
       "11836 1         -0.16063263 -0.3960333    0.55621188 -0.36251979  0.08258460\n",
       "11837 1         -0.53671550  1.6646892   -0.70713666 -0.78244715 -0.78936598\n",
       "11838 1          0.77957456 -0.3960333    0.82255519  0.15863623 -1.51744265\n",
       "      Last.Attendance.vs.Opp Capacity    Playoffs.  Home_Atlanta.Hawks ⋯\n",
       "1     -0.41414101             0.46647597 -0.2788241 -0.2179768         ⋯\n",
       "2      0.45442179            -0.86789653 -0.2788241 -0.2179768         ⋯\n",
       "3      0.69552455             0.50093362 -0.2788241 -0.2179768         ⋯\n",
       "4      0.07621655             0.65427016 -0.2788241 -0.2179768         ⋯\n",
       "5      0.63920136             0.86015462 -0.2788241 -0.2179768         ⋯\n",
       "6     -0.17970810            -0.51470562 -0.2788241 -0.2179768         ⋯\n",
       "7     -0.05520422             0.03144814 -0.2788241 -0.2179768         ⋯\n",
       "8      0.39958080             2.95948695 -0.2788241 -0.2179768         ⋯\n",
       "9     -0.07694299             0.90064236 -0.2788241 -0.2179768         ⋯\n",
       "10    -0.04285264            -1.21075015 -0.2788241 -0.2179768         ⋯\n",
       "11     0.67403281             0.15549568 -0.2788241 -0.2179768         ⋯\n",
       "12     0.83262704             0.41737382 -0.2788241 -0.2179768         ⋯\n",
       "13     0.11302424             0.46647597 -0.2788241 -0.2179768         ⋯\n",
       "14    -4.14629311             0.03144814 -0.2788241 -0.2179768         ⋯\n",
       "15    -0.11572694             1.14873744 -0.2788241 -0.2179768         ⋯\n",
       "16    -0.04507593            -0.86789653 -0.2788241 -0.2179768         ⋯\n",
       "17     0.80742983             0.50093362 -0.2788241 -0.2179768         ⋯\n",
       "18     0.46726743             2.95948695 -0.2788241 -0.2179768         ⋯\n",
       "19    -0.04285264            -1.21075015 -0.2788241 -0.2179768         ⋯\n",
       "20     0.77235135             0.24077836 -0.2788241 -0.2179768         ⋯\n",
       "21     0.68712548             0.15549568 -0.2788241 -0.2179768         ⋯\n",
       "22     0.93316887             0.80157661 -0.2788241 -0.2179768         ⋯\n",
       "23    -0.74664542            -1.10823864 -0.2788241 -0.2179768         ⋯\n",
       "24    -0.87856025             0.65427016 -0.2788241 -0.2179768         ⋯\n",
       "25    -0.17970810            -0.51470562 -0.2788241 -0.2179768         ⋯\n",
       "26     0.74493085             0.41737382 -0.2788241 -0.2179768         ⋯\n",
       "27     0.45442179            -0.86789653 -0.2788241 -0.2179768         ⋯\n",
       "28     0.94799076             1.14873744 -0.2788241 -0.2179768         ⋯\n",
       "29     0.81360561             0.50093362 -0.2788241 -0.2179768         ⋯\n",
       "30    -0.40623600             0.90064236 -0.2788241 -0.2179768         ⋯\n",
       "⋮     ⋮                      ⋮           ⋮          ⋮                  ⋱\n",
       "11809  1.79283853             0.00646634 -0.2788241 -0.2179768         ⋯\n",
       "11810 -1.42375880            -1.47176685 -0.2788241 -0.2179768         ⋯\n",
       "11811  0.05373668            -0.86789653 -0.2788241 -0.2179768         ⋯\n",
       "11812 -0.88671229            -1.24434636 -0.2788241 -0.2179768         ⋯\n",
       "11813  0.35042153            -1.23056330 -0.2788241 -0.2179768         ⋯\n",
       "11814  0.56212753            -0.21233974 -0.2788241 -0.2179768         ⋯\n",
       "11815  0.51741483             1.22195994 -0.2788241 -0.2179768         ⋯\n",
       "11816 -0.11918538            -1.63630213 -0.2788241 -0.2179768         ⋯\n",
       "11817 -0.39141411             0.29763348 -0.2788241 -0.2179768         ⋯\n",
       "11818  0.05546590            -1.00055848 -0.2788241 -0.2179768         ⋯\n",
       "11819  0.67798531             0.73438420 -0.2788241 -0.2179768         ⋯\n",
       "11820 -1.38670407             0.67236043 -0.2788241 -0.2179768         ⋯\n",
       "11821  0.95219029             0.46992173 -0.2788241 -0.2179768         ⋯\n",
       "11822  0.54656455             0.31486231 -0.2788241 -0.2179768         ⋯\n",
       "11823  0.08807406            -0.14945453 -0.2788241 -0.2179768         ⋯\n",
       "11824  0.45442179            -0.86789653 -0.2788241 -0.2179768         ⋯\n",
       "11825  1.08953981             2.73551222 -0.2788241 -0.2179768         ⋯\n",
       "11826  0.35042153            -1.23056330 -0.2788241 -0.2179768         ⋯\n",
       "11827  0.96083640             0.63187269 -0.2788241 -0.2179768         ⋯\n",
       "11828 -0.08361284             0.26662160 -0.2788241 -0.2179768         ⋯\n",
       "11829  0.13155161            -1.74312084 -0.2788241 -0.2179768         ⋯\n",
       "11830  0.51790889             0.24077836 -0.2788241 -0.2179768         ⋯\n",
       "11831 -0.39141411             0.29763348 -0.2788241 -0.2179768         ⋯\n",
       "11832 -0.55149053            -1.63630213 -0.2788241 -0.2179768         ⋯\n",
       "11833  0.70861722             0.73438420 -0.2788241 -0.2179768         ⋯\n",
       "11834 -0.53642161            -0.72317440 -0.2788241 -0.2179768         ⋯\n",
       "11835  0.88400959             0.46992173 -0.2788241 -0.2179768         ⋯\n",
       "11836  0.83707361             0.63187269 -0.2788241 -0.2179768         ⋯\n",
       "11837  0.13426895            -0.14945453 -0.2788241 -0.2179768         ⋯\n",
       "11838  1.79283853             0.00646634 -0.2788241 -0.2179768         ⋯\n",
       "      Day.of.Week_Wednesday Month_April Month_December Month_February\n",
       "1     -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2     -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "3     -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "4     -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "5     -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "6     -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "7     -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "8     -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "9     -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "10    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "12    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "13    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "14    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "15    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "16    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "17    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "18    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "19    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "20    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "21    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "22    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "23    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "24    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "25    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "26     1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "27     1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "28     1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "29     1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "30     1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "⋮     ⋮                     ⋮           ⋮              ⋮             \n",
       "11809 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11810 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11811 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11812 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11813 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11814 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11815 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11816 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11817 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11818 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11819 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11820 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11821 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11822 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11823  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "11824  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "11825  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "11826  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "11827  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "11828  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "11829  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "11830  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "11831 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11832 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11833 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11834 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11835 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11836 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11837 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11838 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "      Month_January Month_June  Month_March Month_May  Month_November\n",
       "1     2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "2     2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "3     2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "4     2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "5     2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "6     2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "7     2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "8     2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "9     2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "10    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "11    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "12    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "13    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "14    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "15    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "16    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "17    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "18    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "19    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "20    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "21    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "22    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "23    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "24    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "25    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "26    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "27    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "28    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "29    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "30    2.1006        -0.08037997 -0.4729413  -0.1927284 -0.4224709    \n",
       "⋮     ⋮             ⋮           ⋮           ⋮          ⋮             \n",
       "11809 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11810 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11811 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11812 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11813 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11814 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11815 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11816 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11817 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11818 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11819 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11820 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11821 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11822 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11823 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11824 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11825 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11826 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11827 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11828 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11829 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11830 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11831 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11832 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11833 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11834 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11835 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11836 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11837 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11838 -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "      Month_October\n",
       "1     -0.1294096   \n",
       "2     -0.1294096   \n",
       "3     -0.1294096   \n",
       "4     -0.1294096   \n",
       "5     -0.1294096   \n",
       "6     -0.1294096   \n",
       "7     -0.1294096   \n",
       "8     -0.1294096   \n",
       "9     -0.1294096   \n",
       "10    -0.1294096   \n",
       "11    -0.1294096   \n",
       "12    -0.1294096   \n",
       "13    -0.1294096   \n",
       "14    -0.1294096   \n",
       "15    -0.1294096   \n",
       "16    -0.1294096   \n",
       "17    -0.1294096   \n",
       "18    -0.1294096   \n",
       "19    -0.1294096   \n",
       "20    -0.1294096   \n",
       "21    -0.1294096   \n",
       "22    -0.1294096   \n",
       "23    -0.1294096   \n",
       "24    -0.1294096   \n",
       "25    -0.1294096   \n",
       "26    -0.1294096   \n",
       "27    -0.1294096   \n",
       "28    -0.1294096   \n",
       "29    -0.1294096   \n",
       "30    -0.1294096   \n",
       "⋮     ⋮            \n",
       "11809 -0.1294096   \n",
       "11810 -0.1294096   \n",
       "11811 -0.1294096   \n",
       "11812 -0.1294096   \n",
       "11813 -0.1294096   \n",
       "11814 -0.1294096   \n",
       "11815 -0.1294096   \n",
       "11816 -0.1294096   \n",
       "11817 -0.1294096   \n",
       "11818 -0.1294096   \n",
       "11819 -0.1294096   \n",
       "11820 -0.1294096   \n",
       "11821 -0.1294096   \n",
       "11822 -0.1294096   \n",
       "11823 -0.1294096   \n",
       "11824 -0.1294096   \n",
       "11825 -0.1294096   \n",
       "11826 -0.1294096   \n",
       "11827 -0.1294096   \n",
       "11828 -0.1294096   \n",
       "11829 -0.1294096   \n",
       "11830 -0.1294096   \n",
       "11831 -0.1294096   \n",
       "11832 -0.1294096   \n",
       "11833 -0.1294096   \n",
       "11834 -0.1294096   \n",
       "11835 -0.1294096   \n",
       "11836 -0.1294096   \n",
       "11837 -0.1294096   \n",
       "11838 -0.1294096   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 2960 × 91</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>intercept</th><th scope=col>V.Pop</th><th scope=col>H.Pop</th><th scope=col>Curr.Win..</th><th scope=col>LS.Win..</th><th scope=col>Last.Game</th><th scope=col>Last.Attendance.vs.Opp</th><th scope=col>Capacity</th><th scope=col>Playoffs.</th><th scope=col>Home_Atlanta.Hawks</th><th scope=col>⋯</th><th scope=col>Day.of.Week_Wednesday</th><th scope=col>Month_April</th><th scope=col>Month_December</th><th scope=col>Month_February</th><th scope=col>Month_January</th><th scope=col>Month_June</th><th scope=col>Month_March</th><th scope=col>Month_May</th><th scope=col>Month_November</th><th scope=col>Month_October</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.592292553</td><td> 0.01123250</td><td> 0.69163671</td><td>-1.028188394</td><td>-1.19500762</td><td>-1.47176685</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 1.24967816</td><td> 1.075911376</td><td>-1.09511662</td><td> 0.02844706</td><td> 0.284505639</td><td> 0.10042563</td><td>-0.86789653</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.02740881</td><td> 1.566559590</td><td>-0.68023570</td><td>-2.70051892</td><td> 1.478203087</td><td> 1.09571560</td><td> 2.73551222</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td> 0.585263161</td><td>-0.46203907</td><td>-0.46914712</td><td>-0.770293357</td><td>-0.63325796</td><td>-1.24434636</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.396033267</td><td>-0.57882037</td><td>-0.43793912</td><td> 0.325967864</td><td> 0.54656455</td><td> 0.31486231</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.16063263</td><td>-0.690422196</td><td> 0.51237329</td><td>-0.08141139</td><td> 0.465280939</td><td> 0.56212753</td><td>-0.21233974</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.34867406</td><td> 0.781522447</td><td> 0.58410917</td><td> 1.67949406</td><td> 1.088043553</td><td> 0.93316887</td><td> 0.80157661</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.06661191</td><td>-0.396033267</td><td> 0.51237329</td><td> 0.73929589</td><td> 0.731883043</td><td> 0.72368614</td><td> 0.73438420</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td> 0.192744590</td><td> 1.48636466</td><td> 1.45246270</td><td> 0.199093456</td><td> 0.49715824</td><td>-0.72317440</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td> 0.879652090</td><td>-0.17111023</td><td>-0.19627289</td><td> 0.448696049</td><td> 0.01939930</td><td> 0.46992173</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 1.24967816</td><td>-0.396033267</td><td>-1.11662897</td><td> 0.15863623</td><td> 0.652275572</td><td> 0.38475891</td><td> 0.00646634</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.16063263</td><td>-0.003514696</td><td>-1.49623133</td><td> 1.01178794</td><td>-0.578323256</td><td>-1.11324019</td><td>-0.44062167</td><td>-0.2788241</td><td> 4.5872557</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.96761600</td><td> 0.585263161</td><td> 0.33801525</td><td>-0.46914712</td><td> 0.048585581</td><td>-0.12338492</td><td>-1.24434636</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.690422196</td><td>-0.63540078</td><td>-0.08141139</td><td> 0.465280939</td><td> 0.63006120</td><td>-0.21233974</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.30947097</td><td> 1.075911376</td><td>-0.13324963</td><td> 0.02844706</td><td> 0.284505639</td><td>-0.51295361</td><td>-0.86789653</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.02740881</td><td>-0.592292553</td><td>-0.70713666</td><td> 0.69163671</td><td>-0.006144556</td><td>-0.13128992</td><td>-1.47176685</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.592292553</td><td>-0.42019314</td><td>-0.13626181</td><td> 0.738102377</td><td>-0.13005477</td><td> 0.26662160</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.87359528</td><td>-0.396033267</td><td>-0.13324963</td><td>-0.43793912</td><td> 0.439159737</td><td> 0.54656455</td><td> 0.31486231</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.592292553</td><td>-0.66254408</td><td>-1.04129373</td><td> 0.777076868</td><td> 0.55545768</td><td> 0.15549568</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 1.62576103</td><td>-0.101644339</td><td> 0.54048600</td><td> 0.52533743</td><td> 0.772101401</td><td> 0.74493085</td><td> 1.22195994</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.77957456</td><td> 0.192744590</td><td> 1.50173062</td><td> 1.45246270</td><td> 0.199093456</td><td>-0.09176488</td><td>-0.72317440</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.96761600</td><td>-0.396033267</td><td> 0.36890153</td><td> 0.73929589</td><td> 0.827660782</td><td> 0.81409968</td><td> 0.73438420</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.199773982</td><td> 0.54048600</td><td>-0.43793912</td><td> 0.818124470</td><td> 0.77235135</td><td> 0.24077836</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.494162910</td><td>-0.59177761</td><td>-0.93514801</td><td>-0.136750564</td><td>-0.31186996</td><td>-1.74312084</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td>-0.396033267</td><td> 0.04512067</td><td> 0.15863623</td><td>-0.267771194</td><td> 0.32547134</td><td> 0.00646634</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.592292553</td><td>-0.56366490</td><td> 0.69163671</td><td>-0.609834547</td><td>-0.85682148</td><td>-1.47176685</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 1.24967816</td><td>-0.003514696</td><td>-1.13755193</td><td> 1.01178794</td><td>-0.836218294</td><td>-0.98923037</td><td>-0.44062167</td><td>-0.2788241</td><td> 4.5872557</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.02740881</td><td>-0.199773982</td><td>-0.66254408</td><td> 0.74456144</td><td>-1.159623646</td><td>-0.78419421</td><td>-1.63630213</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 1.71978175</td><td> 0.585263161</td><td> 0.96508485</td><td>-0.46914712</td><td> 0.048585581</td><td>-0.59200370</td><td>-1.24434636</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td> 0.879652090</td><td>-0.09641228</td><td>-0.19627289</td><td> 0.680884507</td><td> 0.79334903</td><td> 0.46992173</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>2.114249</td><td>-0.1927284</td><td>-0.4224709</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋱</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>1</td><td> 0.21545025</td><td>-0.4941629</td><td> 0.55621188</td><td>-0.002962536</td><td>-1.15962365</td><td>-0.31112887</td><td>-1.47176685</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.40349169</td><td> 1.9590782</td><td>-0.02564581</td><td> 1.166586339</td><td> 0.50508467</td><td> 0.45442179</td><td>-0.40960978</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.2979036</td><td>-0.89843234</td><td>-0.362519792</td><td>-0.01941247</td><td>-0.66018439</td><td>-0.96351651</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 2.00184391</td><td>-0.5922926</td><td>-0.48770926</td><td>-2.021744980</td><td>-0.32954991</td><td> 0.93316887</td><td>-0.17185200</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td> 0.3890039</td><td> 1.36054456</td><td> 0.862688517</td><td> 0.77210140</td><td> 0.74493085</td><td> 0.99540090</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.49751240</td><td> 1.2721707</td><td> 1.05250226</td><td> 0.468714724</td><td> 0.04858558</td><td> 0.31386086</td><td>-1.35805660</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.2979036</td><td>-1.10379387</td><td>-0.965874406</td><td>-0.05963083</td><td> 0.08066311</td><td>-1.58289277</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.02740881</td><td>-0.2979036</td><td> 1.71992725</td><td> 0.874357167</td><td>-0.22921132</td><td>-1.12806208</td><td>-1.63630213</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.1997740</td><td>-0.99030460</td><td> 0.089148761</td><td> 0.23226324</td><td> 0.45219851</td><td>-0.86531221</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td> 2.9403746</td><td>-0.68023570</td><td>-2.021744980</td><td>-1.67292599</td><td>-0.65746705</td><td>-1.35805660</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.87359528</td><td>-0.6904222</td><td>-0.89843234</td><td>-0.965874406</td><td>-1.20937832</td><td>-4.14629311</td><td>-0.86617365</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.16063263</td><td> 2.4497264</td><td>-0.71468780</td><td> 0.394751816</td><td> 0.66595811</td><td> 0.48060713</td><td> 0.43718697</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.96761600</td><td> 0.2908742</td><td>-1.54153820</td><td> 0.862688517</td><td> 0.05231718</td><td> 0.69453642</td><td>-1.35030363</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td> 1.9584331</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 1.90782319</td><td> 0.3890039</td><td> 0.11216259</td><td> 0.454956360</td><td>-0.96143421</td><td>-0.04359374</td><td>-1.63630213</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.25465335</td><td>-0.4941629</td><td>-0.99030460</td><td> 1.147620682</td><td>-1.34454517</td><td>-1.17796245</td><td> 0.60861377</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.40349169</td><td> 2.0572078</td><td> 1.42899841</td><td> 0.952550538</td><td>-0.66083308</td><td> 0.20294371</td><td>-0.67665657</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.59153312</td><td>-0.5922926</td><td> 0.93901299</td><td>-2.156754886</td><td> 0.34504049</td><td> 0.74789523</td><td> 0.15549568</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.25465335</td><td>-0.5922926</td><td> 1.76586338</td><td>-2.021744980</td><td>-0.09362985</td><td> 0.93316887</td><td>-0.17185200</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.02740881</td><td>-0.4941629</td><td> 0.84714072</td><td>-0.002962536</td><td>-0.37764609</td><td>-0.43736197</td><td>-1.47176685</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.21545025</td><td>-0.2979036</td><td> 0.66339619</td><td>-0.965874406</td><td>-0.50452050</td><td>-0.89955793</td><td>-1.58289277</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.30947097</td><td> 0.4871335</td><td> 1.13806956</td><td>-0.513358445</td><td> 0.68917695</td><td> 0.69552455</td><td> 0.07968885</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.63073622</td><td>-0.1997740</td><td>-0.60750349</td><td> 0.597126125</td><td> 0.10994967</td><td> 0.35042153</td><td>-1.23056330</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td>-0.1997740</td><td>-0.81115368</td><td> 0.089148761</td><td> 0.17255763</td><td> 0.40353330</td><td>-0.86531221</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.12142953</td><td> 2.9403746</td><td>-0.31657465</td><td>-2.021744980</td><td>-1.58170909</td><td>-0.09250598</td><td>-1.35805660</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.96761600</td><td> 2.4497264</td><td>-0.54931772</td><td> 0.394751816</td><td> 0.80112496</td><td> 0.76518744</td><td> 0.43718697</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.53671550</td><td> 0.8796521</td><td>-0.79575157</td><td> 0.515510148</td><td> 0.43915974</td><td> 0.54656455</td><td>-0.54657894</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.34867406</td><td>-0.2979036</td><td>-0.31657465</td><td>-0.513358445</td><td>-0.49995965</td><td> 0.19726199</td><td>-1.76465687</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.44269478</td><td> 0.5852632</td><td> 1.02169803</td><td> 0.887515433</td><td> 1.10048222</td><td>-0.11424475</td><td> 1.19353238</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td> 0.02740881</td><td> 1.2721707</td><td> 0.93901299</td><td> 0.468714724</td><td> 0.04858558</td><td> 0.31386086</td><td>-1.35805660</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "\t<tr><td>1</td><td>-0.25465335</td><td>-0.2979036</td><td> 1.80720590</td><td> 0.874357167</td><td>-0.17116421</td><td>-0.06582657</td><td>-1.63630213</td><td>-0.2788241</td><td>-0.2179768</td><td>⋯</td><td>-0.5105691</td><td>-0.3769627</td><td>-0.4300806</td><td>-0.4063786</td><td>-0.4760142</td><td>-0.08037997</td><td>-0.4729413</td><td>-0.1927284</td><td>2.366827</td><td>-0.1294096</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2960 × 91\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " intercept & V.Pop & H.Pop & Curr.Win.. & LS.Win.. & Last.Game & Last.Attendance.vs.Opp & Capacity & Playoffs. & Home\\_Atlanta.Hawks & ⋯ & Day.of.Week\\_Wednesday & Month\\_April & Month\\_December & Month\\_February & Month\\_January & Month\\_June & Month\\_March & Month\\_May & Month\\_November & Month\\_October\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & -0.53671550 & -0.592292553 &  0.01123250 &  0.69163671 & -1.028188394 & -1.19500762 & -1.47176685 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  1.24967816 &  1.075911376 & -1.09511662 &  0.02844706 &  0.284505639 &  0.10042563 & -0.86789653 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.02740881 &  1.566559590 & -0.68023570 & -2.70051892 &  1.478203087 &  1.09571560 &  2.73551222 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 &  0.585263161 & -0.46203907 & -0.46914712 & -0.770293357 & -0.63325796 & -1.24434636 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.396033267 & -0.57882037 & -0.43793912 &  0.325967864 &  0.54656455 &  0.31486231 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.16063263 & -0.690422196 &  0.51237329 & -0.08141139 &  0.465280939 &  0.56212753 & -0.21233974 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.34867406 &  0.781522447 &  0.58410917 &  1.67949406 &  1.088043553 &  0.93316887 &  0.80157661 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.06661191 & -0.396033267 &  0.51237329 &  0.73929589 &  0.731883043 &  0.72368614 &  0.73438420 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 &  0.192744590 &  1.48636466 &  1.45246270 &  0.199093456 &  0.49715824 & -0.72317440 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 &  0.879652090 & -0.17111023 & -0.19627289 &  0.448696049 &  0.01939930 &  0.46992173 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  1.24967816 & -0.396033267 & -1.11662897 &  0.15863623 &  0.652275572 &  0.38475891 &  0.00646634 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.16063263 & -0.003514696 & -1.49623133 &  1.01178794 & -0.578323256 & -1.11324019 & -0.44062167 & -0.2788241 &  4.5872557 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.96761600 &  0.585263161 &  0.33801525 & -0.46914712 &  0.048585581 & -0.12338492 & -1.24434636 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.690422196 & -0.63540078 & -0.08141139 &  0.465280939 &  0.63006120 & -0.21233974 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.30947097 &  1.075911376 & -0.13324963 &  0.02844706 &  0.284505639 & -0.51295361 & -0.86789653 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.02740881 & -0.592292553 & -0.70713666 &  0.69163671 & -0.006144556 & -0.13128992 & -1.47176685 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.592292553 & -0.42019314 & -0.13626181 &  0.738102377 & -0.13005477 &  0.26662160 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.87359528 & -0.396033267 & -0.13324963 & -0.43793912 &  0.439159737 &  0.54656455 &  0.31486231 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 & -0.592292553 & -0.66254408 & -1.04129373 &  0.777076868 &  0.55545768 &  0.15549568 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  1.62576103 & -0.101644339 &  0.54048600 &  0.52533743 &  0.772101401 &  0.74493085 &  1.22195994 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.77957456 &  0.192744590 &  1.50173062 &  1.45246270 &  0.199093456 & -0.09176488 & -0.72317440 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.96761600 & -0.396033267 &  0.36890153 &  0.73929589 &  0.827660782 &  0.81409968 &  0.73438420 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.199773982 &  0.54048600 & -0.43793912 &  0.818124470 &  0.77235135 &  0.24077836 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.494162910 & -0.59177761 & -0.93514801 & -0.136750564 & -0.31186996 & -1.74312084 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 & -0.396033267 &  0.04512067 &  0.15863623 & -0.267771194 &  0.32547134 &  0.00646634 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.592292553 & -0.56366490 &  0.69163671 & -0.609834547 & -0.85682148 & -1.47176685 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  1.24967816 & -0.003514696 & -1.13755193 &  1.01178794 & -0.836218294 & -0.98923037 & -0.44062167 & -0.2788241 &  4.5872557 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  0.02740881 & -0.199773982 & -0.66254408 &  0.74456144 & -1.159623646 & -0.78419421 & -1.63630213 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 &  1.71978175 &  0.585263161 &  0.96508485 & -0.46914712 &  0.048585581 & -0.59200370 & -1.24434636 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 &  0.879652090 & -0.09641228 & -0.19627289 &  0.680884507 &  0.79334903 &  0.46992173 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & 2.114249 & -0.1927284 & -0.4224709 & -0.1294096\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 1 &  0.21545025 & -0.4941629 &  0.55621188 & -0.002962536 & -1.15962365 & -0.31112887 & -1.47176685 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.40349169 &  1.9590782 & -0.02564581 &  1.166586339 &  0.50508467 &  0.45442179 & -0.40960978 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.2979036 & -0.89843234 & -0.362519792 & -0.01941247 & -0.66018439 & -0.96351651 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  2.00184391 & -0.5922926 & -0.48770926 & -2.021744980 & -0.32954991 &  0.93316887 & -0.17185200 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 &  0.3890039 &  1.36054456 &  0.862688517 &  0.77210140 &  0.74493085 &  0.99540090 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.49751240 &  1.2721707 &  1.05250226 &  0.468714724 &  0.04858558 &  0.31386086 & -1.35805660 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.2979036 & -1.10379387 & -0.965874406 & -0.05963083 &  0.08066311 & -1.58289277 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.02740881 & -0.2979036 &  1.71992725 &  0.874357167 & -0.22921132 & -1.12806208 & -1.63630213 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.1997740 & -0.99030460 &  0.089148761 &  0.23226324 &  0.45219851 & -0.86531221 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 &  2.9403746 & -0.68023570 & -2.021744980 & -1.67292599 & -0.65746705 & -1.35805660 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.87359528 & -0.6904222 & -0.89843234 & -0.965874406 & -1.20937832 & -4.14629311 & -0.86617365 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.16063263 &  2.4497264 & -0.71468780 &  0.394751816 &  0.66595811 &  0.48060713 &  0.43718697 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.96761600 &  0.2908742 & -1.54153820 &  0.862688517 &  0.05231718 &  0.69453642 & -1.35030363 & -0.2788241 & -0.2179768 & ⋯ &  1.9584331 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  1.90782319 &  0.3890039 &  0.11216259 &  0.454956360 & -0.96143421 & -0.04359374 & -1.63630213 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.25465335 & -0.4941629 & -0.99030460 &  1.147620682 & -1.34454517 & -1.17796245 &  0.60861377 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.40349169 &  2.0572078 &  1.42899841 &  0.952550538 & -0.66083308 &  0.20294371 & -0.67665657 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.59153312 & -0.5922926 &  0.93901299 & -2.156754886 &  0.34504049 &  0.74789523 &  0.15549568 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.25465335 & -0.5922926 &  1.76586338 & -2.021744980 & -0.09362985 &  0.93316887 & -0.17185200 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.02740881 & -0.4941629 &  0.84714072 & -0.002962536 & -0.37764609 & -0.43736197 & -1.47176685 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.21545025 & -0.2979036 &  0.66339619 & -0.965874406 & -0.50452050 & -0.89955793 & -1.58289277 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.30947097 &  0.4871335 &  1.13806956 & -0.513358445 &  0.68917695 &  0.69552455 &  0.07968885 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.63073622 & -0.1997740 & -0.60750349 &  0.597126125 &  0.10994967 &  0.35042153 & -1.23056330 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 & -0.1997740 & -0.81115368 &  0.089148761 &  0.17255763 &  0.40353330 & -0.86531221 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.12142953 &  2.9403746 & -0.31657465 & -2.021744980 & -1.58170909 & -0.09250598 & -1.35805660 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.96761600 &  2.4497264 & -0.54931772 &  0.394751816 &  0.80112496 &  0.76518744 &  0.43718697 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.53671550 &  0.8796521 & -0.79575157 &  0.515510148 &  0.43915974 &  0.54656455 & -0.54657894 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.34867406 & -0.2979036 & -0.31657465 & -0.513358445 & -0.49995965 &  0.19726199 & -1.76465687 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.44269478 &  0.5852632 &  1.02169803 &  0.887515433 &  1.10048222 & -0.11424475 &  1.19353238 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 &  0.02740881 &  1.2721707 &  0.93901299 &  0.468714724 &  0.04858558 &  0.31386086 & -1.35805660 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\t 1 & -0.25465335 & -0.2979036 &  1.80720590 &  0.874357167 & -0.17116421 & -0.06582657 & -1.63630213 & -0.2788241 & -0.2179768 & ⋯ & -0.5105691 & -0.3769627 & -0.4300806 & -0.4063786 & -0.4760142 & -0.08037997 & -0.4729413 & -0.1927284 & 2.366827 & -0.1294096\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2960 × 91\n",
       "\n",
       "| intercept &lt;dbl&gt; | V.Pop &lt;dbl&gt; | H.Pop &lt;dbl&gt; | Curr.Win.. &lt;dbl&gt; | LS.Win.. &lt;dbl&gt; | Last.Game &lt;dbl&gt; | Last.Attendance.vs.Opp &lt;dbl&gt; | Capacity &lt;dbl&gt; | Playoffs. &lt;dbl&gt; | Home_Atlanta.Hawks &lt;dbl&gt; | ⋯ ⋯ | Day.of.Week_Wednesday &lt;dbl&gt; | Month_April &lt;dbl&gt; | Month_December &lt;dbl&gt; | Month_February &lt;dbl&gt; | Month_January &lt;dbl&gt; | Month_June &lt;dbl&gt; | Month_March &lt;dbl&gt; | Month_May &lt;dbl&gt; | Month_November &lt;dbl&gt; | Month_October &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | -0.53671550 | -0.592292553 |  0.01123250 |  0.69163671 | -1.028188394 | -1.19500762 | -1.47176685 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  1.24967816 |  1.075911376 | -1.09511662 |  0.02844706 |  0.284505639 |  0.10042563 | -0.86789653 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.02740881 |  1.566559590 | -0.68023570 | -2.70051892 |  1.478203087 |  1.09571560 |  2.73551222 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 |  0.585263161 | -0.46203907 | -0.46914712 | -0.770293357 | -0.63325796 | -1.24434636 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.396033267 | -0.57882037 | -0.43793912 |  0.325967864 |  0.54656455 |  0.31486231 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.16063263 | -0.690422196 |  0.51237329 | -0.08141139 |  0.465280939 |  0.56212753 | -0.21233974 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.34867406 |  0.781522447 |  0.58410917 |  1.67949406 |  1.088043553 |  0.93316887 |  0.80157661 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.06661191 | -0.396033267 |  0.51237329 |  0.73929589 |  0.731883043 |  0.72368614 |  0.73438420 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 |  0.192744590 |  1.48636466 |  1.45246270 |  0.199093456 |  0.49715824 | -0.72317440 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 |  0.879652090 | -0.17111023 | -0.19627289 |  0.448696049 |  0.01939930 |  0.46992173 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  1.24967816 | -0.396033267 | -1.11662897 |  0.15863623 |  0.652275572 |  0.38475891 |  0.00646634 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.16063263 | -0.003514696 | -1.49623133 |  1.01178794 | -0.578323256 | -1.11324019 | -0.44062167 | -0.2788241 |  4.5872557 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.96761600 |  0.585263161 |  0.33801525 | -0.46914712 |  0.048585581 | -0.12338492 | -1.24434636 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.690422196 | -0.63540078 | -0.08141139 |  0.465280939 |  0.63006120 | -0.21233974 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.30947097 |  1.075911376 | -0.13324963 |  0.02844706 |  0.284505639 | -0.51295361 | -0.86789653 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.02740881 | -0.592292553 | -0.70713666 |  0.69163671 | -0.006144556 | -0.13128992 | -1.47176685 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.592292553 | -0.42019314 | -0.13626181 |  0.738102377 | -0.13005477 |  0.26662160 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.87359528 | -0.396033267 | -0.13324963 | -0.43793912 |  0.439159737 |  0.54656455 |  0.31486231 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 | -0.592292553 | -0.66254408 | -1.04129373 |  0.777076868 |  0.55545768 |  0.15549568 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  1.62576103 | -0.101644339 |  0.54048600 |  0.52533743 |  0.772101401 |  0.74493085 |  1.22195994 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.77957456 |  0.192744590 |  1.50173062 |  1.45246270 |  0.199093456 | -0.09176488 | -0.72317440 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.96761600 | -0.396033267 |  0.36890153 |  0.73929589 |  0.827660782 |  0.81409968 |  0.73438420 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.199773982 |  0.54048600 | -0.43793912 |  0.818124470 |  0.77235135 |  0.24077836 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.494162910 | -0.59177761 | -0.93514801 | -0.136750564 | -0.31186996 | -1.74312084 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 | -0.396033267 |  0.04512067 |  0.15863623 | -0.267771194 |  0.32547134 |  0.00646634 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.592292553 | -0.56366490 |  0.69163671 | -0.609834547 | -0.85682148 | -1.47176685 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  1.24967816 | -0.003514696 | -1.13755193 |  1.01178794 | -0.836218294 | -0.98923037 | -0.44062167 | -0.2788241 |  4.5872557 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  0.02740881 | -0.199773982 | -0.66254408 |  0.74456144 | -1.159623646 | -0.78419421 | -1.63630213 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 |  1.71978175 |  0.585263161 |  0.96508485 | -0.46914712 |  0.048585581 | -0.59200370 | -1.24434636 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| 1 | -0.53671550 |  0.879652090 | -0.09641228 | -0.19627289 |  0.680884507 |  0.79334903 |  0.46992173 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | 2.114249 | -0.1927284 | -0.4224709 | -0.1294096 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 1 |  0.21545025 | -0.4941629 |  0.55621188 | -0.002962536 | -1.15962365 | -0.31112887 | -1.47176685 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.40349169 |  1.9590782 | -0.02564581 |  1.166586339 |  0.50508467 |  0.45442179 | -0.40960978 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.2979036 | -0.89843234 | -0.362519792 | -0.01941247 | -0.66018439 | -0.96351651 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  2.00184391 | -0.5922926 | -0.48770926 | -2.021744980 | -0.32954991 |  0.93316887 | -0.17185200 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.53671550 |  0.3890039 |  1.36054456 |  0.862688517 |  0.77210140 |  0.74493085 |  0.99540090 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.49751240 |  1.2721707 |  1.05250226 |  0.468714724 |  0.04858558 |  0.31386086 | -1.35805660 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.2979036 | -1.10379387 | -0.965874406 | -0.05963083 |  0.08066311 | -1.58289277 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.02740881 | -0.2979036 |  1.71992725 |  0.874357167 | -0.22921132 | -1.12806208 | -1.63630213 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.1997740 | -0.99030460 |  0.089148761 |  0.23226324 |  0.45219851 | -0.86531221 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.53671550 |  2.9403746 | -0.68023570 | -2.021744980 | -1.67292599 | -0.65746705 | -1.35805660 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.87359528 | -0.6904222 | -0.89843234 | -0.965874406 | -1.20937832 | -4.14629311 | -0.86617365 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.16063263 |  2.4497264 | -0.71468780 |  0.394751816 |  0.66595811 |  0.48060713 |  0.43718697 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.96761600 |  0.2908742 | -1.54153820 |  0.862688517 |  0.05231718 |  0.69453642 | -1.35030363 | -0.2788241 | -0.2179768 | ⋯ |  1.9584331 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  1.90782319 |  0.3890039 |  0.11216259 |  0.454956360 | -0.96143421 | -0.04359374 | -1.63630213 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.25465335 | -0.4941629 | -0.99030460 |  1.147620682 | -1.34454517 | -1.17796245 |  0.60861377 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.40349169 |  2.0572078 |  1.42899841 |  0.952550538 | -0.66083308 |  0.20294371 | -0.67665657 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.59153312 | -0.5922926 |  0.93901299 | -2.156754886 |  0.34504049 |  0.74789523 |  0.15549568 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.25465335 | -0.5922926 |  1.76586338 | -2.021744980 | -0.09362985 |  0.93316887 | -0.17185200 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.02740881 | -0.4941629 |  0.84714072 | -0.002962536 | -0.37764609 | -0.43736197 | -1.47176685 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.21545025 | -0.2979036 |  0.66339619 | -0.965874406 | -0.50452050 | -0.89955793 | -1.58289277 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.30947097 |  0.4871335 |  1.13806956 | -0.513358445 |  0.68917695 |  0.69552455 |  0.07968885 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.63073622 | -0.1997740 | -0.60750349 |  0.597126125 |  0.10994967 |  0.35042153 | -1.23056330 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.44269478 | -0.1997740 | -0.81115368 |  0.089148761 |  0.17255763 |  0.40353330 | -0.86531221 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.12142953 |  2.9403746 | -0.31657465 | -2.021744980 | -1.58170909 | -0.09250598 | -1.35805660 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.96761600 |  2.4497264 | -0.54931772 |  0.394751816 |  0.80112496 |  0.76518744 |  0.43718697 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.53671550 |  0.8796521 | -0.79575157 |  0.515510148 |  0.43915974 |  0.54656455 | -0.54657894 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.34867406 | -0.2979036 | -0.31657465 | -0.513358445 | -0.49995965 |  0.19726199 | -1.76465687 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.44269478 |  0.5852632 |  1.02169803 |  0.887515433 |  1.10048222 | -0.11424475 |  1.19353238 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 |  0.02740881 |  1.2721707 |  0.93901299 |  0.468714724 |  0.04858558 |  0.31386086 | -1.35805660 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "| 1 | -0.25465335 | -0.2979036 |  1.80720590 |  0.874357167 | -0.17116421 | -0.06582657 | -1.63630213 | -0.2788241 | -0.2179768 | ⋯ | -0.5105691 | -0.3769627 | -0.4300806 | -0.4063786 | -0.4760142 | -0.08037997 | -0.4729413 | -0.1927284 | 2.366827 | -0.1294096 |\n",
       "\n"
      ],
      "text/plain": [
       "     intercept V.Pop       H.Pop        Curr.Win..  LS.Win..     Last.Game   \n",
       "1    1         -0.53671550 -0.592292553  0.01123250  0.69163671  -1.028188394\n",
       "2    1          1.24967816  1.075911376 -1.09511662  0.02844706   0.284505639\n",
       "3    1          0.02740881  1.566559590 -0.68023570 -2.70051892   1.478203087\n",
       "4    1         -0.63073622  0.585263161 -0.46203907 -0.46914712  -0.770293357\n",
       "5    1         -0.63073622 -0.396033267 -0.57882037 -0.43793912   0.325967864\n",
       "6    1         -0.16063263 -0.690422196  0.51237329 -0.08141139   0.465280939\n",
       "7    1         -0.34867406  0.781522447  0.58410917  1.67949406   1.088043553\n",
       "8    1         -0.06661191 -0.396033267  0.51237329  0.73929589   0.731883043\n",
       "9    1         -0.53671550  0.192744590  1.48636466  1.45246270   0.199093456\n",
       "10   1         -0.63073622  0.879652090 -0.17111023 -0.19627289   0.448696049\n",
       "11   1          1.24967816 -0.396033267 -1.11662897  0.15863623   0.652275572\n",
       "12   1         -0.16063263 -0.003514696 -1.49623133  1.01178794  -0.578323256\n",
       "13   1          0.96761600  0.585263161  0.33801525 -0.46914712   0.048585581\n",
       "14   1         -0.44269478 -0.690422196 -0.63540078 -0.08141139   0.465280939\n",
       "15   1          0.30947097  1.075911376 -0.13324963  0.02844706   0.284505639\n",
       "16   1          0.02740881 -0.592292553 -0.70713666  0.69163671  -0.006144556\n",
       "17   1         -0.63073622 -0.592292553 -0.42019314 -0.13626181   0.738102377\n",
       "18   1          0.87359528 -0.396033267 -0.13324963 -0.43793912   0.439159737\n",
       "19   1         -0.53671550 -0.592292553 -0.66254408 -1.04129373   0.777076868\n",
       "20   1          1.62576103 -0.101644339  0.54048600  0.52533743   0.772101401\n",
       "21   1          0.77957456  0.192744590  1.50173062  1.45246270   0.199093456\n",
       "22   1          0.96761600 -0.396033267  0.36890153  0.73929589   0.827660782\n",
       "23   1         -0.63073622 -0.199773982  0.54048600 -0.43793912   0.818124470\n",
       "24   1         -0.44269478 -0.494162910 -0.59177761 -0.93514801  -0.136750564\n",
       "25   1         -0.53671550 -0.396033267  0.04512067  0.15863623  -0.267771194\n",
       "26   1         -0.63073622 -0.592292553 -0.56366490  0.69163671  -0.609834547\n",
       "27   1          1.24967816 -0.003514696 -1.13755193  1.01178794  -0.836218294\n",
       "28   1          0.02740881 -0.199773982 -0.66254408  0.74456144  -1.159623646\n",
       "29   1          1.71978175  0.585263161  0.96508485 -0.46914712   0.048585581\n",
       "30   1         -0.53671550  0.879652090 -0.09641228 -0.19627289   0.680884507\n",
       "⋮    ⋮         ⋮           ⋮            ⋮           ⋮            ⋮           \n",
       "2931 1          0.21545025 -0.4941629    0.55621188 -0.002962536 -1.15962365 \n",
       "2932 1          0.40349169  1.9590782   -0.02564581  1.166586339  0.50508467 \n",
       "2933 1         -0.44269478 -0.2979036   -0.89843234 -0.362519792 -0.01941247 \n",
       "2934 1          2.00184391 -0.5922926   -0.48770926 -2.021744980 -0.32954991 \n",
       "2935 1         -0.53671550  0.3890039    1.36054456  0.862688517  0.77210140 \n",
       "2936 1          0.49751240  1.2721707    1.05250226  0.468714724  0.04858558 \n",
       "2937 1         -0.44269478 -0.2979036   -1.10379387 -0.965874406 -0.05963083 \n",
       "2938 1          0.02740881 -0.2979036    1.71992725  0.874357167 -0.22921132 \n",
       "2939 1         -0.63073622 -0.1997740   -0.99030460  0.089148761  0.23226324 \n",
       "2940 1         -0.53671550  2.9403746   -0.68023570 -2.021744980 -1.67292599 \n",
       "2941 1          0.87359528 -0.6904222   -0.89843234 -0.965874406 -1.20937832 \n",
       "2942 1         -0.16063263  2.4497264   -0.71468780  0.394751816  0.66595811 \n",
       "2943 1          0.96761600  0.2908742   -1.54153820  0.862688517  0.05231718 \n",
       "2944 1          1.90782319  0.3890039    0.11216259  0.454956360 -0.96143421 \n",
       "2945 1         -0.25465335 -0.4941629   -0.99030460  1.147620682 -1.34454517 \n",
       "2946 1          0.40349169  2.0572078    1.42899841  0.952550538 -0.66083308 \n",
       "2947 1          0.59153312 -0.5922926    0.93901299 -2.156754886  0.34504049 \n",
       "2948 1         -0.25465335 -0.5922926    1.76586338 -2.021744980 -0.09362985 \n",
       "2949 1          0.02740881 -0.4941629    0.84714072 -0.002962536 -0.37764609 \n",
       "2950 1          0.21545025 -0.2979036    0.66339619 -0.965874406 -0.50452050 \n",
       "2951 1          0.30947097  0.4871335    1.13806956 -0.513358445  0.68917695 \n",
       "2952 1         -0.63073622 -0.1997740   -0.60750349  0.597126125  0.10994967 \n",
       "2953 1         -0.44269478 -0.1997740   -0.81115368  0.089148761  0.17255763 \n",
       "2954 1          0.12142953  2.9403746   -0.31657465 -2.021744980 -1.58170909 \n",
       "2955 1          0.96761600  2.4497264   -0.54931772  0.394751816  0.80112496 \n",
       "2956 1         -0.53671550  0.8796521   -0.79575157  0.515510148  0.43915974 \n",
       "2957 1         -0.34867406 -0.2979036   -0.31657465 -0.513358445 -0.49995965 \n",
       "2958 1         -0.44269478  0.5852632    1.02169803  0.887515433  1.10048222 \n",
       "2959 1          0.02740881  1.2721707    0.93901299  0.468714724  0.04858558 \n",
       "2960 1         -0.25465335 -0.2979036    1.80720590  0.874357167 -0.17116421 \n",
       "     Last.Attendance.vs.Opp Capacity    Playoffs.  Home_Atlanta.Hawks ⋯\n",
       "1    -1.19500762            -1.47176685 -0.2788241 -0.2179768         ⋯\n",
       "2     0.10042563            -0.86789653 -0.2788241 -0.2179768         ⋯\n",
       "3     1.09571560             2.73551222 -0.2788241 -0.2179768         ⋯\n",
       "4    -0.63325796            -1.24434636 -0.2788241 -0.2179768         ⋯\n",
       "5     0.54656455             0.31486231 -0.2788241 -0.2179768         ⋯\n",
       "6     0.56212753            -0.21233974 -0.2788241 -0.2179768         ⋯\n",
       "7     0.93316887             0.80157661 -0.2788241 -0.2179768         ⋯\n",
       "8     0.72368614             0.73438420 -0.2788241 -0.2179768         ⋯\n",
       "9     0.49715824            -0.72317440 -0.2788241 -0.2179768         ⋯\n",
       "10    0.01939930             0.46992173 -0.2788241 -0.2179768         ⋯\n",
       "11    0.38475891             0.00646634 -0.2788241 -0.2179768         ⋯\n",
       "12   -1.11324019            -0.44062167 -0.2788241  4.5872557         ⋯\n",
       "13   -0.12338492            -1.24434636 -0.2788241 -0.2179768         ⋯\n",
       "14    0.63006120            -0.21233974 -0.2788241 -0.2179768         ⋯\n",
       "15   -0.51295361            -0.86789653 -0.2788241 -0.2179768         ⋯\n",
       "16   -0.13128992            -1.47176685 -0.2788241 -0.2179768         ⋯\n",
       "17   -0.13005477             0.26662160 -0.2788241 -0.2179768         ⋯\n",
       "18    0.54656455             0.31486231 -0.2788241 -0.2179768         ⋯\n",
       "19    0.55545768             0.15549568 -0.2788241 -0.2179768         ⋯\n",
       "20    0.74493085             1.22195994 -0.2788241 -0.2179768         ⋯\n",
       "21   -0.09176488            -0.72317440 -0.2788241 -0.2179768         ⋯\n",
       "22    0.81409968             0.73438420 -0.2788241 -0.2179768         ⋯\n",
       "23    0.77235135             0.24077836 -0.2788241 -0.2179768         ⋯\n",
       "24   -0.31186996            -1.74312084 -0.2788241 -0.2179768         ⋯\n",
       "25    0.32547134             0.00646634 -0.2788241 -0.2179768         ⋯\n",
       "26   -0.85682148            -1.47176685 -0.2788241 -0.2179768         ⋯\n",
       "27   -0.98923037            -0.44062167 -0.2788241  4.5872557         ⋯\n",
       "28   -0.78419421            -1.63630213 -0.2788241 -0.2179768         ⋯\n",
       "29   -0.59200370            -1.24434636 -0.2788241 -0.2179768         ⋯\n",
       "30    0.79334903             0.46992173 -0.2788241 -0.2179768         ⋯\n",
       "⋮    ⋮                      ⋮           ⋮          ⋮                  ⋱\n",
       "2931 -0.31112887            -1.47176685 -0.2788241 -0.2179768         ⋯\n",
       "2932  0.45442179            -0.40960978 -0.2788241 -0.2179768         ⋯\n",
       "2933 -0.66018439            -0.96351651 -0.2788241 -0.2179768         ⋯\n",
       "2934  0.93316887            -0.17185200 -0.2788241 -0.2179768         ⋯\n",
       "2935  0.74493085             0.99540090 -0.2788241 -0.2179768         ⋯\n",
       "2936  0.31386086            -1.35805660 -0.2788241 -0.2179768         ⋯\n",
       "2937  0.08066311            -1.58289277 -0.2788241 -0.2179768         ⋯\n",
       "2938 -1.12806208            -1.63630213 -0.2788241 -0.2179768         ⋯\n",
       "2939  0.45219851            -0.86531221 -0.2788241 -0.2179768         ⋯\n",
       "2940 -0.65746705            -1.35805660 -0.2788241 -0.2179768         ⋯\n",
       "2941 -4.14629311            -0.86617365 -0.2788241 -0.2179768         ⋯\n",
       "2942  0.48060713             0.43718697 -0.2788241 -0.2179768         ⋯\n",
       "2943  0.69453642            -1.35030363 -0.2788241 -0.2179768         ⋯\n",
       "2944 -0.04359374            -1.63630213 -0.2788241 -0.2179768         ⋯\n",
       "2945 -1.17796245             0.60861377 -0.2788241 -0.2179768         ⋯\n",
       "2946  0.20294371            -0.67665657 -0.2788241 -0.2179768         ⋯\n",
       "2947  0.74789523             0.15549568 -0.2788241 -0.2179768         ⋯\n",
       "2948  0.93316887            -0.17185200 -0.2788241 -0.2179768         ⋯\n",
       "2949 -0.43736197            -1.47176685 -0.2788241 -0.2179768         ⋯\n",
       "2950 -0.89955793            -1.58289277 -0.2788241 -0.2179768         ⋯\n",
       "2951  0.69552455             0.07968885 -0.2788241 -0.2179768         ⋯\n",
       "2952  0.35042153            -1.23056330 -0.2788241 -0.2179768         ⋯\n",
       "2953  0.40353330            -0.86531221 -0.2788241 -0.2179768         ⋯\n",
       "2954 -0.09250598            -1.35805660 -0.2788241 -0.2179768         ⋯\n",
       "2955  0.76518744             0.43718697 -0.2788241 -0.2179768         ⋯\n",
       "2956  0.54656455            -0.54657894 -0.2788241 -0.2179768         ⋯\n",
       "2957  0.19726199            -1.76465687 -0.2788241 -0.2179768         ⋯\n",
       "2958 -0.11424475             1.19353238 -0.2788241 -0.2179768         ⋯\n",
       "2959  0.31386086            -1.35805660 -0.2788241 -0.2179768         ⋯\n",
       "2960 -0.06582657            -1.63630213 -0.2788241 -0.2179768         ⋯\n",
       "     Day.of.Week_Wednesday Month_April Month_December Month_February\n",
       "1    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "3    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "4    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "5    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "6    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "7    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "8    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "9    -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "10   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "11   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "12   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "13   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "14   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "15   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "16   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "17   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "18   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "19   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "20   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "21   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "22   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "23   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "24   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "25   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "26   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "27   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "28   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "29   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "30   -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "⋮    ⋮                     ⋮           ⋮              ⋮             \n",
       "2931  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2932  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2933  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2934  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2935  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2936  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2937  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2938  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2939  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2940  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2941  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2942  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2943  1.9584331            -0.3769627  -0.4300806     -0.4063786    \n",
       "2944 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2945 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2946 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2947 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2948 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2949 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2950 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2951 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2952 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2953 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2954 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2955 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2956 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2957 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2958 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2959 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "2960 -0.5105691            -0.3769627  -0.4300806     -0.4063786    \n",
       "     Month_January Month_June  Month_March Month_May  Month_November\n",
       "1    -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "2    -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "3    -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "4    -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "5    -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "6    -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "7    -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "8    -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "9    -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "10   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "11   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "12   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "13   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "14   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "15   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "16   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "17   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "18   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "19   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "20   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "21   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "22   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "23   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "24   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "25   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "26   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "27   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "28   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "29   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "30   -0.4760142    -0.08037997 2.114249    -0.1927284 -0.4224709    \n",
       "⋮    ⋮             ⋮           ⋮           ⋮          ⋮             \n",
       "2931 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2932 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2933 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2934 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2935 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2936 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2937 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2938 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2939 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2940 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2941 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2942 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2943 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2944 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2945 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2946 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2947 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2948 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2949 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2950 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2951 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2952 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2953 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2954 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2955 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2956 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2957 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2958 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2959 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "2960 -0.4760142    -0.08037997 -0.4729413  -0.1927284 2.366827      \n",
       "     Month_October\n",
       "1    -0.1294096   \n",
       "2    -0.1294096   \n",
       "3    -0.1294096   \n",
       "4    -0.1294096   \n",
       "5    -0.1294096   \n",
       "6    -0.1294096   \n",
       "7    -0.1294096   \n",
       "8    -0.1294096   \n",
       "9    -0.1294096   \n",
       "10   -0.1294096   \n",
       "11   -0.1294096   \n",
       "12   -0.1294096   \n",
       "13   -0.1294096   \n",
       "14   -0.1294096   \n",
       "15   -0.1294096   \n",
       "16   -0.1294096   \n",
       "17   -0.1294096   \n",
       "18   -0.1294096   \n",
       "19   -0.1294096   \n",
       "20   -0.1294096   \n",
       "21   -0.1294096   \n",
       "22   -0.1294096   \n",
       "23   -0.1294096   \n",
       "24   -0.1294096   \n",
       "25   -0.1294096   \n",
       "26   -0.1294096   \n",
       "27   -0.1294096   \n",
       "28   -0.1294096   \n",
       "29   -0.1294096   \n",
       "30   -0.1294096   \n",
       "⋮    ⋮            \n",
       "2931 -0.1294096   \n",
       "2932 -0.1294096   \n",
       "2933 -0.1294096   \n",
       "2934 -0.1294096   \n",
       "2935 -0.1294096   \n",
       "2936 -0.1294096   \n",
       "2937 -0.1294096   \n",
       "2938 -0.1294096   \n",
       "2939 -0.1294096   \n",
       "2940 -0.1294096   \n",
       "2941 -0.1294096   \n",
       "2942 -0.1294096   \n",
       "2943 -0.1294096   \n",
       "2944 -0.1294096   \n",
       "2945 -0.1294096   \n",
       "2946 -0.1294096   \n",
       "2947 -0.1294096   \n",
       "2948 -0.1294096   \n",
       "2949 -0.1294096   \n",
       "2950 -0.1294096   \n",
       "2951 -0.1294096   \n",
       "2952 -0.1294096   \n",
       "2953 -0.1294096   \n",
       "2954 -0.1294096   \n",
       "2955 -0.1294096   \n",
       "2956 -0.1294096   \n",
       "2957 -0.1294096   \n",
       "2958 -0.1294096   \n",
       "2959 -0.1294096   \n",
       "2960 -0.1294096   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X.train.intercept <- cbind(intercept = rep(1,nrow(X.train)), X.train)\n",
    "X.train.intercept\n",
    "X.test.intercept <- cbind(intercept = rep(1,nrow(X.test)), X.test)\n",
    "X.test.intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T04:27:43.985876Z",
     "start_time": "2019-12-19T04:27:32.579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>$R^2$</th><th scope=col>MAE</th><th scope=col>RMSE</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.5212138</td><td>987.9821</td><td>1373.883</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 3 of type dbl\n",
       "\\begin{tabular}{lll}\n",
       " \\$R\\textasciicircum{}2\\$ & MAE & RMSE\\\\\n",
       "\\hline\n",
       "\t 0.5212138 & 987.9821 & 1373.883\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 3 of type dbl\n",
       "\n",
       "| $R^2$ | MAE | RMSE |\n",
       "|---|---|---|\n",
       "| 0.5212138 | 987.9821 | 1373.883 |\n",
       "\n"
      ],
      "text/plain": [
       "     $R^2$     MAE      RMSE    \n",
       "[1,] 0.5212138 987.9821 1373.883"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fgJGU8+QbYjuarhOpI230ny9RNvI/lD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\n/rPCnSfHAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso <- cvNcvxReg(X = as.matrix(X.train), Y = y.train, lSeq = NULL, nlambda = 100L, penalty = \"Lasso\", phi0 = 0.001, gamma = 1.5, epsilon_c = 0.001, epsilon_t = 0.001, \n",
    "                   iteMax = 500L, nfolds = 5L, intercept = TRUE, itcpIncluded = FALSE)\n",
    "\n",
    "plot(log10(lasso$lambdaSeq), lasso$mse)\n",
    "preds <- as.matrix(X.test.intercept) %*% c(lasso$beta)\n",
    "metrics(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T04:29:18.459509Z",
     "start_time": "2019-12-19T04:28:48.384Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>$R^2$</th><th scope=col>MAE</th><th scope=col>RMSE</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.5152193</td><td>943.7345</td><td>1382.457</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 3 of type dbl\n",
       "\\begin{tabular}{lll}\n",
       " \\$R\\textasciicircum{}2\\$ & MAE & RMSE\\\\\n",
       "\\hline\n",
       "\t 0.5152193 & 943.7345 & 1382.457\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 3 of type dbl\n",
       "\n",
       "| $R^2$ | MAE | RMSE |\n",
       "|---|---|---|\n",
       "| 0.5152193 | 943.7345 | 1382.457 |\n",
       "\n"
      ],
      "text/plain": [
       "     $R^2$     MAE      RMSE    \n",
       "[1,] 0.5152193 943.7345 1382.457"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eR35NdkQ/IRsiWZR64lx5H7yfvIPiRf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\nKKCAAgqMSuD/A0xPqXI6TUGmAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scad <- cvNcvxReg(X = as.matrix(X.train), Y = y.train, lSeq = NULL, nlambda = 100L, penalty = \"SCAD\", phi0 = 0.001, gamma = 1.5, epsilon_c = 0.001, epsilon_t = 0.001, \n",
    "                   iteMax = 500L, nfolds = 5L, intercept = TRUE, itcpIncluded = FALSE)\n",
    "\n",
    "plot(log10(scad$lambdaSeq), scad$mse)\n",
    "preds <- as.matrix(X.test.intercept) %*% c(scad$beta)\n",
    "metrics(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T05:20:31.595419Z",
     "start_time": "2019-12-19T05:11:53.698Z"
    }
   },
   "outputs": [],
   "source": [
    "mcp <- cvNcvxReg(X = as.matrix(X.train), Y = y.train, lSeq = NULL, nlambda = 100L, penalty = \"MCP\", phi0 = 0.001, gamma = 1.5, epsilon_c = 0.001, epsilon_t = 0.001, \n",
    "                   iteMax = 500L, nfolds = 5L, intercept = TRUE, itcpIncluded = FALSE)\n",
    "\n",
    "plot(log10(mcp$lambdaSeq), mcp$mse)\n",
    "preds <- as.matrix(X.test.intercept) %*% c(mcp$beta)\n",
    "metrics(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T05:21:04.151307Z",
     "start_time": "2019-12-19T05:21:04.123Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>$R^2$</th><th scope=col>MAE</th><th scope=col>RMSE</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.5212138</td><td>987.9823</td><td>1373.883</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 3 of type dbl\n",
       "\\begin{tabular}{lll}\n",
       " \\$R\\textasciicircum{}2\\$ & MAE & RMSE\\\\\n",
       "\\hline\n",
       "\t 0.5212138 & 987.9823 & 1373.883\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 3 of type dbl\n",
       "\n",
       "| $R^2$ | MAE | RMSE |\n",
       "|---|---|---|\n",
       "| 0.5212138 | 987.9823 | 1373.883 |\n",
       "\n"
      ],
      "text/plain": [
       "     $R^2$     MAE      RMSE    \n",
       "[1,] 0.5212138 987.9823 1373.883"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "huber.lasso <- cvNcvxHuberReg(X = as.matrix(X.train), Y = y.train, lSeq = NULL, nlambda = 50L, penalty = \"Lasso\", tSeq = NULL, ntau = 5L, phi0 = 0.001, gamma = 1.5, \n",
    "                              epsilon_c = 0.001, epsilon_t = 0.001, iteMax = 500L, nfolds = 5L, intercept = TRUE, itcpIncluded = FALSE)\n",
    "\n",
    "plot(log10(huber.lasso$tauSeq), huber.lasso$mse[which(huber.lasso$lambdaSeq == huber.lasso$lambdaMin, arr.ind = T)[,\"row\"],])\n",
    "\n",
    "preds <- as.matrix(X.test.intercept) %*% c(huber.lasso$beta)\n",
    "metrics(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "huber.scad <- cvNcvxHuberReg(X = as.matrix(X.train), Y = y.train, lSeq = NULL, nlambda = 50L, penalty = \"SCAD\", tSeq = NULL, ntau = 5L, phi0 = 0.001, gamma = 1.5, \n",
    "                              epsilon_c = 0.001, epsilon_t = 0.001, iteMax = 500L, nfolds = 5L, intercept = TRUE, itcpIncluded = FALSE)\n",
    "\n",
    "plot(log10(huber.scad$tauSeq), huber.scad$mse[which(huber.scad$lambdaSeq == huber.scado$lambdaMin, arr.ind = T)[,\"row\"],])\n",
    "\n",
    "preds <- as.matrix(X.test.intercept) %*% c(huber.scad$beta)\n",
    "metrics(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "huber.mcp <- cvNcvxHuberReg(X = as.matrix(X.train), Y = y.train, lSeq = NULL, nlambda = 50L, penalty = \"MCP\", tSeq = NULL, ntau = 5L, phi0 = 0.001, gamma = 1.5, \n",
    "                              epsilon_c = 0.001, epsilon_t = 0.001, iteMax = 500L, nfolds = 5L, intercept = TRUE, itcpIncluded = FALSE)\n",
    "\n",
    "plot(log10(huber.MCP$tauSeq), huber.mcp$mse[which(huber.mcp$lambdaSeq == huber.mcp$lambdaMin, arr.ind = T)[,\"row\"],])\n",
    "preds <- as.matrix(X.test.intercept) %*% c(huber.mcp$beta)\n",
    "metrics(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T06:05:07.399985Z",
     "start_time": "2019-12-19T05:38:15.314Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>$R^2$</th><th scope=col>MAE</th><th scope=col>RMSE</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.5555378</td><td>961.8926</td><td>1323.721</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 3 of type dbl\n",
       "\\begin{tabular}{lll}\n",
       " \\$R\\textasciicircum{}2\\$ & MAE & RMSE\\\\\n",
       "\\hline\n",
       "\t 0.5555378 & 961.8926 & 1323.721\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 3 of type dbl\n",
       "\n",
       "| $R^2$ | MAE | RMSE |\n",
       "|---|---|---|\n",
       "| 0.5555378 | 961.8926 | 1323.721 |\n",
       "\n"
      ],
      "text/plain": [
       "     $R^2$     MAE      RMSE    \n",
       "[1,] 0.5555378 961.8926 1323.721"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "huber.adaptive <- huberReg(X = as.matrix(X.train), Y = y.train, epsilon = 1e-07, constTau = 1.345, iteMax = 1e4)\n",
    "preds <- as.matrix(X.test.intercept) %*% c(huber.adaptive$theta)\n",
    "metrics(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X <- dataset_3[,-ncol(dataset_3)][,-1]\n",
    "y <- dataset_3[,ncol(dataset_3)]\n",
    "training_indices <- 1:(nrow(X)-2960)\n",
    "X_train <- X[training_indices,]\n",
    "X_test <- X[-training_indices,]\n",
    "y_train <- y[training_indices]\n",
    "y_test <- y[-training_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb <- bst(X_train, y_train, cost = 0.5, family = c(\"huber\"), ctrl = bst_control(mstop = 50, trace = TRUE), \n",
    "          control.tree = list(maxdepth = 1),learner = c(\"tree\"))\n",
    "gb.preds <- predict(gb, newdata=X_test, mstop = 50, type=c(\"response\"), n.cores=8)\n",
    "display(metrics(gb.preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb.1 <- bst(X_train, y_train, cost = 0.5, family = c(\"gaussian\"), ctrl = bst_control(mstop = 50, trace = TRUE), control.tree = list(maxdepth = 1),learner = c(\"ls\"))\n",
    "gb.1.preds <- predict(gb.1, newdata=X_test, mstop = 50, type=c(\"response\"), n.cores=4)\n",
    "display(metrics(gb.1.preds))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb.2 <- bst(X_train, y_train, cost = 0.5, family = c(\"gaussian\"), ctrl = bst_control(mstop = 50, trace = TRUE), control.tree = list(maxdepth = 1),learner = c(\"tree\"))\n",
    "gb.2.preds <- predict(gb.2, newdata=X_test, mstop = 50, type=c(\"response\"), n.cores=4)\n",
    "display(metrics(gb.2.preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb.3 <- rbst(x, y, cost = 0.5, rfamily = c(\"tgaussian\", \"thuber\",\"thinge\", \"tbinom\", \"binomd\",\"texpo\", \"tpoisson\", \"clossR\", \"closs\", \"gloss\", \"qloss\"), ctrl=bst_control(),\n",
    "control.tree=list(maxdepth = 1), learner=c(\"ls\",\"sm\",\"tree\"),del=1e-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mars <- earth(X_train, y_train, nfold = 5, ncross = 30)\n",
    "display(summary(mars, digits = 3)) \n",
    "mars.preds <- predict(mars, newdata = X_test, type = c(\"response\"), trace = True)\n",
    "display(metrics(mars.preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-19T05:27:01.325712Z",
     "start_time": "2019-12-19T05:27:01.124Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbind(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "reem <- REEMtree(y ~ . ,data = `, random, subset=NULL, initialRandomEffects=rep(0,TotalObs),\n",
    "ErrorTolerance=0.001, MaxIterations=1000, verbose=FALSE, tree.control=rpart.control(),\n",
    "cv=TRUE, cpmin = 0.001, no.SE =1,\n",
    "lme.control=lmeControl(returnObject=TRUE), method=\"REML\", correlation=NULL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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