{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lab 5 - R Code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Authors: Valerie Dube, Erzo Garay, Juan Marcos Guerrero y Matias Villalba" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Replication and Data analysis" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "ename": "ERROR", "evalue": "Error in library(hdm): there is no package called 'hdm'\n", "output_type": "error", "traceback": [ "Error in library(hdm): there is no package called 'hdm'\nTraceback:\n", "1. library(hdm)" ] } ], "source": [ "library(hdm)\n", "library(xtable)\n", "library(randomForest)\n", "library(glmnet)\n", "library(sandwich)\n", "library(rpart)\n", "library(nnet)\n", "library(gbm)\n", "library(rpart.plot)\n", "library(keras)\n", "library(xtable)\n", "library(glmnet)\n", "library(randomForest)\n", "library(ggplot2)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "set.seed(1)\n", "rm(list = ls())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " cobalt (Version 4.5.5, Build Date: 2024-04-02)\n", "\n" ] } ], "source": [ "library(ggplot2)\n", "library(WeightIt)\n", "library(cobalt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Descriptives" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1.1. Descriptive table (vale)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 15
ywgender_femalegender_malegender_transgenderethnicgrp_asianethnicgrp_blackethnicgrp_mixed_multipleethnicgrp_otherethnicgrp_whitepartners1postlaunchmsmageimd_decile
<int><int><int><int><int><int><int><int><int><int><int><int><int><int><int>
11101000100010275
20001000001000196
30101001000010264
40010000001100202
51110010000010243
61101000001010242
\n" ], "text/latex": [ "A data.frame: 6 × 15\n", "\\begin{tabular}{r|lllllllllllllll}\n", " & y & w & gender\\_female & gender\\_male & gender\\_transgender & ethnicgrp\\_asian & ethnicgrp\\_black & ethnicgrp\\_mixed\\_multiple & ethnicgrp\\_other & ethnicgrp\\_white & partners1 & postlaunch & msm & age & imd\\_decile\\\\\n", " & & & & & & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 27 & 5\\\\\n", "\t2 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 19 & 6\\\\\n", "\t3 & 0 & 1 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 26 & 4\\\\\n", "\t4 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 20 & 2\\\\\n", "\t5 & 1 & 1 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 24 & 3\\\\\n", "\t6 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 24 & 2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 15\n", "\n", "| | y <int> | w <int> | gender_female <int> | gender_male <int> | gender_transgender <int> | ethnicgrp_asian <int> | ethnicgrp_black <int> | ethnicgrp_mixed_multiple <int> | ethnicgrp_other <int> | ethnicgrp_white <int> | partners1 <int> | postlaunch <int> | msm <int> | age <int> | imd_decile <int> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 27 | 5 |\n", "| 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 19 | 6 |\n", "| 3 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 26 | 4 |\n", "| 4 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 20 | 2 |\n", "| 5 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 24 | 3 |\n", "| 6 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 24 | 2 |\n", "\n" ], "text/plain": [ " y w gender_female gender_male gender_transgender ethnicgrp_asian\n", "1 1 1 0 1 0 0 \n", "2 0 0 0 1 0 0 \n", "3 0 1 0 1 0 0 \n", "4 0 0 1 0 0 0 \n", "5 1 1 1 0 0 1 \n", "6 1 1 0 1 0 0 \n", " ethnicgrp_black ethnicgrp_mixed_multiple ethnicgrp_other ethnicgrp_white\n", "1 0 1 0 0 \n", "2 0 0 0 1 \n", "3 1 0 0 0 \n", "4 0 0 0 1 \n", "5 0 0 0 0 \n", "6 0 0 0 1 \n", " partners1 postlaunch msm age imd_decile\n", "1 0 1 0 27 5 \n", "2 0 0 0 19 6 \n", "3 0 1 0 26 4 \n", "4 1 0 0 20 2 \n", "5 0 1 0 24 3 \n", "6 0 1 0 24 2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import data and see first observations\n", "data = read.csv(\"../../data/processed_esti.csv\")\n", "head(data)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t1739 obs. of 15 variables:\n", " $ y : int 1 0 0 0 1 1 1 0 0 1 ...\n", " $ w : int 1 0 1 0 1 1 1 0 1 1 ...\n", " $ gender_female : int 0 0 0 1 1 0 1 0 0 1 ...\n", " $ gender_male : int 1 1 1 0 0 1 0 1 1 0 ...\n", " $ gender_transgender : int 0 0 0 0 0 0 0 0 0 0 ...\n", " $ ethnicgrp_asian : int 0 0 0 0 1 0 0 0 0 0 ...\n", " $ ethnicgrp_black : int 0 0 1 0 0 0 0 1 0 1 ...\n", " $ ethnicgrp_mixed_multiple: int 1 0 0 0 0 0 0 0 0 0 ...\n", " $ ethnicgrp_other : int 0 0 0 0 0 0 0 0 0 0 ...\n", " $ ethnicgrp_white : int 0 1 0 1 0 1 1 0 1 0 ...\n", " $ partners1 : int 0 0 0 1 0 0 0 0 1 0 ...\n", " $ postlaunch : int 1 0 1 0 1 1 0 1 0 0 ...\n", " $ msm : int 0 0 0 0 0 0 0 0 0 0 ...\n", " $ age : int 27 19 26 20 24 24 24 21 27 21 ...\n", " $ imd_decile : int 5 6 4 2 3 2 4 2 2 6 ...\n" ] } ], "source": [ "str(data)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "data$w: 0\n", " w gender_female gender_male gender_transgender\n", " Min. :0 Min. :0.0000 Min. :0.0000 Min. :0.000000 \n", " 1st Qu.:0 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000000 \n", " Median :0 Median :1.0000 Median :0.0000 Median :0.000000 \n", " Mean :0 Mean :0.5807 Mean :0.4181 Mean :0.001223 \n", " 3rd Qu.:0 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.000000 \n", " Max. :0 Max. :1.0000 Max. :1.0000 Max. :1.000000 \n", " ethnicgrp_asian ethnicgrp_black ethnicgrp_mixed_multiple ethnicgrp_other \n", " Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000 \n", " 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 \n", " Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000 \n", " Mean :0.05501 Mean :0.09291 Mean :0.09291 Mean :0.01711 \n", " 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 \n", " Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000 \n", " ethnicgrp_white partners1 postlaunch msm \n", " Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 \n", " 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 \n", " Median :1.0000 Median :0.0000 Median :0.0000 Median :0.0000 \n", " Mean :0.7421 Mean :0.2922 Mean :0.4731 Mean :0.1381 \n", " 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000 \n", " Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000 \n", " age imd_decile \n", " Min. :16.00 Min. :1.000 \n", " 1st Qu.:20.00 1st Qu.:2.000 \n", " Median :23.00 Median :3.000 \n", " Mean :23.05 Mean :3.484 \n", " 3rd Qu.:26.00 3rd Qu.:4.000 \n", " Max. :30.00 Max. :9.000 \n", "------------------------------------------------------------ \n", "data$w: 1\n", " w gender_female gender_male gender_transgender\n", " Min. :1 Min. :0.0000 Min. :0.0000 Min. :0.000000 \n", " 1st Qu.:1 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000000 \n", " Median :1 Median :1.0000 Median :0.0000 Median :0.000000 \n", " Mean :1 Mean :0.5874 Mean :0.4093 Mean :0.003257 \n", " 3rd Qu.:1 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.000000 \n", " Max. :1 Max. :1.0000 Max. :1.0000 Max. :1.000000 \n", " ethnicgrp_asian ethnicgrp_black ethnicgrp_mixed_multiple\n", " Min. :0.00000 Min. :0.00000 Min. :0.00000 \n", " 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 \n", " Median :0.00000 Median :0.00000 Median :0.00000 \n", " Mean :0.07166 Mean :0.08035 Mean :0.08469 \n", " 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 \n", " Max. :1.00000 Max. :1.00000 Max. :1.00000 \n", " ethnicgrp_other ethnicgrp_white partners1 postlaunch \n", " Min. :0.000000 Min. :0.0000 Min. :0.0000 Min. :0.0000 \n", " 1st Qu.:0.000000 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000 \n", " Median :0.000000 Median :1.0000 Median :0.0000 Median :1.0000 \n", " Mean :0.009772 Mean :0.7535 Mean :0.3008 Mean :0.5559 \n", " 3rd Qu.:0.000000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 \n", " Max. :1.000000 Max. :1.0000 Max. :1.0000 Max. :1.0000 \n", " msm age imd_decile \n", " Min. :0.0000 Min. :16.00 Min. :1.00 \n", " 1st Qu.:0.0000 1st Qu.:20.00 1st Qu.:2.00 \n", " Median :0.0000 Median :23.00 Median :3.00 \n", " Mean :0.1238 Mean :23.16 Mean :3.46 \n", " 3rd Qu.:0.0000 3rd Qu.:26.00 3rd Qu.:4.00 \n", " Max. :1.0000 Max. :30.00 Max. :9.00 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "by(data[, !names(data) %in% 'y'], data$w, summary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The observations for each variable are generally balanced between the control and treatment groups. Additionally, most participants are white, with an average age of approximately 23. The mean IMD decile scores are around 3.5, indicating that participants in both groups tend to come from more deprived areas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1.2. Descriptive graphs (vale)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "\u001b[4mBalance Measures\u001b[24m\n", " Type Diff.Adj\n", "prop.score Distance -0.0025\n", "age Contin. -0.0009\n", "gender_male Binary 0.0004\n", "ethnicgrp_white Binary -0.0001\n", "partners1 Binary -0.0010\n", "imd_decile Contin. 0.0021\n", "\n", "\u001b[4mEffective sample sizes\u001b[24m\n", " Control Treated\n", "Unadjusted 818. 921\n", "Adjusted 815.66 921" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Generating propensity score weights for the ATT\n", "w.out <- WeightIt::weightit(w ~ age + gender_male + ethnicgrp_white + partners1 + imd_decile,\n", " data = data,\n", " method = \"glm\",\n", " estimand = \"ATT\")\n", "\n", "bal.tab(w.out)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94AAAgAElEQVR4nOzdd4AU5cH48dk7OI6Do/eioiBFEQtdKQqG0AJoYlc0vAKJqGgERVGMsUVFUAQRiP21BwFBYxR7BIMoagzYUKQjRYq04+5+f+yb/V0AYTluPXj8fP7anZud55ndTfg6Ozsby8/PjwAAOPilFfcEAAAoGsIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEOGH3TXXXBOLxcaPH5/SzQ4fPjwWi913331FO8quA/2UUrdTB86I+2nevHktW7bMyMgoW7bsokWLins6P4V58+bFYrGOHTsW90T2y0H3TgNI0sEXdvF/VwoqW7bs4Ycf/utf//rhhx/evHnzTutXrFjx0EMPzc7OTn6Ixx57bNq0aXtepxCbLdzoqRto/+36WqSnp1esWLFly5YjRoxYu3ZtcU8w5c4///w5c+acdNJJ/fv3z8rKKqrN/vDDD7FY7NBDD42iaM2aNbFYrGHDhkW1cQrBKwIcLEoU9wQKqWzZst27d4/f3rRp08KFCydPnvzXv/51+PDhDz300KmnnppY8+qrr7766qv3aeNDhw7t3r37r371qz2sU4jNFm701A1UVAq+Fjk5OStWrJgzZ86cOXMeffTROXPmVKlSpXinlzrbtm3717/+Va5cub///e8lShTl/5TKlCmTlpZWtmzZ+O0oiuK3KS5eEeBgcbCGXfXq1Z966qmCSxYtWnTLLbdMnDixe/fu06dP/8UvflG4LS9cuHDFihVFMceDb/TC2fW1WL58+SmnnLJgwYIJEyZce+21xTWxVNuyZUsURRUrVizaqovLysqKH6bNzMxMT08/MA/Z/qx4RYCDwsH3UeyPOfTQQydMmHDHHXfk5OT069cv/o9utLtz1J577rlTTjmlUqVKGRkZtWrV6tq160svvRT/069//esjjjgiiqK//OUvsVjspJNOiqLouuuui8Vi06ZNGzduXO3atStUqLDbzUZRlJaW9vrrr3fo0KFcuXJly5Y96aSTXn311cRfBw0aFIvFHn744YIPmT17diwW69Gjx4+NvutAOTk599xzT4sWLbKzszMzM+vXrz9o0KBly5YlVrj++uvjE/7000/79OlTrVq1zMzMY4899sknnyw49IYNG4YNG9a4cePSpUuXKlWqQYMGQ4YM2bBhQ6FfgoSaNWuee+65URR98803+zniXh+S5M7m5eWNGzeuRYsWZcuWzc7O7tSp01tvvVVwhfz8/EmTJrVp0yY7O7t06dKNGze+/vrrf/jhhx+bWO/evStWrBhF0aJFi+IfQ3/55ZdREq/Obt9OuypTpkzimFBWVlbB40PffvvtOeecU7Vq1aysrBYtWkyePHnt2rWxWKxVq1bJ706Sz9uiRYvOOuusKlWqZGVlHXvssQ8++OCuU93rWEnu8q4Sk5w1a1bHjh2zs7OrVq164YUXbty4MT8/f/To0Y0aNcrKymrSpMltt91W8GevC/FO2+te7OEVAThwHKxH7H7MVVdd9cQTT8ybN++55547//zzd11h4sSJ/fv3r1q16hlnnFGtWrWlS5dOmTKle/fujzzyyPnnn3/RRRdlZ2c//PDDrVu3PvPMM2vXrh1FUUZGRhRFb7755vjx43v16rWH/0OfN2/elVdeefLJJ1988cVfffXVtGnTunbt+uqrr3bo0CGZye929J3k5eX16tXrpZdeatSoUb9+/cqVK/f++++PHTt28uTJs2bNip8DFJ/whx9+eMEFF7Rq1eq88877/PPPZ8yYcc4551SvXv2UU06JoignJ6dHjx5vv/32CSecMGjQoJycnL/97W933XXXm2++OWvWrPT09KSe7h/37bffRlF01FFHJZYUYsRkHpLMzkZRdOaZZz733HNNmjTp27fv+vXrp06d2qFDh0cffTTxJrngggsef/zxmjVrDhgwoFSpUq+99trNN988ffr0t956a7fHZvr169eqVatrr722YsWKN9xwQxRFVatWTf7V2evbqUWLFvXr14/fbtmyZeKZXLNmzUknnbR48eITTzyxc+fOS5YsOffcc4cPHx5FUWZmZuLhe92dZJ63devWtWvXbvHixe3bt2/fvv1333133XXXde3adaepJjlWMv8L2kn8gbNnz77//vu7dOnSt2/fqVOnPvLII3l5ebVq1XriiSe6d+++efPmp5566tprr61Tp0781Szce3uve/FjrwjAgSX/YPPhhx9GUXTEEUf82Aq33357FEXnnXde/G78BLX7778/frdp06ZRFH355ZeJ9RcvXpydnd26dev43WeffTaKon79+iVWuPXWW6MoKl++/Msvv5xYuNNmr7vuuiiK0tLSpk6dmljnzjvvjKLoxBNPjN+95JJLoih66KGHCs521qxZURR17979x0bfaaAJEyZEUdSmTZutW7cm1on/u37GGWfE7952221RFGVkZDz22GOJda666qooivr27Ru/+9e//jWKotatW+/YsSO+ZNu2bY0aNYqiaNq0aQV3asyYMT/2VO/6WuTm5i5ZsuTWW29NT09v0qTJpk2bEn8qxIjJPCSZnY0fheratWtiOwsWLMjKyipTpkz82M/TTz8dRdEJJ5ywYcOG+Ap5eXmDBg2Kouiaa675sd1ft25dFEWHHnpoYkkyr85u307Jiz9Fv/nNbxJL/vGPf5QuXTqKog4dOsSXJLM7yTxv8WA988wzEyssX768Ro0a+zpWoXc5PslSpUq9/vrr8SWLFi1KT08vWbJko0aN1qxZE184adKkKIp69OgRv1uId1rh3gAAB6BwPopNaNKkSfSfI0a7+v7772OxWPz057g6deqsXr06Hli7FYvFoihq3LjxXs/ba9myZcGvXAwaNCgzM/Pdd98twu+HPvLII1EUXX/99aVKlUosHDJkSEZGxpQpUxIfQEdR1KJFi/POOy9x9ze/+U0URZ9//nn87vHHHz958uQxY8YkDmBkZGT06tUriqKPP/54n6b01VdfFfxWbJ06dW644YbBgwfPmjWr4PNciBGTf8ied/ahhx6Koujaa69NbKdhw4a33HLLwIEDV61aFUXRxIkToyi67bbbEgfnYrHYn/70p5IlS8af8CQl8+ok/3barRdeeCG+zcSStm3bnnXWWQXXSX539vy8TZ06NYqiwYMHJ1aoUaPG7373u30daz93uWPHjomrqxxyyCFNmzbNycm59NJLK1WqFF8YP5Phq6++it8txDutqN4AAMUutI9io/98W23jxo27/WvPnj3HjRt38sknDxkypFu3bvHDD/FPfPasTZs2e10nflZcQmZmZqNGjebNm/fZZ58l8/C9ys/Pnzt3bhRFbdu2Lbi8XLlyDRs2/OSTTz799NPmzZvHF7Zu3brgOvETwhLld9hhhx122GHx2xs3boyfSxS/YEfBOkxGdnb2aaedlpjh6tWrP/7441GjRs2fP3/ixIm1atUq9IjJP2TPO/uPf/wjiqITTjih4DoFe2X27NnRLs9qhQoVjj766A8//PDbb7895JBD9vo87NOrU7j3Q15e3oIFC9LS0o499tiCy7t37x6P133dnT08b3l5efPnz4+iqFmzZgXXKXgm3z6NVej/Cey0s+XKlYui6Jhjjtlpyf68t4vkDQBwIAgw7NasWRNFUeK/5ncyevTo3NzcBx98sF+/flEUNWnSpEePHgMHDqxXr96eN1u1atW9Dl2zZs2dlsSnEf/Mbv9t2rRp69atGRkZ5cuX3+30Vq9enVgSb9aE+FGT/AInmE+ZMuWuu+6aO3fu1q1b92dW1apV2+kbIbm5uffdd9/gwYNPPfXUefPmlSxZstAjJvmQPezsDz/88MMPP2RmZsY/r9zVli1bNm3aFP34BSyWLl2azL/r+/TqJPN22u0Q27dvL1++fOIpjYufvRe3T7uzh+ctPtauz1vlypULN1bhdjmKop2umBOfZMGF+/neLqo3AMCBIMCw++c//xlFUfyUml2VLFly/PjxI0aMmDZt2ksvvfTaa6/dcccdo0ePfuyxx84444w9bHanf0p3a9fzsuP/5KSlFc1H3rv+A5aQl5eXWCEZEyZMGDBgQHZ29sCBA1u2bFm+fPm0tLQpU6Y88MAD+z/P9PT0yy+//KWXXnr55ZenT5/ep0+fwo1YJJOMP/k5OTn5+fm7fX7iC2OxWPyUsl3tVD8/Zp9enWTeTruKb3zXvSi4pKh2Jz7WrruTm5tbuLEKt8uFsK9vm6J6xgAOBKGF3fbt25944okoirp167aH1eLffRswYMDWrVsffvjhSy+9dMCAAb169Sp4alQhFDwkE1fw8OFu/+Ffvnx58tsvW7ZsVlbW5s2bv//++52uGfHdd99F+3JQ5KabboqiaPr06e3bt08s3MOJhoVw+OGHR1G0YMGCQo9YJJMsXbp0dnb2xo0b16xZs9urJWdmZpYvX379+vWXXHJJoY8qRUX66uxhiPT09I0bN+bm5hb8r4jFixcnbhfh7qSnp2/btm3Lli0FD9oVvM5iUY1VtPb1bXNg7gVA4YT25Ymrrrpq6dKlRx999K4XZYhbtGhRwZbKzMwcOHBg27Ztv//++4ULF+7n6O+9917Bu9u2bfvss8/S0tLihw/jV6PY6WPZOXPm7NMQ8ZO04ieNJaxdu/azzz4rXbp0kpdg2LZt29KlS8uWLVvwX778/Py//e1v+zSZPYufgx8/basQIxbhJONPWsFrCkZRdNttt3Xu3Pndd9+N/nPe2E5XtouiaF+/9VIkr84epKen16tXLzc3N5HLcTs9J0WyO+np6Q0aNIh2+cLBO++8U+RjFaHCvW0OtL0AKLRwwi5+Qa8xY8ZkZWU9+uiju/3Q7aOPPjrssMPOO++87du3JxZu3Lhx4cKF6enp1apVi/6TX/Ejbftq5syZ8VCImzhx4pYtW04++eT4yd3xI1jxCy7EV4h/vaDgFvY6evzUwFtvvbXgLtx66607duw499xzkzziWKpUqUqVKm3atClxpCc/P/+mm26Kf5X4+++/T2pvf1xeXt6YMWNmzpxZqlSp+DcWCzFiEU6yb9++URTdddddievNfvPNN3feeeesWbMaN24c/edZvfHGG+OH1uLefvvt6tWrx78omqQieXX2rEuXLlEUjRkzJrHkn//8Z/wo9U7T2P/diR/2vvvuuxNLvv7667/85S+pGKuoFO5tc6DtBUChHawfxa5YseLXv/51/Pb27dsXL1788ccf5+Xl1a5d+5lnnjnuuON2+6hmzZqdc845TzzxROPGjbt27Vq5cuXVq1fPmDFjyZIll19+efys8MaNG8disRkzZvTr1y8jI+P+++9PZj47duyIoqhfv35du3bt06fP4YcfPn/+/GeffbZUqVK33HJLfJ3TTz/9mmuuefPNN0888cTWrVsvX758+vTpI0aMGDJkSPwcrGRGP//88ydPnjx16tQTTjiha9euJUuWfO+992bOnHnkkUfGL+CXpAsvvPDuu+/u1KlTPHqmT5++bt26Rx55pEuXLk899VTdunXjPx2RjIKvRRRFGzZsmD9//pIlS9LT08ePH1+nTp1Cj1hUkzz//POfe+656dOnH3XUUV27dv3hhx+mTJmycePGiRMnxg8onnHGGVOmTHnyySePO+64M888Mzs7+1//+te0adNKly5d8MIiyQxUJK/OHlx11VWPP/74Aw88sGjRopYtWy5atGjy5MnDhw8v+IPCRbU7f/jDHx599NFnnnlm4cKFbdq0+e6771566aWLL774rrvuKvKxilAh3jYH4F4AFNJPeM28ohG/KG5BaWlpVatW7dSp05gxYzZv3rzT+jtd4Dc3N3fs2LFt27atUqVKenp6+fLl27Vr9+CDD+bl5SUecvvtt1epUqVUqVLHH398/n+uknrnnXfuYbPxa2c8++yzr7/+evv27cuWLVumTJkOHTq8/fbbBR/1ySefnHLKKfHfI2rVqtWUKVPiRwg6duz4Y6PvNFB+fn5OTs7o0aOPP/74rKysUqVKNWrUaNiwYevWrUussNsJf/HFF1EUNWvWLH53y5Yt11133RFHHFGqVKm6dev+/ve/X716dX5+/oUXXlimTJkaNWp8/PHHSV6geCfly5dv2rRpv379Pvroo4IrF2LEZB6SzM7Gn7SRI0cec8wxpUuXLlOmTPv27V977bWCD8nNzZ04cWL8F6VKlChRp06dCy64YP78+T+27/m7u0BxoV+dffLhhx+eeuqp2dnZ5cqV69Chw2uvvfbJJ5/s9C7a6+4k+bzNnz+/V69eFSpUyMzMbNq06cSJE+OHk1u1arWfYyVjtw+M/45Lwe3HL2KSeCEK994uxBsA4AAUy9/dl/iAg8h7773XunXr7t27T58+vbjnAkBxCuccO/g5WLly5YsvvrjTFxrih073ei1GAIJ3sJ5jBz9Pr7zyyvnnn9+2bds33ngjfmW49evXjxw5MvrPL2sd+DZv3hz/Jbc9q1ix4q6XegZgz3wUCweT7du3d+rU6Z133jnqqKO6deu2efPmqVOnLlmypE+fPpMnTy7u2SXlueeeS+arpn/605+GDx/+E8wHICTCDg4yGzduHDVq1DPPPLNo0aLc3NyGDRuee+65gwcPLlHi4DgAv2LFivfff3+vqzVq1Kh+/fo/wXwAQiLsAAAC4csTAACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIE4OC5Vn/Ddd99t27atuGcBAOxe6dKlK1euXNyz+Pk6yMJu27ZtmzdvLu5ZAAC7l56eXtxT+FnzUSwAQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB3wf1asWHHyySd//fXXubm5J5988ty5c3+yEVM90J7F9/ef//xn8U4DYP8dZNexAwpn3bp1Z5xxRsWKFZ988sm9XmUqLS1t9OjRRxxxxL6O8uGHH2ZlZTVs2LCw0/w/ubm5Tz/99MyZM5cvX56Tk1OjRo1f/vKXZ599dlqa/xYF2BNhBz8LM2bMOOaYY77++uvZs2efeOKJe145Fos1a9asEKM888wzbdq02f+wGz9+/Ouvv37VVVcdeeSRURR98MEHo0eP3rZt229/+9v93DJA2IQdhC8vL2/69Ol9+/b96quvXnjhhYJh9+WXX44cOfKbb76pWbPmueeeG1+Ym5vbuXPnu+66q0mTJt26dRs9enS885YuXXreeec9/vjjtWvX/tvf/vbkk0+uWLGibNmyJ5100iWXXHL11Vd/9NFHc+fOnTFjxgMPPLB27dr77rvvo48+2rx5c8OGDX//+9/HK223I+5k7ty5Xbp0ad26dfxu586dy5cvn5+fH7/79ddfjxs3bsGCBfn5+U2aNLn88str166dl5fXqVOnYcOGvfTSS8uXLy9duvR11133yiuvzJ07d926db/5zW/OOuus7du3d+nS5Q9/+MOrr766YsWKKIouvfTSnTL3x6YNcFDwuQaE77333lu/fn3Hjh1/+ctfzpkzJ940URTl5eVdf/31hxxyyPPPP3/bbbe98MILSW5w2bJld9xxx+WXX/7iiy+OGTPm3//+97PPPjtq1Khq1aoNGjTogQceiKJo+PDhURQ99NBDU6ZMadq06TXXXLNt27YkR6xfv/6bb775+eefJ5a0aNGiZcuW8dsjRoyoVKnSs88++/TTT2dlZd12221RFKWlpaWnp0+fPv3WW2/93//93/Lly19xxRVHH330pEmTrr766kmTJq1bty7+GfTUqVNvuOGGp556qm/fviNGjFi3bl3BoXc77X15sgGKk7CD8E2dOvXkk08uXbp0/fr1jzjiiBkzZsSXz58/f8WKFX379s3MzKxevfqvf/3rJDe4adOm/Pz87Ozs9PT0WrVqjR8/fqdjb59//vn8+fMHDRpUrly5UqVKXXTRRTk5Oe+++26SI1566aXxo2XnnHPOrbfe+sILLxTMr3Hjxl1xxRWZmZllypQ55ZRT4ofu4n/q3Llz6dKl09PTmzRpUrp06Xbt2kVR1LRp09zc3OXLl8fX6dKlS6VKlaIo+sUvfpGZmTlr1qy9TjvJpwWg2PkoFgK3fPnyOXPm3HPPPfG7Xbt2ffzxxy+88ML09PRVq1bFYrHq1avH/1S7du0kt9mgQYOePXv+7ne/a9SoUfPmzU899dSdHrtkyZIoik4//fSCC5ctWxZFUTIjZmdnX3/99YMHD543b96nn37617/+dcyYMVddddUvfvGLKIq++OKLRx999Ntvv42iaPv27bm5uXl5efGjcVWqVIlvISMjo3LlyonbURTl5OTE79aqVSt+Iz09vXLlyqtWrUpm2gAHBWEHgXvhhRfy8vKuueaa+N28vLwtW7a88847HTp0iLdOLBZL/GnPm0ocGIvFYldeeeU555zz3nvvzZo16/HHH7/uuutOPvnkxJqlSpWKoujll1+OR1XC3//+9+RHzM7ObteuXbt27QYOHDh27NhRo0Z16tRpxYoVV199dd++ff/85z9nZGT84x//iH94mpjYbm8XtGPHjsTt3Nzcgt+0/bFpAxwsfBQLIduxY8dLL73Ut2/fSf/x4IMPtm/fftq0aVEUVa1aNT8/f+XKlfGVFy1atNPDMzIyYrFY4iSzxKeZubm533//fY0aNXr16nX77bf37Nlz6tSpBR8YPxT35ZdfJpbEj3vtdcQoilauXHnjjTcm1olr2rTp9u3bc3JyPvvss7y8vLPOOiveXv/+97/39TlZunRp/Mb27dtXr15drVq1vU4b4GAh7CBkb7zxxqZNm/r06VOjgNNOO+3DDz9cunRpkyZNypUr98gjj2zatGnJkiVTpkzZ6eHxU+g++OCDKIq2bt36/PPPx5f//e9/79+//+eff56Xl7d27dqvv/46nkSZmZlLly7dtGnTYYcddtxxx40bN27lypW5ubnTpk3r16/fmjVr9jpiFEVVqlRZvHjxtdde++67765YsWLlypXvvvvuAw880Lx588zMzBo1auTm5n766ac5OTkzZ8789NNPoyhavXp18s/JK6+88vXXX2/fvv3JJ5/Mzc1t06ZN4k8/Nu19fNYBio2PYiFk06ZNa9++ffny5QsubNasWd26dadNm/a73/3u9ttvHz169Omnn167du2BAwdeffXVic9b4wYPHjx69Oi33367UqVK55133qxZs3Jzc7t06bJq1arrr79+7dq15cqVa9Wq1e9+97soinr27Dlx4sTXX3/9mWeeGT58+JgxY/r165efn1+vXr3bb789ftLbXkdMT08fPXr0Y489Nm7cuDVr1uzYsaNGjRodOnQ477zzoihq0qTJWWeddd1118VisXbt2t18881/+MMf/ud//mfixIlJPie9e/e+++67P//884oVK/7pT38qX758bm5u4q8/Nm2Ag0Jsp/9LPcAtWbJk8+bNxT0LCFz8OnYjR448/vjji3suRSm+X3/+858TV04Bilx2dnbNmjWLexY/Xz6KBf5Lbm5u/AJy2dnZxT0XAPaNsAP+yxtvvHHppZe2bdu2fv36xT0XAPaNj2IBgCLjo9ji5YgdAEAghB0AQCCEHQBAIIQdAEAghB0AQCAOsm/FNmnSZLc/LgkAHAiaNm06e/bs4p7Fz9dB9pNiy5cvnzhxYsHfdgQADhDPPPPMfffdV9yz+Fk7yMIuiqLq1avXq1evuGcBAOzMbysXO+fYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEokRKt75p06YJEyZ8/PHHOTk5DRs2HDhwYLVq1XZd7cUXX3z++efXrFlTu3btCy64oEWLFimdFQBAkFJ7xG706NGrVq0aMWLEnXfemZWVddNNN+Xl5e20zsyZM59++ukBAwaMHz++c+fOEydO3Lx5c0pnBQAQpBSG3erVq+fMmdO/f/969erVqlVr4MCBS5cu/eSTT3Za7emnn+7bt2/z5s2rVavWq1evCRMmZGVlpW5WAAChSmHYffHFFyVLlqxXr178btmyZevUqfPZZ58VXGfNmjUrVqyIouiyyy77zW9+c9VVVy1YsCB1UwIACFgKz7HbsGFDdnZ2LBZLLClfvvz69esLrrNmzZooil599dWhQ4eWL1/+qaee+uMf/zh+/Pjy5cvHV/j0008fe+yxxPoZGRmpmzAAwEEttV+eKFh1e3DmmWfWqVMniqLf/va3r7/++vvvv9+pU6f4n1atWvXqq68m1kxPT0/FPAEAApDCsKtQocKGDRvy8/MTebd+/fqKFSsWXKdSpUpRFJUpUyZ+Nz09vVKlSuvWrUus0Lp166lTpybuNm3aNHUTBgA4qKUw7Bo0aJCTk/PVV1/Vr18/iqINGzYsXry4cePGBdepVKlSxYoVFyxYEF9n+/bt3333XfXq1RMrlC5dunbt2om7+fn5qZswAMBBLYVfnqhUqVKbNm3Gjh379ddfL126dNSoUUcccUSTJk2iKHrllVdeeOGFKIrS0tJ69uz51FNPzZs3b/Xq1Q888EBmZqbr2AEAFEJqz7G77LLLJkyYcOONN+bm5h511FHDhw+Pfyw7b968DRs29OzZM4qi0047bfPmzXffffemTZsaNmx48803Z2ZmpnRWAABBih1cH25WrFjxueeeS3y1AgA4cEyaNOmPf/zj4sWLi3siP19+KxYAIBDCDgAgEMIOACAQqf3yRHjy8/NnzpxZ3LNgZ7Vq1Yp/4RoAfs6E3b7ZsWPHqaeeWtyzYGeVK1c+7LDDinsW/H+///3vf/vb3xb3LAB+doRdYRxaqWL/E9sU9yyIoij6bNWqR997f8369d9/8UVxz4UoiqL8HTvyNm9evnx5cU8E4OdI2BVG3YoVhnTuWNyzIIqi6MVP5z/63vul2nU4+s6RxT0XoiiK1iQOqAIAACAASURBVL/15pdXXl7cswD4mfLlCQCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQJQo7gkAJOWjjz76/PPPi3sW/H+PPPLI/PnzK1SoEIvFinsu/J+yZcu+8cYbxT0LipOwAw4Ojz766N13313cs4ADWvny5Yt7ChQzYQccTAac1OaQihWLexZEURTd9veZm7ZtO+pvr2RWqVrccyGKoujT3/SJ1q4t7llQzIQdcDA5p/nxbeodWtyzIIqi6N433t60bVtxzwL4L748AQAQCGEHABAIYQcAEIjUht2mTZvuvvvuCy+88Nxzz73ppptWrVq1h5Vnzpz5q1/9avbs2SmdEgBAqFIbdqNHj161atWIESPuvPPOrKysm266KS8vb7drfv/994888khGRkZK5wMAELAUht3q1avnzJnTv3//evXq1apVa+DAgUuXLv3kk092u/L48eM7duyYlZWVuvkAAIQthWH3xRdflCxZsl69evG7ZcuWrVOnzmeffbbrmrNmzfrqq6/OOeec1E0GACB4KbyO3YYNG7Kzswv+1Ez58uXXr1+/02qbNm0aP378FVdckZmZuetG3nnnnRtuuCFxd7frAAAQpfoCxcn8gOBf/vKX448//thjj93tX0uUKJGdnV3U8wIACFAKw65ChQobNmzIz89P5N369esr/vdvAc2bN++DDz647777fmwjrVu3njp1auJuRT8lBADwI1IYdg0aNMjJyfnqq6/q168fRdGGDRsWL17cuHHjguu88sorP/zww8CBA+N3N23aNGrUqGOPPXbYsGGpmxgAQJBSGHaVKlVq06bN2LFjL7vssoyMjEmTJh1xxBFNmjSJouiVV17ZunVrz549Bw4ceNFFFyUecsUVV1xwwQWtWrVK3awAAEKV2nPsLrvssgkTJtx44425ublHHXXU8OHD4x/Lzps3b8OGDT179szOzi54Cl0sFsvOzi5XrlxKZwUAEKTUhl1WVtbgwYN3XT5kyJDdrv/oo4+mdD4AAAHzW7EAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIFIKuxWrVp14YUX1q5dOz09PbaLVE8RAPg5Gzhw4K75kdC6devinuABpEQyKw0aNOj555/v0KHDqaeeWqJEUg8BACgSZ5111tFHHx2//cUXX9x7772nn356x44d40tq1KhR5CPOmzfvuOOOy8/PL/Itp3rcpCrttddee+6553r16lXoYQAACqdjx46JjHvjjTfuvffek046adCgQakb8e23307dxlM6blIfxW7ZsqVt27b7ORIAQIqcdNJJ7du3nz59et26dRPR8uabb5566qnlypXLyso6/vjjH3zwwYIPeeqpp1q2bJmVlVWuXLnmzZs/9dRT8eW//OUvL7vssiiKYrFY8+bNoyhq3759u3bt3n777ZYtW5YuXbp27dp33nlnTk7ONddcU7t27ezs7M6dOy9cuDCx5T2MG9/Uhx9+2KlTp3LlylWrVu3ss89etWrVbscthKTC7oQTTvj0008LNwAAQKqVKlVq/fr1Q4YMGTZs2HXXXRdF0cyZMzt16rR9+/Ynnnhi6tSprVq16tev38iRI+PrP/3002effXadOnWeffbZJ598smrVqmefffaMGTOiKBozZkz8U8o5c+Y89thjURRlZGR88803I0aMGD9+/BdffNGqVauhQ4d269YtKyvrn//854wZM+bMmRNvsr2Om5GRsWjRogEDBgwbNuzLL7+8//77n3322aFDh+523EJI6qPYUaNG/f73vx89enSbNm0KNwwAQOrEYrGPP/548uTJffr0iS8ZMmRIvXr1XnrppaysrCiKTj311GXLlv3xj3+85JJLMjMzFy5ceMoppzz11FMZGRlRFLVr165y5cpPPvlk9+7dGzRoUKVKlSiKCh42W7JkyfTp05s1axZF0ZVXXvn8889v3rz5hhtuiKKodu3aPXv2nDJlSjLjRlG0ePHiJ5988sQTT4yiKH6y4CuvvBJF0W7H3VdJHbG7/PLLly9f3rZt2zJlyhy2i0KPDQBQVDIyMnr06BG/vWrVqg8//LB79+5paWlb/6Nbt24bN2785JNPoigaNmzYzJkz41UXRVG5cuVq1Kjx7bff/tjGy5QpE6+6KIpq1qwZRVHBs9Rq1qz5ww8/bNy4ca/jRlGUlZUVr7q4OnXqrFixoqiehKSO2KWlpR155JFHHnlkUY0KAFC0qlSpUrJkyfjtZcuWRVF0zz333HPPPTuttmTJkhYtWmzYsOGuu+56/vnnv/322x9++CGKotzc3EMPPXQPG0/cTk9Pj6KocuXKOy3Jzc3d67hRFFWtWrXg8hIlSuTl5e3rzv6YpMLurbfeKqrxAABSIVF1Cb/97W8vvvjinRbWr18/iqKePXv+4x//uPrqq3/5y19WqFAhFot16dKlqGayh3FTbR8uSrdmzZrZs2cvW7YsLS2tTp06bdu2zc7OTt3MAAAK55BDDomiKDc3d7eXL/7yyy/feuutiy+++JZbbokv2bFjx9q1a+vVq5fScX8CSYVdXl7e0KFD77333pycnMTCMmXKjBgxYsiQISmbGwBAYVSqVKlly5ZTpkz5/vvvK1SoEF/46KOPfv755zfeeGO8Z+rUqZNY//7779+6dWtubm78bvyHtXbs2LGvv8uw53H3urVCj5uQ1MNGjhw5cuTIPn369OjRo2bNmnl5eUuXLp08efLQoUOrV69+wQUXFG5sAIAUueOOO0499dQOHTr84Q9/qFGjxttvv/3nP//53HPPLVGiRP369evWrTthwoRjjz22cuXKzz///Ny5czt27Dh37tzXX3+9ZcuWtWrViqLo1ltvPeqoo04//fSiGnevj92fceOSCruHHnroyiuvTFyCJa5///4DBgy45557hB0AcKDp0KHDa6+9dtNNNw0aNGjr1q316tW75ZZbrrjiiiiKSpYsOXny5Msuu+zss8/Ozs7u3bv31KlT33rrrYsuuuj000+fPXv2xRdf/MILL9x8882HH374vgbWHsbdq/0ZNy6psFu4cGH37t13Xd6rV69CX0APAGBfdezYcbc/pfrqq6/uuvCkk076+9//vtvtNG/e/N133y24pEePHt99913i7gcffPBjGz/ssMN2msPtt99+++23JzPurvOcNGnSpEmT4rfr1KlTcNxCSOo6diVKlNi8efOuy3NycuLf7wUAoNglFXbHHXfc3XffvX379oILt27dOm7cuP25ODIAAEUoqY9ihw0b1qNHjwYNGnTr1q127dr5+fmLFy+eMWPGihUrXn755VRPEQCAZCQVdt26dZs8efKwYcPGjx+fWNi0adOJEyd27tw5ZXMDAGAfJHuVlN69e/fu3XvZsmVLly6NxWJ169atXr16SmcGAMA+2bfL39WqVSt+hRUAAA40ewq7Ro0a9e3bd9iwYY0aNdrDagsWLCjqWQEAsM/2FHYVKlQoXbp0/MZPNR8AAAppT2E3e/bsnW4AAHDASuocu+bNmz/22GONGzfeaflf//rX66+//t///ncKJgYAEEVR9P333+/21yb2R1paWvny5Yt2mweCpMJu7ty5P/zww04Ld+zY8emnn3711VcpmBUAQBRF0TPPPPM///M/ZcqUKdrNbtq06bnnnuvSpUvRbrbY7SXsYrFY/EaLFi12u8Lxxx9fxDMCAPiPzZs3H3roof/617+KdrPHHHPMbn8u9WC3l7CbN2/em2++efnll/fq1atKlSoF/xSLxWrVqnXxxRencnoAAFEURZd3bFcxK2v/t7N8w4YH3pm1/9s5MO0l7Jo1a9asWbMXX3zxzjvvbNCgwU8zJwCAnQxs1/aIKpX3fztzv10ScNilJbPShg0bvvjii1RPBQCA/ZFU2C1evNhViAEADnBJhd3YsWMnTZo0ZcqUnJycVE8IAIDCSepyJ3fddVeJEiX69OmTkZFRpUqVkiVLFvzrN998k5KpAQCwL5IKu7y8vKpVq3bq1CnVswEAoNCSCrt33nkn1fMAAGA/JRV2cWvWrJk9e/ayZcvS0tLq1KnTtm3b7Ozs1M0MAKC4rFu37tJLL3399de3bt3apk2b++6777DDDivuSe1dsh/FDh069N577y345YkyZcqMGDFiyJAhKZsbAEDxuPDCC9euXfviiy9mZWWNGDGiR48eH330UXp6enHPay+SCruRI0eOHDmyT58+PXr0qFmzZl5e3tKlSydPnjx06NDq1atfcMEFqZ4lAMBPZvHixS+88MIHH3zQrFmzKIrGjh1brVq1119/vXPnzsU9tb1IKuweeuihK6+8cuTIkQUX9u/ff8CAAffcc4+wAwBC8v7772dmZsarLoqiihUrNm7c+L333jvwwy6p69gtXLiwe/fuuy7v1avX/Pnzi3pKAADF6bvvvqtUqVIsFkssqVq16qpVq4pxSklKKuxKlCixefPmXZfn5OQc+B82AwDsq4JV92NLDkBJhd1xxx139913b9++veDCrVu3jhs3rnnz5qmZGABA8ahevfrq1avz8/MTS1atWlW9evVinFKSkjrHbtiwYT169GjQoEG3bt1q166dn5+/ePHiGTNmrFix4uWXX071FAEAfkotWrTYtm3b3Llz4wewVq9ePX/+/BNPPLG457V3SYVdt27dJk+ePGzYsPHjxycWNm3adOLEiQf+WYQAAPukVq1ap5122oABAx588MHSpUsPHjz4+OOPb9euXXHPa++SvUBx7969e/fuvWzZsqVLl8Zisbp16x4UByQBAArhwQcfvOyyy7p27ZqTk9OuXbupU6ceFOfY7cMvT6xcufKjjz5auXJlWlramjVrjj32WG0HAASpXLlyDz/8cHHPYp8lFXbff/99//79n3/++R07diQWxmKxc84554EHHihTpkzKpgcAQLKSCrsrrrhiypQpffv2bd++feXKlXfs2LFy5coXX3zxf//3f7Ozs++///5UzxIAgL1KKuymTp06adKknX5hon///tdcc82kSZOEHQDAgSCp69ht3rz5F7/4xa7Lu3TpsmXLlqKeEgAAhZFU2B111FELFy7cdfmCBQtcoBgA4ACRVNjdcccdl19++TvvvJO4BHNubu6LL744duzYUaNGpXJ6AAAkK6lz7IYPH75o0aJ27dqVKVMmfomT5cuXb9mypW7duueee27BH9xYsGBBqmYKAPyM/eK+B0oWxS/UbytwiY/wJBV227dvr1+//pFHHplYUrNmzZRNCQDg/2zbtm3dunVZWVlrtm3f+9rJycrKWrNmTU5OTlFt8MCRVNjNnTs31fMAANjVypUrY7FYvXr1inaz69at++6774p2mweCffjliTVr1syePXvZsmVpaWl16tRp27ZtdnZ26mYGAHDIIYdUqFDhX//6V9Fu9phjjqlVq1bRbvNAkFTY5eXlDR069N577y140LJMmTIjRowYMmRIyuYGAPB/Dr3hxpJVquz/drZ9++3iu+7Y/+0cmJIKu5EjR44cObJPnz49evSoWbNmXl7e0qVLJ0+ePHTo0OrVq+904WIAgCKXfdwJperW3f/tbK7w7/3fyAErqbB76KGHrrzyypEjRxZc2L9//wEDBtxzzz3CDgDgQJDUdewWLlzYvXv3XZf36tVr/vz5RT0lAAAKI6mwK1GixObNm3ddnpOTk14UV5QBAGD/JRV2xx133N133719+39dP2br1q3jxo3zk2IAAAeIpM6xGzZsWI8ePRo0aNCtW7fatWvn5+cvXrx4xowZK1asePnll1M9RQAAkpFU2HXr1m3y5MnDhg0bP358YmHTpk0nTpzYuXPnlM0NAIB9kOwFinv37t27d+9ly5YtXbo0FovVrVs3/qOxAABB+uyzz/r27fv+++/vOHh+Xjapc+zatm374osvRlFUq1atFi1aNG/eXNUBAAF7+umnTz755IYNGxb3RPZNUmG3ePHiBQsWpHoqAAAHiG3bts2ePbtPnz7FPZF9k1TYjR07dtKkSVOmTCn4k2IAAKG64IILDjnkkOKexT5L6hy7u+66q0SJEn369MnIyKhSpUrJkiUL/vWbb75JydQAANgXSYVdXl5e1apVO3XqlOrZAABQaEmF3TvvvJPqeQAAsJ/2HnZbt279+OOPc3Jyjj766PLly/8EcwIAoBD28uWJe+65p1q1aq1atTrppJOqVq16ySWXbNu27aeZGQBAcVmxYsWSJUvWrFkTRdGSJUuWLFmyadOm4p7U3u3piN3kyZMHDx582GGHXXzxxVlZWW+88ca4cePS0tLGjBnzk80PAOCn17p160WLFsVv161bN4qiUaNGDR48uFgntXd7CrvRo0cfdthhn3zySdmyZeNL+vXr98ADD9xyyy3lypX7SaYHAFAMDtKLfuzpo9gPP/zw/PPPT1RdFEUDBw7Mycn55JNPUj8xAAD2zZ7CbtOmTXXq1Cm4JH73oPiMGQDg52YvX55IS/uvFWKxWBRF+fn5KZwRAACFktRPigEAcODby3XsFi5cOHv27MTdtWvXRlG0YMGCChUqJBa2bt06RZMDACB5ewm722677bbbbttp4RVXXFHwrk9mAYBUy92yObcozvLP3bpl/zdywNpT2I0YMeInmwcAwB7MP+fM4p7CQWBPYXfjjTf+VNMAANi9LVu2NGjQoGi3GeolPvb+W7EAAMXl5JNPXrlyZSq23KZNm1RstngJOwDgwHXooYdeffXVxT2Lg4bLnQAABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEokRKt75p06YJEyZ8/PHHOTk5DRs2HDhwYLVq1XZaZ+3atQ8++OBHH320ffv2ww8//KKLLjryyCNTOisAgCCl9ojd6NGjV61aNWLEiDvvvDMrK+umm27Ky8vbaZ2bb7559erVf/zjH0ePHl2lSpWbbrpp69atKZ0VAECQUhh2q1evnjNnTv/+/evVq1erVq2BAwcuXbr0k08+KbjOxo0bq1ateskllxx++OE1a9a84IILNmzYsHjx4tTNCgAgVCn8KPaLL74oWbJkvXr14nfLli1bp06dzz77rFmzZol1srOzhw0blri7Zs2atLS0KlWqJJZs3LhxyZIlibtpaU4KBADYvRSG3YYNG7Kzs2OxWGJJ+fLl169f/2Prb9y4ccyYMb17965YsWJi4fvvvz9kyJDE3VKlSqVotgAAB7vUfnmiYNXt2ZIlS/70pz8de+yxffv2Lbi8du3ap512WuLuPffcU5TzAwAISArDrkKFChs2bMjPz0/k3fr16wsejUv46KOP7rjjjrPPPrtHjx47/enII4+89tprE3fvvPPO1E0YAOCglsJT1ho0aJCTk/PVV1/F78a/FdG4ceOdVvv3v//95z//+corr9y16gAASF4Kw65SpUpt2rQZO3bs119/vXTp0lGjRh1xxBFNmjSJouiVV1554YUXoijavn376NGjf/WrXx166KGr/8PlTgAACiG159hddtllEyZMuPHGG3Nzc4866qjhw4fHP5adN2/ehg0bevbsOX/+/BUrVjzxxBNPPPFE4lEDBgzo3r17SicGABCe1IZdVlbW4MGDd12e+KJrs2bNpk2bltI5AAD8TLgsHABAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAMD/a+/uYpq89wCO/wttoy0tBRGllCkRV9SyF3VMfGEbhc1MxwlZNJtkZ3OK65K5i20JbnHqSPYmJmNZiKZZvGA3LjHMjCZzsMUoM2cOF1S25TBl7MhLhDXTFnDVAj0XT3zWw9kQFHzon+/nivb58++vF5Kvz9MXSRB2AAAAkiDsAAAAJEHYAQAASIKwAwAAkARhBwAAIAnCDgAAQBKEHQAAgCQIOwAAAEkQdgAAAJIgKY5c3QAAC+1JREFU7AAAACRB2AEAAEiCsAMAAJAEYQcAACAJwg4AAEAShB0AAIAkCDsAAABJEHYAAACSIOwAAAAkQdgBAABIgrADAACQBGEHAAAgCcIOAABAEoQdAACAJAg7AAAASRB2AAAAkiDsAAAAJEHYAQAASIKwAwAAkARhBwAAIAnCDgAAQBKEHQAAgCQIOwAAAEkQdgAAAJIg7AAAACRB2AEAAEiCsAMAAJAEYQcAACAJwg4AAEAShB0AAIAkCDsAAABJEHYAAACSIOwAAAAkQdgBAABIgrADAACQBGEHAAAgCcIOAABAEoQdAACAJAg7AAAASRB2AAAAkiDsAAAAJEHYAQAASIKwAwAAkARhBwAAIAnCDgAAQBKEHQAAgCQIOwAAAEkQdgAAAJIg7AAAACRB2AEAAEiCsAMAAJAEYQcAACAJwg4AAEAShB0AAIAkCDsAAABJEHYAAACSIOwAAAAkQdgBAABIgrADAACQBGEHAAAgCcIOAABAEoQdAACAJAg7AAAASRB2AAAAkiDsAAAAJEHYAQAASIKwAwAAkARhBwAAIAnCDgAAQBKEHQAAgCQIOwAAAEkQdgAAAJIg7AAAACRB2AEAAEiCsAMAAJCEflJ37+/v93q9586dC4fDTqfT4/GkpqbewhoAAADc1OSesauqqurt7d29e3dlZaXJZKqoqBgeHr6FNQAAALipSQw7v9/f1NS0bdu2zMxMu93u8Xi6urpaWlrGuwYAAABjMYlhd/78eYPBkJmZqdxMSEhwOBytra3jXQMAAICxmMTX2AWDQYvFotPp1HsSExMDgcC41ly8ePHYsWPRv/Lcc8+ZzeZJm/omIpGIEOL0fzoWVbyn1QyINnD9uhDi+ql/nf3Heq1ngRBCREIhIURVVdUnn3wysTv/9ttvQohNB2tmGAwTuzNujX9gQAjx783/1MXFaz0LhBBiqLfn+tBQdna2hjMEAoFwOKzhAJjcN09EF9utrWlra/voo4/Um/n5+T09PRMw2W1Qzi/yMsApYqYQTnv64OAgL82cKowGYbWYzeYJ/w+YXq9PTEwU/OubMubbkvR6/fXr17UeBDc4HDqdzmazaTiCzWZzuVwaDoBJDDubzRYMBiORiJpugUAgKSlpXGuWLFny3nt/nhtbtmzZiB0wzbW3t2/YsKG4uHjXrl1azwJML6+88sqJEycaGhr4swxMHZMYdgsXLgyHw21tbVlZWUKIYDDY0dGxaNGica1JTU0tLCycvCEBAACkMYlvnkhOTs7Ly6uurm5vb+/q6vrggw8WLFiwePFiIURDQ0NdXd3oawAAADAuk/s5di+//PK8efP27NlTXl5uNBp37typXHI9c+bMd999N/oaAAAAjItOeZsnAAAAYh3fFQsAACAJwg4AAEAShB0AAIAkJvcDioFJ1d/f7/V6z507Fw6HnU6nx+NJTU3VeihgWlA+x+DChQtHjhzRehYAf+KMHWJYVVVVb2/v7t27KysrTSZTRUUF3z8B3AGNjY1vvPGGw+HQehAAIxF2iFV+v7+pqWnbtm2ZmZl2u93j8XR1dbW0tGg9FyC/cDi8b9++FStWaD0IgJEIO8Sq8+fPGwwG5at7hRAJCQkOh6O1tVXbqYDpoKCgYPbs2VpPAeAvEHaIVcFg0GKxRH+cdWJiYiAQ0HAkAAC0RdghhvElJQAARCPsEKtsNlswGIz+6pRAIJCUlKThSAAAaIuwQ6xauHBhOBxua2tTbgaDwY6OjkWLFmk7FQAAGiLsEKuSk5Pz8vKqq6vb29uVj9RasGDB4sWLtZ4LkN/ly5f9fn9fX58Qwu/3+/3+UCik9VAAhBBCF30lC4gtV69e9Xq9zc3NQ0NDS5Ys8Xg8XIoF7oCtW7f29vaOuKe4uFireQCoCDsAAABJcCkWAABAEoQdAACAJAg7AAAASRB2AAAAkiDsAAAAJEHYAQAASIKwAwAAkARhB2CCPfXUUwkJCVpPcROrV6/Ozs7WegoAmGCEHQAAgCQIOwAAAEkQdsC0MDw8vGfPnoyMjBkzZixbtqyhoWH79u1Go1FdcPz48aKiIqvVajKZli5devDgQfVQfn7+mjVrmpub3W631WpNTU19+umn1a8KjUQiFRUVys45OTmHDx8e8dCj7Lx69er8/Hyfz5eRkbFy5cqbPgtlksbGxtzc3JkzZ6anp1dWVobD4R07dqSnp1sslsLCwl9++UVdf+jQodzcXJPJZLValy9ffujQob/beZQhASCWRABMA2+//bYQYuPGjV9++eXHH3+clpaWm5trNpuVo1999VV8fHx+fn5dXV19fb3H4xFC7Nu3TznqdrszMjIeeOCBhoaGnp6ew4cPx8fHP/vss8rR999/XwhRWlra0NDw6aefulwup9M5xp0LCgruueee7Ozs6upqn89302fhdrsdDscjjzzy/fffd3R0lJSUCCEKCwvfeuutzs7O48ePW63WdevWKYuVjCspKfH5fD6fb+3atUII9VFWrVrldDrHMiQAxBDCDpDf8PDwnDlzXC7X8PCwcs+3334rhFDz6/7778/KyhoYGFB/pbi42GKx/PHHH5FIxO12CyG++eYb9ajb7bbb7crOdrvd5XKph7q7uw0Gw7h2rq2tHeMTUdafOXNGudnY2CiEWLlypbqgtLRUfeh33nmnoKDg2rVrys1AIKDX60tLS5Wb0WE3+pAAEEO4FAvI79KlSz09PUVFRTqdTrnnwQcfdLlcys+9vb3Nzc3r1q2Li4sL3fD444/39fW1tLQoa0wm06pVq9QNHQ7HpUuXhBAdHR3d3d0FBQXqobS0tOXLl499Z6PRuH79+rE/F7PZfO+996qPJYSIvoablpY2MDDQ19cnhHj99de//vpr9XKz1WqdO3fuxYsXR2w4liEBIFYQdoD8enp6xI0MUjmdTuWH7u5uIcSHH344M4pyObKzs1NZM3v27Ojf1ev1w8PDQggl70YctdvtY985JSXFYDCM/bmkpKSoP8fHxwshZs2aNeKeoaEhIUQwGNy1a1dOTk5iYqJer9fr9Z2dncrY0cYyJADECr3WAwCYdNeuXRNCxMX9z3/k1LN3iueff76srGzEL2ZlZY2+cyQS+f87la4a487jqrpxeeKJJ06ePFleXr527VqbzabT6R577LG/W3xrTx8AphrCDpBfcnKyuHHeTtXa2qr8cNdddwkhhoaGVqxYMd6dlXN1ynk71a+//nr7O9+mCxcunDhxoqysTHnXiBBicHDw999/z8zMHLFSwyEBYMJxKRaQX2ZmZmJi4hdffKHe09TUpL6ALDk5OTc398iRI1euXFEX1NTU7Ny5c3BwcPSd58+fn5KScvToUfUS588//3z27Nnb3/k2hcNhIYTD4VDv2b9/fygUGnE2UdshAWDCEXaA/PR6/ZYtW3744YfNmzfX19d7vd6NGzdGvxli7969V69efeihh2pqaurr6998882tW7d2dXXp9Tc5qR8XF/fiiy+2tbVt2LChtrb2wIEDjz766NKlS29/59uUlZWVkZHh9Xo///zzkydPvvbaa7W1tQ8//PCPP/547NixgYGB6MVaDQkAE0/rt+UCuBNCodD27dtTUlLMZvOaNWtOnTq1adOmhIQEdUFjY2NRUZHFYjEYDHfffffevXvD4bByyO12z5s3L3q3LVu2qH89BgcHd+zYMXfuXKPRmJOT89lnn7300ktGo/HWdh7diPXt7e1CiHfffVe9p7y8XAhx+fLlSCTS1NSUl5dnMpnmzJnzwgsvBAKBurq6lJSUpKSk1tbW6I87GX1IAIghushfvfYZgPQKCwt/+ukn5T2hAAA5cCkWmBaqqqqefPJJ9UVjV65cOX369H333aftVACAicUrSIBpYdasWbW1tSUlJWVlZaFQqKqqKhgMvvrqq1rPBQCYSJyxA6aFZ555pqampqura9OmTZs3b9bpdD6fT/mGrini6NGjulEdOHBA6xkBYKrjNXYApoT+/n71A/D+Unp6elJS0p0aBwBiEmEHAAAgCS7FAgAASIKwAwAAkARhBwAAIAnCDgAAQBKEHQAAgCQIOwAAAEn8F/ugaoo9QWLsAAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bal.plot(w.out, var.name = \"gender_male\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we saw in section 1.1., there is a similar percentage of males and females participants in each treatment group in the sample. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94AAAgAElEQVR4nOzdaWBU5d3w4TMJhBAIYd8REVBBKWoBAQVUUMtWQK2tG2ipQCsqWkGpWKx7RQRFEIFHrbbuIruPVcStilIqbgVccGFHdsIakrwfpp03D+sQiIHb6/o0c3LmnH/OTOTnLGdi+fn5EQAAR76U4h4AAIBDQ9gBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEIvywu+mmm2Kx2NixY4t0s0OGDInFYg899NCh3cvuO/ohFd0vdfjs8SDNmzevRYsWaWlpZcuW/fbbb4t7HAB+7I68sJs3b17s/ypbtuwxxxxzwQUXPP7441u2bNll/QoVKtStWzczMzP5XTz55JNTpkzZ9zqF2Gzh9l50Ozp4u98XqampFSpUaNGixdChQ9euXVvcAxa5yy67bM6cOaeffnqfPn0yMjIO1WY3b94ci8Xq1q0bRdGaNWtisdhxxx13qDa+b8k8+H94c+bMady4cSwWu+OOOwouL8YDBXB4KlHcAxRS2bJlO3fuHL+cnZ29aNGiiRMnvvjii0OGDHnsscfOPvvsxJo33njjjTfeeEAbHzRoUOfOnX/+85/vY51CbLZwey+6HR0qBe+LnJycFStWzJkzZ86cOU888cScOXMqV65cvOMVne3bt3/66aflypX7+9//XqLEofxTKlOmTEpKStmyZeOXoyiKX/4BJPPg/yHl5OTcfvvtd999dywW2/2nxXigAA5PR2rYVatW7Zlnnim45Ntvv73zzjvHjx/fuXPnadOmnXPOOYXb8qJFi1asWHEoZjzy9l44u98Xy5cvP+ussxYsWDBu3Lg//OEPxTVYUdu6dWsURRUqVDi0VReXkZERf5o2PT09NTX1h3nK9jB8+J199tlvvfXWgAEDsrKybr311t1XKJYDBXDYOvJeit2bunXrjhs37t57783Jyendu3f8H91oT+9Re+GFF84666yKFSumpaXVrFmzY8eOL7/8cvxHF1xwQf369aMo+p//+Z9YLHb66adHUXTzzTfHYrEpU6aMGTOmVq1a5cuX3+NmoyhKSUmZNWtWu3btypUrV7Zs2dNPP/21115L/LR///6xWOzxxx8veJPZs2fHYrEuXbrsbe+77ygnJ+eBBx5o3rx5ZmZmenp6gwYN+vfvv2zZssQKt9xyS3zgzz77rEePHlWrVk1PTz/ppJOefvrpgrveuHHj4MGDGzVqVLp06VKlSjVs2HDgwIEbN24s9F2QUKNGjUsuuSSKom+++eYg97jfmyT5y+bl5Y0ZM6Z58+Zly5bNzMxs3779W2+9VXCF/Pz8CRMmtGrVKjMzs3Tp0o0aNbrllls2b968t8G6d+9eoUKFKIq+/fbb+MvQX375ZZTEvbPHh9PuypQpk3jyKSMjI3H5+uuvj8ViEydOjD/SsrKyMjMz27ZtO3PmzAM6bruPsceHX5KHd79Hb2+/9T7+GOO2bdv26quv3n///aVKlTqgAwXw4xRO2MXdcMMNJ5100pIlS1544YU9rjB+/Phf/OIXn3766YUXXnjTTTd17Njxgw8+6Ny585NPPhlF0RVXXHH55ZdHUdSyZcsRI0Zce+21URSlpaVFUfTmm28OHDiwXbt2F1544d72Pm/evI4dO2ZkZFx55ZUdOnR49913O3bs+OabbyY5/B73vou8vLxu3boNGDAgOzu7d+/egwYNOvbYY0ePHt2sWbPEm/fjA3/44YennXbali1bLr300g4dOnz00UcXX3zx66+/Hl8nJyenS5cu99xzT5kyZfr37//b3/42NTX1vvvu69ChQ25ubpID78N3330XRdEJJ5yQWFKIPSZzk2R+2SiKfvnLX1511VVbtmzp1atXt27dPvjgg3bt2sXv9LiePXteeeWV3377bd++fa+//vry5cvfcccdp59++qZNm/Y4W+/eve+6664oiipUqDBixIgRI0ZUqVIl+Xtnvw+n5s2bN2nSJH65RYsWiSMZv/msWbM6d+5cvnz5K6+8sn379u+888655577xhtvHOhxKzjGPh78+z28+z16e/yt9/3HGDdr1qz27dvv8RDt+0AB/EjlH2k+/PDDKIrq16+/txXuueeeKIouvfTS+NX4G9Qefvjh+NX4vwFffvllYv3FixdnZma2bNkyfvX555+Poqh3796JFeL/fmdlZb3yyiuJhbts9uabb46iKCUlZfLkyYl1hg0bFkXRaaedFr961VVXRVH02GOPFZz2vffei6Koc+fOe9v7LjsaN25cFEWtWrXatm1bYp0hQ4ZEUXThhRfGr959991RFKWlpT355JOJdW644YYoinr16hW/+uKLL0ZR1LJly507d8aXbN++/fjjj4+iaMqUKQV/qVGjRu3tUO9+X+Tm5i5ZsuSuu+5KTU1t3LhxdnZ24keF2GMyN0nml40/vdSxY8fEdhYsWJCRkVGmTJlNmzbl5+c/++yzURT99Kc/3bhxY3yFvLy8/v37R1F000037e3XX7duXRRFdevWTSxJ5t7Z48MpeYlH2tSpUxML44+0xGM4meO2xzF2f/glc3iTOXp73N1+/xgLik9y++23H+ABA/hxCe0ZuyiKGjduHP33GaPdrV+/PhaLxd9nHVe7du3Vq1fHA2uP4u/abtSo0X7ft9eiRYuC7zrv379/enr6u+++ewg/H/qXv/wliqJbbrml4CtTAwcOTEtLmzRpUuIF6CiKmjdvfumllyau/uIXv4ii6PPPP49fPeWUUyZOnDhq1KjU1NT4krS0tG7dukVR9PHHHx/QSF999VXBT8XWrl37j3/844ABA957772Cx7kQe0z+Jvv+ZR977LEoiv7whz8ktnPcccfdeeed/fr1W7VqVRRF48ePj6Lo7rvvTrxDKxaL3X777SVLlowf8CQlc+8k/3Dah1atWsVfvo+7+uqrMzIy3n///fgjLZnjdkBj7PvwJnP09ri7QvwxArBvR+qHJ/Yh/iabvb2C1rVr1zFjxpx55pkDBw7s1KlT9erVo/++TrRvrVq12u868bclJaSnpx9//PHz5s1buHBhMjffr/z8/Llz50ZR1Lp164LLy5Urd9xxx33yySefffZZs2bN4gtbtmxZcJ34G8IS5Xf00UcfffTR8cubNm2Kvx0qfsKOgnWYjMzMzPPOOy8x4erVqz/++OMRI0bMnz9//PjxNWvWLPQek7/Jvn/Zf/zjH1EU/fSnPy24zoABAxKXZ8+eHe12VMuXL3/iiSd++OGH33333VFHHbXf43BA985BPh5OO+20gldLlSrVoEGDjz/+eMmSJRUrVkz+uCU5xr4Pb/JHb5fdFfqPEYC9CTDs1qxZE0VRxYoV9/jTkSNH5ubmPvroo717946iqHHjxl26dOnXr1+9evX2vdkqVarsd9c1atTYZUl8jPhrdgcvOzt727ZtaWlpWVlZexxv9erViSXxfyYT4k+Z5OfnJ5ZMmjTpvvvumzt37rZt2w5mqqpVq+7yiZDc3NyHHnpowIABZ5999rx580qWLFnoPSZ5k338sps3b968eXN6enrp0qX3eNutW7dmZ2dHez9TxtKlS5MJuwO6d5J5OO1DtWrVdlkSj62VK1fGryZ53JIcYx+H94CO3i67K/QfIwB7E2DYffDBB1EUxd9RtLuSJUuOHTt26NChU6ZMefnll19//fV777135MiRTz755D4+FRG/4X53nXjlKyH+T2BKyqF5yXv3OEvIy8tLrJCMcePG9e3bNzMzs1+/fi1atMjKykpJSZk0adIjjzxy8HOmpqZee+21L7/88iuvvDJt2rQePXoUbo+HZMj4wc/JycnPz9/j8YkvjMVif/zjH/e4hV2yZm8O6N5J5uG0D7s/ouL7jT8Ckz9uBzlGdIBHb5fdFfqPEYC9CS3sduzY8dRTT0VR1KlTp32sVqNGjb59+/bt23fbtm2PP/741Vdf3bdv327duu3tlApJKviUTFzBpw/3+A//8uXLk99+2bJlMzIytmzZsn79+l1Ok/H9999HB/I80G233RZF0bRp09q2bZtYeGjf23TMMcdEUbRgwYJC7/GQDFm6dOnMzMxNmzatWbNmj2dLTk9Pz8rK2rBhw1VXXXUwT6Qdwntnv+KPq4LWr18f/feZvB/gzk04+KNXRH+MAD9OoX144oYbbli6dOmJJ57YsWPHPa7w7bffFmyp9PT0fv36tW7dev369YsWLTrIvb///vsFr27fvn3hwoUpKSnxpw/T09Oj3V6WnTNnzgHtIv4mrfibxhLWrl27cOHC0qVLJ3muh+3bty9durRs2bIF/+HPz8//3//93wMaZt/ib66Pv0RYiD0ewiHjB63gOQWjKLr77rvjp6SJoujUU0+NomiXM9tFUXSgn3o5JPdOMuJPSyds2rRpwYIFqampderU+WHu3IIKffSK9I8R4McpnLBbsmTJJZdcMmrUqIyMjCeeeGKPL7p99NFHRx999KWXXrpjx47Ewk2bNi1atCg1NbVq1arRf/Nr92dEkjFz5sx4KMSNHz9+69atZ555Zrly5aL/PoMVP99EfIX4xwsKbmG/e4+/G+muu+4q+CvcddddO3fuvOSSS5J8kqNUqVIVK1bMzs5evHhxfEl+fv5tt90W/yhx/Lmfg5GXlzdq1KiZM2eWKlUq/uHNQuzxEA7Zq1evKIruu+++xClzv/nmm2HDhr333nuNGjWK/ntUb7311vhTa3Fvv/12tWrV4p8ATdIhuXeSMXPmzPhHFuIef/zxHTt2tG3btly5cgdz3Ar34C/c0UvmjxGAA3WkvhS7YsWKCy64IH55x44dixcv/vjjj/Py8mrVqvXcc8+dfPLJe7xV06ZNL7744qeeeqpRo0YdO3asVKnS6tWrp0+fvmTJkmuvvbZSpUpRFDVq1CgWi02fPr13795paWkPP/xwMvPs3LkziqLevXt37NixR48exxxzzPz5859//vlSpUrdeeed8XXOP//8m2666c033zzttNNatmy5fPnyadOmDR06dODAgfH3YCWz98suu2zixImTJ0/+6U9/2rFjx5IlS77//vszZ8489thj4yfwS9Lll19+//33t2/fPh4906ZNW7du3V/+8pdzzz33mWeeqVOnTvyrI5JR8L6Iomjjxo3z589fsmRJamrq2LFja9euXeg9HqohL7vsshdeeGHatGknnHBCx44dN2/ePGnSpE2bNo0fPz7+hOKFF144adKkp59++uSTT/7lL3+ZmZn56aefTpkypXTp0gMHDkzyOESH7t7Zr0svvfScc84577zzGjRo8MUXX/ztb38rWbJk/Fxx0UEct8I9+At39JL5Y5w9e/Z9990XX3/hwoVRFD3zzDPz5s2LL3nwwQcTn7kG4D9+2NPmHQLxk+IWlJKSUqVKlfbt248aNWrLli27rL/LCX5zc3NHjx7dunXrypUrp6amZmVltWnT5tFHH83Ly0vc5J577qlcuXKpUqVOOeWU/P+eGXXYsGH72Gz83BnPP//8rFmz2rZtW7Zs2TJlyrRr1+7tt98ueKtPPvnkrLPOin/x0amnnjpp0qT4kxxnnHHG3va+y47y8/NzcnJGjhx5yimnZGRklCpV6vjjjx88ePC6desSK+xx4C+++CKKoqZNm8avbt269eabb65fv36pUqXq1Knzu9/9bvXq1fn5+ZdffnmZMmWqV6/+8ccfJ3mC4l1kZWU1adKkd+/eH330UcGVC7HHZG6SzC8bP2jDhw//yU9+Urp06TJlyrRt2/b1118veJPc3Nzx48fHvxSrRIkStWvX7tmz5/z58/f2u+fv6QTFhb53khc/RGPGjHnttdfatWtXtmzZsmXLtmvX7q233kqsU+jjlp/cg3/3w7vfo7fH7ez3jzF+wuS92fe9A/DjFMvf04f4gMPTkCFD7rzzzlGjRsW/2gEACgrnPXYAAD9ywg4AIBDCDgAgEMIOACAQPjwBABAIz9gBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABKJEcQ9wYL7//vvt27cX9xQAwJ6VLl26UqVKxT3Fj9cRFnbbt2/fsmVLcU8BAOxZampqcY/wo+alWACAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIO+A/VqxYceaZZ3799de5ublnnnnm3Llzf7A9FvWO9i3++37wwQfFOwbAwTvCzmMHFM66desuvPDCChUqPP300/s9y1RKSsrIkSPr169/oHv58MMPMzIyjjvuuMKO+R+5ubnPPvvszJkzly9fnpOTU7169Z/97GcXXXRRSor/FwXYF2EHPwrTp0//yU9+8vXXX8+ePfu0007b98qxWKxp06aF2Mtzzz3XqlWrgw+7sWPHzpo164Ybbjj22GOjKPrXv/41cuTI7du3//rXvz7ILQOETdhB+PLy8qZNm9arV6+vvvpq6tSpBcPuyy+/HD58+DfffFOjRo1LLrkkvjA3N7dDhw733Xdf48aNO3XqNHLkyHjnLV269NJLL/3rX/9aq1at//3f/3366adXrFhRtmzZ008//aqrrrrxxhs/+uijuXPnTp8+/ZFHHlm7du1DDz300Ucfbdmy5bjjjvvd734Xr7Q97nEXc+fOPffcc1u2bBm/2qFDh6ysrPz8/PjVr7/+esyYMQsWLMjPz2/cuPG1115bq1atvLy89u3bDx48+OWXX16+fHnp0qVvvvnmV199de7cuevWrfvFL37xq1/9aseOHeeee+7vf//71157bcWKFVEUXX311btk7t7GBjgieF0Dwvf+++9v2LDhjDPO+NnPfjZnzpx400RRlJeXd8sttxx11FEvvfTS3XffPXXq1CQ3uGzZsnvvvffaa6+dMWPGqFGj/v3vfz///PMjRoyoWrVq//79H3nkkSiKhgwZEkXRY489NmnSpCZNmtx0003bt29Pco8NGjR48803P//888SS5s2bt2jRIn556NChFStWfP7555999tmMjIy77747iqKUlJTU1NRp06bdddddf/vb37Kysq677roTTzxxwoQJN95444QJE9atWxd/DXry5Ml//OMfn3nmmV69eg0dOnTdunUFd73HsQ/kYAMUJ2EH4Zs8efKZZ55ZunTpBg0a1K9ff/r06fHl8+fPX7FiRa9evdLT06tVq3bBBRckucHs7Oz8/PzMzMzU1NSaNWuOHTt2l+fePv/88/nz5/fv379cuXKlSpW64oorcnJy3n333ST3ePXVV8efLbv44ovvuuuuqVOnFsyvMWPGXHfddenp6WXKlDnrrLPiT93Ff9ShQ4fSpUunpqY2bty4dOnSbdq0iaKoSZMmubm5y5cvj69z7rnnVqxYMYqic845Jz09/b333tvv2EkeFoBi56VYCNzy5cvnzJnzwAMPxK927Njxr3/96+WXX56amrpq1apYLFatWrX4j2rVqpXkNhs2bNi1a9ff/va3xx9/fLNmzc4+++xdbrtkyZIois4///yCC5ctWxZFUTJ7zMzMvOWWWwYMGDBv3rzPPvvsxRdfHDVq1A033HDOOedEUfTFF1888cQT3333XRRFO3bsyM3NzcvLiz8bV7ly5fgW0tLSKlWqlLgcRVFOTk78as2aNeMXUlNTK1WqtGrVqmTGBjgiCDsI3NSpU/Py8m666ab41by8vK1bt77zzjvt2rWLt04sFkv8aN+bSjwxFovFrr/++osvvvj9999/7733/vrXv958881nnnlmYs1SpUpFUfTKK6/Eoyrh73//e/J7zMzMbNOmTZs2bfr16zd69OgRI0a0b99+xYoVN954Y69evf785z+npaX94x//iL94mhhsj5cL2rlzZ+Jybm5uwU/a7m1sgCOFl2IhZDt37nz55Zd79eo14b8effTRtm3bTpkyJYqiKlWq5Ofnr1y5Mr7yt99+u8vN09LSYrFY4k1miVczc3Nz169fX7169W7dut1zzz1du3adPHlywRvGn4r78ssvE0viz3vtd49RFK1cufLWW29NrBPXpEmTHTt25OTkLFy4MC8v71e/+lW8vf79738f6DFZunRp/MKOHTtWr15dtWrV/Y4NcKQQdhCyN954Izs7u0ePHtULOO+88z788MOlS5c2bty4XLlyf/nLX7Kzs5csWTJp0qRdbh5/C92//vWvKIq2bdv20ksvxZf//e9/79Onz+eff56Xl7d27dqvv/46nkTp6elLly7Nzs4++uijTz755DFjxqxcuTI3N3fKlCm9e/des2bNfvcYRVHlypUXL178hz/84d13312xYsXKlSvffffdRx55pFmzZunp6dWrV8/Nzf3ss89ycnJmzpz52WefRVG0evXq5I/Jq6+++vXXX+/YsePpp5/Ozc1t1apV4kd7G/sAjzpAsfFSLIRsypQpbdu2zcrKKriwadOmderUmTJlym9/+9t77rln5MiR559/fq1atfr163fjjTcmXm+NGzBgwMiRI99+++2KFSteeuml7733Xm5u7rnnnrtq1apbbrll7dq15cqVO/XUU3/7299GUdS1a9fx48fPmjXrueeeGzJkyKhRo3r37p2fn1+vXr177rkn/qa3/e4xNTV15MiRTz755JgxY9asWbNz587q1au3a9fu0ksvjaKocePGv/rVr26++eZYLNamTZs77rjj97///W9+85vx48cneUy6d+9+//33f/755xUqVLj99tuzsrJyc3MTP93b2ABHhNgu/0k9zC1ZsmTLli3FPQUELn4eu+HDh59yyinFPcuhFP+9/vznPyfOnAIccpmZmTVq1CjuKX68vBQL/B+5ubnxE8hlZmYW9ywAHBhhB/wfb7zxxtVXX926desGDRoU9ywAHBgvxQIAh4yXYouXZ+wAAAIh7AAAAiHsAAACIewAAAIh7AAAAnGEfSq2cePGe/xySQDgcNCkSZPZs2cX9xQ/XkfYV4otX758/PjxBb/bEQA4TDz33HMPPfRQcU/xo3aEhV0URdWqVatXr15xTwEA7Mp3Kxc777EDAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIRIniHgAOyubNm997773inoJd1a9fv169esU9BcCPjrA7MHl5eS+++GJxT8H/t3Tp0uuuu664p2BXd9xxx80331zcUwD86Ai7A5Obm3vhhRcW9xTsqkT9BtU6dS7uKYiiKNr29aI106YW9xQAP1LCrjDqVqzQ57RWxT0FURRFC1eteuL9f6YeVbd6ryuKexaiKIo2vPWmsAMoLsKuMOpUKD+wwxnFPQVRFEUzPpv/xPv/LO4pAOCw4FOxAACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEoUaRbz87OHjdu3Mcff5yTk3Pcccf169evatWqu682Y8aMl156ac2aNbVq1erZs2fz5s2LdCoAgCAV7TN2I0eOXLVq1dChQ4cNG5aRkXHbbbfl5eXtss7MmTOfffbZvn37jh07tkOHDuPHj9+yZUuRTgUAEKQiDLvVq1fPmTOnT58+9erVq1mzZr9+/ZYuXfrJJ5/sstqzzz7bq1evZs2aVa1atVu3buPGjcvIyCi6qQAAQlWEYffFF1+ULFmyXr168atly5atXbv2woULC66zZs2aFStWRFF0zTXX/OIXv7jhhhsWLFhQdCMBAASsCN9jt3HjxszMzFgslliSlZW1YcOGguusWbMmiqLXXntt0KBBWVlZzzzzzJ/+9KexY8dmZWXFV/jss8+efPLJxPppaWlFNzAAwBGtaD88UbDq9uGXv/xl7dq1oyj69a9/PWvWrH/+85/t27eP/2jVqlWvvfZaYs3U1NSimBMAIABFGHbly5ffuHFjfn5+Iu82bNhQoUKFgutUrFgxiqIyZcrEr6amplasWHHdunWJFVq2bDl58uTE1SZNmhTdwAAAR7QiDLuGDRvm5OR89dVXDRo0iKJo48aNixcvbtSoUcF1KlasWKFChQULFsTX2bFjx/fff1+tWrXECqVLl65Vq1bian5+ftENDABwRCvCD09UrFixVatWo0eP/vrrr5cuXTpixIj69es3btw4iqJXX3116tSpURSlpKR07dr1mWeemTdv3urVqx955JH09HTnsQMAKISifY/dNddcM27cuFtvvTU3N/eEE04YMmRI/GXZefPmbdy4sWvXrlEUnXfeeVu2bLn//vuzs7OPO+64O+64Iz09vUinAgAIUtGGXUZGxoABA3ZfPnDgwMTllJSUnj179uzZs0gnAQAInu+KBQAIhLADAO377yAAACAASURBVAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACERSYbdq1arLL7+8Vq1aqampsd0U9YgAwI9Zv379ds+PhJYtWxb3gIeREsms1L9//5deeqldu3Znn312iRJJ3QQA4JD41a9+deKJJ8Yvf/HFFw8++OD5559/xhlnxJdUr179kO9x3rx5J598cn5+/iHfclHvN6lKe/3111944YVu3boVejcAAIVzxhlnJDLujTfeePDBB08//fT+/fsX3R7ffvvtott4ke43qZdit27d2rp160JsPTs7+/7777/88ssvueSS2267bdWqVftYeebMmT//+c9nz55diB0BAD9mp59+etu2badNm1anTp1EtLz55ptnn312uXLlMjIyTjnllEcffbTgTZ555pkWLVpkZGSUK1euWbNmzzzzTHz5z372s2uuuSaKolgs1qxZsyiK2rZt26ZNm7fffrtFixalS5euVavWsGHDcnJybrrpplq1amVmZnbo0GHRokWJLe9jv/FNffjhh+3bty9XrlzVqlUvuuiieCDtvt9CSCrsfvrTn3722WeF2PrIkSNXrVo1dOjQYcOGZWRk3HbbbXl5eXtcc/369X/5y1/S0tIKsRcA4EeuVKlSGzZsGDhw4ODBg2+++eYoimbOnNm+ffsdO3Y89dRTkydPPvXUU3v37j18+PD4+s8+++xFF11Uu3bt559//umnn65SpcpFF100ffr0KIpGjRoVf5Vyzpw5Tz75ZBRFaWlp33zzzdChQ8eOHfvFF1+ceuqpgwYN6tSpU0ZGxgcffDB9+vQ5c+bEm2y/+01LS/v222/79u07ePDgL7/88uGHH37++ecHDRq0x/0WQlIvxY4YMeJ3v/vdyJEjW7VqlfymV69ePWfOnBEjRtSrVy+Kon79+l122WWffPJJ06ZNd1957NixZ5xxxhtvvJH89gEA4mKx2Mcffzxx4sQePXrElwwcOLBevXovv/xyRkZGFEVnn332smXL/vSnP1111VXp6emLFi0666yznnnmmfiTSm3atKlUqdLTTz/duXPnhg0bVq5cOYqigk+bLVmyZNq0afGGuf7661966aUtW7b88Y9/jKKoVq1aXbt2nTRpUjL7jaJo8eLFTz/99GmnnRZFUfzNgq+++moURXvc74FK6hm7a6+9dvny5a1bty5TpszRu9nbrb744ouSJUvGqy6KorJly9auXXvhwoW7r/nee+999dVXF198caF+BQCAKC0trUuXLvHLq1at+vDDDzt37pySkrLtvzp16rRp06ZPPvkkiqLBgwfPnDkz8VJhuXLlqlev/t133+1t42XKlEk8M1WjRo0oigq+S61GjRqbN2/etGnTfvcbRVFGRka86uJq1669YsWKQ3UQknrGLiUl5dhjjz322GMPaNMbN27MzMwseD6UrKysDRs27LJadnb22LFjr7vuunjG7uKdd96J53DcHtcBAKhcuXLJkiXjl5ctWxZF0QMPPPDAAw/sstqSJUuaN2++cePG++6776WXXvruu+82b94cRVFubm7dunX3sfHE5dTU1CiKKlWqtMuS3Nzc/e43iqIqVaoUXF6iRIm9vVGtEJIKu7feeqtwW0/mLHf/8z//c8opp5x00kl7/GmJEiUyMzMLt3cA4McjUXUJv/71r6+88spdFjZo0CCKoq5du/7jH/+48cYbf/azn5UvXz4Wi5177rmHapJ97LeoHcBJ6dasWTN79uxly5alpKTUrl27devW+06u8uXLb9y4MT8/P5F3GzZsqFChQsF15s2b969//euhhx7a20Zatmw5efLkxNVdbg4AsLujjjoqiqLc3Nw9nr74yy+/fOutt6688so777wzvmTnzp1r165NvH+siPb7A0gq7PLy8gYNGvTggw/m5OQkFpYpU2bo0KEDBw7c260aNmyYk5Pz1VdfxRN148aNixcvbtSoUcF1Xn311c2bN/fr1y9+NTs7e8SIESeddNLgwYML89sAAERRxYoVW7RoMWnSpPXr15cvXz6+8Iknnvj8889vvfXWeM/Url07sf7DDz+8bdu23Nzc+NX4c1I7d+480O9l2Pd+97u1Qu83IambDR8+fPjw4T169OjSpUuNGjXy8vKWLl06ceLEQYMGVatWrWfPnnu8VcWKFVu1ajV69OhrrrkmLS1twoQJ9evXb9y4cRRFr7766rZt27p27dqvX78rrrgicZPrrruuZ8+ep556auF+GQCAuHvvvffss89u167d73//++rVq7/99tt//vOfL7nkkhIlSjRo0KBOnTrjxo076aSTKlWq9NJLL82dO/eMM86YO3furFmzWrRoUbNmzSiK7rrrrhNOOOH8888/VPvd720PZr9xSYXdY489dv311ydOwRLXp0+fvn37PvDAA3sLuyiKrrnmmnHjxt166625ubknnHDCkCFD4ik6b968jRs3du3aNTMzs+DrubFYLDMzs1y5coX4TQAAEtq1a/f666/fdttt/fv337ZtW7169e68887rrrsuiqKSJUtOnDjxmmuuueiiizIzM7t37z558uS33nrriiuuOP/882fPnn3llVdOnTr1jjvuOOaYYw40sPax3/06mP3GxZL5PrL09PQZM2acddZZuyyfMWPGBRdcsGXLlkLsuHAqVKjwwgsvtG/f/gfb4y5ycnLS0tJOr1/v9Wt+W1wzUNCMz+Z3H/dYqTPbnzhs+P7XpuhteOvNL6+/9o477oifIBT4UZkwYcKf/vSnxYsXF/cgP15JnceuRIkSe6y3nJyc+Od7AQAodkmF3cknn3z//ffv2LGj4MJt27aNGTPmYE6ODADAIZTUe+wGDx7cpUuXhg0bdurUqVatWvn5+YsXL54+ffqKFSteeeWVoh4RAIBkJBV2nTp1mjhx4uDBg8eOHZtY2KRJk/Hjx3fo0KHIZgMA4AAke5aU7t27d+/efdmyZUuXLo3FYnXq1KlWrVqRTgYAwAE5sNPf1axZM36GFQAADjf7Crvjjz++V69egwcPPv744/ex2oIFCw71VAAAHLB9hV358uVLly4dv/BDzQMAQCHtK+xmz569ywUAAA5bSb3HrlmzZk8++WSjRo12Wf7iiy/ecsst//73v4tgMACAKIqi9evXJ/NFWQckJSUlKyvr0G7zcJBU2M2dO3fz5s27LNy5c+dnn3321VdfFcFUAABRFEXPPffcb37zmzJlyhzazWZnZ7/wwgvnnnvuod1ssdtP2MVisfiF5s2b73GFU0455RBPBADwX1u2bKlbt+6nn356aDf7k5/85If8svsfzH7Cbt68eW+++ea1117brVu3ypUrF/xRLBarWbPmlVdeWZTjAQBEURRde0abChkZB7+d5Rs3PvLOewe/ncPTfsKuadOmTZs2nTFjxrBhwxo2bPjDzAQAsIt+bVrXr1zp4Lcz97slAYddSjIrbdy48YsvvijqUQAAOBhJhd3ixYudhRgA4DCXVNiNHj16woQJkyZNysnJKeqBAAAonKROd3LfffeVKFGiR48eaWlplStXLlmyZMGffvPNN0UyGgAAByKpsMvLy6tSpUr79u2LehoAAAotqbB75513inoOAAAOUlJhF7dmzZrZs2cvW7YsJSWldu3arVu3zszMLLrJAACKy7p1666++upZs2Zt27atVatWDz300NFHH13cQ+1fsi/FDho06MEHHyz44YkyZcoMHTp04MCBRTYbAEDxuPzyy9euXTtjxoyMjIyhQ4d26dLlo48+Sk1NLe659iOpsBs+fPjw4cN79OjRpUuXGjVq5OXlLV26dOLEiYMGDapWrVrPnj2LekoAgB/M4sWLp06d+q9//atp06ZRFI0ePbpq1aqzZs3q0KFDcY+2H0mF3WOPPXb99dcPHz684MI+ffr07dv3gQceEHYAQEj++c9/pqenx6suiqIKFSo0atTo/fffP/zDLqnz2C1atKhz5867L+/Wrdv8+fMP9UgAAMXp+++/r1ixYiwWSyypUqXKqlWrinGkJCUVdiVKlNiyZcvuy3Nycg7/F5sBAA5Uwarb25LDUFJhd/LJJ99///07duwouHDbtm1jxoxp1qxZ0QwGAFA8qlWrtnr16vz8/MSSVatWVatWrRhHSlJS77EbPHhwly5dGjZs2KlTp1q1auXn5y9evHj69OkrVqx45ZVXinpEAIAfUvPmzbdv3z537tz4E1irV6+eP3/+aaedVtxz7V9SYdepU6eJEycOHjx47NixiYVNmjQZP3784f8uQgCAA1KzZs3zzjuvb9++jz76aOnSpQcMGHDKKae0adOmuOfav2RPUNy9e/fu3bsvW7Zs6dKlsVisTp06R8QTkgAAhfDoo49ec801HTt2zMnJadOmzeTJk4+I99gdwDdPrFy58qOPPlq5cmVKSsqaNWtOOukkbQcABKlcuXKPP/54cU9xwJIKu/Xr1/fp0+ell17auXNnYmEsFrv44osfeeSRMmXKFNl4AAAkK6mwu+666yZNmtSrV6+2bdtWqlRp586dK1eunDFjxt/+9rfMzMyHH364qKcEAGC/kgq7yZMnT5gwYZdvmOjTp89NN900YcIEYQcAcDhI6jx2W7ZsOeecc3Zffu65527duvVQjwQAQGEkFXYnnHDCokWLdl++YMECJygGADhMJBV2995777XXXvvOO+8kTsGcm5s7Y8aM0aNHjxgxoijHAwAgWUm9x27IkCHffvttmzZtypQpEz/FyfLly7du3VqnTp1LLrmk4BduLFiwoKgmBQB+xM556JGSh+Ib6rcXOMVHeJIKux07djRo0ODYY49NLKlRo0aRjQQA8B/bt29ft25dRkbGmu079r92cjIyMtasWZOTk3OoNnj4SCrs5s6dW9RzAADsbuXKlbFYrF69eod2s+vWrfv+++8P7TYPBwfwzRNr1qyZPXv2smXLUlJSateu3bp168zMzKKbDADgqKOOKl++/KeffnpoN/uTn/ykZs2ah3abh4Okwi4vL2/QoEEPPvhgwScty5QpM3To0IEDBxbZbAAA/1H3j7eWrFz54Lez/bvvFt9378Fv5/CUVNgNHz58+PDhPXr06NKlS40aNfLy8pYuXTpx4sRBgwZVq1ZtlxMXAwAccpkn/7RUnToHv50t5f998Bs5bCUVdo899tj1118/fPjwggv79OnTt2/fBx54QNgBABwOkjqP3aJFizp37rz78m7dus2fP/9QjwQAQGEkFXYlSpTYsmXL7stzcnJSD8UZZQAAOHhJhd3JJ598//3379jxf84fs23btjFjxvhKMQCAw0RS77EbPHhwly5dGjZs2KlTp1q1auXn5y9evHj69OkrVqx45ZVXinpEAACSkVTYderUaeLEiYMHDx47dmxiYZMmTcaPH9+hQ4cimw0AgAOQ7AmKu3fv3r1792XLli1dujQWi9WpUyf+pbEAAEFauHBhr169/vnPf+48cr5eNqn32LVu3XrGjBlRFNWsWbN58+bNmjVTdQBAwJ599tkzzzzzuOOOK+5BDkxSYbd48eIFCxYU9SgAAIeJ7du3z549u0ePHsU9yIFJKuxGjx49YcKESZMmFfxKMQCAUPXs2fOoo44q7ikOWFLvsbvvvvtKlCjRo0ePtLS0ypUrlyxZsuBPv/nmmyIZDQCAA5FU2OXl5VWpUqV9+/ZFPQ0AAIWWVNi98847RT0HAAAHaf9ht23bto8//jgnJ+fEE0/Mysr6AWYCAKAQ9vPhiQceeKBq1aqnnnrq6aefXqVKlauuumr79u0/zGQAAMVlxYoVS5YsWbNmTRRFS5YsWbJkSXZ2dnEPtX/7esZu4sSJAwYMOProo6+88sqMjIw33nhjzJgxKSkpo0aN+sHmAwD44bVs2fLbb7+NX65Tp04URSNGjBgwYECxDrV/+wq7kSNHHn300Z988knZsmXjS3r37v3II4/ceeed5cqV+0HGAwAoBkfoST/29VLshx9+eNlllyWqLoqifv365eTkfPLJJ0U/GAAAB2ZfYZednV27du2CS+JXj4jXmAEAfmz28+GJlJT/s0IsFouiKD8/vwgnAgCgUJL6SjEAAA5/+zmP3aJFi2bPnp24unbt2iiKFixYUL58+cTCli1bFtFwAAAkbz9hd/fdd9999927LLzuuusKXvXKLABQ1HK3bsk9FO/yz9229eA3ctjaV9gNHTr0B5sDAGAf5l/8y+Ie4Qiwr7C79dZbf6gxAAD2bOvWrQ0bNjy02wz1FB/7/65YAIDicuaZZ65cubIottyqVaui2GzxEnYAwOGrbt26N954Y3FPccRwuhMAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgECUKO4BAJLy+eeff/fdd8U9Bbtq27ZtWlpacU8B/IewA44MjzzyyP3331/cU7Cr8ePHZ2VlFfcU/EfJkiW7d+9e3FNQnIQdcCS5uNkptcqXK+4piKIoGvPWu5t37LjyyiuLexD+v6ysrPXr1xf3FBQnYQccSfqe3qpVvbrFPQVRFEVPvD93844d1X79mxJlyhT3LERRFK3825NRbm5xT0ExE3YAFF7lC3+ZXrlKcU9BFEXRmmlTorVri3sKiplPxQIABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEokSRbj07O3vcuHEff/xxTk7Occcd169fv6pVq+6yztq1ax999NGPPvpox44dxxxzzBVXXHHssccW6VQAAEEq2mfsRo4cuWrVqqFDhw4bNiwjI+O2227Ly8vbZZ077rhj9erVf/rTn0aOHFm5cuXbbrtt27ZtRToVAECQijDsVq9ePWfOnD59+tSrV69mzZr9+vVbunTpJ598UnCdTZs2ValS5aqrrjrmmGNq1KjRs2fPjRs3Ll68uOimAgAIVRG+FPvFF1+ULFmyXr168atly5atXbv2woULmzZtmlgnMzNz8ODBiatr1qxJSUmpXLlyYsmmTZuWLFmSuJqS4k2BAAB7VoRht3HjxszMzFgslliSlZW1YcOGva2/adOmUaNGde/evUKFComF//znPwcOHJi4WqpUqSKaFgDgSFe0H54oWHX7tmTJkttvv/2kk07q1atXweW1atU677zzElcfeOCBQzkfAEBAijDsypcvv3Hjxvz8/ETebdiwoeCzcQkfffTRvffee9FFF3Xp0mWXHx177LF/+MMfEleHDRtWdAMDABzRivAtaw0bNszJyfnqq6/iV+OfimjUqNEuq/373//+85//fP311+9edQAAJK8Iw65ixYqtWrUaPXr0119/vXTp0hEjRtSvX79x48ZRFL366qtTp06NomjHjh0jR478+c9/Xrdu3dX/5XQnAACFULTvsbvmmmvGjRt366235ubmnnDCCUOGDIm/LDtv3ryNGzd27dp1/vz5K1aseOqpp5566qnErfr27du5c+ciHQwAIDxFG3YZGRkDBgzYfXnig65NmzadMmVKkc4AAPAj4bRwAACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2wP9r7/5jqq7+OI6fi/eCXLiXe0Eu7HqRL4GgUi3DMQm6NpBwaXxH1oovm/XF0Ft/2A/niOakuWImLbFyNtpcs82sPwiH08qRk0tzhoRKWxIwEiQSb+m9aJIXvN8/Put2g77+vn64p+fjL87nnM/5vP1ssJfn8wsAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAktAGdfYLFy7U19efOHHC6/VmZGQ4HA6LxXITYwAAAHBNwV2xq6urGx4erq6urq2t1ev1GzduvHLlyk2MAQAAwDUFMdi5XK62trZVq1alpKRYrVaHwzE4ONjZ2XmjYwAAAHA9ghjsuru7dTpdSkqK0oyOjrbZbF1dXTc6BgAAANcjuBMQ0AAACWFJREFUiPfYeTweg8Gg0Wj8W2JiYtxu9w2N6e/vP3jwYOAuzzzzTFRUVNCqvgafzyeEOHpqYO7GTWrVgEAXL18WQlw+cvj4v5epXQuEEMI3OiqEqKur++ijj27vzGfPnhVC/GfHzuk63e2dGTfHdfGiEOLkf1dowqapXQuEEGJ8+Mzl8fE5c+aoWIPb7fZ6vSoWgOA+PBGY2G5uTG9v77vvvutv2u32M2fO3IbKboGyvshtgFNEpBAZ1pljY2PcmjlVhOuE0RAVFXXb/wOm1WpjYmIEv31Txr9MZq1We/nyZbULwR9sNo1GYzKZVCzBZDLdfffdKhaAIAY7k8nk8Xh8Pp8/urndbrPZfENjMjMzN236c20sKytrwgz4h+vr63viiSeKi4s3bNigdi3AP8vLL7/c0tJy4MAB/iwDU0cQg93s2bO9Xm9vb29aWpoQwuPxDAwMzJ0794bGWCyWxYsXB69IAAAAaQTx4YnY2NicnJxt27b19fUNDg5u2bIlNTV13rx5QogDBw40NTVdfQwAAABuSHDfY7dmzZrk5OTXXnutsrIyPDx8/fr1yiXXY8eOffPNN1cfAwAAgBuiUR7zBAAAQKjjW7EAAACSINgBAABIgmAHAAAgieC+oBgIqgsXLtTX1584ccLr9WZkZDgcDovFonZRwD+C8h6Dnp6exsZGtWsB8CdW7BDC6urqhoeHq6ura2tr9Xr9xo0b+f4EcAc4nc5XX33VZrOpXQiAiQh2CFUul6utrW3VqlUpKSlWq9XhcAwODnZ2dqpdFyA/r9f71ltvLVy4UO1CAExEsEOo6u7u1ul0yqd7hRDR0dE2m62rq0vdqoB/gvz8/Pj4eLWrAPA3CHYIVR6Px2AwBL7OOiYmxu12q1gSAADqItghhPGREgAAAhHsEKpMJpPH4wn8dIrb7TabzSqWBACAugh2CFWzZ8/2er29vb1K0+PxDAwMzJ07V92qAABQEcEOoSo2NjYnJ2fbtm19fX3KK7VSU1PnzZundl2A/M6dO+dyuUZGRoQQLpfL5XKNjo6qXRQAIYTQBF7JAkLLb7/9Vl9f39HRMT4+npmZ6XA4uBQL3AHPPvvs8PDwhC3FxcVq1QPAj2AHAAAgCS7FAgAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYApHXy5MmsrCyNRtPa2qp2LQBwJxDsAATRsWPHNBqNKod+//33s7KyJnwgAQDkplW7AAAyczqdqhz38OHDL7300ttvv63T6SoqKlSpAQDuPFbsAPxFVlZWTk7OV199lZ2drdfrY2Njy8vL3W63f8Du3buVLqPRuGDBgt27d/u78vLy7Hb73r17k5KSHnjggSVLlqxZs0YIodFoFixYIISw2+0PPvhgR0dHQUGB0Wi0WCylpaWBi2qHDh0qLCw0Go16vf7+++/fsWPH/5tcCDE0NFRRUZGcnDx9+vTExMTly5efPHlSGRwfH3/kyJHnnnsuyGcLAKYWVuwA/EVERERPT09lZeXWrVvT09M///zz8vLy8+fPNzQ0CCE++eST0tLSkpKS6upqIcR7771XWlpqMBiWLl2q7OtyudatW1dVVZWcnJyenr5u3bo9e/a0tbVFRUUJIcLDw3/44YfVq1fX1NTce++9TqfzySefjIiI+PDDD4UQzc3NRUVFubm5u3btioiIaGhoWLly5blz59auXTt5ciHEY4899uOPP77++ut33XXX0NDQpk2bFi1a1NfXp9fr09LS1DuFAKAeHwAEyM3NFUK0tLT4t6xcuVII0d/f7/P5ampq8vPzf//9d6XL7XZrtdqysjKlWVBQIIRoaGiYsK+/qQxobW0N3GK1WpWf58+fn5aWdvHiRX9vcXGxwWC4dOnS5MmVRcRXXnnFP7inp6empmZwcDDwn/PBBx8IIZxO562cEwAIFVyKBTBRVFRUXl6ev2m324UQ3333nRCiqqqqubk5PDxc6TIajYmJif39/f7B4eHhy5Ytu8rker1eyY4Km832888/CyGGh4c7OjqWLl0aFhY2+odHHnlkZGSks7Nz8uSRkZFxcXEff/xxc3PzlStXhBCpqalVVVVWq/V2nAMACEkEOwATJSQkBD7KGhcXJ4Q4c+aMEMLj8WzYsOGee+6JiYnRarVarfb06dNKrlLMmDFDp9NdZfL4+PjAplarVXb/6aefhBBbt26NDOBwOIQQp0+fnjy5Tqfbs2dPWFjY4sWLLRbL448/vmvXrrGxsdtxAgAgVHGPHYBrUNJSWFiYEOLRRx/9+uuvKysrlyxZYjKZNBpNUVFR4OCrp7prKi8vn/wQq/+GuQmT5+bmdnd3Hzp0aP/+/fv27SsrK9uyZUtLS0tkZOSt1AAAoYtgB2CioaGh8fHxadOmKU1lrS4hIaGnp6elpaWiouKNN95QusbGxn799deUlJRbP+isWbOEEOPj4wsXLrz+vaZNm5afn5+fn19bW7t9+/bnn3/+008/ffrpp2+9HgAIRVyKBTDRpUuXvvzyS39z//79ERER2dnZXq9XCGGz2fxd27dvHx0dHR8f/39TKZd0r+cKaWxsbHZ2dmNj4/nz5/0bd+7cuX79+r/dvb29/amnngp8VcrDDz8shDh79uw1jwUAsmLFDsBESUlJL7744qlTp9LS0r744ovGxsYVK1aYzebo6OikpKT6+vr77rsvLi7us88+a29vf+ihh9rb2w8ePJidnT15KuVRhpqamszMzOXLl1/9uJs3by4sLFy0aNHatWsTExOdTuebb75ZVlam1f7NX6qZM2fu27fv+++/f+GFF2bNmvXLL7+88847RqOxpKRECNHa2qq80075mNjevXuVZlFRUVJS0i2fIQCYqtR+LBfA1JKbmztnzpyjR4/a7Xa9Xm82mysqKkZGRpTetra2nJwcvV6fkJCwevVqt9vd1NQ0Y8YMs9nc1dVVUFCQnJwcONvAwMD8+fN1Ol1GRobP55s8YML7UJxOZ2FhocFg0Ol06enpmzdv9nq9StfkfY8fP15SUmKxWHQ6ndVqLSkp+fbbbwOnnaypqem2ni0AmFo0Pp/vziZJAFNaXl6ey+Xyf8IBABBCuMcOAABAEgQ7AAAASRDsAAAAJME9dgAAAJJgxQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJPE/dgJGd1B8wesAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bal.plot(w.out, var.name = \"partners1\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a similar manner, we note an equal percentage of participants with 2 or more sexual partners as well as those with 1 sexual partner in the last 12 months from the beginning of the study, per treatment or control group." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bal.plot(w.out, var.name = \"age\")\n", "bal.plot(w.out, var.name = \"imd_decile\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see a higher percentage of participants aged between 23 and 27 in the treatment group. Also, there is a higher perceptange of participants aged between 21 and 22 and 27 and 29 in the control group." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the case of the IMD decile, an index that measures poverty in the UK, we can see the same proportion of participants in each group." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Linear Regression analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.1. Regression 1: $y = \\beta_0 + \\beta_1 T + \\epsilon$ (Vale)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "lm(formula = y ~ w, data = data)\n", "\n", "Coefficients:\n", "(Intercept) w \n", " 0.2115 0.2652 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lm(formula = y ~ w, data = data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We found that the adjusted R-squared is 0.076 and the ATE is approximately 0.27. This means that the treatment explains only 7.6% of the increase in the STI tests between the control and treatment groups. Additionally, receiving the treatment (i.e. being invited to use the internet-based sexual health service) increases, on average, the probability of taking an STI test by 26.5%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.2. Regression 2: $y = \\beta_0 + \\beta_1 T + \\beta_2 X + \\epsilon$ (vale)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "lm(formula = y ~ w + age + gender_female + ethnicgrp_white + \n", " ethnicgrp_black + ethnicgrp_mixed_multiple + partners1 + \n", " postlaunch + imd_decile, data = data)\n", "\n", "Coefficients:\n", " (Intercept) w age \n", " -0.163972 0.255827 0.012437 \n", " gender_female ethnicgrp_white ethnicgrp_black \n", " 0.092892 0.049888 -0.039745 \n", "ethnicgrp_mixed_multiple partners1 postlaunch \n", " -0.035726 -0.059088 0.077025 \n", " imd_decile \n", " -0.004108 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lm(formula = y ~ w + age + gender_female + ethnicgrp_white + ethnicgrp_black + ethnicgrp_mixed_multiple + partners1 + postlaunch + imd_decile + msm, data = data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compared to the previous regression, when we include additional variables (age, gender, ethnic group, number of sexual partners, the indicator for Randomised after SH:24 made publicly available, the indicator for men who have sex with men, and the socioeconomic level measured by deciles), we observe an increase in the adjusted R-squared of approximately 0.3 percentage points (from 0.1). Additionally, the ATE decreases by 0.003 percentage points, indicating a negative bias.\n", "\n", "It is worth mentioning that we omit gender_male to avoid collinearity with gender_female. Similarly, we exclude the less relevant ethnic group (i.e. the one with fewer observations) to prevent the same issue." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.3." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe that the ATE using double lasso is akin to the OLS with confounders, along with the adjusted R-squared." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.4." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general, the three ATEs are very similar, but we consider that the models that include the cofounders (OLS with controls and DL) are better estimated." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Non-Linear Methods DML" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "rm(list=ls())\n", "DML <- as.data.frame(read.table(\"../../../data/processed_esti.csv\", header=T ,sep=\",\"))\n", "#DML <- as.data.frame(read.table(\"C:/Users/Erzo/Documents/GitHub/CausalAI-Course/data/processed_esti.csv\", header=T ,sep=\",\"))\n", "\n", "set.seed(1234)\n", "training <- sample(nrow(DML), nrow(DML)*(3/4), replace=FALSE)\n", "data_train <- DML[training,]\n", "data_test <- DML[-training,]\n", "Y_test <- data_test$y\n", "\n", "y = as.matrix(data_train[,1]) # outcome: growth rate\n", "d = as.matrix(data_train[,2]) # treatment: initial wealth\n", "x = as.matrix(data_train[,-c(1,2)]) # controls: country characteristics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DML function for Regression tree" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "DML2.for.PLM.tree <- function(data_train, dreg, yreg, nfold=10) {\n", " nobs <- nrow(data_train) #number of observations\n", " foldid <- rep.int(1:nfold,times = ceiling(nobs/nfold))[sample.int(nobs)] #define folds indices\n", " I <- split(1:nobs, foldid) #split observation indices into folds\n", " ytil <- dtil <- rep(NA, nobs)\n", " cat(\"fold: \")\n", " for(b in 1:length(I)){\n", " datitanow=data_train[-I[[b]],-c(2)]\n", " datitanoy=data_train[-I[[b]],-c(1)]\n", " datitanowpredict=data_train[I[[b]],-c(2)]\n", " datitanoypredict=data_train[I[[b]],-c(1)]\n", " \n", " dfit <- dreg(datitanoy) #take a fold out\n", " yfit <- yreg(datitanow) # take a foldt out\n", " dhat <- predict(dfit, datitanoypredict ) #predict the left-out fold\n", " yhat <- predict(yfit, datitanowpredict ) #predict the left-out fold\n", " dtil[I[[b]]] <- (d[I[[b]]] - dhat) #record residual for the left-out fold\n", " ytil[I[[b]]] <- (y[I[[b]]] - yhat) #record residial for the left-out fold\n", " cat(b,\" \")\n", " }\n", " rfit <- lm(ytil ~ dtil) #estimate the main parameter by regressing one residual on the other\n", " coef.est <- coef(rfit)[2] #extract coefficient\n", " se <- sqrt(vcovHC(rfit)[2,2]) #record robust standard error\n", " cat(sprintf(\"\\ncoef (se) = %g (%g)\\n\", coef.est , se)) #printing output\n", " return( list(coef.est =coef.est , se=se, dtil=dtil, ytil=ytil) ) #save output and residuals\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DML function for Boosting Trees" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "DML2.for.PLM.boosttree <- function(data_train, dreg, yreg, nfold=10) {\n", " nobs <- nrow(data_train) #number of observations\n", " foldid <- rep.int(1:nfold,times = ceiling(nobs/nfold))[sample.int(nobs)] #define folds indices\n", " I <- split(1:nobs, foldid) #split observation indices into folds\n", " ytil <- dtil <- rep(NA, nobs)\n", " cat(\"fold: \")\n", " for(b in 1:length(I)){\n", " datitanow=data_train[-I[[b]],-c(2)]\n", " datitanoy=data_train[-I[[b]],-c(1)]\n", " datitanowpredict=data_train[I[[b]],-c(2)]\n", " datitanoypredict=data_train[I[[b]],-c(1)]\n", " \n", " dfit <- dreg(datitanoy) #take a fold out\n", " best.boostt <- gbm.perf(dfit, plot.it = FALSE) # cross-validation to determine when to stop\n", " yfit <- yreg(datitanow) # take a foldt out\n", " best.boosty <- gbm.perf(yfit, plot.it = FALSE) # cross-validation to determine when to stop\n", " dhat <- predict(dfit, datitanoypredict, n.trees=best.boostt) \n", " yhat <- predict(yfit, datitanowpredict, n.trees=best.boosty) #predict the left-out fold\n", " dtil[I[[b]]] <- (d[I[[b]]] - dhat) #record residual for the left-out fold\n", " ytil[I[[b]]] <- (y[I[[b]]] - yhat) #record residial for the left-out fold\n", " cat(b,\" \")\n", " }\n", " rfit <- lm(ytil ~ dtil) #estimate the main parameter by regressing one residual on the other\n", " coef.est <- coef(rfit)[2] #extract coefficient\n", " se <- sqrt(vcovHC(rfit)[2,2]) #record robust standard error\n", " cat(sprintf(\"\\ncoef (se) = %g (%g)\\n\", coef.est , se)) #printing output\n", " return( list(coef.est =coef.est , se=se, dtil=dtil, ytil=ytil) ) #save output and residuals\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DML function for Lasso and Random Forest" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "DML2.for.PLM <- function(x, d, y, dreg, yreg, nfold=10) {\n", " nobs <- nrow(x) #number of observations\n", " foldid <- rep.int(1:nfold,times = ceiling(nobs/nfold))[sample.int(nobs)] #define folds indices\n", " I <- split(1:nobs, foldid) #split observation indices into folds\n", " ytil <- dtil <- rep(NA, nobs)\n", " cat(\"fold: \")\n", " for(b in 1:length(I)){\n", " dfit <- dreg(x[-I[[b]],], d[-I[[b]]]) #take a fold out\n", " yfit <- yreg(x[-I[[b]],], y[-I[[b]]]) # take a foldt out\n", " dhat <- predict(dfit, x[I[[b]],], type=\"response\") #predict the left-out fold\n", " yhat <- predict(yfit, x[I[[b]],], type=\"response\") #predict the left-out fold\n", " dtil[I[[b]]] <- (d[I[[b]]] - dhat) #record residual for the left-out fold\n", " ytil[I[[b]]] <- (y[I[[b]]] - yhat) #record residial for the left-out fold\n", " cat(b,\" \")\n", " }\n", " rfit <- lm(ytil ~ dtil) #estimate the main parameter by regressing one residual on the other\n", " coef.est <- coef(rfit)[2] #extract coefficient\n", " se <- sqrt(vcovHC(rfit)[2,2]) #record robust standard error\n", " cat(sprintf(\"\\ncoef (se) = %g (%g)\\n\", coef.est , se)) #printing output\n", " return( list(coef.est =coef.est , se=se, dtil=dtil, ytil=ytil) ) #save output and residuals\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3.1. Lasso" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "DML with Lasso \n", "fold: " ] }, { "ename": "ERROR", "evalue": "Error in rlasso(x, d, post = FALSE): no se pudo encontrar la función \"rlasso\"\n", "output_type": "error", "traceback": [ "Error in rlasso(x, d, post = FALSE): no se pudo encontrar la función \"rlasso\"\nTraceback:\n", "1. DML2.for.PLM(x, d, y, dreg_lasso, yreg_lasso, nfold = 10)", "2. dreg(x[-I[[b]], ], d[-I[[b]]]) # at line 8 of file " ] } ], "source": [ "cat(sprintf(\"\\nDML with Lasso \\n\"))\n", "dreg_lasso <- function(x,d){ rlasso(x,d, post=FALSE) } #ML method= lasso from hdm\n", "yreg_lasso <- function(x,y){ rlasso(x,y, post=FALSE) } #ML method = lasso from hdm\n", "DML2.lasso = DML2.for.PLM(x, d, y, dreg_lasso, yreg_lasso, nfold=10)\n", "\n", "coef_lasso<-as.numeric(DML2.lasso$coef.est)\n", "se_lasso<-as.numeric(DML2.lasso$se)\n", "prRes_lassoD<- c(mean((DML2.lasso$dtil)^2));\n", "prRes_lassoY<- c(mean((DML2.lasso$ytil)^2));\n", "prRes_lasso<- rbind(coef_lasso,se_lasso,sqrt(prRes_lassoD), sqrt(prRes_lassoY));\n", "rownames(prRes_lasso)<- c(\"Estimate\",\"Standard Error\",\"RMSE D\", \"RMSE Y\");\n", "colnames(prRes_lasso)<- c(\"Lasso\")\n", "prRes_lasso " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The message treatment providing information about Internet-accessed sexually transmitted \n", "infection testing predicts an increase in the probability that a person will get tested \n", "by 25.13 percentage points compared to receiving information about nearby clinics offering \n", "in-person testing. \n", "By providing both groups with information about testing, we mitigate the potential reminder \n", "effect, as both groups are equally prompted to consider testing. This approach allows us to \n", "isolate the impact of the type of information \"Internet-accessed testing\" versus \"in-person clinic \n", "testing\" on the likelihood of getting tested. Through randomized assignment, we establish causality \n", "rather than mere correlation, confirming that the intervention's effect is driven by the unique \n", "advantages of Internet-accessed testing, such as increased privacy, reduced embarrassment, and \n", "convenience" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3.2. Regression Trees" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# Tree\n", "X_basic <- \"gender_transgender + ethnicgrp_asian + ethnicgrp_black + ethnicgrp_mixed_multiple+ ethnicgrp_other + ethnicgrp_white + partners1 + postlaunch + msm + age+ imd_decile\"\n", "y_form_tree <- as.formula(paste(\"y\", \"~\", X_basic))\n", "t_form_tree <- as.formula(paste(\"w\", \"~\", X_basic))\n", "yreg_tree <- function(dataa){rpart(y_form_tree, dataa, minbucket=5, cp = 0.001)}\n", "treg_tree <- function(dataa){rpart(t_form_tree, dataa, minbucket=5, cp = 0.001)}\n", "DML2.tree = DML2.for.PLM.tree(data_train, treg_tree, yreg_tree, nfold=10)\n", "\n", "coef_tree<-as.numeric(DML2.tree$coef.est)\n", "se_tree<-as.numeric(DML2.tree$se)\n", "prRes_treeD<- c(mean((DML2.tree$dtil)^2));\n", "prRes_treeY<- c(mean((DML2.tree$ytil)^2));\n", "prRes_tree<- rbind(coef_tree,se_tree,sqrt(prRes_treeD), sqrt(prRes_treeY));\n", "rownames(prRes_tree)<- c(\"Estimate\",\"Standard Error\",\"RMSE D\", \"RMSE Y\");\n", "colnames(prRes_tree)<- c(\"Regression Tree\")\n", "prRes_tree " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The message treatment providing information about Internet-accessed sexually transmitted \n", "infection testing predicts an increase in the probability that a person will get tested \n", "by 23.08 percentage points compared to receiving information about nearby clinics offering \n", "in-person testing. \n", "By providing both groups with information about testing, we mitigate the potential reminder \n", "effect, as both groups are equally prompted to consider testing. This approach allows us to \n", "isolate the impact of the type of information \"Internet-accessed testing\" versus \"in-person clinic \n", "testing\" on the likelihood of getting tested. Through randomized assignment, we establish causality \n", "rather than mere correlation, confirming that the intervention's effect is driven by the unique \n", "advantages of Internet-accessed testing, such as increased privacy, reduced embarrassment, and \n", "convenience" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3.3. Boosting Trees" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "yreg_treeboost<- function(dataa){gbm(y_form_tree, data=dataa, distribution= \"gaussian\", bag.fraction = .5, interaction.depth=2, n.trees=1000, shrinkage=.01)}\n", "treg_treeboost<- function(dataa){gbm(t_form_tree, data=dataa, distribution= \"gaussian\", bag.fraction = .5, interaction.depth=2, n.trees=1000, shrinkage=.01)}\n", "DML2.boosttree = DML2.for.PLM.boosttree(data_train, treg_treeboost, yreg_treeboost, nfold=10)\n", "\n", "coef_boosttree<-as.numeric(DML2.boosttree$coef.est)\n", "se_boosttree<-as.numeric(DML2.boosttree$se)\n", "prRes_boosttreeD<- c(mean((DML2.boosttree$dtil)^2));\n", "prRes_boosttreeY<- c(mean((DML2.boosttree$ytil)^2));\n", "prRes_boosttree<- rbind(coef_boosttree,se_boosttree,sqrt(prRes_boosttreeD), sqrt(prRes_boosttreeY));\n", "rownames(prRes_boosttree)<- c(\"Estimate\",\"Standard Error\",\"RMSE D\", \"RMSE Y\");\n", "colnames(prRes_boosttree)<- c(\"Regression Tree\")\n", "prRes_boosttree " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The message treatment providing information about Internet-accessed sexually transmitted \n", "infection testing predicts an increase in the probability that a person will get tested \n", "by 25.28 percentage points compared to receiving information about nearby clinics offering \n", "in-person testing. \n", "By providing both groups with information about testing, we mitigate the potential reminder \n", "effect, as both groups are equally prompted to consider testing. This approach allows us to \n", "isolate the impact of the type of information \"Internet-accessed testing\" versus \"in-person clinic \n", "testing\" on the likelihood of getting tested. Through randomized assignment, we establish causality \n", "rather than mere correlation, confirming that the intervention's effect is driven by the unique \n", "advantages of Internet-accessed testing, such as increased privacy, reduced embarrassment, and \n", "convenience" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3.4. Random Forest" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "cat(sprintf(\"\\nDML with Random Forest \\n\"))\n", "dreg <- function(x,d){ randomForest(x, d) } #ML method=Forest\n", "yreg <- function(x,y){ randomForest(x, y) } #ML method=Forest\n", "DML2.RF = DML2.for.PLM(x, d, y, dreg, yreg, nfold=10)\n", "\n", "coef_RF<-as.numeric(DML2.RF$coef.est)\n", "se_RF<-as.numeric(DML2.RF$se)\n", "prRes_RFD<- c(mean((DML2.RF$dtil)^2));\n", "prRes_RFY<- c(mean((DML2.RF$ytil)^2));\n", "prRes_RF<- rbind(coef_RF,se_RF,sqrt(prRes_RFD), sqrt(prRes_RFY));\n", "rownames(prRes_RF)<- c(\"Estimate\",\"Standard Error\",\"RMSE D\", \"RMSE Y\");\n", "colnames(prRes_RF)<- c(\"Random Forest\")\n", "prRes_RF " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The message treatment providing information about Internet-accessed sexually transmitted \n", "infection testing predicts an increase in the probability that a person will get tested \n", "by 24.14 percentage points compared to receiving information about nearby clinics offering \n", "in-person testing. \n", "By providing both groups with information about testing, we mitigate the potential reminder \n", "effect, as both groups are equally prompted to consider testing. This approach allows us to \n", "isolate the impact of the type of information \"Internet-accessed testing\" versus \"in-person clinic \n", "testing\" on the likelihood of getting tested. Through randomized assignment, we establish causality \n", "rather than mere correlation, confirming that the intervention's effect is driven by the unique \n", "advantages of Internet-accessed testing, such as increased privacy, reduced embarrassment, and \n", "convenience" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3.5. Table and Coefficient plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Table" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "prRes.D<- c( mean((DML2.lasso$dtil)^2), mean((DML2.tree$dtil)^2), mean((DML2.boosttree$dtil)^2), mean((DML2.RF$dtil)^2));\n", "prRes.Y<- c(mean((DML2.lasso$ytil)^2), mean((DML2.tree$ytil)^2),mean((DML2.boosttree$ytil)^2),mean((DML2.RF$ytil)^2));\n", "prRes<- rbind(sqrt(prRes.D), sqrt(prRes.Y));\n", "rownames(prRes)<- c(\"RMSE D\", \"RMSE Y\");\n", "colnames(prRes)<- c(\"Lasso\", \"Reg Tree\", \"Boost Tree\", \"Random Forest\")\n", "\n", "table <- matrix(0,4,4)\n", "# Point Estimate\n", "table[1,1] <- as.numeric(DML2.lasso$coef.est)\n", "table[2,1] <- as.numeric(DML2.tree$coef.est)\n", "table[3,1] <- as.numeric(DML2.boosttree$coef.est)\n", "table[4,1] <- as.numeric(DML2.RF$coef.est)\n", "# SE\n", "table[1,2] <- as.numeric(DML2.lasso$se)\n", "table[2,2] <- as.numeric(DML2.tree$se)\n", "table[3,2] <- as.numeric(DML2.boosttree$se)\n", "table[4,2] <- as.numeric(DML2.RF$se)\n", "# RMSE Y\n", "table[1,3] <- as.numeric(prRes[2,1])\n", "table[2,3] <- as.numeric(prRes[2,2])\n", "table[3,3] <- as.numeric(prRes[2,3])\n", "table[4,3] <- as.numeric(prRes[2,4])\n", "# RMSE D\n", "table[1,4] <- as.numeric(prRes[1,1])\n", "table[2,4] <- as.numeric(prRes[1,2])\n", "table[3,4] <- as.numeric(prRes[1,3])\n", "table[4,4] <- as.numeric(prRes[1,4])\n", "# print results\n", "colnames(table) <- c(\"Estimate\",\"Standard Error\", \"RMSE Y\", \"RMSE D\")\n", "rownames(table) <- c(\"Lasso\", \"Reg Tree\", \"Boost Tree\", \"Random Forest\")\n", "table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Coefficient Plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94AAAgAElEQVR4nOzdd3xUVf7/8c+dml4gQMIGaQKhaFCjAiqCsjaqorKIgOhXyIoFsIEiYFBUQIiyrCwILrqCa0PBAqsrYFdWpCy9iQEJMaSHTMrM/f1x3fnFZDKZCZlMOHk9//CROXPO3M/MPd55c9touq4LAAAAzn6mYBcAAACA+kGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDs/TJ06VdO0JUuWBLsQP2zbtk3TtH79+gW7EA+2bdt2ySWX2Gy2iIiIo0ePVm/x9wM/G1dQTWp9L88++6ymafPnz2/Iqs5EHVZuY569qpo+fbqmaX/5y1+CXQiAOlIq2BlfA96tX7/e9xd87bXX1q5d634YGxvbtm3byMjIANRey6IbQFZW1pNPPtm7d+8WLVrY7fbExMQ+ffrMnz//119/DdASR48evWXLlssvv3z8+PFhYWHVW/z9wAO3ghp+dVR/L/VbQ9BXd0P+39RgQkJCNE3Lzs72a1TDzy4AKtMV8uOPP4pIeHj40Jpt3brV9xeMj4+/6667AldwAyza+EyuvPJK791WrlxpRCur1ZqSkvLHP/6xW7duNptNRGJjYzds2HDmlVThcDhEJCoqqry8vKaWxiOIM6GmGp555hkRmTdvXh1eqjGsbl/4OHsbD7vdLiK//vqrX6Maw+xye/zxx0Vk0aJFwS4EQB1ZgpImAyo+Pv69994789c5fPhwZmbmmb9O41/0G2+8MXbsWJPJ9Pjjjz/yyCNRUVFGe1ZW1tNPP/3iiy/ecMMNW7ZsueCCC+pxoSUlJSISGxtrsVhqamkkgjgTAlFDI1ndMDSG2QVAKcFOlvXJ+Pd9x44dfez/1ltv9e/fPzY21mq1JiQkXHfddR999JHx1PDhwyt/Spdddpmu648++qiIvPTSS0af6dOni8j777//9ddfX3nllREREXFxcWPHji0oKHC5XAsXLuzSpUtoaGjXrl3nzJnjcrncy83Pz586dWpSUlJISIjNZjv33HMfeuih/Px8L4vWdd3lci1btqxXr14REREhISFJSUnTp08vKiqq/I5++umnESNGNG/ePDQ0NDk5efny5bXu8ygoKIiJian8vqow3vX48ePdLWVlZenp6SkpKREREXa7vWPHjhMnTjx+/HjlUd6rHTp0aJV52L179yotBw4cqPKB67rudDoXL16ckpISHh4eERFx1VVXbd68uUqplfvX+qG5V+J///vfYcOGGcclk5OTV61a5X11eJk81bVu3TokJKS0tNTd8v333xsveOzYMXdjVlaWpmkpKSlV3ovHGow9dgsWLNi1a9fAgQNjYmJCQkKSk5PfeOONmsrQA7O6a/0Mq69ujyvXl9l75ivU4H0i+bIgj6rssavz7Kp16Y899pjxyosXL27dunV0dPSVV14pImvXrq1S0rp160TkqquuMh563/jonvbY+TXVAQRd0w12S5cuFZEWLVpMmDDhiSeeuPPOO5s1a6Zp2quvvqrr+gcffHDHHXeISK9evRYuXPjmm2/q1XJDWlqaiEybNi0mJmbEiBETJ05MTEwUkdGjRz/66KNt2rRJTU0dM2aMcYTLeFld18vKyq644goRueiiix566KEHHnigS5cuInLxxRdXVFTUtGhd12+//XYRSUhIePDBBx977LFevXqJSM+ePQsKCowOOTk5bdq0EZG+fftOnz59woQJ8fHx48aN8x7s/vrXv4qIESY8KikpOXr0qPuh0+m8/vrrRSQpKemBBx544oknjIcJCQk//fSTu5v3ateuXTtnzhwRiY2NXbhw4cKFC1977bUqLXl5edW/+2+++WYR6dat2z333DNq1KiIiIjKn231/rV+aMZKnDVrVnR09DXXXDN58uSBAwca37L//ve/a1od3idPdWPHjhWRL774wt3y3HPPGRPj9ddfdze++eabIvL4449XeS8eazCC3YMPPhgTE3PNNdc8+OCDQ4YMERFN0z799NOGXN21fobVV3f1levj7D3zFWrwPpF8WZBHVYJd3WaXL0ufNWuWiEyZMiUsLGzkyJF333333/72NxEZO3ZslZJGjx4tIq+88oruw8ZHrxbs/J3qAIKu6Qa78847T0QOHjzobsnIyIiMjOzVq5fx8K233hKRyue+VPkqMr5Z7Xb7xo0bjZajR4+azWar1ZqUlHTq1Cmj8eWXXxaRQYMGGQ/feecdYzvu3pKWlpYmJSVV/td29UX/85//NDbH7i27y+W69957RWTq1KlGy4wZM0RkxIgR7lEnTpyIj4/3HuyMfQbPP/+8Lx+a/r8Nfe/evR0Oh7vR2DNx6623+l5tbm6uiLRt29b9ItVbqnzgq1evFpHrr7/e/dHt3bs3LCwsPDy8sLCwen9fyjBWos1me+2119zLfeihhyp/R1ZfHbVOnir+8Y9/iMjTTz/tbrn22mt79+7dtm3byvvG7rnnHhH5/PPPq7+X6jV4rPyRRx4RkTFjxngsQw/M6vblM6x15foye+trhdY6kXxZkEdVgl3dZpcvSzeCcnR0tPuEyJycHJvNFhsbW1ZW5n4ph8MRHR0dGhpqvJQvG58qwc7fqQ4g6JS6KtaQmZl5cw1efPFFd7e8vDxN08LDw90tiYmJ2dnZ33zzjV+L69evn/t2DOecc855551XXl5+3333NWvWzGgcNGiQiBw6dMh4eOGFF7777ruLFi0ym81Gi81mM45V7dixo6alLFu2TESeeeYZ91WEmqbNnj3barWuXLnSaHn//fdFZNKkSe5R8fHxf/7zn73Xf/jwYRExNt++MBb3xBNPGN9hhocffthms7333nvGqVS+VFsHr7zyiog89thj7o+uS5cuTz/9dGpqalZWVvX+vpdx8cUXG/tIDLfccouI7N+/v6ZK/J08AwYM0DTt888/Nx6Wl5d/+eWXvXv3TklJ2bx5s7vbxo0bIyMje/fu7eVDqKJXr16VK7/xxhtFxLh3jEeBWN0Gfz/DKnyZvfW1QmudSPU7gf39ZHxZuqZpItK1a9drrrnGaImNjb322mtzc3M/++wz90utX78+Pz9/6NChxkvVYeNTX9tJAA1GwWBXXFz8Tg3cJzaJyODBg3Vd79+//4oVK9wnLxtHx/zSs2fPyg+NU9HPP//8Ki3ur8B27drdeOONKSkpIlJYWJiZmZmZmWlcolj5a7KKb7/9VkT69OlTuTEmJqZHjx4nTpz4+eefXS7Xnj17RCQ5Oblyn0svvdR7/YWFhSLi410ndF3/4YcfqlcSFRXVpUuXsrKyXbt2+VKtL8uq7quvvhKRiy66qHLjpEmT5s+f36FDh+r9fS/DONTlFhsbK15Xh7+Tp1WrVueff/7XX3/tdDpF5Pvvvy8uLu7Tp0/fvn337dt38uRJEcnKytqzZ8/VV1/t1+UFVSo3/jmRn59fU/9ArG6PldT6GVbm4+ytrxVa60Sq3wns7yfj+9Kr/BvgtttuE5G3337b3WIc3DeOxkqdNj71tZ0E0GAUvEKtY8eOBw8erLVbenq60+lcsWLFXXfdJSLdunUbNGhQampq+/bt/VpcXFxc5YfGv6QrNxotuq67W95777358+f/8MMPxj0galVSUlJUVCQixplA1R0/fjwmJqasrCwkJCQ0NLTyU82bN/f+4kbuNI6U1aqoqMjhcNhstujo6CpPtWjRQkSys7N9qfacc87xZXGVFRcXFxcXV3+DNfGrDOOQn1v1VVZFHSbPH//4x/nz5//4448pKSkbN27UNO3KK6/MyMgQkc2bN996662bNm0SkWuvvdaXd+dmfOxuJpPJe+X1vrrdLf5+hlUWVOvsra8VWutEqvcJ7Ncn49fSq6z9IUOGREREvPfee0uWLDGbzQ6HY926dS1btnTv1RP/Nz71tZ0E0GAUDHY+slqtS5YsmTlz5tq1az/++OPPPvts7ty56enpr7322q233hq45S5dunTChAmRkZGpqamXXHJJdHS0yWR67733jHOfPTK+CTRNM85Dqi4+Pt74nqj+bWHsIvKiS5cuW7du/c9//mOcFO+dl+8kl8tldPCl2loXVJ0RWYy7oBmL8KXUei/DUIfJYwS7zz//PCUl5bPPPuvevXtcXFyzZs2ioqKMYLdx40YRqfwdHAj1vrrrpSpfZm99rdBaJ1JAZ06t/Fq61Wqt/FRYWNjQoUNff/31zZs3X3XVVR999FFhYeG4cePc+4DrsPEJ1nYSQJ013WBnSEhImDBhwoQJExwOx9///vf77rtvwoQJQ4cOrXxGUf0yLpT74IMP+vbt6270fsJKSEhIdHR0fn7+xIkTq/wb3c3pdJrN5tLS0pKSksq7Imq9RdaAAQNWr169cuXKadOmeTwIqOv6U089dcsttyQlJUVERISFhZ0+fTovL8+4a4ab8YsFLVq08KXaOggNDY2MjCwsLDx16lSVvaQeBaiMyvyaPH379g0JCfn8888nTpz4zTff/N///Z+ImEymPn36GKfZbdq0qVOnTh6PKdejel/d9VJVRERErbO3vlZorROpAWaOF2e49Ntuu+31119/5513rrrqKuOyDPdxWKnTxsfQ8NtJAHWm4Dl2Pjp69OiJEyfcD0NCQlJTU/v06ZOXl2ecYB4IpaWlx48fj4iIqLxh1XW91h86M042cp9975aTk2P8YTabO3XqJNVOgv7yyy+9v/LIkSNbtmx56NAh42q46ubMmTNjxgzjQIyIGCfoGGcpVS5j3759oaGhxu3oaq22boxFf/rpp5Ubn3nmmQEDBnz99dfV+weoDKnT5AkJCbn88su//PLLr776yuFwuC+4ueKKK3bv3r179+69e/f6exy2DgKxus+cj7O3vlZorRMpcDPHF2ey9GuuuSYuLm7dunUlJSXr1q1LSkoy3qzUdeMTlO0kgDPRRIPd9u3b27Vrd/vtt5eVlbkbCwsLDx8+bDabW7ZsKSIhISEicurUqXpcrt1ub9asWVFRkXFylYjoup6WlmacEJ2Xl2c0Vl+08UU7a9asyj/l+cUXX7Rq1cq4yE5EbrjhBhFZsGCBu8ORI0eWL1/uvaTQ0NDly5drmjZ37tw777yz8j6SzMzMiRMnTp8+PSoqyrhpi7uSOXPmVP7o5syZU1FRMWrUKONf8L5UWwfG3eDmz59fXFxstPz000/z5s375ptvunbtWr1/fZVRZXX4Mnk8+uMf/3jq1Cnj0zZuJysiV1xxha7rc+fOFa8n2NXXbAzE6q4Xvsze+lqhtU6kAE1gj+r2P3tNLBbLLbfckpGRsXDhwuLi4spX4/q48amszlMdQBApeCj2xIkTxh1GPOrZs+dTTz2VnJx82223rVq1qmvXrtdff33z5s2zs7M//PDDY8eOPfDAA8Yp2127dtU07cMPP7zrrrtsNttLL71UL+XdcccdCxYsuPrqq41vlw8++CA3N3flypXXXnvtG2+80aZNm1GjRlVf9K233vree++tXr36ggsuGDFiRGRk5H//+9+1a9eGhoY+/PDDxis/+OCDr7766ptvvnn48OHevXv/+uuvH3/88d133z1//nzvJQ0aNOitt9668847X3nllZUrV5533nktW7Y8ceLE/v37y8rK2rRp8+GHH7qT0+jRo999993333//oosuuv76661W63fffffvf/+7c+fOzz77rNHHl2rrYPTo0W+//fYHH3zQvXv366+/vri4+L333issLFy2bJlxpWEV9VVG9dVR6+Tx6Jprrnn00Ufffvtt4wQ7o/GSSy6x2+2rV6+22Wzu3Xi+1OB7/VXU++quF77M3vpaobVOpABNYI/q9j+7F7fddttLL7307LPPapo2atSoyk/5svGp3N+X7SSARqehbpjXEIwbFHt39dVXG52N3xTq06dPXFyc2WyOjo6+4oorVqxYUfm3v5599tm4uDi73X7hhRfqNdyguMqvsBt7Yvbs2eNuMe4j4L41a0lJyeOPP96xY0e73d6mTZt77rknOztb1/U77rgjPDw8Pj5+x44d1RdtFLxs2bLevXtHRkZaLJbExMQxY8ZUXpCu63v27Bk6dKjx61LnnXfesmXLjD0Bl156aa2fXnZ2dlpaWq9eveLi4iwWS2xsbN++fV9++eWSkpIqPcvLy9PT0y+88MKwsDC73Z6UlDRt2rTc3NzKfWqttg43KDYW/fzzz59//vmhoaHh4eF9+/b97LPPvPSvtQyPK/HAgQMikpyc7G6psjp8mTzVuVwuYyfHvffeW7n98ssvF5F+/fpVbqz+XqrU4GPlNanH1e1LJb6sXF9mb32tUO8TyZcFeeTxBsX+zq46v02Dy+Vq166d/G9ncGW+bHyq3KC4blMdQBBpum/3IwAAAEAj10TPsQMAAFAPwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ6/cblc+fn5p0+fDnYhaKSMGVJSUhLsQtBIOZ1OZgi8MGaIw+EIdiGKswS7ADQi5eXlJhNZH57pus4MgRfGDDGbzcEuBI2UMUMsFoJHYLGNBgAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBGWgL56UVHR0qVLd+zYUV5e3qVLl9TU1JYtW1bpk5GRsXLlyj179ui63r59+9GjRyclJYlITk7OihUrtm/fXlZW1qFDh3HjxnXu3Dmg1QIAAJzVArvHLj09PSsra+bMmfPmzQsLC0tLS3O5XJU7VFRUPPHEE+Hh4XPnzn3++edbtGjx5JNPlpSUiMhTTz2VnZ395JNPpqenx8XFpaWlORyOgFYLAABwVgtgsMvOzt6yZcv48ePbt2/funXr1NTU48eP79y5s3Kf4uLioUOHpqam/uEPf0hISLjllluKi4tPnDhRWFjYokWLiRMndujQISEhYcyYMQUFBRkZGYGrFgAA4GwXwEOxBw4csFqt7du3Nx5GREQkJibu27cvOTnZ3Sc6OvrGG280/i4sLFy7dm1iYmKbNm2sVuu0adPc3U6dOmUymeLi4mpalq7rgXkTTYj7M+TDhEfMEHjHDIF3zJB6pGlaTU8FMNgVFBRERkZWXnZ0dHR+fn71ni6X65ZbbikvL+/Ro8fs2bOtVmvlZwsLCxctWjRs2LDY2NiallVaWlpUVFSPxTdZpaWlpaWlwa4CjRczBN45HA5Om4EXJSUlxglXqDOz2ewlEQX24gkvibIyk8n0wgsv5Obmfvjhh4899tjzzz8fERFhPHXs2LHZs2f37Nlz7NixXl7BbDbb7fZ6qLhpKy0tNZvNFktgZwXOUrqul5WVmUymKv/0Agwul6u8vJxtCGpibEOYIWfOe7gK4IcbExNTUFCg67q7gvz8/JoyZmJiYmJiYvfu3W+77bbNmzcPHDhQRLZv3z537tyRI0cOGjTI+7KsVitfNmfI5XKVlpZaLJbIyMhg14LGyOl0lpWVWa1WZgg8qqioyMvLs1qt7n+ZA5VVVFSUlZXZbLbw8PBg16KyAF480alTp/Ly8kOHDhkPjasfunbtWrnPjz/+OH78ePeRHU3T3EF+9+7dzz333JQpU2pNdQAAAJCABrtmzZr17t178eLFR44cOX78+MKFCzt27NitWzcR+eSTT9atWycinTp1cjgc6enpGRkZmZmZL7/8ssPhuOiii8rKytLT04cMGdK2bdvs/+G8DQAAAC+0gF6ccvr06aVLl/74449Op7N79+6pqanGodh58+YVFBTMnj1bRI4ePfrKK6/s3r1b07Rzzjnn9ttvT05O3r59+xNPPFHl1SZMmGAcokUguFyunJwcu93OgTZ45HQ6c3NzmSGoiXEoNiQkhEOx8MiYIaGhoRyKDajABjucRQh28I5gB+8IdvCOYNcw+K1YAAAARRDsAAAAFEGwAwAAUAQ3CWzqPpKPfpVfRUTX9CJ7kdVqDZEQ46n+0v8cOSeo1QEAAD8Q7Jq6jbJxn+wTkXIpPxp2NEIi4iXeeKqzdCbYAQBwFiHYNXXjZfxpOS0i2Xr2DNeMC/QL7jbfbTzVTtoFszIAAOAngl1T10k6GX9kSmakHhmvxydLcnBLAgAAdcPFEwAAAIog2AEAACiCQ7EAvNkv+0ukRERcmqvAUmA1WcPlt7vGt5f2URIV1OoAAL9DsAPgzV/lr4flsIhUaBVZEVmhWmisxBpPzZAZKZIS1OoAAL9DsAPgzdVy9QVygYic0k+9Iq900DsMlIHGUwmSENTSAABVEewAeDNYBht//Kz//C/Xvy7SLxprGRvckgAANeHiCQAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARlmAXAAA4i2VLdoVUiEiFVBSYCuyavUiKjKeaSTOb2IJaHdDkEOwAAHU3U2ZmSIaIOM3OvMg8u2aPkAjjqdkyO1mSg1od0OQQ7AAAdXe5XJ4jOSKSrWf/U/tnV+l6lVxlPNVcmge1NKApItgBAOpupIw0/jikH/rG9c1l+mUTLRODWxLQlHHxBAAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKILfilXTjh3yxRf+DSkI0X6+0vTdL+bFO/0baLHIhAn+DQEAAIFAsFPT0aOyYYN/Q4rC5Of25vwdJu0z/wbabAQ7AAAaBYKdmvr2la5d/RuyP08fXap37KgvGOTfQBPH8wEAaBwIdmqKjpboaP+GOLLEfFjCwvRzzw1MTQAAIMDY2QIAAKAIgh0AAIAiOBQLNEXHj8tPP/k35KRFy2uv/ZSrfZXt30CzWXr18m8IAKBuCHZAU/Tdd/L3v/s3pCha23u35dftluOf+DcwPJxgBwANhGAHNEU9esjYsf4NOVii/6BLbKzu70Cr1b/+AIA6UyTYlZeXOxyOYFdxdispKRERXdcLCwuDXQsCLiFBEhL8GxJ38rQ9W4+Pd15zqd8zhDnVFJyW02IXp9PJNgQe6bouImVlZS6XK9i1nN00TYuIiKjpWUWCndlsDg0NDXYVZzebzSYiIhqfJDyy20NERNNMzBB4ZHfZRcRkMoXamCHwwOl0lpWVWSwWtiFnSNM0L88qEuxMJpOJ++SeGbPZLCKaJhaLIrMC9ctk+m1TwgyBR2anWZyiaRozBF6YTCZmSEARhgAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFGEJdgFocJmZsn179WZbSW5Ms1+bZx+QDRs8jAoLkyuuCHhtAADgDBDsmp5Dh2Tx4urNYbbTbe7a2/5Qpnyb7WFUfDzBDgCARo5g1/R06SKPPlq9ueTEYZf2Y2FUa4/Pit0e8MIAAMCZIdg1PXFxEhdXvbl8b3TFUasjJFYuu6zhiwIAAGeOiycAAAAUQbADAABQBIdiAfxeRYU4HNWbtdOnzeK0OEulqMjDKJNJwsICXhsAwCuCHYDf+/JLWbCgenOz0OIu47b32HpE/vKdh1Ft2ni82hoA0JAIdgB+r0ULjxfQlBdmWF3f6yaT58trmjcPeGEAzkKTZfJJOSkiulkviywzm82W/2WPmTKzi3QJanUKItgB+L3u3aV79+rNxdu+d5x8Oyeuk/yfpxviAIAnYRIWIREikqfnfW/9/hw5p7v8toUxizmopamJYAcAAALlaXna+GOvvneSc9JAfeB99vuCW5LauCoWAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEVwuxMAQFWffCIff+zfkLwYbe8ws2Of5dg3/g2MiJC0NP+GAKgJwQ4AUFVOjhw86OeQKO3kSVPFQS3Kz4ExMf71B+AFwQ4AUNWIETJihH9DNh913XJCv/RS1zvTAlMTAB9wjh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAirAEuwAAwFnlq69k5crqzc3tBR2G7EzakSNv7PIwKjFRZswIeG1Ak0ewAwD4o7xcioqqN5tPZ8eUZUXm6lLUzsOokpJA1wVACHYAAP/06yf9+lVvzvluc3HeiBPtLpWnVjV4TQB+wzl2AAAAiiDYAQAAKIJgBwAAoIjAnmNXVFS0dOnSHTt2lJeXd+nSJTU1tWXLllX65OTkrFixYvv27WVlZR06dBg3blznzp1F5NixY6+88sq+ffsqKirat28/evTobt26BbRaAACAs1pg99ilp6dnZWXNnDlz3rx5YWFhaVQZ25YAACAASURBVGlpLperSp+nnnoqOzv7ySefTE9Pj4uLS0tLczgcuq6npaXFxsYuXbp05cqVPXr0mDVrVmFhYUCrBQAAOKsFMNhlZ2dv2bJl/Pjx7du3b926dWpq6vHjx3fu3Fm5T2FhYYsWLSZOnNihQ4eEhIQxY8YUFBRkZGQUFBRkZmYOGDAgLCzMbrffcMMNDofjxIkTgasWAADgbBfAQ7EHDhywWq3t27c3HkZERCQmJu7bty85OdndJzIyctq0ae6Hp06dMplMcXFx0dHRSUlJ69ev/8Mf/mC1WtevX9+qVat27drVtCxd16vvC4RfnE6niOj/+wOowuXSjT+YIfBI143/6swQeOT+mmaGnCFN00ymGnfMBTDYFRQUREZGaprmbomOjs7Pz6+pf2Fh4aJFi4YNGxYbGysiU6dOnTFjxqhRo0QkNjZ2xowZNputprGlpaVFnm6YCd8VF58WEdFdubm5wa4FjdHp06dFRHfpzBB49NsM0Zkh8KzIXCThUl5enlvIDDkjZrPZSEoeBfbiicqpzrtjx47Nnj27Z8+eY8eOFZGKioq0tLSkpKSnn37aarV+9NFHM2fOXLRoUU3vxGw22+32equ7SbLZrCIimsYnCY8sFrOIaJowQ+CRxWKWctGEGQLPrJpVREwmEzPkDHkPVwEMdjExMQUFBbquuyvIz8/3mMy2b98+d+7ckSNHDho0yGjZuXPnkSNHnn322ZCQEBG5+eabP/744y+//HLw4MEel2W1Wq1Wa2DeR1NhfNSaaJGRkcGuBY1RSEiI5ItozBB4ZrfbpUQ0k4kZAo9CnaHiFLPZHBnGDAmgAF480alTp/Ly8kOHDhkPjasiunbtWqXb7t27n3vuuSlTprhTnYjoul7ltLmKiorAlQoAAKCAAAa7Zs2a9e7de/HixUeOHDl+/PjChQs7duxo3Ivuk08+WbdunYiUlZWlp6cPGTKkbdu22f/jcDiSkpJiY2NXrFhRVFRUVlb27rvvFhcXp6SkBK5aAACAs11gz7G7//77ly5dOmvWLKfT2b179+nTpxuHZbdt21ZQUDB48OA9e/ZkZmauWrVq1ar//6PREyZMGDhwYFpa2sqVK1NTU51O5znnnDNz5syEhISAVgsAAHBWC2ywCwsLmzRpUvX2hx9+2PgjOTl57dq1Hse2bdt2xowZASwOAABALfxWLAAAgCIIdgAAAIoI7KFYAACgnqIi8fdnAX4VKQ3XCku1TD/vT2w2S4sW/g1pygh2AADAP++/L//8p39D8lua/jvOnPm95duN/g1s0UKWL/dvSFNGsAMAAP455xy57DL/huzX9R9KNbvd74FRUf71b+IIdgAAwD9XXCFXXOHfkA2H9XVZeteurkf7B6YmiAgXTwAAACiDYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKsAS7AAAAoJB9++Tbb6s3RzpOtEg6Fn/kO/k53MOoFi3khhsCXlsTQLADAAD159Aheeed6s0RYTmt2xz6w8EC+W+uh1FduhDs6gXBDgAA1J/LLpPOnas3523/otz0SHbrHnLnAg+jQkICXljTQLADAAD1JzpaoqOrN1ecOOQ8bS4PiZBzz234opoOLp4AAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARViCXUD9KCsrKykpCXYVZ7fTp0+LiK7r+fn5wa4FjZHxv5juYobAM4ejVER0l4sZAo9KHA7hW6Y+mEymyMjImp5VJNhZLJbw8PBgV3F2s9vtIiKaxicJj+z2EBHRTMwQeGaz2cTBNgQ1+m2GCDPkTGma5uVZRYKdyWQymTisfEbMZrOIaCIWiyKzAvXLZPptU8IMgUfGDNE0jRkCj8wmk4hoGtuQwCIMAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYQEdm5c+e6deucTmduXu67775bUFAQ7IoAAIDfCHZN3Zo1a3r06HH++efPnz/f6azIysoaPnx4y5YtJ0yYcOLEiWBXBwAA/ECwa7oqKipSU1NvuummXbt2VXmqtLR06dKlPXv2/Oqrr4JSGwAAqAOCXdN17733/u1vf/PSISsr6/rrr9+5c2eDlQQAAM4Ewa6JWrNmjfdUZygsLBw5cqTT6WyAkgAAwBki2DVR06dP97Hnrl27Vq9eHdBiAABAvSDYNUXbt2/fvXu37/3feOONwBUDAADqC8GuKfr222/96v/1118HqBI0fuXl5a+88sp11133pz/9qaysdM/u3V26dHnwwQcPHjwY7NIAAFUR7JqizMxMv/rn5uY6HI4AFYPGbOvWrT169Ljzzjs3bNhQVFQkIrqu79+/f8GCBd27d3/sscc4/xIAGhWCXVMUHh7uV3+r1Wqz2QJUDBqtTZs29e3bd//+/R6fLSsre+aZZ2699VaXy9XAhQEAakKwa4o6dOjgV//27dubTEyVpuXnn38ePnx4cXGx927vvvvujBkzGqYkAECt+LZuigYMGODXHrgbbrghcMWgcZo+fXpOTo4vPefPn3/48OFA1wMA8AXBrimKiooaO3asj51tNtuf//zngNaDxubUqVO+3+PG+J2SgNYDAPARwa6JmjVrVsuWLX3pOWnSpM6dOwe6HjQqGzZsqKio8L3/hx9+GLhiAAC+8yPYORyOLVu2rFmzJjs7W0T82u6jsWnduvU777wTERHhvdvgwYPnzJnTMCWh8Thy5Ihf/TkUCwCNhK/B7vnnn2/ZsuUll1xy0003Gfevmjlz5rhx44h3Z6/LL7/866+/7t69u8dnbTbbI488smbNGrPZ3MCFIehOnz7tV3+Hw8F9TwCgMfAp2C1btuyhhx7q37//kiVL3I1dunT5xz/+sXDhwoDVhoA777zztm/f/tprrw0cODAqKkpENE3r1KnTpEmTdu7c+dxzz5Hqmqb4+Hi/+rdq1YqpAgCNgcWXTn/5y19SU1Nfeuklh8ORmppqNI4ZM2bv3r0vv/zyww8/HMgKEVhms/n222+//fbbd+/974SjAzp16rxx/0fBLgpB1rt3b7/69+nTJ0CVAAD84tMeu/379w8fPrx6e79+/fw9FweNnBbsAtAYXHTRRZ06dfK9/4gRIwJXDADAdz4Fu6ioKI+/KJWfnx8aGlrfJQEIMk3T0tLSfOx8wQUXePyHHwCg4fkU7M4///z58+eXlJRUbszJyUlLS+vVq1dgCgMQTH/605/GjRtXa7eYmJhVq1bxwyQA0Ej4dI7d448/PmDAgPPPP3/gwIEismzZsiVLlqxZs6akpKTy5RQAVLJkyRJN01asWFFTh9atW69ZsyYpKakhqwIAeOHTv7P79eu3YcOGyMjIF154QURWrFixcuXKpKSkTz755LLLLgtwhQCCw2azLV++/O233+7atWuVp0JCQiZOnLht27ZLLrkkKLUBADzyaY+diFx99dVbt27Nysr65ZdfRKRt27axsbGBLAxAozB8+PDhw4fv2LFj3Ufvf2h+IT4+fs2a+QMGDKj17tYAgIbn0x67lJSUPXv2iEjLli179uzZs2dPI9W988473bp1C2yBABqB888//7rrrjWbLc2aNx82bBipDgAaJ5+C3Q8//FBcXFylsaKiYteuXYcOHQpAVQAAAPBbLYdiNe23+5pdfPHFHjtceOGF9VwRAAAA6qSWYLdt27bNmzc/8MADQ4cOjYuLq/yUpmmtW7e+++67A1keAAAAfFVLsEtOTk5OTv7oo4/mzZtX/U70RUVFJ06cCFhtAAAA8INP59itX7/e4+8Lfffdd9ygGAAAoJHw9XYnH3744erVq3/++WeXy2W0OJ3OXbt22e32gNUGAAAAP/gU7N54442RI0daLJb4+Phjx461bt06JyfH4XD079//oYceCnSJAAAA8IVPh2Lnz59/3XXX5eTkZGRkmM3mDRs2FBYWvvjii7quX3HFFYEuEQAAAL7wKdjt37//3nvvjYyMNB7qum6xWO67776ePXtOmzYtkOUBAADAVz4Fu/LycrPZbPwdHh6el5dn/D18+PA1a9YEqjQAAAD4w6dg17Vr1+XLl5eVlYlImzZtNmzYYLTn5OTk5+cHsDoAAAD4zKeLJ6ZMmTJ69Ojc3NxPP/30pptumjNnTlZWVmJi4tKlS5OTkwNdIgAAAHzhU7C7/fbbLRbLTz/9JCJTp0799ttvly1bJiJt2rR54YUXAlofAAAAfOTrfez+9Kc/GX+EhYX961//OnjwYHl5+bnnnmu1WgNWGwAAAPzga7AzFBYWOp1OETF+N7a4uFhEYmJiAlEZAAAA/OJTsDt8+PD999+/adMmI8lVoet6fVcFAAAAv/kU7O66664ff/xx2LBhCQkJ7vueAAAAoFHxKdht2bLlX//6V58+fQJdDQAAAOrMp/vYhYeHt2vXLsCVAAAA4Iz4FOxGjx69YsWKQJcCAACAM+HTodg5c+YMHDhw/fr1vXv3bt68eZVnp06dGoDCAAAA4B+fgt2CBQs+/fRTEfnqq6+qP0uwAwAAaAx8CnYvvvji8OHDJ0+eHB8fz1WxAAAAjZNPwS4nJ+fFF19s3bp1oKsBAJyNTp48uXXrj652ztzc3K1bt15wwQWapgW7KKAp8uniiW7duv3666+BLgUAcNb54osv+vfv37p167lzn6uoqDh69OhFF13Upk2befPmORyOYFcHNDk+Bbv09PQpU6bs2LEj0NUAAM4WLpdr6tSpffv23bRpk8vlqvzU8ePHH3nkkUsuueTIkSPBKg9omnw6FPvYY48dPXo0OTk5IiKi+lWxP/30U/3XBQBo3B588MH09HQvHXbu3HnllVdu2bKlVatWDVYV0MT5FOxMJlOXLl26dOkS6GoAAGeFtWvXek91hoyMjDvuuOPjjz9ugJIAiI/B7vPPPw90HQCAs4Wu677f6Gr9+vUbN27s379/QEsCYPDpHDsAANy2bt26Z88e3/v/4x//CFwxACrztscuKSlp7Nix06ZNS0pK8tJt79699V0VAKDx8vcwDod9gAbjLdjFxMSEhoYafzRUPQCAxu6XX37xq//x48cDVAmAKrwFu2+//bbKHwAA2Gy2gPYHUGc+nWOXkpLi8XSKd955p1u3bl4GFhUVLViw4I477hg1alRaWlpWVlb1Pjk5OfPnzx89evSIESOmTZu2f//+Kh3+/e9/DxkyhHAJAI1E27Zt/erfrl27wBQCoCqfgt0PP/xQXFxcpbGiomLXrl2HDh3yMjA9PT0rK2vmzJnz5s0LCwtLS0urchNLEXnqqaeys7OffPLJ9PT0uLi4tLS0yjcrz8vLW7lyJf/aA4DG49prr/Wr/3XXXRegSgBUUUuw0zTN+L2/iy++WPs9q9U6c+bMHj161DQ2Ozt7y5Yt48ePb9++fevWrVNTU48fP75z587KfQoLC1u0aDFx4sQOHTokJCSMGTOmoKAgIyPD3WHJkiX9+vULCws7s7cJAKg3bdu29T2r2Wy2O++8M6D1AHCr5T5227Zt27x58wMPPDB06NC4uLjKT2ma1rp167vvvrumsQcOHLBare3btzceRkREJCYm7tu3Lzk52d0nMjJy2rRp7oenTp0ymUzuBX3zzTeHDh2aNGnSpk2bvNfpcrmq7wuEX5xOp4joIhUVFcGuBY2Ry6UbfzBDICLPPPPM5s2bS0pKau15zz33dOjQgWkDp8slIrrONuRMaZpmNptreraWYJecnJycnPzRRx/NmzevU6dOfi24oKAgMjLS2OFniI6Ozs/Pr6l/YWHhokWLhg0bFhsbKyJFRUVLliyZPHlySEhIrcsqKysrKiryqzxUUVx8WkREd+Xl5QW7FjRGp0+fFhHdpTNDICKJiYkLFy5MTU313q1v376PPPIIcwYiUupwiIiusw05U2az2UhKHvn0yxPr168/ffr0iRMnEhISRKSkpOSf//znqVOnbrzxxg4dOngZWDnVeXfs2LHZs2f37Nlz7NixRsvy5csvvPDCnj17+jLcbDb7kv/ghc1mFRHRND5JeGS1WkRE04QZAsOoUaPi4uLGjx+fk5PjscMdd9wxf/58u93ewIWhcTKbLeJkG1IPTCZv59H5FOz27t175ZVXTp48eerUqRUVFX379v3Pf/4jIrNnz964ceMFF1zgcVRMTExBQYGu6+54l5+f7zFjbt++fe7cuSNHjhw0aJDRsm3btq1bt/7lL3/xpTwRsVqtVqvVx87wyPg/TRMtIiIi2LWgMfrt61ljhuD/Gz58+IABA9LT0998882C07/Fu+jo6BtuuGHSpEmXXHJJcMtDo2K32+S0aGxDAsynq2Iff/zxVq1a3XLLLSLyxhtv/Oc///nrX/968ODB7t27z5kzp6ZRnTp1Ki8vd182a1wV0bVr1yrddu/e/dxzz02ZMsWd6kTkk08+KS4uTk1NHTVq1KhRo/Lz8xcuXPjMM8/4/f4AAIEUHR09c+bMXbt2rVy50mq1de/ePTc3d9WqVaQ6ICh82mP35ZdfLly4sGPHjiLy7rvv9ujR489//rOITJw40cvvQDdr1qx3796LFy++//77bTbbyy+/3LFjR+O+d5988onD4Rg8eHBZWVl6evqQIUPatm2bnZ1tDIyIiEhNTR03bpz7pSZPnjxmzJhLL730TN4qACBw7Ha7VqJZrVbfT8IBUO98CnZ5eXnG2XVOp3PTpk3uK2FbtGhx8uRJLwPvv//+pUuXzpo1y+l0du/effr06cb/8Nu2bSsoKBg8ePCePXsyMzNXrVq1atUq96gJEyYMHDgwMjLS3aJpWmRkZFRUVB3eIQAAQBPhU7Br1arV4cOH+/fv/9lnn+Xm5rpvX5SRkdG8eXMvA8PCwiZNmlS9/eGHHzb+SE5OXrt2ba0FvPrqq77UCQAA0JT5FOyuueaa6dOnHzx4cPXq1R07duzbt6+IZGVlvfDCC5dddlmAKwQAAIBPfAp2s2fP3rVr17PPPhsXF7du3Trjtnj333//0aNHX3vttQBXCAAAAJ/4FOwSEhK++eabgoKC0NBQ911FHnrooRdeeKFVq1aBLA8AAAC+8na7k7y8vLKyMvfDqKioyveKS0lJyczMTE9PD2B1AAAA8Jm3YBcbG7t06VL3w6KioqlTp+7fv9/d8sUXX0yePDmA1QEAAMBnPt2g2FBUVPTcc88dPnw4cNUAAACgzvwIdgAAAGjMCHYAAACKINgBAAAogmAHAACgCIIdAACAImq5QfFPP/307bffGn/n5OSIyN69e2NiYtzPBrI2AAAA+KGWYPf8888///zzlVu4cR0AAEDj5C3YzZw5s8HqAAAAwBnyFuxmzZrVUGUAAADgTHHxBAAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAImr5rVgAAIA6c7lc77///ltvvXU4Y5/+SNn27TuuuuqqgQMHjhs3rlmzZsGuTkHssQMAAAGxa9eulJSUm266afXq1SdPnhTRy8rKNm7c+NBDD5177rnLli0LdoEKItgBAID69+WXX/bp0+fHH3/0+Gxubu748eMffvjhBq5KeQQ7AABQz37++ecbb7yxoKDAe7f58+ez365+EewAAEA9e+yxx7Kzs33p+eijj+bk5AS6nqaDYAcAAOrTiRMnVq9e7WPn3Nzcv//974Esp2kh2AEAgPr00UcfuVwu3/uvW7cucMU0NQQ7AABQnw4ePOhX/wMHDgSokiaIYAcAAOpTYWGhX/1rvcYCviPYAQCA+tSyZUu/+sfHxweokiaIYAcAAOrTxRdf7Ff/Sy+9NECVNEEEOwAAUJ+uuuqq5s2b+97/5ptvDlwxTQ3BDgAA1Ce73f7444/72DklJWXIkCEBradJIdgBAIB6dt999w0YMKDWblFRUa+++qqmaQ1QUhNBsAMAAPXMYrG89dZb3rNdixYtPvzww65duzZYVU0BwQ4AANS/mJiYjz/+eMGCBdXPtzOZTKNHj966devll18elNoURrADAAABYbFYJk+efPz48Y8//njosGEmszkhIWH58uW//PLLq6++mpiYGOwCFWQJdgEAAEBldrv9uuuus4VrP5x+OzHxD3feeWewK1IZe+wAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARlmAXUD9KS0tLSkqCXcXZrbi4WER0Xc/Lywt2LWiMTp8uERHdxQyBZyUlDhHRXS5mCDwqcZSIiIttyBkzmUxRUVE1PatIsLPZbBaLIu8lWEJCQkRENC0yMjLYtaAxCgkJkULRTMwQeBYSEiKlbENQI7s9RErExDbkjGma5uVZRcKQpmlmsznYVZzdjA9Q+98fQBUm02+bEmYIPDK+a9gaoyYmjW1IQ+AcOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEZZgF4AGt327rFxZvTm8PDv0+sK4X/fIlCkeRjVrJtOnB7w2AABwBgh2TU9RkRw8WL3ZHOJwmCOcFrvHZyU+PuCFAQCAM0Owa3ouu0zWrq3enJeVuffw/4VdeJk8PK3hiwIAAGeOc+wAAAAUQbADAABQBMEOAABAEZxjB+D3Cgvl5MnqzeYTx8xahbW02PPlNTabnHNOwGsDAHhFsAPwez/8IAsWVG+OshVY7yxtnn3A8w1x2rSRxYsDXhsAwCuCHYDfa91arr22erOjIvdk6KkTiZ0k7moPo5o1C3hhAIDaEOwA/F7nztK5c/XmouM/Z2Z8e6xdH7l6YsMXBQDwBRdPAAAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCIIdgAAAIog2AEAACiCYAcAAKAIgh0AAIAiCHYAAACKINgBAAAogmAHAACgCIIdAACAIizBLgABsWOHfPGFf0N+qdBKztd+/tm0eLF/Ay0WmTDBvyEAACAQCHZqOnpUNmzwb4gjSmwpelmZ3wNtNoIdAACNAsFOTVdfLRdf7N+QLNEfCHdemlQxaah/AzXNv/4AACBACHZqCguTsDA/x7jEXqFH2PR4e0BKAgAAgcbFEwAAAIog2AEAACiCYAcAAKAIzrEDmqJ335W//92/IfkRpsJx2g8/moe84N/A8HBZvdq/IQCAuiHYAU1RbKyce65/Qwqj5GgrV5uOrnMz/RsYGupffwBAnRHsgKaof3/p39+/IT87XRlOZ/8uFY8OD0xNAIAzxjl2AAAAiiDYAQAAKCKwh2KLioqWLl26Y8eO8vLyLl26pKamtmzZskqfnJycFStWbN++vaysrEOHDuPGjevcubOPYwEAAOAW2GCXnp5eVFQ0c+ZMu92+atWqtLS0F1980WT63W7Cp556ymazPfnkk6GhoUafl19+OSQkxJexAICGVlIi+fnVm815OSZxmctLJNPT9TUWi8TFBbw2oMkLYLDLzs7esmXLwoUL27dvLyKpqamjR4/euXNncnKyu09hYWGLFi1uv/32Nm3aiMiYMWM2b96ckZERGxtb61gAQBB8+aUsWlS9Odqeax93usUvO2X8eA+jOnSQ9PSA1wY0eQEMdgcOHLBarUYyE5GIiIjExMR9+/ZVDmeRkZHTpk1zPzx16pTJZIqLi9u7d2+tYytzuVxOpzNgb6VJcIpTRHRdLy8vD3YtaIycOjMEIiJa8+Zar17V20skJysk+9c/dHKGenhWWrVyMXOaNuNrWteFbciZs1qtNT0VwGBXUFAQGRmpaZq7JTo6Ot/TDnxDYWHhokWLhg0bFhsb6+/YsrKyoqKi+qq8aSo0FUqEOJ3O/KIaP2c0ZUWmImYIRETOOcfjPrkTJ4//Urj3j5vUIgAAIABJREFUcMd+uTeP8Tyw5m04moLS0lIR0XXdy7c5fGE2m2NjY2t6NrDn2FVOZt4dO3Zs9uzZPXv2HDt2rL9jRcRisYRyF9QzYxe7iJhMJj5JeGTMEE3TmCHwyNiFwDYENbFYrFIhmibMkDPk/XqDAAa7mJiYgoICXdfdES0/P99jxty+ffvcuXNHjhw5aNAgf8caLBaLxcLNls9IqCtUKsRkMoWHhge7FjRGIc4QcYrZbA63M0Pggd3+W/QPD2eGwAObzQh2zJDACuBFpp06dSovLz906JDxsKCgICMjo2vXrlW67d69+7nnnpsyZYo71fk+FgAAAG4BDHbNmjXr3bv34sWLjxw5cvz48YULF3bs2LFbt24i8sknn6xbt05EysrK0tPThwwZ0rZt2+z/cTgcXsYCAADAo8Aevrz//vuXLl06a9Ysp9PZvXv36dOnG4dWt23bVlBQMHjw4D179mRmZq5atWrVqlXuURMmTBg4cGBNYwEAAOBRYINdWFjYpEmTqrc//PDDxh/Jyclr1671aywAAAA84occAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEQQ7AAAARRDsAAAAFEGwAwAAUATBDgAAQBEEOwAAAEUQ7AAAABRBsAMAAFAEwQ4AAEARBDsAAABFEOwAAAAUQbADAABQBMEOAABAEZZgFwAAaHROnpTMTP+GHDqlOe2Sn69t3+7fQItFunf3bwiAmhDsAABVbdokr7/u35DcaK10nHbwoPbEK/4NjImRV1/1bwiAmhDsAABVde0qw4f7N+RkqCuvvbOH7f+1d+9hUdb5/8c/NwMznEYRQZQ8ImEqnrAckoPmGZXJtFIzxMt1t2hbrfbqsCSCoHjoSs3VrnW3dLVEzPDA2lrkWl2eKlgzlVI8hg0gIqIIDAwz8/vj/jVfvoh+s8x7uOf5+Iu573vgjd7M53V/DvdtHdfhzt7o6XlnxwO4DYIdAKC5/v1F//539pazVrHPanso2JYY/dvUBOBnYPEEAACAStBjBwAA7p4TJ8QXX9y82bfie92QWr/y02Lt2hbeFRQkHn/8N6/NBRDsAADA3VNcLD755ObNnvprVoOH1lwtvmxhr+jVi2B3VxDsAADA3RMVJcLCbt5cUXburLT01IPDxMwZLbyLRTR3CcEOAADcPW3birZtb95scWs0l3vX6QNFaOi9L8p1sHgCAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKsEjxVzd38XfL4qLQogb0o0zmjP1or5e1Mu7nhJP9Ra9Fa0OAADcAYKdqzstTp8Sp4QQNmHrYuviIXl8K76Vd40X4xUtDQAA3BmCnatLF+lWYRVC2Oy2q9ev6nQ6X19feZeX8FK0NAAAcGcIdq7Okd5swtZgb9DZdb7CV9mSAADAL8PiCQAAAJUg2AEAAKgEQ7EAbqdW1NqETQhRI2qswtogGm6IG/IuL+GlERpFqwMA/C8EOwC3kyySz4lzQgizm/mkdPKSuJQv8uVdC8SCB8WDilYHAPhfCHYAbqeX6KUXeiGEXdj7mfu5u7trtVp5VxvRRtHSAADNEewA3E6SSJK/sNqsV2uv6nQ6vVavbEkAgFth8QQAAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJblAMAADuzObNYuvWO3vLZT+36gTp4Fca48o7e2NgoHj33Tt7iysj2AEAgDvj7y9CQ+/wLe3tpUG2kFBbaOWdvdHP786Od3EEOwAAcGfi4kRc3J295aTVXma1TujV+Kdpv01NEEIwxw4AAEA1CHYAAAAqQbADAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAqQbADAABQCYIdAACAShDsAAAAVIJgBwAAoBLuShdwd9TX19fV1SldhRpYLJaqqiqlq4AzstvtQoiGhgbOELTohnRDeAmr1coZghbJZ0hjY2NVHWfIr+Lm5tamTZtb7VVJsNNqtVqtVukqWjebzXb16lUPDw9fX1+la4EzkhtsrVbLGYIW+Vh9hE1oNJq2bdsqXQuckXyGuLu7t/XmDPkNqSTYSZKkdAmtnuPfkH9MtIgzBLfHGYLb4wy5N5hjBwAAoBIEOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAqQbADAABQCYIdAACAShDsAAAAVEIlNygGACgiVaSWilIhxHW364VS4TX7tZPipLzrJfHSA+IBRasDXA7BDgDwy9WK2hvihhBCEtIQyxCNRiO/FEJYhVXR0gBXRLADAPxyb4g35C8arY1V1VWenp48TRhQEHPsAAAAVIJgBwAAoBIEOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAqQbADAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAqQbADAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAqQbADAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAq4a50AQAAQLV+ED9YhEUIUSyKa6Xay+LyGXFG3tVZdPYUnopWp0IEOwAA8FtZLBaXiTIhhNnNfElc+kT65Kg4Ku9aKpb2EX0UrU6FCHYAAOC3Mk6MqxbVQgirzVpbW6vVanU6nbwrQAQoWpo6EewAAMBvZbKYLH/RaG+sqq/ycvPy0fkoW5K6sXgCAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7PA/3N3d3dw4JdAySZLc3d01Go3ShcBJyWcInyG4Fc6Qe0Oy2+1K1wAAAIC7gOAMAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHALgLNm3aVF5ebjaby8vLla4FcF3uShcAAFADDw+P5ORkf3//AQMGzJgxQ+lyABfFI8Xw/zU2Nubm5h45csTf33/KlCndunVTuiI4l8bGxh07duTn58tnyP333690RXAuN27cmD17tt1uX7t2bYcOHZQuB06HVube0KSlpSldA5Rns9nS0tLKysoMBsPZs2ezsrJ69erVsWNHpeuCs7BYLGlpaZcvXzYYDKdOncrOzg4ICAgJCVG6LjgRk8kkSZLZbN6zZ09kZKSPj4/SFcGJ0MrcMwzFQgghcnJytFrt/PnzJUm6dOlSXV0d/TFo6oMPPvDw8EhNTZUkyWg0rlu37q9//Wu7du0GDx6sdGlQnt1uP3z4cElJyeTJk6dNm5aSkpKcnJyZmUm/HRxoZe4ZFk9ACCEOHz48fPhwSZLeeeedkydPpqene3t7X7x4saamRunS4BQOHTr08MMPS5IkhJAk6ZlnnhkyZMjf//53peuC8urr6xcuXLh169bz589rtVq9Xp+RkeHr65ucnFxeXl5VVfXtt98qXSOURytzzxDsIIQQ3t7eRUVFTf/ehBApKSnFxcVKlwan4OPjc+zYMcdLSZJmzJhRWlpaXV2tYFVwBmvXrm3Xrt2qVatefvllvV5fV1en1WrlbPfCCy/MmzevrKxM6RqhPFqZe4ahWNfVdB5rnz59srOzg4ODV6xYIf+9lZSU1NXVMbnVlTnG1x555JEJEya8+eabERERI0eOlPfW1NS0adOGeVQurqKi4sCBA1u2bJEkyWQybdy48auvvtLpdK+++urSpUt37doVHBwcExOjdJlQQLOlEnFxccuWLaOVuQdYPOGims1jPXDgQOfOnX/88ceGhoagoKCysrI33nhjypQpffv2VbpSKKO+vn7RokUFBQU2m81gMISFhVVVVW3evNlut3ft2rW0tHTlypXTpk0LDQ1VulIoqb6+fteuXRaL5dChQ2+//Xb37t2TkpIkSfr000+NRmN4eDjNtmu6eanEqFGj9Hp9QUEBrcxvjR47F3XzPNb09PT//Oc/WVlZubm53t7eiYmJcXFxSpcJxcjja/JqCSFEXV3d7Nmzg4KCsrOzs7OzdTrdrFmzxowZo3SZUFi7du2effbZ3Nzc++67b8mSJWFhYUKI69evnz17VunSoKQWl0oMGDAgKCiIVua3RrBzUYcPH37ssceazWMdOHBgTExMbW1tQECAVqtVukYo5jbja++9915ZWVlQUJCnp6fSZUIZzYbYxo4dO3bsWMfeqqqqjRs3JiYmKlghFNdiE3Px4sURI0aMGzfu8uXLtDK/HYZiXdT+/fttNtuRI0eazmOdN2/egAEDQkJCNBqN0gVCSbcZX4uPj/fz83N355rQRd3mbmQnTpzYv3//mjVr4uLiRo8erXSlUNKtmph+/foFBQXp9Xpamd8On84uxGazLVmy5Omnn+7WrRvzWHGzpqslGF9Di25zN7KjR4+aTCb5+lDZIqEURytDE6Mggp3K2Wy26urqtm3bms1mk8lkNpu7du0qhIiKijIajbm5uVlZWXFxcTU1NW+99VZCQoL8FwgXVF9fv2TJkqtXr3bu3HnsTxx7GV+D7FZDbP7+/k8//bTS1UEBLbYy3bp1o4lRCs+KVTObzbZq1Sp3d/e5c+cuWbJEvrZOSEhwHPCvf/0rKyurpqaGeaxYsWKFRqOZO3euY7WEm5ubTqc7ceLEqVOnPvroo4kTJ06ePFnpMqEMR0/MP/7xjx49etjt9qZDbLNmzXr11Vd79+6tdJm4127fytDEKIJgp1ry31tlZWVKSopOpzOZTCkpKQ0NDWvWrPHz83McZrFYmMeKioqKP/zhD1u2bJFPlaarJb7//nuTyTRu3DjG11yW3BPzz3/+Mz09/dChQzcPsb344osbNmygM8bV/JxWhibm3mPxhDrJf28VFRULFizQ6XRCiDZt2gwZMuTzzz/Pz8+PiYnx8PCQj9RoNMxjxW1WS7z22mvR0dE8q9uVvfHGGwUFBWFhYQMGDOjatWtNTQ13I8PPbGVoYu49gp0KyX9vX3zxhUajiYmJcVxG6/V6g8Gwe/fur776Kjo62pHt4LJsNtvu3bt37drV2Nj44IMPfvbZZ1qtdu7cufHx8YGBgVartaioiJvVISQkZNeuXUVFRSNGjPD09IyIiPD19c3Nzd2xY8eBAweefPLJCRMmKF0j7ilaGWfGUKzaOPrG58yZs3DhQq1Wm5mZ2b59e8cBpaWlycnJ7du3d8yPgWtqaGjIyMiw2WydO3fOy8uLiYl58cUX5Ql2Qoiqqqq//OUviYmJkZGRytYJZyB/bvj7+2dkZMifGwyxuSxaGSdHj53abNiwoaSkJCUlJSAgwGAw5OXlff7550OHDr35iqpt27Y9e/ZUtlooaP369Xa7ff78+Q899NCFCxf2799fVlYWGRlZWFjI3cjQ2NiYk5OzcePG//73v0FBQd26dWvWE8MQm8uilXFy9NipTUVFhV6vl2c8iJ+unG6+oqqurtbr9QrVCKcwderU119/vX///u+9994333xjNBpXrlwZHR3d0NDwzTffpKSksFrCZVkslrS0NI1GM3jw4AMHDpw+ffr5558fNWoUPTEQtDJOjx47tfH29m76VAD5yunmKyrH3yRc1rlz54YNG/bNN9/k5uYuWbKkV69eJ0+e/O67786dOzd79uzY2FilC8Q9JV/ky2Px2dnZVVVVaWlpDzzwwOjRo69du7Z58+awsLCwsDB6YkAr4+TosXMJt7qiAp577rnJkyePGjVKCLF8+fLp06dbLJaQkBCl68I9Jc+aCgsLmzhxohDij3/8o9FodNyh2m63Z2ZmFhcXr1u3TtATg5bQyjgPN6ULwF2wadOm8vJys9lcXl7e4gGdOnXKzMxsaGg4fPjwPa4NTq6ysvLKlStCiKtXrxYVFXXq1IlU52occ+EdUyp9fHyOHTvmOECSpBkzZpSWllZXVwshSHW4Ga2M82AoVg2+++67d95558svv6ypqenfv3+Lx+j1+pEjR/br1+8e1wZl2Wy2tWvXenl5BQUFtXiAxWLZsmXL2bNnt27dOmXKlF69et3jCqGsZveYFUI0NDR4eXllZ2d36NDBkfJNJlN+fv7UqVMd66bhasxm87p16/r06XOrddC0Mk6CHjs1iI+Pv379+vnz52+/hpHrbBdktVorKirS09OPHz/e4gHTp09/4YUXfH19n3nmmfHjx9/j8qCsm1NdeXn5888/HxISEhcXt3r16qysrGvXrp0/f37NmjUzZ850c6PJcF0Wi+XEiRMpKSk3bty41TG0Ms6AOXZqcP78+b179xYWFt64cSMzM7NDhw5KVwQn0tDQkJmZWVhYuGDBgpsvpmtra728vOiGcUFyqjOZTJmZmY5Ul5ycbDQajUajEGL79u3Z2dlms1mn082aNYu7ELsss9n8t7/97bvvvvP19T1z5kzPnj0zMjJ8fX2VrgstI9i1bna7/fDhwyUlJY888ohWq5Wvpch2aOZW2e7atWvz589PTEx88MEHFSwPirBYLIsXL3acFc1Snay+vr6srCwoKMjT01PBUqGsVatW1dXVvfLKKxqNpqioaOnSpW3btiXbOS3m2LVi9fX1ixYtKigosNlsBoNBr9dHRUUdPHhwz549kZGRFovl1KlTPOITQgiNRjN06NBTp059+OGHvXv3lufbyalu6NChjsWPcClNz4qAgIBVq1Y1S3VCCHd3dz8/v6b3toALWr58+cyZM7t06SKEaN++/UMPPbRt27aCgoLo6GieO+KECHat2OrVq729vVNTU6OionQ6XV1dnbu7+7Bhww4ePJiTk5OXl9ejR4/Q0FCly4RTaJbtPD095VQ3ffp0pUuDYhxnxY4dO2JjY59++mmlK4IzysvLs9lsQ4YMkV/q9fqKiopjx44VFBTExsbyQFhnQ7BrrSoqKtauXbt06VJ3d3eTybRmzZq33norNze3d+/eTz31lBBi2LBhI0aMULpMOJGm2e7QoUPR0dGkOshnxZkzZ77++mtHby7QlI+PT1ZWVmBgoGOV9Llz54YOHZqfn3/+/PmoqChly0MzBLvWqr6+fteuXRaL5dChQ2+//Xb37t2TkpIkSfr000+NRmN4eHi3bt2UrhFOx5HtBg8eTKqDrMWReriyxsbGnTt3bt68+ejRo8HBwREREdXV1e+//75Wq73//vsrKirefffdhISEnj17ZmVlGY1GBmSdCsGutfLy8mrXrt1nn32m1Wrnzp0bHx8fGBhotVqLiorGjBmjdHVwXhqNJiYm5lb3O4RrItvBwWazpaWllZWVGQyGs2fPZmVl9erVa/z48Y2NjVu2bNm1a1dubu6TTz4ZERFhs9k++eSTJ554glmYToX/jNaksbFxx44d+fn5/v7+U6ZMGTt2bNNp71VVVRs3bkxMTFSwQrQKGo1G6RKgAPmmFUePHh0xYsSMGTOanQZarTY5OTkzM/PkyZPcY9aV5eTkaLXa+fPnS5J06dKlurq6+++/X5KkhISEESNGFBYWhoSEhIaGWiyWTZs2jRkzhhXTzobbnbQaFoslLS1No9EMHjz4wIEDp0+ffv755+VHfJ44ceLUqVMfffTRxIkTJ0+erHSlAJzR6tWra2pqQkNDc3JyevfunZycfPO0d6vVSu53cS+99NJjjz0WExPzzjvvnDx5Mj093dvb++LFi/7+/j4+PkKIc+fOLV26tK6ubsCAAS+88ALddc6GodhWIzs7u6qqKi0t7YEHHhg9evS1a9c2b94cFhYWHBycl5f3ww8/PP3004888ojSZQJwUjk5OWlpaeHh4QaDIScn5/jx41FRUc1iHM+WwP79+20225EjRxypTggxb968fv36BQYGCiFsNpu/v39cXNykSZM4YZwQPXatxh//+Eej0egYe7Xb7ZmZmcXFxevWrVO2MADOrKGhYevWrZcvX75y5crixYvljRcvXpw/f36PHj1ef/11blfhyj7//PPdu3e7ubnNmjWrT58+QoiDBw8uW7YsODh4xYoVcqorKSl58cUXN2zYIL+EkyNrtxo+Pj7Hjh1zvJQkacaMGaWlpdXV1QpWBcDJLVu27NixYyaT6fjx4/v27ZM3dunSZdGiRefPn1+8eLHNZlO2QijCZrOtXbt269atkZGRkiQtWrTIYrEIIaKiooxGY0lJSVZWlslkKioqWrx4cUJCAqmutWBovNWYMGHCm2++GRERMXLkSHlLTU1NmzZt5EkPANCM2Wy+ceNGXV3d8uXLhRAbNmxYs2aNr6+vfKdZOdv997//ZTTNBclPCr5y5cqKFSu8vLxiY2PnzJlTVVUlD7bOmTMnKCgoKysrNzfX29s7MTExLi5O6ZLxcxHsnFezNbDDhg377rvvVq9efenSpQkTJlRWVq5Zs2bmzJl8KANo0cqVK2tra0NDQyVJEkLMnj1bo9EsXbr0tddec2Q7+TlRcClyqqusrFywYIFOpxNCFBcXd+nSJSAgwHFMfHz8uHHjLl++HBAQwG3qWhcWTzgpeQ3s5cuXDQbDqVOnsrOzAwICpk6dqtPpPvzww23btn322WdPPvkkV1EAbiUkJGTnzp0XL14cMWKE3H4PHDjQbDa/++67PXr0uO+++5QuEAqQU53JZEpPT5fPivLy8kWLFmm12oaGBi8vLz8/P/lIjUaj1+tZJd3qsHjCSW3evPn06dOpqamSJNnt9nXr1u3Zs2fBggWDBw+ur68vKysLCgri7kEAbq+0tDQ5OTkoKCgtLc3xibFx40ZPT8+pU6cqWxsUYbFYFi9eXFhYuGDBgn79+pWXlycnJ0dFRVVXVxcUFFRVVbVr127QoEEDBw6MiYkh1bVGBDsnxRpYAL9Ms1kcvr6+ycnJHTt2TE1N5WoQQoiGhobMzMzCwsKkpCT5mWBGo1EIYbfbT58+nZ+f//XXX0dGRvLUwVaKYOcszGZzTU1N+/bt5ZevvPJKYGDgyy+/7DjgwoULc+fO3bx5s16vV6hGAM6uxTuZ9+3bl2yHpuRsd+TIkbi4uKSkJKXLwd3EHDunkJOTk5GRkZOTU1FRYTAYhBBarTY7O7tDhw4hISHyMSaTKT8/f+rUqfI8aAC4WYt3Mo+IiJg0adKuXbv8/PwcHylwZfLTgc+cOfP111/zdGCVIdgpzGazvfXWWydOnJg3b17Pnj23bds2dOhQPz+/7t27V1VVbd682W63d+3atbS0dOXKldOmTQsNDVW6ZADO4scff1y1atXgwYMd6xbXrVs3atQo+YNCkqTBgwefO3du375906dPHzlyZO/evRWtF05EznanTp368MMPyXZqwp0ylCSvTqqoqFiyZEn//v3j4+M7duxYV1d3+PDhixcvJiUlJSYm7ty5MyEh4ZVXXpk4ceKYMWOULhmAE9FqtcXFxSkpKTdu3JC33OZO5sziQDNarTY5Oblv377p6enHjx9XuhzcHfTYKebmNecXLlz417/+deLEiSNHjnzwwQceHh5TpkwxGo2xsbEJCQnyw14AwMHHxycyMnLPnj0HDx6Mjo7WarXM4sAdcfTbubm59e3bV+lycBcQ7BTT2Ni4d+/eM2fOhIeHBwUFlZeXp6amjh8//uWXX46Pj5cf7zhy5Mi2bdv6+fm5u3MraQAt8PHxGTRoUF5enpztwsLCmMWBO6LRaGJiYsLDw5UuBHcHq2KVdKs15/Kuxx9/fMmSJVxCAbgVs9m8Zs2a8+fP33ffffn5+d27d8/IyPD19d2+fXt2drbZbNbpdLNmzZowYYLSlQK4Rwh2CrvVmvPTp08vXLhww4YNHh4eCpYHwJmtXLnSbDa/8sorGo3mhx9+SE9Pb9OmjZztuJM54JpYPKEwee5qRETEvn37HHNXr127tmLFijlz5pDqANzG/v37hw8fLj8eoFu3bhkZGaWlpfJaCp1O161bN1Id4GqYY6e8ZmvOPT0958+fHx0dHR8fr3RpAJza3r176+vrH374YfmlXq+32+2HDx8uKCgYPnw4c3MBF8RQrLNwzLcLCAiIjY3lWS4AWlRfX79u3boDBw4MHjw4LCxsw4YN8+bNGzlypLx3z549tbW1Pj4+48aNU7ZOAIog2DkROdv16tWLVAfgVpYtW2a1Wvv37799+3ZPT8/g4OD8/Pynnnpq8uTJV65cWbBgwV/+8pcePXooXSYAZRDsnIvVapWnywBAM2azub6+fuHChW+++aYkSVevXk1OTpYkKTw8/NNPP9VoNFarNSEhYfLkyUpXCkAxBDsAaB2WLFlSW1sbGBg4d+5ceYuc7dzc3ObNm1daWtqjR4+uXbsqWyQAZbEqFgBah5kzZ/7444/5+fnXrl2Tt7Rr1y4zM1N+5LTBYCDVAaDHDgBajdLS0uTk5Pbt26enp3t7e8sbr169eujQIe5CDEAQ7ADAmdlsto8++uj777/v37//2LFjJUlqMdsBgIxgBwBOqqGhISMjw2azde7cOS8vLyYm5sUXXyTbAbgNblAMAE5q/fr1drt9/vz5Dz300IULF/bv319WVhYZGanX6w0Gw+7du9u2bduzZ0+lywTgRLgvOQA4qb17977++uuSJL333nvl5eUvvfTSypUrrVbrk08+WV5evnr1ar1er3SNAJwLq2IBwElFREQEBwcfPHhw//79CxcuHD58+MCBA48ON2SXAAAHa0lEQVQePfqnP/2prKyMVAfgZsyxAwCn8Pnnn+/evdvNzW3WrFl9+vRxbH/uuecmT548atQoIcTy5cunT59usVhCQkKUqxSA86LHDgAUZrPZ1q5du3Xr1sjISEmSFi1aZLFYHHsrKyuvXLkihLh69WpRUVGnTp1IdQBuhTl2AKAkm822atWqK1eurFixwsvLKzY2ds6cOVVVVYGBgfIBjz76aFZW1unTp8+dO/f444+7u/O5DeCW+IAAAMXIqa6ysnLBggU6nU4IUVxc3KVLl4CAAMcx06dP79ix47fffvvMM88YDAbligXQCjDHDgCUIac6k8mUmZkpp7ry8vKXX37Z19d31KhRgwYN6t69u9I1Amhl6LEDAGVYrdbr168XFxcXFRX169evvLw8OTl5+PDh1dXVO3bs2LBhQ7t27QYNGjRw4MCYmBiNRqN0vQBaAXrsAEAxDQ0NmZmZhYWFSUlJWVlZRqPRaDQKIex2++nTp/Pz87/++uvIyMjp06crXSmA1oFgBwBKkrPdkSNH4uLikpKSlC4HQOvG7U4AQElarTY5OTkiImLfvn3Hjx9XuhwArRvBDgAUJme7vn37pqenk+0A/BoEOwBQHtkOwF1BsAMAp+DIdidPnlS6FgCtFYsnAMCJWK1W7mwC4Bcj2AEAAKgEQ7EAAAAqQbADAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwAuIS0tTZKkDh06WCyWm/fOmTNHkqTo6Ohf8J1HjRrVvXv3X1vfT6ZNm+br63u3vhsAV0OwA+Aq3NzcKisrP/roo2bb6+rqtm3b5uHh8TO/z9GjRyVJutvVAcBdQLAD4Crc3NwMBsOGDRuabd+xY0ddXd2AAQN+5vfZv3//3S4NAO4Ogh0AV9HY2Dhx4sR///vfly5darp948aNjzzyiE6na7rxiy++GD16dJs2bby9vSMiItavXy9vHzdu3Ny5c4UQkiQ9+OCD8kZ3d/fz58/HxcXp9Xq9Xj916tTKykrHt9qzZ09sbKxer/fy8goPD1+xYoXjzvB2uz09Pb1Lly6enp79+vX78MMPf7tfH4ArINgBcCFPPPGEzWZ7//33HVtMJtPevXunTZtmtVodG//zn/+MHDmyoaEhKytr165dBoPhd7/73ZtvvimE+Otf//roo48KIfLz89977z35eKvV+thjj8XGxr7//vvPPvvstm3b/vznP8u7du7cOWHCBB8fn/fff3/37t1jx47985///Oqrr8p733jjjdTU1GHDhu3evTslJWXhwoVHjx69N/8UANTJDgAuIDU1VQhRV1c3atSovn37OrYvXbrUy8vr+vXrBoMhKipK3jho0KDQ0NCamhrHYUajUa/X19XV2e323/3ud00/PEeOHCmE2L59u2PL0KFDO3ToIH/9wAMPdO3atb6+3rF30qRJHh4eFRUVNpstODg4PDzcsaukpMTDw8PHx+cu//IAXAY9dgBcy6xZswoLC/Pz8+WXGzdunDRpkl6vdxxQXl7+zTffTJgwwc3NzfyT8ePHV1dXHz9+vMXv6enpOWnSJMfL0NDQiooKIURJScnJkyfHjx+v1Wode+Pj4y0Wy5dffnnx4sWSkpIRI0Y4dnXq1MkxvAsAvwDBDoBreeyxx/R6vbyEIj8///vvv585c2bTA0pKSoQQb731llcTzz77rBDixx9/bPF7BgUFNV0n6+HhYbPZhBAmk0kIcd999zU9uFOnTvJPKSsrE0IEBgY23RscHHwXfkkArspd6QIA4J7y9vZ+4okntmzZsmLFio0bN3bq1Gn06NE3HzZ79uzf//73zTaGhobe0c+S054c8hzsdrsQws3Nzf7TEoqmmk71A4A7RbAD4HISExPXr1+fl5e3devWxMREjUbTdG/Xrl2FEFarNTIy8lf+oM6dO4uf+u0c5JedO3eW++rkfjuHCxcu/MofCsCVMRQLwOXExMSEhIRkZGRUVFQ0G4cVQvj7+w8ZMmTnzp1VVVWOjZs2bZo/f35jY6P4qR9O/vr2OnbsGB4evnv3brPZ7Ni4fft2b2/vhx9+uHv37gEBAR9//LGjS6+oqOjbb7/99b8gAJdFsAPgciRJmjlzZkFBwYABA/r373/zAcuXL6+trR02bNimTZvy8vJSUlLmzJljMpnc3d3FT9PgMjMzc3Jy/s+ftWzZsrKyskcffTQ3N/fjjz9+7rnnPv7445SUlDZt2ri5uSUlJZ09e/aJJ57Yvn373/72tzFjxkRERNz13xeA6yDYAXBFM2fOlONdi3uHDRu2b9++oKCg559/fuLEiR988MHixYv/8Y9/yHt///vfDxo0aNGiRa+//vr/+YPGjx//8ccf19bWPvXUU5MmTfryyy/Xr1//2muvyXtTU1Nfe+21Q4cOTZ8+/e233161atXDDz/c4tNsAeDnkFqcvQsAAIBWhx47AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCX+H0zzVeffFV7cAAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table_ci<-as.data.frame(table)\n", "table_ci$CI_Lower_1 <- table_ci$Estimate - 2.576 * table_ci$'Standard Error'\n", "table_ci$CI_Upper_1 <- table_ci$Estimate + 2.576 * table_ci$'Standard Error'\n", "table_ci$CI_Lower_5 <- table_ci$Estimate - 1.96 * table_ci$'Standard Error'\n", "table_ci$CI_Upper_5 <- table_ci$Estimate + 1.96 * table_ci$'Standard Error'\n", "table_ci$CI_Lower_10 <- table_ci$Estimate - 1.645 * table_ci$'Standard Error'\n", "table_ci$CI_Upper_10 <- table_ci$Estimate + 1.645 * table_ci$'Standard Error'\n", "\n", "ggplot(table_ci, aes(x = rownames(table_ci), y = Estimate)) +\n", " geom_point(size = 4) +\n", " geom_errorbar(aes(ymin = CI_Lower_5, ymax = CI_Upper_5), width = 0.2, color = \"blue\", alpha = 0.7) +\n", " geom_errorbar(aes(ymin = CI_Lower_10, ymax = CI_Upper_10), width = 0.1, color = \"red\", alpha = 0.7) +\n", " geom_errorbar(aes(ymin = CI_Lower_1, ymax = CI_Upper_1), width = 0.05, color = \"green\", alpha = 0.7) +\n", " labs(title = \"Estimated Coefficients with Confidence Intervals\",\n", " y = \"Estimate\",\n", " x = \"Method\") +\n", " theme_minimal() +\n", " theme(axis.text.x = element_text(angle = 45, hjust = 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3.6. Model" ] }, { "cell_type": "markdown", "metadata": { "vscode": { "languageId": "r" } }, "source": [ "To choose the best model, we must compare the RMSEs of the outcome variable Y. In this case, the model with the lowest RMSE for Y \n", "is generated by Lasso (0.4716420), whereas the lowest for the treatment is generated by Boosting Trees (0.4983734). Therefore, DML \n", "could be employed with Y cleaned using Lasso and the treatment using Boosting Trees." ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 4 }