{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "# Strutural Equation Modelling (SEM) with Jupyter and Anaconda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is intended as an example of how to \"do SEM\" using different tools that are accessible in a Jupyter IPython Notebook installed with Anaconda. The notebook uses a Python3 kernel and interfaces with several R packages. Alternatives would be to run an R kernel in the notebook directly, or to use an RStudio markdown notebook and the reticulate package to access Python functionality from R."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\">Stefan Rank (strank at strank.info)</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To Do for future revisions:\n",
    "    \n",
    "* Integrate instructions on using different hosted / collaborative notebook options for this tutorial:\n",
    "\n",
    "  * CoCalc: https://cocalc.com/\n",
    "  * Google Colaboratory in Google Drive: https://colab.research.google.com/\n",
    "    * (Python works, conda & R aren't available so most of the meat of this notebook won't work. If you block third-party cookies, you might have to allow cookies from \\[*.\\]googleusercontent.com to get output working)\n",
    "  * Microsoft Azure Notebooks: https://notebooks.azure.com/\n",
    "  * Binder: https://mybinder.org/\n",
    "  * A JupyterHub server: https://jupyterhub.readthedocs.io/en/stable/ (if I ever get to installing one)\n",
    "\n",
    "* Turn it into a RISE presentation https://rise.readthedocs.io/en/docs_hot_fixes/ (ideally also using the \"Split Cells Notebook\" extension for better visual organization)\n",
    "\n",
    "* Integrate other SEM tools available in R:\n",
    "\n",
    "  * lavaan, openmx, semplot (DONE)\n",
    "  * sem (less well documented/supported)\n",
    "  * metasem and metafor for meta-analysis (goes beyond normal SEM)\n",
    "  * simsem's semtools\n",
    "  * bigsem, ctsem https://github.com/cdriveraus/ctsem, gsem, lavaan.survey, regsem, semdiag\n",
    "  \n",
    "* Compare with other tools:\n",
    "\n",
    "  * Onyx GUI for SEM http://onyx.brandmaier.de/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For local use, I recommend jupyterlab as the interface, usually acessible as http://localhost:8888/lab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## Installing Required (and Optional) Components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the code in this notebook, a local (or hosted) installation of Anaconda is required. The following installs R components available through Anaconda. We use the --yes option to avoid prompts for confirmation (impossible in the notebook) but that also means you cannot check what will be installed before it proceeds. To avoid this, you could run the installations from an Anaconda prompt or using the Anaconda Navigator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R Studio is not strictly required, but it is a useful alternative way of using R. Also note that this assumes an Anaconda installation/environment that you have permission to change. On Windows, that means choosing the recommended \"for this user only\" option on install of Anaconda. Otherwise, these installations require Administrator / root rights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda install --yes r-essentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# !conda install --yes rstudio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda install --yes r-lavaan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing and Enabling the Python-R bridge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need `rpy2` which provides the bridge between this python notebook and R. (Also installing tzlocal due to a current dependency bug in rpy2.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda install --yes tzlocal rpy2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's **enable the rpy2 extension**, so that we can then execute R code with the %%R magic command at the top of a cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are on Windows, and you do not get text output from %%R cells showing up in the notebook, but instead in the console window where jupyter is running, this is a bug in rpy2 on Windows, and there's a workaround to capture stdout by running the following cells, see https://github.com/vitorcurtis/RWinOut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "install.packages(c(\"R.utils\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "100  1259  100  1259    0     0  11550      0 --:--:-- --:--:-- --:--:-- 11550\n"
     ]
    }
   ],
   "source": [
    "!curl -O \"https://raw.githubusercontent.com/vitorcurtis/RWinOut/master/RWinOut.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "%load_ext RWinOut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing Interface Conveniences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally we want to see plots directly in the notebook. The simplest way is to request inline plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But assuming you are running this in a JuyterLab interface, you might want the `ipympl` library to get interactive widget plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We install ipympl with pip, as it is not yet readily available with conda:\n",
    "!pip install ipympl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nodejs is needed for the interactive features if using JupyterLab,\n",
    "# the corresponding package for normal notebooks, widgetsnbextension, should already be installed.\n",
    "!conda install --yes nodejs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "jupyter-widgets-jupyterlab-manager-0.38.1.tgz\n",
      "yarn install v1.9.4\n",
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      "[1/5] Validating package.json...\n",
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      "warning css-loader > cssnano > autoprefixer > browserslist@1.7.7: Browserslist 2 could fail on reading Browserslist >3.0 config used in other tools.\n",
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      "info fsevents@1.2.4: The platform \"win32\" is incompatible with this module.\n",
      "info \"fsevents@1.2.4\" is an optional dependency and failed compatibility check. Excluding it from installation.\n",
      "[4/5] Linking dependencies...\n",
      "warning \"@jupyterlab/vdom-extension > @nteract/transform-vdom@1.1.1\" has incorrect peer dependency \"react@^15.6.1\".\n",
      "[5/5] Building fresh packages...\n",
      "success Saved lockfile.\n",
      "Done in 75.75s.\n",
      "yarn run v1.9.4\n",
      "$ webpack\n",
      "Hash: f1eac10162dd2d44bf4d\n",
      "Version: webpack 4.12.2\n",
      "Time: 39111ms\n",
      "Built at: 2018-11-02 20:57:26\n",
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      "                                                        index.html   1.53 KiB                                          [emitted]  \n",
      "Entrypoint main = vendors~main.44f7c1af2649541ea6c6.js vendors~main.44f7c1af2649541ea6c6.js.map main.5a992934528990838951.js main.5a992934528990838951.js.map\n",
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      " [YuTi] (webpack)/buildin/module.js 497 bytes {vendors~main} [built]\n",
      " [eTbV] ./node_modules/codemirror/mode sync ^\\.\\/.*\\.js$ 2.78 KiB {0} [built]\n",
      " [yLpj] (webpack)/buildin/global.js 489 bytes {vendors~main} [built]\n",
      "    + 2390 hidden modules\n",
      "\n",
      "WARNING in jquery\n",
      "  Multiple versions of jquery found:\n",
      "    2.2.4 ./~/jupyter-matplotlib/~/jquery from ./~/jupyter-matplotlib\\src\\mpl_widget.js\n",
      "    3.3.1 ./~/jquery from ./~/@jupyter-widgets\\base\\lib\\widget.js\n",
      "\n",
      "\n",
      "WARNING in vega-lite\n",
      "  Multiple versions of vega-lite found:\n",
      "    2.5.1 ./~/vega-lite\\build\\src from ./~/vega-lite\\build\\src\\compile\\selection\\selection.js\n",
      "    2.6.0 ./~/vega-lite\\build from ./~/vega-lite\\build\\src\\index.js\n",
      "\n",
      "Check how you can resolve duplicate packages: \n",
      "https://github.com/darrenscerri/duplicate-package-checker-webpack-plugin#resolving-duplicate-packages-in-your-bundle\n",
      "\n",
      "Child html-webpack-plugin for \"index.html\":\n",
      "     1 asset\n",
      "    Entrypoint undefined = index.html\n",
      "    [KTNU] ./node_modules/html-loader!./templates/partial.html 567 bytes {0} [built]\n",
      "    [YuTi] (webpack)/buildin/module.js 497 bytes {0} [built]\n",
      "    [aS2v] ./node_modules/html-webpack-plugin/lib/loader.js!./templates/template.html 1.22 KiB {0} [built]\n",
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      "        + 1 hidden module\n",
      "Done in 48.33s.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Node v8.9.3\n",
      "\n",
      "> C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\npm.CMD pack @jupyter-widgets/jupyterlab-manager\n",
      "Node v8.9.3\n",
      "\n",
      "> node C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\jupyterlab\\staging\\yarn.js install\n",
      "> node C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\jupyterlab\\staging\\yarn.js run build\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jupyter-matplotlib-0.3.0.tgz\n",
      "yarn install v1.9.4\n",
      "info No lockfile found.\n",
      "[1/5] Validating package.json...\n",
      "[2/5] Resolving packages...\n",
      "warning css-loader > cssnano > autoprefixer > browserslist@1.7.7: Browserslist 2 could fail on reading Browserslist >3.0 config used in other tools.\n",
      "warning css-loader > cssnano > postcss-merge-rules > browserslist@1.7.7: Browserslist 2 could fail on reading Browserslist >3.0 config used in other tools.\n",
      "warning css-loader > cssnano > postcss-merge-rules > caniuse-api > browserslist@1.7.7: Browserslist 2 could fail on reading Browserslist >3.0 config used in other tools.\n",
      "[3/5] Fetching packages...\n",
      "info fsevents@1.2.4: The platform \"win32\" is incompatible with this module.\n",
      "info \"fsevents@1.2.4\" is an optional dependency and failed compatibility check. Excluding it from installation.\n",
      "[4/5] Linking dependencies...\n",
      "warning \"@jupyterlab/vdom-extension > @nteract/transform-vdom@1.1.1\" has incorrect peer dependency \"react@^15.6.1\".\n",
      "[5/5] Building fresh packages...\n",
      "success Saved lockfile.\n",
      "Done in 107.61s.\n",
      "yarn run v1.9.4\n",
      "$ webpack\n",
      "Hash: f1eac10162dd2d44bf4d\n",
      "Version: webpack 4.12.2\n",
      "Time: 36409ms\n",
      "Built at: 2018-11-02 21:00:13\n",
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      "     vendors~@jupyter-widgets/controls.741a7524652a40694e8a.js.map    303 KiB       vendors~@jupyter-widgets/controls  [emitted]  vendors~@jupyter-widgets/controls\n",
      "                                                        index.html   1.53 KiB                                          [emitted]  \n",
      "Entrypoint main = vendors~main.44f7c1af2649541ea6c6.js vendors~main.44f7c1af2649541ea6c6.js.map main.5a992934528990838951.js main.5a992934528990838951.js.map\n",
      " [0] multi whatwg-fetch ./build/index.out.js 40 bytes {main} [built]\n",
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      " [ANye] ./build/index.out.js 35.9 KiB {main} [built]\n",
      " [RnhZ] ./node_modules/moment/locale sync ^\\.\\/.*$ 2.88 KiB {main} [optional] [built]\n",
      " [YuTi] (webpack)/buildin/module.js 497 bytes {vendors~main} [built]\n",
      " [eTbV] ./node_modules/codemirror/mode sync ^\\.\\/.*\\.js$ 2.78 KiB {0} [built]\n",
      " [yLpj] (webpack)/buildin/global.js 489 bytes {vendors~main} [built]\n",
      "    + 2390 hidden modules\n",
      "\n",
      "WARNING in jquery\n",
      "  Multiple versions of jquery found:\n",
      "    2.2.4 ./~/jupyter-matplotlib/~/jquery from ./~/jupyter-matplotlib\\src\\mpl_widget.js\n",
      "    3.3.1 ./~/jquery from ./~/@jupyter-widgets\\base\\lib\\widget.js\n",
      "\n",
      "\n",
      "WARNING in vega-lite\n",
      "  Multiple versions of vega-lite found:\n",
      "    2.5.1 ./~/vega-lite\\build\\src from ./~/vega-lite\\build\\src\\compile\\selection\\selection.js\n",
      "    2.6.0 ./~/vega-lite\\build from ./~/vega-lite\\build\\src\\index.js\n",
      "\n",
      "Check how you can resolve duplicate packages: \n",
      "https://github.com/darrenscerri/duplicate-package-checker-webpack-plugin#resolving-duplicate-packages-in-your-bundle\n",
      "\n",
      "Child html-webpack-plugin for \"index.html\":\n",
      "     1 asset\n",
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      "    [KTNU] ./node_modules/html-loader!./templates/partial.html 567 bytes {0} [built]\n",
      "    [YuTi] (webpack)/buildin/module.js 497 bytes {0} [built]\n",
      "    [aS2v] ./node_modules/html-webpack-plugin/lib/loader.js!./templates/template.html 1.22 KiB {0} [built]\n",
      "    [yLpj] (webpack)/buildin/global.js 489 bytes {0} [built]\n",
      "        + 1 hidden module\n",
      "Done in 46.88s.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Node v8.9.3\n",
      "\n",
      "> C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\npm.CMD pack jupyter-matplotlib\n",
      "Node v8.9.3\n",
      "\n",
      "> node C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\jupyterlab\\staging\\yarn.js install\n",
      "> node C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\jupyterlab\\staging\\yarn.js run build\n"
     ]
    }
   ],
   "source": [
    "# install the jupyterlab extensions:\n",
    "!jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
    "!jupyter labextension install jupyter-matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now enable widget-based plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, since this is a long document, the following extension adds a table-of-contents sidebar to the JupyterLab interface:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!jupyter labextension install @jupyterlab/toc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing More R Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for some R packages that are not readily available in an Anaconda default install. They might be available through the *conda-forge* \"channel\" - however, at the time of writing, I cannot recommend this, as the performance of `conda install` is abysmal when using R packages from that repository."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using a server-hosted notebook, some or all of these packages might already be installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "install.packages(c(\"semPlot\", \"OpenMx\", \"semTools\", \"sem\", \"gpairs\", \"GGally\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A note to avoid possible confusion: `lavaan` provides a function `cfa` as a convenience for confirmatory factor analysis. There is also an R package called `cfa` - however, that one is not related to SEM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing and Configuring Python & R Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import all the basic packages in Python, such as `pandas`, `numpy`, `matplotlib`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also import `seaborn` for simple high-level plots with decent looks. It complements the default plotting provided by `matplotlib`. See here, for a useful brief overview of looking at data using some of seaborn's plot types: https://elitedatascience.com/python-seaborn-tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, some of the libraries that are statistics-oriented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf # the R-like interface for statsmodels\n",
    "import statsmodels.graphics as smg\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for loading R packages, we will be using."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "library(lavaan)\n",
    "library(semPlot)\n",
    "library(OpenMx)\n",
    "library(semTools)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In R, there are also several packages providing convenient high-level plots, such as generalized pairs plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "library(GGally)\n",
    "library(gpairs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " For the purpose of getting nice visual output, we will also set some defaults for plotting libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "defaultfigwidth, defaultfigheight = 10, 9\n",
    "\n",
    "# set a slightly larger default size for plots. default dpi is 100\n",
    "plt.rcParams['figure.figsize'] = [defaultfigwidth, defaultfigheight]\n",
    "\n",
    "# enable seaborn's defaults for nicer plots overall:\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is currently no built-in way to set default dimensions for plots in R. The %R magic command from the `rpy2` library accepts width, height, and units parameters like this: `%%R -w 10 -h 9 -u in -r 100` but it would be nice to set defaults."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is python, there is a way around that using monkey-patching. Note that this is usually a Bad Idea(TM) and should be avoided if possible. It is also purely cosmetic for the purposes of this notebook, so it can be safely ignored. :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these are the defaults we want to set:\n",
    "default_units = 'in' # inch, to make it more easily comparable to matpplotlib\n",
    "default_res = 100 # dpi, same as default in matplotlib\n",
    "default_width = 10\n",
    "default_height = 9\n",
    "# try monkey-patching a function in rpy2, so we effectively get these\n",
    "# default settings for the width, height, and units arguments of the %R magic command\n",
    "import rpy2\n",
    "old_setup_graphics = rpy2.ipython.rmagic.RMagics.setup_graphics\n",
    "\n",
    "def new_setup_graphics(self, args):\n",
    "    if getattr(args, 'units') is not None:\n",
    "        if args.units != default_units: # a different units argument was passed, do not apply defaults\n",
    "            return old_setup_graphics(self, args)\n",
    "    args.units = default_units\n",
    "    if getattr(args, 'res') is None:\n",
    "        args.res = default_res\n",
    "    if getattr(args, 'width') is None:\n",
    "        args.width = default_width\n",
    "    if getattr(args, 'height') is None:\n",
    "        args.height = default_height        \n",
    "    return old_setup_graphics(self, args)\n",
    "\n",
    "rpy2.ipython.rmagic.RMagics.setup_graphics = new_setup_graphics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are borrowing example data from this excellent course offered at Harvard, S090A1: https://canvas.harvard.edu/courses/8737/pages/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual data is from the Zambian Early Childhood Development Project. The full sample has more than 1600 Zambian six-year-olds, from a study led by Günther Fink and Stephanie Zuilkowski."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is in the proprietory stata format, so we first need to convert it and import it. We will use `pandas.read_stata` but this could also be accomplished in R with the `foreign` package. First, we write a helper function to download the data file if it is not in the current directly. Defining a function will allow us to re-use it later for other datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os.path\n",
    "import urllib.request\n",
    "\n",
    "def downloadIfMissing(filenameData, remoteLocation):\n",
    "    '''Check if the file exists. If not, try downloading from remoteLocation.'''\n",
    "    if not os.path.isfile(filenameData):\n",
    "        with urllib.request.urlopen(remoteLocation) as response:\n",
    "            with open(filenameData, 'xb') as destinationFile:\n",
    "                destinationFile.write(response.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure we have the small data file available in the current directory, if not, try to download it:\n",
    "filenameSmallZambiaData = \"S090_InClass_Zambia.dta\"\n",
    "downloadIfMissing(filenameSmallZambiaData, \"https://canvas.harvard.edu/courses/8737/files/1839865/download\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1613"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the data into a pandas dataframe\n",
    "smallZambiaDF = pd.read_stata(filenameSmallZambiaData)\n",
    "len(smallZambiaDF) # should return 1613"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure we have the full measurement data file available in the current directory, if not, try to download it:\n",
    "filenameMeasureZambiaData = \"S090_InClass_Zambia_Measurement.dta\"\n",
    "downloadIfMissing(filenameMeasureZambiaData, \"https://canvas.harvard.edu/courses/8737/files/1994882/download\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1623"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the data into a pandas dataframe\n",
    "measureZambiaDF = pd.read_stata(filenameMeasureZambiaData)\n",
    "len(measureZambiaDF) # should return 1623"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have the data in a dataframe, let's get an overview of what kind of data we are dealing with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's have a look at the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>childid</th>\n",
       "      <th>male</th>\n",
       "      <th>urban</th>\n",
       "      <th>ece</th>\n",
       "      <th>reasoning</th>\n",
       "      <th>socemo</th>\n",
       "      <th>vocab</th>\n",
       "      <th>vocabsq</th>\n",
       "      <th>wealth</th>\n",
       "      <th>books</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>ECE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>18.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No books in home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No ECE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No books in home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>Female</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No ECE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>19.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No books in home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No ECE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>12.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No books in home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No ECE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>25.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Books in home</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   childid    male  urban     ece  reasoning  socemo  vocab  vocabsq  wealth  \\\n",
       "0      101    Male  Urban     ECE        3.0    1.05   18.0    324.0     3.0   \n",
       "1      102    Male  Urban  No ECE        4.0    1.00   19.0    361.0     2.0   \n",
       "2      103  Female  Urban  No ECE        4.0    1.80   19.0    361.0     3.0   \n",
       "3      104    Male  Urban  No ECE        5.0    2.35   12.0    144.0     3.0   \n",
       "4      105    Male  Urban  No ECE        5.0    1.50   25.0    625.0     2.0   \n",
       "\n",
       "              books  \n",
       "0  No books in home  \n",
       "1  No books in home  \n",
       "2  No books in home  \n",
       "3  No books in home  \n",
       "4     Books in home  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smallZambiaDF.head() # equivalent to [:5] i.e. first five entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1613 entries, 0 to 1612\n",
      "Data columns (total 10 columns):\n",
      "childid      1613 non-null int16\n",
      "male         1613 non-null category\n",
      "urban        1613 non-null category\n",
      "ece          1613 non-null category\n",
      "reasoning    1613 non-null float32\n",
      "socemo       1613 non-null float32\n",
      "vocab        1613 non-null float32\n",
      "vocabsq      1613 non-null float32\n",
      "wealth       1613 non-null float32\n",
      "books        1613 non-null category\n",
      "dtypes: category(4), float32(5), int16(1)\n",
      "memory usage: 53.9 KB\n"
     ]
    }
   ],
   "source": [
    "smallZambiaDF.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>childid</th>\n",
       "      <th>male</th>\n",
       "      <th>urban</th>\n",
       "      <th>ece</th>\n",
       "      <th>reasoning</th>\n",
       "      <th>socemo</th>\n",
       "      <th>vocab</th>\n",
       "      <th>vocabsq</th>\n",
       "      <th>wealth</th>\n",
       "      <th>books</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1613.000000</td>\n",
       "      <td>1613</td>\n",
       "      <td>1613</td>\n",
       "      <td>1613</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>1613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No ECE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No books in home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>810</td>\n",
       "      <td>814</td>\n",
       "      <td>1110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4158.768754</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.451333</td>\n",
       "      <td>1.647789</td>\n",
       "      <td>21.475512</td>\n",
       "      <td>489.346558</td>\n",
       "      <td>2.957222</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2376.382896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.521065</td>\n",
       "      <td>0.450903</td>\n",
       "      <td>5.307204</td>\n",
       "      <td>216.253510</td>\n",
       "      <td>1.430792</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4312.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.611111</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>484.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8125.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            childid    male  urban     ece    reasoning       socemo  \\\n",
       "count   1613.000000    1613   1613    1613  1613.000000  1613.000000   \n",
       "unique          NaN       2      2       2          NaN          NaN   \n",
       "top             NaN  Female  Urban  No ECE          NaN          NaN   \n",
       "freq            NaN     810    814    1110          NaN          NaN   \n",
       "mean    4158.768754     NaN    NaN     NaN     4.451333     1.647789   \n",
       "std     2376.382896     NaN    NaN     NaN     2.521065     0.450903   \n",
       "min      101.000000     NaN    NaN     NaN     0.000000     0.444444   \n",
       "50%     4312.000000     NaN    NaN     NaN     4.000000     1.611111   \n",
       "max     8125.000000     NaN    NaN     NaN    10.000000     3.000000   \n",
       "\n",
       "              vocab      vocabsq       wealth             books  \n",
       "count   1613.000000  1613.000000  1613.000000              1613  \n",
       "unique          NaN          NaN          NaN                 2  \n",
       "top             NaN          NaN          NaN  No books in home  \n",
       "freq            NaN          NaN          NaN              1171  \n",
       "mean      21.475512   489.346558     2.957222               NaN  \n",
       "std        5.307204   216.253510     1.430792               NaN  \n",
       "min        0.000000     0.000000     1.000000               NaN  \n",
       "50%       22.000000   484.000000     3.000000               NaN  \n",
       "max       30.000000   900.000000     5.000000               NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smallZambiaDF.describe(include='all', percentiles=[]) # describe categorical and numerical columns, don't bother with percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       1613\n",
       "unique         2\n",
       "top       Female\n",
       "freq         810\n",
       "Name: male, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smallZambiaDF.male.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    810\n",
       "Male      803\n",
       "Name: male, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smallZambiaDF.male.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    356\n",
       "4.0    340\n",
       "2.0    311\n",
       "5.0    307\n",
       "3.0    299\n",
       "Name: wealth, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smallZambiaDF.wealth.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 727.225x648 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visually check relations between numeric variables\n",
    "grid = sns.pairplot(smallZambiaDF, hue=\"ece\", height=defaultfigheight/6, kind='scatter')\n",
    "# we have to use an explicit height per facet-figure here, since a grid of figures doesn't follow the matplotlib default size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 727.225x648 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.pairplot(smallZambiaDF, hue=\"ece\", height=defaultfigheight/6, kind='reg') # linear regressions on top of scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: pandas.scatter_matrix is deprecated, use pandas.plotting.scatter_matrix instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(smallZambiaDF)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.swarmplot(x=\"male\", y=\"socemo\", data=smallZambiaDF)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:191: FutureWarning: from_items is deprecated. Please use DataFrame.from_dict(dict(items), ...) instead. DataFrame.from_dict(OrderedDict(items)) may be used to preserve the key order.\n",
      "  res = PandasDataFrame.from_items(items)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i smallZambiaDF\n",
    "pairs(smallZambiaDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PtvKKZvNQZnOlLawQfbC1aStC93B+UwCwKaj9mN8XXx7SQFvY+J0vvqRvX1bezNyw05bqY6gTVWK1/LGfY4BqwaWiCtUSTOHsTjXb+398uVJZtlme4cA9lNksSV/HBlNjlHRWkJcFEAAc/CTHKa8bPFh4//M7uvm5/FE6fueL1TviK8kv3jc/n//9v+ovO705e3p4eEp08/MvJyrSl6szZ9++fHfTXhaFZ0zXSa8VUVEnf7////7T7H8JRJ2QtFqkfdYsVIv0UknMX/2nb1d9Wy1tO5fKb37f2d/fD9zHh3BXAYMs57gDgFwQI1RQc352ShM39aZxO3/z7cvDic9Xv7gh/H//p8RqLzf03eHp56urvUCkIn1JdEP6jPYX83WyWhQzAAbq5O/pJCB1Qk5Wi/RSaTab0/6tlj/9yU6d/OGfE6ur/6OwVnA+PoS7SlANDCkjZXl4mV/YPygAgNEzcUWnAet99fD05ue9UGP8TvImnXx633/z5u8G8lVlL/3MZJ28+3kE6oRQLWqs18nk1YHBpgNUJ4S7ShDFl6vVisR3j65ff/RdpVKpWh0UEoG7x87OzsbUvooBAN9Oz/THDZuYuGw7uzLxa/r/+utL31C+9DVzdTJxub6sEn4doDoh56oFlwoRTfz6z7XWCwDcVYIvnH147dX3dRslIFXGH3w90xC7nfJ/hJ7skf8j9O9O+/u+MkGnp2dE8kN4X1ytTHz5xSj+MI06UYVqUfrVryboD6gTuaBcKiP+AFKbgGlQ5+QjUcLGrnkP3DGkjMDXMw3Z3yk/Mw15slOdGXlNrc8OP38It+7gN2ZmJ5RDPZy/qbyjic/Hic6ITt/9w/md3ttnp/9Mf35lnMiDenKN6To5FRIDglwnJK0W6dLRrpYILhU1AbmrjPIDSI1KjrvN4SCRKgMAYNr4nbuzdPjy2zfizVsY7qGXgnpjZnbi9LD6vvdO5R1NXg18zqnpOhmNPFyxWv7UX4JqwaWiCneVIJLnuFcqFYu57X08trjL2sy0mtD0m9Ysv8thsQDAn/E7X6xeKa6+XD3sL5m4nDelP/7y6ioRSQZuG+L8jb8HbjNXJ8YmlfF7nZBYLT/s03/qLbFfLX964+PhIAmXigbcVWA4HgN38QcIIZzV+kFc53cKy+96UiwNniOCeKdgIEVg7UZmdTWj9ebA+MtNYfKdwTGZ5S8Vr/3IeJ2oLxu/87fzv5ke193Eh25kVud/rzVdvKFqGVzlVwGoFmuXSrM/j1UgPz6Eu4rTvI4EVFJlLi5Sz6qJ2kwtYWlgGR4D91FzcXExNjZmJ5Xfk+vS6w8DWIFLBQAAwC3x5WpVda7UeNVixgwCdwgsTIcEAAAwyoIXCSBwDwJPrsvgfRhGAS4VAADwxEjet52fORWBO4BjRvKuBAAAAKo0U2UsQ+DuD1zNL8DPTvk/Qk/2yP8Rur/Tyw5t2kssF8V0Q0d2p9En0nrhDpbgZrFkoFocvFSMFM5bUQYvFZt7993mrC8VP26odano3JkZ/frK1QNo+ARMtiFw9we+5xdgvlN+ZhrS3ymjuxImYDK+U606kT1jmk2t8T2GcHlD1ruzVrjZvXBSrMGSnbpUrO2d26Kk69jcu683l/LpXYX1hqP8ANJSX59ZKfeyZS6uP9rbyYZt7BqBOwAAAACA4zqF3MLh7F6lEr58nSM7sTtmTgUAAAAAcNzJp+bcQ0mUHs4+nGt+OrFR4vAW925xaXG31X+VXCnlY4ZKbmyk1ypEFH2wtZkJ2SoKAAAAAMBf4om5J68L9+JZscX99cHcQzvdVYcH7p12y3yI3S0urdFKqRSjbnFpcSMibG+pKAAALrwvrv7wjoiIJma1JhAfso5s9vHzN9++PDwV3xU30VrOK4drpv3T6v6+7P3+tPeXxVwu44/1Chnyp39fXK1MfPnFb8ztiguXB/rr3385PW3qIpEu/7v/qX/22nXlm0pRY/jg3/+0//f/9OW0yhqy+4ziU/OvnD5m0FavHdCHsfvJ7ctFY7SSLDMcDrLbPo5G7iqX9xvU1RvOO+1WMh4jIgp9loju1hv5WEyzKAAA3p2/+faH09m/m//NX0//mzffvnxZvKIMGoV1vly9M07nauucv/nx8JQmLhecnZ6qPpv7y//IrJumg4adtZF1Bmsmcnt19fKs3xdXfzidnRGjdmkI/+0Eh2GZrQrRuiSIiOh98Yd3NDE7WMy73vrnb759+S1xWB9Eg+f7f/77ly+LN4zXifBlZXV1XPj/D//591Eh7teoK9n6/FaKGiMXj+B98YcT+vV/p1rGwH1G5VNz+eUHmPNgOMhOu9WqLKZ3iUgSpDc21o4fbJUyoW5xaXGjIQ/dpSF6KDJFtXaXYiH1ogTSAbBlQ3NoDROhPwCQ5Xc9KdbICgDgnffVw9Obn9/5FTWJxu8kbx7+8PP7zI0bauuME6muc/7mx0OakITtdH52ShO/U2kv6y9nPq6YA4actZF1lDUj2bT4w7uJ2S/vjBPR+5/f0YQQwhPRjZnZiZfv/uH8DmdBma0K0bokBhtNpcX0K6dXTvX9HQ5/hRg435v//eQfaobr5PxN5d3EzS97VXJjZvbXL//rP5zTnXGNulKsz+VFosXIxUPqF0Of/NOk9qlp/+k3f+38wYNLhgXu3fbxZZZ6t7i0VAxvZkKNeiWa2AoRUSgzn0wLDeoSnXaLIsaK6r0pDdbFQXaEWFZr0DedsXgsv+tVsaQ4a7AP0yGBY87PTmni5pV+KH3jdzdJ8UQV1yG1df707sfDic+/nKi8vHzgnp2eTkxcIQWt5TzSP2sj65y/UdaMuOWbyjvqBzJ0I7O6yug0nGOvQjT/9O9/fnfz89WZs2/ltTRxRXxiXJmYoHdn53SDt2eI7HwjV2+SInLXqhM6PZ24+ZfiGY3f+dv53wh5Nup1dSZf/4vVO46fECtGLh66vBj+/cv/qihB8WnyxacmyFRmTr24SD2rJmoztQSTVJlQZrOUEV9Eplr1DlGIiFq7/bZzomikS/RjL3WGkiulu5GoiaJgOK7mF+Bnp45M9HDr1i0W4687CBMweb/Ts9NTIULSaQMX11Fx/u7//sPE56s36E3lcuH7n98RTfy42kvTFTO2tZZzSe+sjawjBBqymukTWpTvaqQKVA9PJ2b/krcg1VaFaP/pb2RWbxCdnyk2Ob2M1M9OT+n09IwoQHXSa1a/bGK+mZj//TSRVl0p1+f64yNjpKJI52LQ/TQRUf9T89e/snukOkb8AaSYgEkzVSZetRC0E9kYx12R2p4vlfKXry7D8m77mKbiCNDt4X9+AaY7ZT3TkCdnahwmYDK+Uzfv4KcG2jaFdc7f/PiHP0+s3iCSHvL52SkR3by7KrQnn7/5drVIq5kbkuXNZnP63/xTbzmzE3GcqZoRAg1Su4Le/yxpbh/UT+72RwaE0Qq5onFJaG1z43c36d1hLzvm/E1FK3uCRwbr5Oz0lN79sEqfr65miIjeF1f/87u/mp4e1/j4kHL9b9/4KMddyUhFCfQ/TST51Pzqj6xmJqbRfgC5Y9g47o2N9FKx23vRbR8LXU5j8WRlX1g8sIIoHIn2Vui+rfX6qaoXBQDgSwZaxmjiyrjwOE3cVmQPjt/5YnX1MqIYvzJB735+r73cP0zVzOeacen7n9/Rzd+pvCuMJnLzc9+EY0YrxPSf/kZm9fOb735YXV1dXf2RkrMT5JscK6N1QkQTs1+KF8mN303+8x+q+h+TwfVvnh5WffXxkTFSUUQ07NPkv09NkNTXZ5J9M7lCx15pw1rcY/mt9lI/KSb6YGsz1lu8Uk8Li6MPtvKK9vRQZnOlLawgbqRRFAAA7/rZw3pjMWhkGJ//w7tTOj3df1frLzl8uXrqp9/vB0iH4Pv17/9u+o6BvGrdmvlh9bKdWKiZ3wuD1Qnt7er98iaEcTd4ZCTR3Mlk9BuZXtsy0fkbTpvcbdTJlYkJIoOhq5X1vSX9NE3MfvnFX1q/MLQ+Tb30O84/NUHm/Mypw1NlBlLTL8UGU2MMraBRFAAA38avTNDp6Rn9Riem7K/TyzDurzN+44vVO9QUBnaUjq/8vrj6Aw0G8BMTV7SX80Ha2a/ZbNL4f6t61oPb6NVMj6Rmmk0iRT89ca3Lke34pHGyhtYx+6cfHK77/B/eDfTL5IfsfNufTFwkNEGHsv6Zv9b5mKitz9HHR0b4NDUvR301cPHoFtUjuzD4/9QE2cmn5tzDncGZU1/VTogsB+7DUmUAAEDIJq68+RNRf6gTZQ5Hb51zvXWkq8/MTkgSId7//E6IurSWc8rIWZusGSJS7ad3/ubHQ5KkQfDJRoWY/dOP/+XNiX4iyPmbHw9Pbyb5bFEdON93/8+J2TrpLSY6f1M5+fWk3sdEsT7fHx8ZK5+UIfzxqQmweGLu4LUkO6ZTeH0wl7DYL5WIbHROBQD/wRCZlt3IrH5eXP1hn/4TSUeqGGjaEtZ5uXpIZGQ0i/E7X3xeXO2P1XY5lYzWck5pnLWdmiHVBnchGeCwV0oPhw2JWhXy7j/v/5d/0q8Qs3/68TtffH662suP4LAqRAPnO5kwc5GM3/niS/q2/1efmP27v/2N7sdEvj7nHx8ZI58mU1Q/NZOJee4ndgsM52dOHbu4uHDu+JwhHdF8bGzs7Ew55lHQxnGXnqbqsBg6m6dSKRajGd66datcLnOyU6bDp6RSKZ0zNVWOy38IVItyucFRZZpWZyR1eUP3j5OHvbA7eAslO3gwgSnK5t59vTmLkgO2odYDaBRiFVIZDtI6rapGi7s/6IcjjJpROdypnZVdKIcRr8ZxD1K1NJvy4c+USywXxXRDR3an9QCwXLiDJbhZLBmoFgcvFSOF81aUwUvF5t59tznrS8WPG2pdKjp3Zh7CBtY7dTBw14LA3R902lBNNYuaakZ1ZKdmG24xjruSV+O4+7FatOpE9ozhs6XK/d3ZbHoMZIu7U5eKtb1zW5R0HV83mTtYdT69q7DeUOvB4UmsQsy+LXj4fETgDgAAAABBw+7bgocwqgwAAAAAgA/w2OIu+7Fb67dv/awmy+96UqyRFQAAAABglPEYuEtHlSHtTNYgjSpDirMGP8JgiwAAAMAOj4E7gB8FMpfOEfg+AwAA4AgE7gDAELsBiAAAAEYNOqcCAAAAAPgAWtz9wcHEd/dz6Nnt0dczDaFaeNgjAAA4yL8PIEd2igmYoMfBof7dnzXA/h4DOdMQqoXFHhH6AwB4yL8PIK92ahZSZQAAAAAAfACBOwAAAACADyBwh1F3dHTk9SEAAAAADKcTuDc20ul0Op1eKnatlKy6uc0yAQAAAABGlFbg3i0urdFKqVQqbSVqixsNk8Wqbm6zTAAAAACA0aUVuHfarWQ8RkQU+iwRrdT7UXa/yTwti7sbGwNLVDfXKhMAAAAAAIbQGA6y2z6ORu4K/w9FpqjW7lIsRI2NteMHW6VMqFtcWtxolPIxE5uTepkC6aTospzjK1euWD07P3FnGLujoyOXRztyf4++wH+1BOxSaTabQ5dYLorpho7sbnp62tnCHSzBzWLJQLU4eKkYKZy3ogxeKjb37rvNWV8qftxQ61JxhCePSP6fywKNwL3TblFEsbRRr0QTWyEiCmXmk+l6Ix+LNTbSaxXh7Uq6QkTRB1ubEbXN1cvskQbrYsWNj4+fnZ2prj8+Pq5Tv5bf9bBY6VmrrqO1uVlBmlVnxCd6YF1ykC4V2TOm2Wxae+q4vCHr3dl89Fo+PE+KNViyU5eKtb1zW5R0HZt79/XmUj69q7De0L8PIEd26t0ETOFIVP2N1u5ierf3/2ikS7FYvlTKEzU20vW42ALfVdtcs0wYzqlvgfpfIVhwZI9MZxpyv06c2mnAqoVpnQAAgAt8+gDyaqcWjF1cXKgtlwTi3eLSYnu+lI/JonOt9Y1sfrkQAAAAAACG0+qcGo5EK/vFLhF139b6fUpj8WRvITU2Bod0jOUHonDVzVUXAgAAAADAcFot7kTUz16PPtjazIRkC471NikAACAASURBVAeXmt586NYAAAAAACChE7gDAAAAAAAvdGZOBQAAAAAAXiBwBwAAAADwAQTuAAAAAAA+gMAdAAAAAMAHNCZgAs5gAibV5SM+0UPAqsW1OvHjzMo2izV4qZj6E3Cysp3CVavF+KXC/2oWimL6CXJkBdblq65g81IZ5bvKUI4/aywUqDvdaaeQe0rf7GTDRFRfn6klqstxe0dokNYktawDd9lMS4aGgzT+LGFxTXteJqZ+BAAAAD+yEMM4HvaYLVA3cOcO28C9W3y+26Kk+GppjVZKpRh1i0uLGxFOJk796quvhP+8ePHC2yMBPxKvH9YsX5/8HyEAAASDtCHSqaePqYeL00344ezOjnOlOYBl4N4tPq9NJaOt/utOuz9dauizRHS33sjHuIjcnYDo3xNOfU33xa8cvjhIAACAgIovV91Jk9HDLnDvFp/XEl9/Tc8r7d6C9nE0clf4fygyRbV2l2L9dJlbt26JWx4dHcnK0glZ9KMZUxuKSyyXqfOunTJVV5ifn9ffxCn7+/ve7lSH8lt1gFuXlSeLUB4AAGCksArchbB9M0RFcVGn3aKI1vrSYN2rHHdhic1uMarv2s9xd7/3pLe7NtIxCLR4VV3294s/NAAAcKBTyC1sfxgTXlxcf7QndE/lAJvAXQzbqXu5MByJMtkZAAzw6muezf0yHVUGAABcwOImbLZM251TO4XcwuHsXmWHhCFlbv+UW8gRJ7E7k8C9+7bWarUW07u912vp4wdbm5nIVKveIQoRUbd9TFNxrWFlAgS570x5GKW5v2uEpAAAwDkWLUfsxofVcPKpOfdwJ0zUEV6Hsw/nXtVOiAIbuIcym6WM8N+B4SAj0cp+8W4sE+q+rbWS81o9U3Wyz8UcazEHekRy3EHVSGUQIcfdE6lUimn55XKZafkeslN1Aa4WayxXpi9q0vFPmS/OWiDt4OcmH1WRFyavTh/U6svxfk/UTuH1wfTsPU+PScSsc2p/xHYiutlbFMps/i9/n15M7xL9N//DalFzRBkjOe4289FdyHGX9ZLEOO4APqXsLq9FeAabXT/AVKtiaC0FvlqsGVpjyhV8VJNGPjUGP18+OmuB8TuGlNm7jXJb0BbOfvM4t7Ber94jam7fT74ahRz3/ojt1C0uLdYbFBP++3/85Urpf+uN495wcBx3IUp2Nh1FP8sFOTDALXROBQAAsC6c3akSEcV3qlmPD0WOTeAeymyW+v+9HLLd7XHcxbwam7E1i28F4AjkuKtC51QAAIBAYjtzKgkdVYV0dkfHcReC6f39felbpvLLzea4a70re6k1jriwmjJHX//IQR9y3F0+BgAAAB2BGFWmvj7zpDw2Jl10cZF6Vl32fvol1jOnLi3utij6YCtGxGQcd9nKWqupRjxmc9zFd2WTEBkM4GRbCS+FVnwjOe4j1bTs1U4BAADAjkCMKhNfrlaXJa87hdxTusdD1E5sA/fe4DKNjfRScWszg3HcbRippmXlTq2N1cMUz6kyAAAA4JRw9uG1me/r2cC3uPfE4sm1eocoZn0cd4Pz2KPD6KgZqe8zSJUBAADwQOfkI1HC66MQsAncGxvp/cjWZiZERNSoV4Tc9rDlcdx1qK4sC/Rl62glmqvmzQ89KjvBk8GR4wFMwagyAAAAVqnkuF9//B0Pze3EKnCP5bd+/rfizKm/fVzKh8jOOO5aa8oSx4euJmuJV2001SpTzHFXXW6NalY9YhewCaPKAACAJwLROVU1x50XjFJlGj/+x18/2PoPmZDQRXWp+FebmRDDcdxdYDBdB9yEHHcAAAB+BKJzqtwI5Lg36pXkfElIYQ9l5pO79Q5RyO1x3CHwkOPu8jEY59oXXXRoAeNSqZTwHyPzvWutLC7XfxdTygMER/Bz3GP50mVI3s9xtzGOu1P0h2B3pExr2/IcgQEABIA04E6lUvqBtdbKsqhdWCI+s0ztAgB4pZLjnnpW4aG5ndiPKtMtLq0dP9jKh4ga1sdxd+poZCU70miKHHcA/kk/VtJmAs8pA0GnHB0dDb2ZyFbgp2YcrBatliCbE/YZWYHdH9c4I8dg9lLh5zrR4kjND21D9FedyFirIiOXClNO/ZY77KdaeY47V5gG7o2N9Nrxg/7oMgEdx92dlICRSuY2vtORqhbje/TvqDLsdir9bnx0dKTzfVv2RZp17FUul/UzMu28K76lVUuybfVrxtSuyV7VmWqrNj5Jn2y5arXoTMCnQ3UF2VkYTL1VXc1yZRqsSVOXiqlPkJEVHP+U6Z+1wSMc2qDmSJ149e1Op4ps3lWCoVPI3d/+QESptcpyvL4+8/rq3k427PVhERHDwL1bXFrcnVopbYo5MyHr47gz4nl/U+MDz49UMrdypwbDDjfxnOPu01FlHNlpsJ8lYEe5XDaegK61snS5/rvIk4HRxMOoMmYLVIwqU//+FT3aq2SpkHta6MSzy3snuaeF23yE7mwC937UPjhsDJNx3E3h9qHO7YEBAATG0N83jKysH5EjXocR5/moMg4VeG0yTEST15q1E6JwuP8fZ4/DEiaBe/dtrUXUWktX+kuSK6V8zPlx3M3ysIFWH3LcAQAAADgQT8w9eV24F8/GE3NPavXl+GTQR5UJZTZLGSHFvdIL2omI/D2OO3AIOe4AAADgqHrtgD6M3U9uE9EYlZPl4M+c2gvaow9WHtTW2v1lGMcdHIYcd5ePAQAAIOgcHFVGZWTJi4vUs6r1uZxYBe6xfKmUJ+oWa+IiS+O4z8/PO3hU3AY63B4Y+BFGlQEAAE8EonOqg+TfATqF3FO6Z6fxnvU47hIdi+O4Owg57gGDVBlVGFUGAAA8EYjOqSrN5AKbjeVEFM4+vDbzfT3LX4u7ioCO4+6OkYpQje8UqTIuHwMAAEDQsZyAqWO3n6uLgTt/47j7yEhFqMqdIkIFAAAAv1FpvLfZz9XFwB3juGvj9sAAAAAARkynkFvY/tALuC+uP7I6capqjrstbgbuFsdxd5CzXV0dhBx3a5R1tb+/7/IxuL/HoWTV4skRclgtAADAmvQB5PiDgNGTRdE5tVPILRzO7lV2qJB7St/s3P4pt5Ajq7H7AO5z3EOZzZL4AuO4g8Mc+Y5nfCZFBzmyU63veD6tFqZ1Yod0zCsW6weYTlWglswaWmO+rlLjB+/r01Rl54w4rA2zt3HHnzVOFHjyqTn3cCdM1BFeh7MP5145NHOqn3LcMY47OM2pKI3nDrgelszzyDmukc5gr/88sPNuIBmvOpkRrKuhpJUpMFJLfqlJ2dnZ/Cj55awFR0dHLO4qHt6OLNzGWY/2aH79X/71Xz35w6f//eHf/HLiz/6MpqZ/+X8tHfzV/7wyPf3bYUU1m83BBSo57qlnFZ/kuFsaxx0/uAMAAACAW377+OAf+/85ICJ6fPCPj60V5fwANT4Yx53Fd0qey+Sw0REAAAAAzOoUcve3PxBRaq2yHK+vz7y+ai9Znsdx3JWRq04sqx/mWtuQqzIBAAAAwIfq37+iR3uVLBVyTwudeHZ57yT3tHDbRuiOcdz9ARMwAQAAAPjNtckwEU1ea9ZOiMLh/n+sFvcLBw9tGGEc9y4Rdd/W+v1UwZBzBU927clOfdTNCAAAAKAvnpg7eF3oCP+p1XujytjBusW9W1xabM8LAz9aHMfdQu74V199RUT7+/t28tGFQojoxYsXdg5G/y3lu2hsBgAAAPC/eu2APozdT24T0RiVk2XOZ07tFp/vtigpvsI47uAonw5Y7tROMY67aiGOHAwAAFjgx3HcFRMwOchfo8p0i89rU8loq/8a47gDQECkUilHylGOzx0AlisnkLXhiGBXqVOfJpEvzlrg1QxK7KrIj+O4swzcnccucO8Wn9cSX39Nzyvt3gIr47gLvB0BRv+l/beGvgtgh5j0xZo0qWwUKO9Uwn1MuVwLh7MeOkW1EvTrJ8C14Qid60qrYn1UpUY+NQY/Xz46a4HxO4aU2buNcltGpK3dTj19TD1cOJuBS2UCJsHFRepZddlCzgyrwF0I2zdDVBQXuT6Ou7Ioa2XKEtCR484PZV3Nz8+7s2v7U4PhDw0AABBofkmVEcN26l4uNDyO+ygQvoaOWgul4zz8Vi3u2sP2bIT+AAAAI4VJ4N59W2u1Wovp3d7rtfTxg63NDO/juItj0aguR5ANfuFVQG9/v/gqAgAAwdIp5Ba2P/SyZS6uP7I3cSqbwD2U2SxlhP9Kh4OkSLSyX7wby4S6b2ut5LxWz1Q+Z04VF5raUMzcUM2sEBtrh+ZdYAImMM6rHyJs7hejygAAAAe0E9NNh92dQm7hcHavskOF3FP6Zuf2T7mFHNmJ3Zl1Tm1spNcqwn9v9ha5N467VlEGN1R9S1ioM8q7hTJV31WNPHjICfFwp1rR2Eh9n0FICgAAnGPxqDJbpu1RZRSJ6Z1CbuHVh7Gx6dnbJgPuk0/NuYc7YaKO8DqcfTj3ytbMqaxy3JfWaKVUiglN7vUGxWIYxx0cN1LfZ5R7RCgPAABcYfFwNFWmY6PK1NeTK71BMy+uP9qrVi2F2pNXpw9q9eV4f/iYTuH1wfTsPRvHxSZwD2U2S/3/Xg7Z7vY47mKaiiw9XXVKVJuUZcr6LCJRHgAAAIB79fWZJwdzz6oV++PBhLPfPM4trNer94ia2/eTr/jMcZe6TGe3MY67MhdcCMr39/eNNDqaTbRQXa6Vj258lHcLRwIAAAAALoovV6v3CrlkckV4ff3Rd9aD7XB2p0pEFN+pZp04OLYzpy4t7rYo+mArRmRrHHett1SXKGmtI8tWN1imzijvZrdVXY4g3jjkuNtc01kYVQYAAIIhnN2p9APtTiGXTH4gmxG8Q1gG7r3BZRob6aXi1mYG47iD05DjbnBNd2BUGeBWKpWSvjQy37u4ierKsgINlgkQeIHonKo5qkzz8KdO1uPInXmqDFEsnlyrd4hizMdxd20qHAAA8AtlkJ1KpfTjbOkmypWVBRopE2AUBKJzqvPTnTqITeDe2EjvR7Y2MyEioka9IuS2hy2P4y5ShubCyvoT3RtMTzdyAMp33clxH6mcEK92CgEmvaKkPWo8pxoCOuLo6MjUjYWHanG8NpQ9pkSm7sYG70iy1fxSpX68VPQ5ciHpXDwCf9WJjLUqMnKpAGtsAvdYfuvnfyvOnPrbx6V8iOyM466zK1Pfqwzmo3OY4z5SOSHKnXLYqZfnHHdQkl5RR0dHOle1wYkjnAoxy+WyfvuQnXfFt4xkVQ09EuO7tlw5Qou12QYznfX1K8f4zdbg8chWkzXAGzwv1dVsVulQpi4Vy58grRUc/8Kmf9YGj9DUc9lynbD76q5Pp4ps3lWANUapMo0f/+OvH2z9h0xI6KK6VPyrzUwI47iDw0bq+4zxHHd0TgWQKpfLZnPcpZsoV1YWaKRMAPCLTiF3f/sDEaXWKsvx+vrM66s2B3F0DqNUmXolOV8SUthDmfnkbr1DFHJ7HHeA0YTOqQAyYlRt/DLT/wlCGqY7NuELAHCh/v0rerRXyVIh97TQiWeX905yTwu3+QjdWaXKlC5D8n6Ou41x3HWYek7LVtbqzKpfpurMSta2NbgVAAAAAP8CMaoMEV2bDBPR5LVm7YQoHO7/x1S5bLAeVaZbXFo7frCVDxE1HBjHXUa/T6qStTxFFrvQ2QpBvHHIceeWa0M8YTZiAAB+BGNUmcTck9eFe/FsPDH3pFZfjk+efCRK2CvUKUwD98ZGeu34QX90GYzjDk5DjrvLxwAAABB09doBfRi7n9wmojEqJ8tE1x9/F/f6sATMAvducWlxd2qltCnmzISYj+MOAAAAAGDDCI7j3o/aB4eNsTKOu9lkGH1GWiht7tFaIyiaTq1BqozNNZ2FIf8BAIAdp5IwfZ1jySRw776ttYhaa+lKf0lypZSPWRzH3UEupDcwynEfqQjV+E6RKmNwTXfwPOQ/AAC4gIfOqWYLVHROra/PPCmPjSk3vLhIPasue5szwyRwD2U2Sxkhxb3SC9qJiDCOu3UjFaEqd4poDAAAgH+ed051okCuU2V+wajcxkY6vXb8YOWBpEPq4DjulXqD0b4BAAAAAAKHVefUWL5UyhN1izVxEZtx3E1xoeHWWgIWWpStGakMIlwkAAAArugUcgvbH3rZMhfXH3EzcSrzcdwlOhbHcXcQt5PbYRx3a0YqgwjDQfJG2txgZPlI0akE1I81Q+vN1xVr/OB9fZqq7JxR8GqDD51CbuFwdq+yQ4XcU/pm5/ZPuYUccRK7uxi4Yxx3AFdgVBkXSGe8F49E5+ucE3OC+IbZyoGhrFWpX6pddnY2P0p+OWvB0dGR5ZPVedfD21EgOqeefGrOPdwJE3WE1+Hsw7lXozJzqoThcdzZPYm5baHk9sDAjzCqDAAAeCIQnVMnr04f1OrL8f7wMZ3C64Pp2XvOHoRVLgbuFI5EK5vpyr8Q0V/8BSW/0BpTBqkyHh6JvyDHHQAAABwVzn7zOLewXq/eI2pu30++GtEcdyIi+tdE/+LyLiG4kOPu8jEAAAAEXzi7UyUiiu9Usx4fihzjwD2U2SyJLzrtVvLfCYO3d4tLi/VGPubyQO5OzbnlPmWItr+/7/5heLJTAAAAACBXW9wtDQeJSFHgVPuu+712HNkjmpYBAAAAfDAcJIt+0zyXiSDVOB5+iODwu6WsWvDjDAAAuEP6AHL8QcDoyaIYVYZrYxcXFy7tqltcek5fb2ZCRESNjfR+ZKv3AgAAAAAA9P3CvV2FIlOtdm9IzG77mKYiiNoBAAAAAIxxMXCncCRa2S92iaj7ttZKxl3umQoAAAAA4F8upsoQETU20msVIoo+QJoMAAAAAIBxLgfuAAAAAABghZupMgAAAAAAYBECdwAAAAAAH0DgDgAAAADgAy5OwGQYJmBSroOZU1WXO3Iu7teJUzsNWLW4VicsPuOcF2vwUjH1J+BkZTuFD73ZGiyZ29UsFMX0E+TICqzLV13B5qUyyneVoRx/1lgokM8JmKanp1WX8xi4c+Krr74S/vPixQtvjwTADvFKZg2fFACAEScN6Ofn5x0p0+yEqWZnoOczcNeCwB1MQAjoC2bvWfzs16sjBwAAR7D4qZbdT3B+hMDdHy3rDgY0voiNfHGQAg6/zHh1z7K5X6apMgAAAMbU12eelMfGlG9cXKSeVZfj7h+RBI+Bu/LRq/Mw1n9Om9pQXCJ7S/8l6+MUKAMaDuNFBxnJL9RZbpYvoj1fHCQAAIDPxZer1eXe/+vrM7VEL1ivr8/UPDwsAbvAvVtcWtxt0eAsqYZmTvWqc6qwRPmWwQ4oTDungqqR+j6jPFlcKgAAACOF1XCQjY3F9nypVCqVVqZ2nxe7RETd4tIarZRKpdJWora40WC0awAAAAAAuzonH70+BBlGLe6NeiUZzxMRUSxfigkLO+1WMh4jIgp9loju1hv5WIzN7gVid2aek9cN8rBt1ZNdoy3ZDnROBQAAT7C4jZst07FRZerryZXyBaVMlcYam8C92z6ORiKytJhu+zgauSusEIpMUa3dpVg/XebWrVvi1kdHR7LyLOeO66zjco67kMKhNaSRhRx313iya4M57qAKnVNdkEqxvZWXy2Wm5bNjv2aUj4BR5tSVxuEVxfpDJHLh3KUxjJs4/LMGZVSZTiG3sP1hLLVWSdRmXhfuxbPhTuH1wfTsPdtF28Qsx721254vlfJE3eLS8+Jnm5lQp92iiNbq0ju1UznuWgVqbcg0x13rSJQbIkgF8AXj8aXwUDe7vn9pnamRevD7ubMw9MoZWrHc1qqpL2lmP0fSrVxg4QuntTOSbQ4M1NdnnhxMP96rZMNEFN87yS0kt8cuLq4/3suGvT44ZoF79MFdIQ8mFJlq7b/tZjLhSJTVzszxxfiPMkiVAQAAAGBv8t5edfkyQg9nd6pZDw9nEJvAPRSZorbKwla9QxQiom77mKbiWsPKgAJSZdw/BgAAABg94bDn7eraGLW4x+JTaz82MvmYkNqeuBsionAkWtkv3o1lQt23tVZynmnPVICRhc6pAADgiYB0Tq2vJ1cG+g/wMPWSgFWqTCy/Uk+n00REyZVSPkREFMpsrrTTi+ldocOqZtzu4ARM+uto9Tpl0TlVtoI44o1Aq9Mq6BupDCLje0TnVAAA8EQgOqfW158c8BKnK7CbgCmWL5Xy1C0uLZod99GFzqmqy73qnKrchU4J7uM5x32kMogwARMAAIAr5hJcRu3EMnAnIuoWn++2KCm+EiZgilG3uLS4ESnlg58s49REniMVoSp3iggVAAAAXBG/9/j100In7v0QMipYBu7d4vPaVDLa6r92ewImAAAAAAAz6t+/an4Yu5/cvlwU/Bx3IWxPfP01Pa/0xpfhbAIm5XJHctxlyesyWg3waFEGB6FzKgAAeCIQnVPjy9XqsqkiXMQqcBfC9s0QFcVFPE3ApLqcXY67qSNEjrtXOw0MdE4FAABPBKJzKtfYBO5i2E7dy4XcTMDkR8hxd/8YACBgUqmU8B8jE8WbWhkAAqS+PvOkPDYmXRTwVJnu21qr1VpM7/Zer6WPH2xtZribgMmpnqMAAMA5MRAX/q8fjptaGQCCRZEq0ynkntKkV4cziEngHspsljLCf7vFpcX2fG/8GEzABACekP5oI+1Rwy1p4GjN0dGR8ZkliKdqcfZIlJ2mBAb7Puks5KfGjNC5ovx7qRhk7dOkdeWIePgp2P6NwhQjl0owhSevNWsnRDyMMsN2OMhB3E3ApLUauwmYLG+IHHc7q7HA8wRM6JyqJE2+Ojo60kkAM9iPhfXzUmzi1c/X1H9XfEurYqXblstlU7mhWis78pXDwZRWnT5OqtWiur7qQoN/I4PHqbWaU1ea/o8Gpi4Vy58g2QquBZ2yczd4hEP7nvGQSK31Z7Vz37B5V2HaOVV//A/jZLNeqsycKhdfrvKQJkPEPnAPZTZLkpfCrExDuNY5VXU1PjunIsfdyGpu4nkCJh46p7qWh/bixQvpyxFtDQIDyuWy8bR1UysDgMjzzqmOFNgp5O5vfyCi1FplOV5fn3l9dW/H4qju9fXkysA9xGa6PLPAvVtcWtwVhnBProhTLTU20msVElrcM/ZT3MXvXrKHtzVIeQcACDBTvyqY/QkCAIKi/v0rerRXyVIh97TQiWeX905yTwu3rYTu9fUnB852a2UUuDc2FmuJrdJmSIjgl4pbm5mQAzOnirG1qUjd2layzR35bmDZSOWEeLVTAAAAAKJrk2Ei6qe228pxn0s4mmXzCycLEzXqleR8r0U9lJlPttodks+cWqk3nNvhV199Fez28nMFT3btyU7R4gUAAABuiSfmDl4XOsJ/anWizslHq0Xde/zxdaHj4MGxaXGP5UuXjemNeiUauevwzKnWZimy046r1XXVkfLRwAwAAADAgXrtgD6M3U9uE9EYlZNlouuPv7PUbl7//lWzX1QPrznuPd3i0trxg618iKjh5Mypxjv+m1ph6LZedU4FMM6/o8r4ZacAAKCK6agyjApUjCqjGMfdOgeL6mEauDc20mvHYjdUzJxqA3LcwTgeRpXxaqe4cgAAPBSMUWV4xnZUmamV0uVw7SHuZk71kZEa91C5UzfHizWI53HcAQAAwKr6+syT8tiY8g3zWS4qRXGZKtOP2geHjQkbnDnVSEY7ctyBRuz7jPFx3AEAAMAqlqkynULuKU3aKJFJ4N59W2sRtdbSlf6S5EopHzM6cypy3K0VAgAAAAD8sjWyJBGjwD2U2SxlWBRsl3+HjFRG87IJe93hyU7BLNnVgksFAADcIX0AOf4gYPRkUXROZSq+XLU1rjvrUWWkHJiAaWQ5lZjh/kSAjuwRv0KY4sifGJfKUNJBbFms71/6Zzo69eAUgzXmx4q1cMzcnqblA+P2jKwxext3/FnD2YTHDqbL97gZuA9OwLRbb+RjiNwNcjAcQa/KwHOqwnGp6CiXy7Il+k8LRll2HFLWDAXuHN2kWp9krEo5r3Zrpzb0pDw566OjIxYff5tV4QkLt3HWoz26eQDNZnNwQTwxRx+vfreT7aXGdAq5hU8PbfRNdTNwd2ICJtmvJPo/mjD6SUW/cyoyBAAAAACAqF47mHtYvUxoD2cfzs3U6stxzkaVUdUxOgETAAAAAIDPTV6dPnhduBe/bHF/fTA9e89GiS4G7piACQAAAABGRTi7U51cn0kme2nuF9cf7Yl5M5a4GLgbnoDJyHCQQ9+yvKHnZfoozddzmIAJAAAAeBZfrlYcGhae3O2canQCJgCDMAGTy8cAAAAAHvqFi/sKZTZXpnYX0+n0Yi2xhbEgAQAAACC4OoVcMplMJpPrdSKqr8/kCh1bBbrZ4k5EsXyplHd3l+4Qp3Z68eIFi/IxjrsjBzMiMI47AAB4wo/juLOcgKn+/St6tFfJUiH3tNCJZ5f3TnJPC7dtpLm7HLgbonz06o/AaKoopmXqvGunTADgTSqVcqQcraGsA8ByFWGQMR3Ga9Wnl5bly8an52uHJzM3jWA923ZtMkxEk9eatROicLj/H6vF8Ri42++cKrR/7+/vu9k5VaD6LovOqWIbP2uMfkMAdjABkzuU8aXwHDUedwZsxkQlC1UU+Dqxz0it+roatS4PnYvH1+drh4VvuWZvU8pt9flxAibVFvf6+sxKWT7jqfnpTuOJuSevC/fi2Xhi7kmtvhyfPPlIlDB1fIPYBe7d4tLibouIog+2NjO9AWQaG+m1imyZP7HOjZHx9fAp/orGAgCpMgAA4AnpbdypFkZTgZYjj5L6+syTj4/3KrbGbRRKqh3Qh7H7yW0iGqNyskx0/fF31udNZRe4NzYW2/OlUoyosZF+XvxsMxOibnFpjVZKpRh1i0uLG5ES+qcaxsnwKV418yMaAwAAAFd0Tj5OP/7GftRORPHlatW5oSCJmI0q06hXknEhLI/lS73G9U671VsY+iwRrdQbhpi1CwAAIABJREFUbPYNAAAAAGDJyaemkJjOIzYt7t32cTQSkaXFdNvH0chdYYVQZIpq7S7F+uky0sQpZeqVssF1fn6eiPb39z3vnKr/0lqZYJCvM4j43yMAAMDombw6/fGkQ3EHYvf6+syT8thArrz5RPkBzHLcW7vt+VIpT9QtLgmpMp12iyJaq0uDdeOdU5UrG3zXwc6p+i8tlKkan41UhGp8p5xkEHm1R4PfEoVvuS7Y39+3WQK+nAAA+BqL27jZMm13Tg3fnqUFe4M29slTZTqF3MKnBI857hR9cFdIlQlFplr7b7uZTDgStVqYyz1BOTRSEapyp4jnTPHqarG5X3ROBQDwOxYPIFNlOvIoCWd3nn2aEbqUStlsLCeicPbh3EytvhznrMU9FJmitsrCVr1DFCKibvuYpuK+HlYGAAAAAIIovlytONurVFCvHXA5HGQsPrX2YyOTjwmp7Ym7ISIKR6KV/eLdWCbUfVtrJee1xpQxlX0uXaJMCXAwx10sXJYPgBx3AAAAAFBQyXGfe1bhMVUmll+pp9NpIqLkSikfIiIKZTZX2unF9K7QYVVzLEidNAnlzx8u57iT4usBctwDs1MAAAAYeQ72KHV+OEh2EzDF8qVSnrrFpcV6Ix/DiO32IMfd/WPwL6+qC3N1AbdSqZTwH2sTRqoWpTr3u/iuPv/OG69/+oExIqfJQiA6pyqi7U4h95QmTRXKDLvAnYioW3y+26Kk+AoTMAGwh86pAFLSYPrWrVt2QjFpUalUSlaUwahddVtf0D/9wJCOTx3g02QkGJ1T5cKT15q1EyIexnZnGbh3i89rU8loq/96cAKmXd80xLs2XagOpMrYWY0FjOPuL9Lakz6VPWc81DPr6Oho6DWj32uIPKorB3eq2r5uZ6YOrZUtfDzZ/enNlm/tUhEW8vNpsl+fWr/GGOm6xk89sDaqT6L4ctVOXrqT2AXu3eLzWuLrr+l5pTe+jI0JmERDHzPG37U8AZP+yow6pyJVxshqbuJ5HHdQktbe0dGRqbkaVN9yKuoql8tm544w/q74lsFPkLI0nbbGoc1alqtI/w9k6khUl+tXi6zHkZFuVNZa+LTqVr8047U69NKSMnupiAuVZzF0p06doIx4JJYPQOcqGnqpOHJXMfsu6+9+qoZeKoGgkuMusD8cpH2sAnchbN8MUVFcZGMCJq23VJcYeddO51QZ/b6qRso00jkVAACsKZfL0hx3O9+6pUUpY1bpu0PLsXwMHtI//cA4OjoSGxMDfJqgLZ6Yo49XvxPnX+oUcgufHnodsfewCdzFsJ26lwttTMAk0spacSSbRSjE/uyPAOBagtnITsoGpjgYfukXZec3Cl8w1ZDvX4jXLQtE59R67WDuYfUyod3+rEkOYhK4d9/WWq3WYnq393otffxgazODCZisG6lkbq92Ghj+HVXGLzsFAABVgeicOnl1+uB14V78ssX99cH07D17hTqFSeAeymyWMsJ/u8WlxfZ8b/wYgxMwgdJIJXMrd4rgzBSfjirjyE5xqQAAgD3h7E51cn0mmeyluV9cf7Qn5s14je1wkIOMTsBkp2+o8Xed6pxqcFvMnAoAAADgC/HlasXZmZMcwixw7xaXFneFkSCTQ1aVM9WE5njn1KFl6kPnVAAAAADHOdV7ytedoxgF7o2NxVpiq7QZEiL4peLWZiZkZQImB3udkrt/KtmR29w1ctwBAAAAXNAp5O5vfyCi1FplOV5fn3l9lZtcGTaBe6NeSc6XhL6nocx8crfeIQr5dQImHiDHXXW1kfo+Y3yP6JwKAACe4GFUGbMFKkeV+f4VPdqrZKmQe1roxLPLeye5p4XbfITubAL3WL50GZI36pVo5K4zEzApWZ4DDznuATBS32eMT8CEzqkAAOAJz0eVcajAa5NhIpq81qydEIXD/f84exyWsO6c2i0urR0/2MqHiBoWJ2DSIZv8SElWlHTGLzdz3PXLRI47AAAAAB/iibknrwv34tl4Yu5Jrb4cnzz5SJTw+rAETAP3xkZ67fjB1mYmROTMBEyBIWTAG098H6mcEK92CgAAACOvXjugD2P3k9tENEblZJno+uPveJh9iViPKjO1Uroc9THkjwmYxFZ8rjodj1ROiHKniOOBQ9IEPyPLRxCqiIXA16r+iQTmNB1huTZQjbriy9Uql0NBErEK3PtR++CwMWFMwATgAnROdYFyRnRTo76OAmkVjeDpMyLWalCrtFwu46Nk0NHRkbUxry2/a6TyA9E5lWtMAvfu21qLqLWWrvSXJFdK+RiTCZj0WeucaucwTHVOFReiRRkchM6pAADgiUB0Tq2vzzwpj41JF11cpJ5Vl3nIlmESuIcym6WM9c0d/AtZ65xq5zD0O6fKBncXFhrpnIocdwAAAAD25KkynUJu4VOCh6id2I8qI2VlAiYQIMdddTXl8v39fVbHpMH9PQIAAIBrwtmHczO1+nKch9jdzcAdEzCBwxz5UuFJxqQjO8XvEgAAAMzVawejMRzkIEsTMNlszhQiG9VCjDfWWjsG1a20irKQcO9JQy9al31BdrXgUgEAAHdIH0COPwgYPVmUM6cqc9znnlV4aG4nVwP3jsUJmCx3cLa2oedlqgbxTjUJu9+6jKZl9/n0hwhcKgAAfmf2Nu74s8aJArkeDnLs4uLCpV11i0vP6evebEyNjfR+pD81EwAAAAAA6PuFe7sKRaZa7Y7w/277mKYiiNoBAAAAAIxxMXDvTcDUJWGg914/VQAAAAAAGM7FVBkiosZGeq1CwgRMSJMBAAAAADDK5cAdAAAAAACscDNVBgAAAAAALELgDgAAAADgA+zGce8WlxZ3WzSYz24oxx3juCvXwTjuqst9OmC5UzsNWLW4VicsPuOcF2vwUjH1J+BkZTuFD73ZGiyZ29UsFMX0E+TICqzLV13B5qUyyneVoXgYx10xAdOlTiF3f/vD9Uff7WTDtg/NnOnpadXlrFrcGxuL7flSqVQqrUztPi92iYi6xaU1WimVSqWtRG1xo8Fo1wAAAAAANtTXZ2YWXl1bq1Tcj9p1MGpxb9QryXieiIhi+VJv3MdOuz8GZOizRHS33sjHmI4IOT8/L/znxYsXLPfjja+++sqdHUlrz5Od6lB+4xf/6KzZn3gZc3wCAECAORUzeBDF1deTK+Xrj/eqPIXsAjaBe7d9HI1EZGkx3fZxNHJXWCEUmaJau0uxfrrMrVu3xK2Pjo5k5emEOEaiH9V1LJdpbUPLZYIO91NcnNo107QQAAAAT7B4MJkt0+z6ilSZ+vqTAxob+7B9P7k98MbFRepZdTluqnSnMctxb+2250ulPFG3uPS8+NlmJtRptyiitbo0WHcqx12rQNmG4jdC4SsdnznuAJbx9jsJAAAEFYsGNXadXjTEl6vVZXtFsMMscI8+uCvkwYQiU639t91MJhyJstqZ92TRv9YKliMbD6N5T3aNby8AAAAAMmwC91BkitoqC1v1DlGIiLrtY5qKczZ1qhBb289dlpbmVBukf3NCHNkp4ngAAABwS6eQW9j+MCa8uLj+aI+bDqqMWtxj8am1HxuZfExIbU/cDRFROBKt7BfvxjKh7ttaKznPtGcq55wN68FDSEQBAAAQBCLHvVPILRzO7lV2wpevc8RJ7M4qVSaWX6mn02kiouRKKR8iIgplNlfa6cX0rtBhdZTjdnDGSGUQGd+jV9Vif78c/rSSSqWYll8ul5mW7wnLlRbI2rDJ5hXIc5Wy+3C5f9bSATZ8gV0VBSLH/eRTc+7hzmWUHs4+nHtVOyEKcuBOFMuXSnlDC+WUD2+/jCqjfNfgSw7jFV8YqQwi1T7WBtd0R1BH2lGOc6VFeHibXT+QVCtBv34CXBs26deYzvXGf5UO/bCY/UyRd2dt6iAFFs7OEfxfGF6bvDp98LpwL54VW9xfH0zP3vP2oPrYBe7WuTyqzND1pWktQ/coe1d4KQYTWu9iVBkAAAAADoSzO3uUW0huj1COu6hbXFpsz5fyvbyYhmxsdzBopHJCvNopAAAAAFE4u1PNen0QqtgG7t3i890WJcVXS2u0UirFqFtcWtyIiOH86LDckXGkckKUO0UcDwAAwL9AdE7lGsvAvVt8XptKRlv91512KxmPERGFPktEd+uNfMzZyH3oYOoQMCP1QwS+vQAAAOcC0TmVa+wC927xeS3x9df0vNIb0b3bPo5G7gr/D0WmqNbuUqyfLiPtKqHsqGG2I6l+l1CbZdrpnCojftNwavD4UTNSP0QY75wKAAAAgcQqcBfC9s0QFcVFnXaLIlrrS4N1+51TZQvn5+eF/wgt8UMb5nXKtNk51chWiMYAAAAAQIlN4C6G7dS9XBiORJnsTIVrc+IAAAAAgCAQOe719Zkn5bEx5ZoXF6ln1eW4qdKdxiRw776ttVqtxfRu7/Va+vjB1mYmMtWqd4hCRNRtH9NU3M/DyricTz9Sydxe7TQwMAETAAB4Iig57nNrFSFA7xRyT+kbYTDI+vpMzXbJdjEJ3EOZzVJG+O/AcJCRaGW/eDeWCXXf1lrJec/HlFFtmJfl1ShX1o/UZWU61fY/Usncyp1qxXMj9X3G+B4xARMAAEAguTkBUyizudJOL6Z3hXHcnYzb3cyNQR4OP0bq+ww6pwIAAIw41oF7KLNZkryM5Uul/LBtlOGIuERsDheHYTEVu7BY2ZHgCREYAAA7qVRK+rJcLhvfRLayhaIARkcgcty55maLu1FGRpURIvj9/X1TDZ8sVnak5RWjygAAMCILtYUl+gG3dBPpyqpFKYcwBhhZQclx5xezwL1bXFrcFeZeSq6Ic6Q2NtJrFRJSZTJ+7pvqtpFK5vZqpxBg0itKOmuE55SBoFOOjo6Gfo54qxbHa0MnpDbVc8Z3NSkyUqV+vFT0OXIhDf0+5uvnlLUqMnKpBEJ8uSqOHBPO7uyoLfcMo8C9sbFYS2yVNkNCBL9U3NrMhLrFpTVaKZVi1C0uLW5ExHB+KKSVj1Qyt3Kn6JzqyR6DRHpFHR0d6VzVstYadoG1oFwu67cP2XlXfEvr4pFuO/RIjO/acqUJDdtmG8x01tevHNVqMTI3iP4KWm35Bs/L2SvQYCaPqUvF1CdIZwV2Hy79szZ4hEN/Cfd1s65OFdm8qwBrbAL3Rr2SnC8JLeqhzHxyt94hCnXarWQ8RkQU+iwR3a038jGbHVTFlHcW8G2BfyP1fQadUwGsKZfLZhPTpZtIV7ZQFAD408Bo7jyM4C5gE7jH8qXLkLxRr0Qjd4m67eNo5K6wLBSZolq7S7F+uoz0pzd2+YI89JlgVwgAAKgSw2vjzflaP0EgUgfQwUOg5UDn1Pr6zJOPj/eqlXB/SaeQm8md7AkDunuJdefUbnFp7fjBVj5E1Gi3KKK1njRYN5gmYYHnfSaMFIIgHgAAAPyIaaDlVCrEsKkzO4XXB3PPqgMxeji78+zTzNPCba9Dd6aBe2MjvXYsdkMNR6IsdwYAAsycCgAAYNXJp+ZcQpkWE0/MPamdEAU0cO8WlxZ3p1ZKl/MshSJTrXqHKERE3fYxTcUNDCuDRHPBSPXCNL7TkaoWzJxqsBCbJQAAwGibvDr98aRDcVmE3jn5SJTw5pAusQnc+1H74LAx4Ui0sl+8G8uEum9rreS8k1OnBt1I9cJU7tTIQAcuQ+dUAAAAGR5y3M0WqMhxD9+epYWF9cmB3qidwtNX9HjP8/6pTAL37ttai6i1lq70lyRXSvlYKLO50k4vpneFcdwRtwMAAAAEB7edCU0VGM7uVCfXZ2bWxZFk6usLr67J0t4N6hRyC9sf+qPTXH9ks4Mrk8A9lNksZVTfieVLpfywzdm1I3L7RRBNpwAAAAC8iC9Xq1qvjOsUcguHs3uVHSrkntI3O7d/yi3kyE7sznpUGSvYJSGwKNmRseQxqgwAAABAsJx8as493AkTdYTX4ezDuVe2eri6HLg3NtJrFRJSZTIG+qYCAAAAALhmYOolKfPTME1enT6o1Zfj/W06hdcH07P3bBycm4F7t7i0RiulUoy6xaXFjYis76oL/DtGjbIZfn9/3/3D8GSnOnioFt7qBAAAwCvS57Ljz0dGD1yVCZhobq0iBOgdIcUlGyai+vpMzWTZ4ew3j3ML6/XqPaLm9v3kKz5z3DV02q1kPEZEFPosEd2tN/IxdFAFAM5J53VmsX4g6VQC6scs/RrzdX0aPHhfnKPlg/TF2QXb9PT04IJPv6JfhXpLfznxZ39GU9PTv5UtV9dsNuWLwtmdKhFRfKeadeJoXQzcu+3jaOSu8P9QZIpq7S7F+uky0gtXOosqmjMBzMIETA6Szm+vP9K8nXcDRlppgpE6fWcpK1NgpEo5r3Zrpzb0pDw566OjI2vHzOiugtuRPX/zv/7j3/T//9vHBwdqyz3jYuDeabcoovWmNFhXjuHt+KXJc5kcxi7gL5iACQAAgA8DGfPms+TlXAzcw5GosRWVj16dh7H+c9rahlyVCQAAAAA+1CnkntCzakUM1evrM7mCnTT3XzhzYEaEIlOtdm84nG77mKYiGFYGAAAAAILp5FNz+uqkZMHk1enmpxMbJbJrce8WlxZ3WyQZ+jEciVY205V/IaK/+AtKfqHVMxWpMlprAgAAAIBPxJf3TnILM4eP93ayYaqvJ1fKqWcVHlNlGhuL7flSKUbU2Eg/L37WH7X9XxP9C6NdAgAAAADwI5y9HE4mvlypLNssj1Hg3qhXkvE8ERHF8qVe03qn3Ur+O2Hw9m5xaRHDQQIElmtzJrx48cKdHQEAAHiOTeDebR9HIxHZLKmWhoMU+KUjKbvOqU6NE+L+IFBMhwpx5Fw8GRgLI6j4XSqVYlq+1kh5/mKzloJRCYxYGPzbX/Xp+EfMX6dvhCcDwA+tRrOPNscfwRYKVJuAyUGdQm5h+0N/VBl+J2Bq7bbnS6U8Ube4JKTKYDhIY2UiGgPwBWUTgynCQ1e1kCBNyKJVSzqnL10BdMhqT79K/VifQy8P459BP56+ERbuQmarTrktmNEp5BYOZ/cqOyTMwHr7p9xCjuzE7swC9+iDu0IeTCgy1dp/281kDA8HCWCQU19yPPmyhG9oAAAQMBYebY4/Dc0WyLLF/eRTc+7hTpioN6piOPtw7lXthIizwD0UmaK2ysJWvUMUot5wkHEMBwn2IFXG1HLW8P0HAGDE+TFVhqXJq9MHtfpyvD+OTKfw+mB69p6NEhm1uMfiU2s/NjL5mJDanrjbHw5yv3g3lgl139ZayXnOe6aKvevQ+w38xaczpzqyU8TxAADAjXD2m8e5hfV69R5Rc/t+8hW3Oe6x/Eo9nU4TESVXSvkQEVEos7nSTi+md4UOq5pxO28zp+q/tP/W0HeNrGCc+5ENYikAAAAYReHsTpWIKH45KqQt7CZgiuVLpbyhhXK8dU6V9Rz1pHMqRpVx5GAAAAAAdNXXZ56Ux8aUb1xcpJ5Vl+1Mn2Qfu8AdgDl0TgUAAOBHUDqnzq1VhAC9I4wGkw0TUX19pubIEdrBOnDvFpcW2/PCrEtE1JCN7Q5gBzqnOnIwAAAAjkDnVNZ+wbT0bvH5bkvyammNVkqlUmkrUVvcaDDdNQAAAABAkLBsce8Wn9emklExcu+0W8l4jIgo9Fkiultv5GOcDyzDD3ROBQAAABhx7AL3bvF5LfH11/S80hvRvds+jkbuCv8PRaao1u5SrJ8uI52OSzmhF0aVQedURw4GAAAA2AlKjju/WAXuQti+GaKiuKjTblFEa31psI5RZbTWBBl0TnW5ZA73i88LeCuVSgn/KZfLqstlZKsBBEwgctzjy1Vx5JhwdmdHbbln2ATuYthO3cuF4UiUyc64hlmcmELnVNXlmIAJwB3S6DyVSolBuVbULlsNAMAsJoF7922t1Wotpnd7r9fSxw+2NjORqVa9QxQiom77mKbiGFYGAFwhDeiliXnc0on8DDo6OrIws5u4hIdasl8JSkOrxXhu5NBttbA4Lzv7tXCpjI+P83CFmGK22pVZuzK+u6s4YpQaRwZGc+dhBHcBk8A9lNksZYT/DgwHGYlW9ot3Y5lQ922tlZw33jNV2W4tLNnf33fyuAEgoKQN80dHR/ZT7FjHXmKjrOWEQJKctcEfZ6SlDW0VNvKrkc1aMtgybfb3K/1qMZgbqV+yPv3zMrhTC3Wrs19Tl4pwhFqlDT1+gys4/hEz+JkSVzA1MaIjdxWz73ryDXDopRKQHPf6+syTj4/3qpVwf0mnkJvJnewJA7p7yc0JmEKZzZV2ejG9K4zjrhm369S4GMHrrzw/P09E+/v77DqnCrugwS8P7DqnYlQZAADelMtl1Rx36XLlJm4cGYBHApHj3im8Pph7Vh2I0cPZnWefZp4WbnsdurMO3EOZzZLkZSxfKuWHbWMqaVX/z+NC51Qhgn/x4oXBpq+hexz6zd6OgI0qw0PnVPErHGv4fQmAQ+VyWfUupwzQAz8vDEBQnHxqziWUaTHxxNyT2glRsAN3lwgt8egAOmr82znVAuVB4qcMAAAAp01enf540qG4LELvnHwkSnhzSJeYBe7d4tJib9bU5Eovx52osZFeq5CQKpNB31Q7lFlDjEi/DnmyUwAAAAC3hG/P0sLC+uRAb9RO4ekrerznef9URoF7Y2OxltgqbYaECH6puLWZCXWLS2u0UirFqFtcWtyIiOE8AAAAAPhdMDqnhrM71cn1mZl1cSSZ+vrCq2uytHdvsAncG/VKcr4ktKiHMvPJ3XqHKNRpt5LxGBFR6LNEdLfeyMcQuQMAAAAEQyA6pxIRUXy5WtV65SU2gXssX7oMyRv1SjRyl6jbPo5G7grLQpEpqrW7FOuny0jHQB06eKpI1i9QZ0xiJeNvyYaeVN1QWOiLUWXMwnSYvoOZUwEAAAKJdefUbnFp7fjBVj5E1Gi3KKK1njRYtzwVovI7luq3Lv3ZTLW+q4lfElS3Oj8/98WoMhbwPB2mcnmAx3ix80FwB8+XCgAAuMypfnGud3sbmHpJiodpmJgG7o2N9Nqx2A01HImy3JlrXOugCUON1PcZjCoDAACcY/FgMlumAxMw0dxaRQjQO4XcU/pGGLy9vj5TM1UyC2xHlZlaKV3OsxSKTLXqHaIQEXXbxzQVD9CwMvqt+AAAAACBx6JVSyzTt034TmITuPej9sFhY8KRaGW/eDeWCXXf1lrJeU56pnoSc2PgeQAAAAAwhUng3n1baxG11tKV/pLkSikfC2U2V9rpxfSuMI67Ztxu+XcWrc6pYt6zLC9Ztr740s4PPeK2QzvOmtojOqcC8Ebapd7bQnimf4KBP32mVGsvSFU69FyCdLLWWK4BVJ1/MQncQ5nNUkb1nVi+VMoP21z6O4upn0WUHRNlP9kYeWlzICGtbY13eEXnVOVOjXdOdY37u8aXGa6YndBe512/zN1rgVhLps4xwBXioKOjo6G15OuaVH7EBENPytdnbYrONWD5dqT/7ujULc9YjyoDwNBIfZ9B51QAAOAcD51TzRao6JwaX66KI8eEszs7ass943Lg3thIr1VISJXJ8NY3VTZeu51CAAAAAEbN/8/e24XGkWX5vkvdXDgPl6piyEyK0YctekqQXTmeFzWiREtI6oErT4EehsQSKrcx/aAqCWFdvbSgDE3QYIP7RcdGyFV+uBRuTyIVyTwIqq0DfZRCqqNGjDlw3XKJyT6NbGcmFJmJqWou3MNc+uR9iMxQKD527IjYEbEj8v97yojYsfaKlZE7Vu5Ye61AF6dKKzBMwnTca8WVO3R7Z2eEasWVxfV+w9pVSQgoETgcehAahrmE8FPOR9UpAAAAkGzCdNyrlbPJ0REiot4PxgYfHx2vjkTrucOZBolEyFxC+LGMQnpE+BAAAIAEE6LjXqu8HOy/pn7u7b9Mh5UajXTCZfQLnPVVVH3O26lPcYOQOM4Fmt0RTKOSHGaRzSYAAABAVOify8KfjwE9cK0KMMlLiI57tXJG/XYH9c66OaOI8HXTMssMNKtMwqZRYzq1LKpTzC4DAACQCrePNuGP4MSnvulptVohdVUrrvyGftlek3q8PlPol3CBKgAAAAAAAFLyg/C66u2/fFapqp9rlZd0uR9eOwAAAAAAAHyE6LhTX/9gqVCskVpatb1OFQAAAAAAAOBMiKEyRJLncQcAAAAAAEBaQnbcAQAAAAAAAF4IM1QGAAAAAAAA4BE47gAAAAAAAMSAMCun8qIl4Awi27r8Yhl53PUtQ/7sXwf/CvCYxdWm5xMZNgluk+c+UWk2m+ZbznGPkAaaAh5OF6USWcE+i62V/xMjFxtQyv9yuTw0NBQXsd4kC1QmMaJ89h7r04OQnLATu7y+isBaTnam9j3jXiuuzKwfn28fr8/MzMzMzKwUaxat2UcBAAAAAAAA1vibcW8niZnsbNeKK3fo9s7OCNWKK4vr/Tur+pSP7KPWpNNp/Waj0TDsVPeYG+v3e0OTxhDF08bcWE+j0ejp6eEXAgAAAAAAuhPvM+7H6zMzd17euH1j8HxftdJJz977wdhg6ej4whnsoxZoHq1GOp02uL/apt1+b+hPtxPF00asEAAAAAAA0LV4n3EfWd3ZWSWqFQ+1XbXKy8H+a+rn3v7LdFip0Ugv19Hh4WFNzLNnz1xpYhe+yQjrdHtI28kOFfUfSMqWMD8/r30uFAo++/KGXgcievDgQSRq6DGoFL5lIlfAjF6lqL4jTQcZbpJwcPUKTgbK5XLkEsIUa5BsGUJq7lqgMvKLsgurNXTns/fYnR70rRLHE+1uFSEraoaHh926gpJ0KjDG3Q6hi1OrlTPq93ZUbyy3iwPs2ntYCmZ3yHKxnaseGWcZJLD/OdjtDPqzEB18KsDunbGfscnf0rI7/wr40dYOOwmO+vtv4FMBISpFS6PRUF8MxsJ397lKL5GLUw0NErOi1KcofZtYry4VaDpRt0rCThS1qDT8xalRdeoWoY57X/+2fXtXAAAgAElEQVSg56NWtFotQ7QMI8ZdfV6a93tDL81OFE8by8YehKg3U6FQ0N9VkWSV0evgSo5/BTjNQswUIuw8Ld6yynhWwNWmq6wyepXs/nYGnVVG04FTAVEqUUT09PSoP2Hzj13djIUrDwAAQGaEOu69/ZfPjqpEvURUq7yky6O93EdtsHzUNRoNy8lvrbGQnD48T1lXT2K7xq1WKxZ/8gAA/GiT7vp1OPDdAQAA+EFsAaa+/sFSoVgjotofDjsrUTmPAgBAXLHMf0W6ufZWqxWBWgAAAJKF2AJMvfn7tyszizOPiQZvPLw/QkRUK64sVuZ3VkcsjzqhDyOxS7piechup1ky51HHZq1Wy+4sxiGkgwQgGRhenWlDkGHEwM8cAACAZ3w77r35+zv67XayGZsGpqNM9AHuDE/a8pDdTnOUvP6oOivGTstoJ8ScvJLnkCEdJB7qAMQOxt9ywyZSvgIAAPCD2Bl3ECyGtIMaYeYflEEHA3YqqYSgmKUC0eZAlCFrp9ksCcsLqf+nzf+X2/zPH1E0AAAAOIHjDgBIPq6SSDrWedDeoWnOt7rfUayaKUs9S/XXVVF+CkR4K1gBAAAgjvRIPtmjPSDNqSE1LA/Z7TRL5jzq2MythuZDkn8XAMQXdhJJPTx1Hiyj2ryJdcwU6V9bCsyDT2Qe9+CUSYyoWCdil+ru6p4T3RLfAkymylZH9yY+3b3oAbZa03f310adRNmZWsYZd/3DRnueNZtN/bNN/0wyHNKOmp+F+nzSlkc1sZbPUfWoYf2ZvgHj6Wt3qNlsamvaNPUsm5HpSRxJHnfPcoLL4y4wM7q3PO6iMrW71dbSJnTxTraT7Ni1twaaAh5OF6USxQoEvgMAkoSo9NaRpMkOoNPRtf39Nf2O6tbCr2nAh0Sx6SABACDhYBE5AAAAj/QN/Kj86rUPATLOuOvhmYvin7JSH7eMMBgeaYy8NN60MkgGAHQb+DMAAADdwejavmOYDAupHXe2h63B7x/ztHRsw9mdh9ffeHgDIDlBhLUw1sAAAACIOdWtheuf/6mzmvG9j588muvzIU6k414rriw+PtPvmby9szpi18BwEDjDzntoh9gcfN50sESUYqJU8pw40o8CAWWrdKVSQFkaZdAhCPDvGgAAAB/VrYXre1NPSo9oa+HX9KtHP/164foC+fHdRTruvfn7O/nOxvH6TKH/2kXHvFo5g7cOAIgX+ln24Lx2vHADAIDE8fpV+erNR31EVXW7b+7m1c8OXxNJ4bjrOF6/8/LGw9XeCztrlZeD/ddcSOF8g+w2xt0xhF3aGPdCoeA5MYsoPOsQnGJ6lczC+fO0hK9AcBhUsuw3aE3YZglBASHo/engFEZuGQAASCIDl4aeHh6tjXbC2qtbXzwdmvrIh8RAHPdasVCanN/pNeyuVs7OSoszj4nIFCgzPDysfdbn0eRMbe4qA7pjYx5potoAAKQlZGcak+4AgLgjKidvJLl9/Xf65s2bizv65n71ycL1e0f7HxGVP//55Ge+Y9yDKMB0vD5T6H94P29w3GvFlcXDsfb+WnHlN/RLUxsiupjHnae2iMCjMoiNXSJqAOTHWwEmgycd9PhgWYwp0AJMploh3YVlfRPYxHI/zGLeCZtY7hfyWnJ6enp3d9e/nPA7NTnu3gmxANPxUWlw7KHZI78QAt/bf/nsqEpk5bhrdFumBcZkGwowcZrF1SYKMKEAk1SoATOOtVQF4rMIYiIrpxoaJKbcqU9R+jaxLn0q0HSibpVuOBEIRLzjbue3u6ULIz7xohwAeYhkCNKW0MR3NGgebG7s1TtbmanlpfEY/5kSRvNgswCzXAS3iiUwS8Kobi38/PM/EdH0ndLa6NG9iS8u+YuVEe641yovB8euWfntFyJoapWXk6OrojtPOgJTMZLXBHyidBCY/k+sWci9bgIVEJUd0qdKQr4dqdJBGmbi2RPz2lFzJCHniW6PGg6p6/K1naLEBk/zYHNjj6aWlban0TzY3NhQ6rNKPhuuInLRNsuH8+/+ZGiIYBYi3Co2wCyCmZ6eDqg9d1DN0b98Rh8/Kc3R1sKvt6qjc2tPXi/8euunkqSDJCKiauWM+vU7asWVxcr8zurIyOrDykpnbergjYf3kRYSABAO3mLcXaXBEb4Gpqenp9FoBBrjLpbT4sYeXZgfTI0vLdPmRulgItu9k4aaWd50QqJhFtwqlsAsQaDPdyIKfT4VDn400EdEAz8qH74m6uvrfPDau3DHfWR156JD3pu/v6P7mDefYk0X5kcLNB2kqDSICU4H6VkrP+kgA8ow6CEdpHBNkpEOMipiOACevjih3KzRvUiNLynj2pYuCiDXmUVsHmxu1HNT9b29OlFudjnzXwr/49up37c3lYnG5sZJLr7hAudm0a9Z82CWkm5zbP7St5tKbM1S8XOrvHulWigUzDZJ0q2ih2GW+Ss/7OyzvVXib5a4Mzp29dMvtj4anRsdu/rp4dHa6MDrPxON+ZAYUB53MbRaLZnTv4QjFgAQPvENMY+MZqNOmRzrv0bzYHNjLzOrLGXVz0pRiwA42avPKkrbOfsv9Pp8kyir91tihzizlHSb5XJ5KL5m+f57PzZ5/t2YovxMbaW3CXXfrVL4ekxpLxW1vVUo7maJPUeHT+lPPT+f/JyIemh3cpfovU9+O+p4nj0/EKUaAAAAgcRw0j2TZkzqne7v1XOzbfcrNT6Zo5MXp52DufcvxPAaNmMOzGLGu00GLl2Ixk2QTci1WV6/6oJbJe6Mru3vly7iL427lDPuWjim8HSQnKVYw8cxm77lMrUwPwvRwacCZuxW7/Fvej5RlAJ+tLVDbcbQlrNrDw0MOz2c7l8l/0jlLquR7lFrwUm90aQs4wvJZM4tm85k6KTRpKzxABG9k5HoG/CPKLNkEmQW7zZ55y27dgnAnVneoaqEt4qoMVny3L526gnM426HjI67vhaJWOT02unis9nybkAed0u7IY+7Jcjj7h9JfGUts7sk+rBIpTNUrzeIjN/AaVEpZZaXuvRlPcxi5u23M/QcNjGSlFtFVOiv5CHELtQ7ujd5e3f6TmmN7k3e3iWi9z7+rZ9Jdxkdd2CH8LyHZhyz8gWng+eEgJGrFML3ohGtSq6+Iw86iMqG6QfZ/t6r7wnDrMrklezEVMac/qJ5UDqhzGyKqEFUP/mmOd4+3KjX1cAAqZ/O/jk3i35vd5ulH7eKFa5/Qd/RWzEyiwSZGSPh6N6nf/74SWmur7q18PS9j588mnt9b+JfjubWPIe5w3EHAIA2cvrHcanKlBq/NnWysbFJWgILNQVGbnYpS223ZG//dDyfbXsjudluiMPVzPLhP76r7oFZcKtY4tosA2PxMosEmRmjo/r1XvnqzUd9VP06sVllgliYJe1iL86HcRDpIBnBGGJ18NMpv0pmgaJCZYJQwI+2rlSyPN1/XIorHXhCZaJFHRwYyayiRdrhS0dqfElJF5UNZa+zJ3NeS6aTk1pRiEiX48+B5kHsk9m1zbJdoK/ae/yb5fuDGKeDJNwqNrgzSzsdpAMJMEusGV27ezj588nP28lkqlu//ow+eeInq4y8jjsFkw6SXdDEs1j2UT9iAQAhIOdcuwHVd5dcyWxeUWzrdVzMSW25LzX+T/PvDqWYp8SQbF6Zv1IeaifvM8BllotN3k6AWbzdKuVOHStTk+TcKpxmaZvC4VZJiFlizOhaqbSmbc092p/zJ0+w414rriw+PutsTd7eWb1Yjel4feZOidTCqflesX0DAIAnYuG1AwAAAIId92rlzOytd6gVV+7Q7Z2dEaoVVxbX++3aaQS9Sky2V8/wGwAICMckkvqUrIzUNOysNZ6PujqRPyGm5PnUAACgC6huLVz//E9th7b13sdP/CVyF+u41yovB/uv2R2tVs4mR0eIiHo/GBt8fHS8OsLw3ENwqaXy2gnFGgEIDEa0vToO8KwlkCSUzpA9lkcsPHgAQHcS9erV6tbC9b2pJ6VHfefbC+THdxfruFcrZ2elxZnHRGQOlNF79b39l+mwUqORTriM3rJBrDuOC3bPV3V/mGkHVfTp+YLWQe1LbwFHbyN8sxjyFYapQKFQ4HS/UqlUhDYhN2axO9duk6eBB2L0h1nse8LzcOHoJIQp1iDZMuLc3LVAZeQXZROFb+zOZ++xOz3oWyWOJ9rdKuFPE0QyMWHXqakA0+tXajKZDn1zN69+dviaSA7HvVZ5eR69XiuurBT7dJHs1coZ9dudqnfWu3mlplo6x3J/+MqQrpJOCDoYygbxF2AKE8uiVOH3TsyhKiqtDAWYXJ2rSQi5AFOXv+aye/RyUi7brbb0RUBiOSUbGghUJtai9G189h7r0/WIulUSdmKEz2VLAspByX2ZA5eGnn6x9dHonDbj/sXToamPfPQu1HHvzd/f0dZC9/ZfPjuqEmmOe1//oBthIQSgxy7GPcx0kHav4Ls2HSRDz3DSQboinHSQbPVkTgcp1Q8fAABAQumbe/SEFq5Pfi5pjDsTvSdfq7yky6NOaWWCSAdpOGrwlSWJYQUABIe66r2bJ9oBAACERZ//FJB6hDrux+szhf5Onsda5eXk6Kr+cF//YKlQvDaS76394fBsct4hpwwwEVrgMqOyfchx9gxNNMJUyVKf8NceOJpFBpV4dOD5foMg1l57LLK5AwAACAKhjvvI6sPKSmdt6uCNh/dHSE3tXpnfWR2h3vz925WZxZnH5wfZBJ0OkiSLlsGTGAAAAAAgKRzdm/h096I322pN391f81w8VXCozIUo9/Nd2sbI6s7OKnERjj8tj9dOXb9ODgAQU06LyvYJERFlpuxKqzu0sa7LflpUSpmLO3n6kopzhd+5sjw0ZKVw82BzY69OZH1NZiOcfl3Y7uRjys0q+WwQegcJx5fo1ibs9vGA+95uX/67xtPOad8W51bhERs4UWdmjITRtf39Nd12dWvh+qsxz147hRrjDnwT5uJU4ToEoYxZJfOJiVmc6motBBanJh55/uc3Dza361PLyniKmgebGxvFtNmTdGrTPPhyr04Zw1mnxe0Tykxd2KOUMsuKklI/b2yS5D6a/sL/9T9vbBSzZuOcFjf2MrPKUtbymkxGOC0q268HxpRf/Ky9sZmR3AgGvj/53VdON4xLmzi0jwU8vyMV4+Vn84qS1x1VtutTE+qpjXo9amcdGOibu3l14vBobVSWGXcQEFqSO7s050F/FqKDTwXMWEp2ten5RFEK+NHWDrUZQ1vOrj00MOz0cLp/lboBmWL8Tvf36rlZ1TNIjU/m9rZfnOazWTdtmgdf7lHmottuNYvYPCidZHLL7W88OzGV2Tj5pjkusVdy4cJzfz/w/NBknOZB6SQztazuM1yTlRFOX5xQ5srft3Mrx8EIBk7/+Pw7hxuGaZOvC9uvDSJZ7eMCz++IrG+Ji8e3TzJTy+1rbzbqlHlfHjsElJlRuMxgOTp8SjTmQ4CMjrv6JG61WiHEuIfTCyf6ousG/E9a+/zsXwf/CvCYxdWmz7lw/wr41NbSJmTKiG95umPX3hpoCng4XZRKBMKk2ahTJqf9ici+nyOTx8Fu0zz4ci8zu5wpbegcktMXJ7lZZaKxqd/ZqNczuR9r33BqfEkZD+KahGG48P5LOTJ57oyrsDRCNq8oARaoCpxm4y/0zt+xbximTV4PjCkz/9sFm8h/JzjC8zsim1tCJ+WgdEId95/UX0wm7H/44Q/CkQz7dp2aCjBZxLhfvVtKWqiM9iQOIR1ks9nUv24WJdbbiZq7Y9cSAOANV/P0jKOeTwxCrN2rFU6xYmjU65RJs/thtVHddiVLByX97mxeyRI1L8QCtacPz+ccpQ/v5jHOBfTTrtZGsDghM/XjGD00GvXv6C0/Npn/YZnoW972cYHzVmHfEurdcE37TZy+OCHKfKm0o9xD+r2EHwAZScgld6fGGHf/yOi4AzuCy/HHn5Uv6DyDHvIDClfJrQ5dniKT3GTJjCr/o2GQDb8gQxBie3p6OGc3Qp4OqDealHXoUW2jue3E8RBs1Ot0sq3QbDui97SobB7ELn7XxjidVYRaxAcHzYNNfVxEfBFkEy82lBme35Ge0xcXptup2agTUe6aou5qHmwqRZL8vy7gQGrHPdAgFnWiXZo4USKkgwQAcCNTmLsRnknmTDp1PtvuQvTUsuZ4ZN/PbW/vn47HyxOxMU4n2uO0qChFnplR1UvNzSqxd9uF2cS1DSXH1cuajt+uu2hDBFEqnaE9y8B5ECR26SDHDicOxzwlhZTXcQ/6mSThM0+eNBEAAGCJPr/cO1c+HBrPZOjEYWYwbd2m+c1Jnerbynm07t6GUp9VrvzQXgy582UixubCWdgFN1+k8nWh8DqjpiCJGenMO1QNwibe28uAh1vFgMlvB3JgGyozuu8x0F20414rriw+PiMiosnbO6sjdgctjwMHgk4HGagOQShjVsksxPPi1BAU8KOtK5UsTxe+OJWtg2fzAjsajUZPT0/I//b1s3jlcplS/3uG6vUGkepwWHoPqbRlm1RWNyGoy+Nut/DSasYw/KV3bjBceOWVhXFOi8o2uZoebh5sHr4emFV+EUsnLZV+i75zuGHc2sS9DaXD5jfCj2F1K5GlWSL+vUSSASZ+aWecEOu4H68vHo493LnfqzrpK8WH9/O954erlTN46wAAkCCy7+dou3QwkR1PdZJamPwNnjY8XU1MZTbaYozJIeXkwoWf/PG1xYVnJ6YyG+d/R05fnLBDtJsHX+7RlQ9/GlsnNXtpgA7ZN4Nbm7htLyN+fyMWq1stzBLl72V3d5e/8fT0tKv2UnV6Eckrpx4flSbnd1RPvTc/P/n4qEp07rjXKi8H+6/xCgs6ghMx7gCAuKMmtI126MjmldmisqHsEekTV1wohWrTxi2p8aVl2myLibwMJA8XLnxgzMo4qfGl5YNNRdkmIueLUuOL6l8Vnn91vlP69DoX6P/p/OzzAvOGcWcT9+1lhOd3ZI/FhDtRanxptqgoiroVT7PEH/GVU3sYucP9cbw+U+i/MON+vD5zR0v4ZZh617/LCCJFPwCgm9FH6bAXk8Qoq4x61M5x58kqE+N04CIYGhoy74RNLPfDLOadsElwwoe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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i smallZambiaDF\n",
    "ggpairs(smallZambiaDF) # from library GGally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fG12Jcczw+4KAXpS6g2NcMl33SFxEt7ZE5tq3IJpTC0Nbryiib/AoqVWiROewc7yOM9lQgatIshuHaMwbdwJRkQv4WezYAH+0IFbFmRRhQwiTTN1RRI94HwysuI7RiK6LcQ2kFrvGbnrg5WgPQ95LCgiSifp2LiF1yXqShzaTL8DmKssw3TsaXFSOsAlMkujBC8nk6cBK8FcSPZUt4CNX9G8i73/z67fzzSc9HiXVQJToDLbM1iiyh6m7D1VBgUhGWzzn6WDJHhhGCeiy+TQ2V5mvz9blz52plMeGAMaE5DY27scvboezOzf65QrZMaF/QCDyj7/+W5d1uA6PkmohSnSO6OUBfHVEdwTzARLx3MS5PJtOF2ULaAgRAzpKyz2HHYw/KQojRXR/LACFwu7qkrpvHtF/989fnjnR4VFSLUSJzmA3IHpUUBjRwTHRYLNyJyT6JOiTtp586s5lIr4wYHpp6h6ZSP1N9gxG9I/f9HuUVAvZj+g12PTAttiM3SFbHUq2IF7H4wgTn9BMBc+WgNuImfxJSNguWXfW+JANCREiuhu9gMAUEl2M6OuIzjt1Y6Kb109PH48P5uMx+A7szRO9RmqxG9ltj3YoBQXuWBMWSHZLTMOx/XKoIy2ESCmf0Lk8YTrOGuZo72t3Wx4bjWMEBhPdGRdz3H+c/WVfO0R0EP1NlYr7CT2QKZu/6/7vFKjuIBrRGbtFtkZ8DonuFeIRoI7onmWWTp4cbj3OYwjYJi63jHNDBvjD3fASeTAcxwIYEv2N2/YcZ4kOHYm+dequl9daYNMD22JzRC8P4MVET3Q88Eb4oEpiKjqZuaQiorMn7SqeN9xQAtQj1oM8dpRfWIIbdjEueOlK9A0yPyL6FdgW2PTAttiM3Q2JHsRA2W4bTOdN9z/k3MwfCI9OYXOV2Sp9ZBYn7SK2g8GUddN7l8pLL1cV0ZXoLbDpgW2xGbvdMRFri4nOGBjYDXNFM1ssrx1JfJRnKBeip8joA7f/5N79rCDmt11Q89go4ycwdpDC9ktE9wHekfEaIrp+131A7AYRnauI2D0zC2XNIc0QW8L+FVUi+sSlBviTJI786BPGdjZEs3j/hghuEKn9C7H1eiK6PkpqQOzlRMdEjiqixTZxTuTMRIxfgIs7WlVEL6kN+UOK6ETPvUV/clh32ANG9NKjlOhVUou90m7wu8h7QYFDLYm61jyJe9Y6mi/jlFnGzp2x8eyU8onZBgkbHY1T9/AvTNhR2XVFdH2U1HpsemBbbM7uhhEdh/DIbnQ4pReKkTb0e17ZuJzGZs4Un7KrNfRBIOwAFcJg4tqPrlh8cXavJLpw4hsTXS+vNcCmB3bHFtnq+qdEdHGHYLctQ9y2W5R9fixAB4l2C+fmBiPH0HlDyEvnKB1i+/EwJDoJ8K6YC+bWO4GPUCWDRXRddW+BTQ9si83ZHdExoK/IfK9QRvQZncmf2UmweDa83ZwSUvQ91pHaI7l6GWyIYHyR/+j5bI9BbJ/39iP6BlM8ImMTfR3ssNgReBTioz3he1HqPvd4tBbuYrvx0RSNLeJZl0T0+BD0H0Jwvy+N7VmNyS1oA31JYpcXCqrd+wmV53/u0oT/nl5eW45ND2yLzdidYKtQEK2Hu4geUB/oQSjXdYHbE99Hvlx3qe/UfnRBw4yP6Ci+J7HJKoLBqTsT0SOid4zoG/QTKs9/7rLv371jzNhflOic3a4b2sAD0THYGO49TPIju10snSnNivHET58ExWZ2Rmbayg2q3Fiyg/vPEJN8tGHfD0neajTMWVz84uy+BqKPnbrfKNHDiB7x2tM4N0cX7cah3q9JMTwHRA9HqYB/rE/wmOP463ntGTe7ycdwIaKjhAOAwqBo7o5ZF9HZFk5Rmi/t2QeJzER/+fJbJfpybHpgW2yO6AKN8wXRkCDbTShvKWcgtN2l1gabCWgi77GBVjdXiI8gp+rHEPvZxVkgQLMZDsREMCiw43OJrOF9sjaip7C5PRhvRT8h8v67dqc+YWY5Nj2wO3Y90ePYX2E3CtGI6agyN5cmLqaZsYk6PGBsx0kM4GFwlhBa6XFCCAfjRgmXe0Snmoq6K4m+TUTPEN28/fuXff9xkXGbSyczCpx8YGwgH3Ft2YLILi55Feq1mzBb4IOoMX7afEmaMS1juxlkG6sxF4EUSNYhbPcCAQwmt5zTJLElw/OFy4m+WFjYl8/fT6n7jxLV7UUjesJuH1OliB5G8nhHMCYE5tiOjgM0oEOD+CueTEWndlDg7J1HD5TLO+aK2AQGLzM43ttt9iU4f8bMitRdKGXtDo9sFtHtV2CHjOhHIfpO2HmihwWAWWqooYzdKK0GnBj7vZTn4tmUEz0mqJ12o1zevsvYFMYNEeBWGty2O0mcBwR2l3KadwCv2r+fUDl/BfYVvvyvd6Ehh5D9yFgjPYlOD6TYAZ2jKTlTEBzKEz2I8v7UnM1+gQt12UYR3XgKO5eSiG7fU9gExuUbczSPI/rFUVcc0fXyWgtseuB22BLRxQIUyIMCjB2umsVLXNiRlFKBRbzduZP0toW5BFO9hE1O0UdwnyLATHbmxSUC3MmwZygXCqUbR3QlegtsemB3bB+NM7zmUnZDN9IR3XFkdhvxoZu72+m7H0bCE8v7BFDdOBS7AsJ39B5hW1vI8MNH84DZiO34fK6C6PqEmRbY9MD+2AFLy4nu64vr5ez20TBmOqb86ojuQPCyn3Hh3BHdDQh+EAiwvT52lD8aEz20C+hLo4jOn/jGqbs+YaaF9BxEGOzoGNSfybtMdL/h6+Hs9tFQZLobDVInQbH5UYAEcG8mk7gbTHs8+Nk4HJrk1Gna7qCwUxiic2e0MqJDX6KXHqVEr5Ja7LV2hxE9LEgwX9yRmesmee64JZ4EwaaqEO5ACwmemGGloU8ItSnPbaLgBiyO6GB9QMpkogthmi3cgeiMgj5hZr3UYq+0O2TriogeFsQRHS0EOILZGGsI6RInnY/obhP8+OXAjTRruOwyJKK7j+jMrKU+OwmIzr0E2QJ3SmWFhyC6PmGmgdRir7RbzL9tQaQh7sDZLbXb9XFAIR3Ry/PHM048mXJ/4xm/rUEiecLfAUw2onMvyYjOthzfnCnVdv2EHhiV6Kp7C6nFXml3LizXRHScN2O7/TQhniUbyxGXa5MQyp0EwU4pYZwEw33sZwgDIYxLRJi/MH/HtiYi+lqic3ZzRyrRz3uV6HUFvr7wPSIj3mLWw6zpJEmWTyK921VD9ROSwg6WLADNODJEF+yWd4VlO0b0uJ9Q0ctrDaQWe6Xd4HeRd8+4PNGZiiS7wS+4kTTaRnJ/ziJ4aUR3IwYZWmidqG4BG6/JYaNtcEcktz6BIKITu3n6iucZFR6C6Hp5rQE2PbAtNmd3Ka8XRfSwozuqRSHWpvVZnhf4JMhVKNeNp3gw0DDYKD5bGBfRTTBXd8roJcBKE7WgcKPUPe4nZaJEPy7RPUkzvGYKjLADsRVzHwVzJqKjz4Y61TGt0Cfggqvl2uXdV2SrM756FtuNCAgmiuiI8FH+XhbRK4guxfmcT9oS/ePxEs11jj4g9vKIHhX4jkc7OmJNJIbyHIwbewDwp+KI7sYVsKmEq4hdJmCxL4cSGDGiB8Hcsx1bxTZSeeFBUvdpjj7Nz5XoK6QWu5HdlEeGpa+wIy6Q7OYYbrlj7BBg4zDCp4bx2KgS44KvQaEbB2RupImx7fhCYewAFkR0KCS6aHJZ4RGIfll1f/38XYm+QmqxW9lNSZrgsxj7EUMiu9HwIYnj02yGdD4Zn9iBws8Q7FzAzw+CRB5QtdhW/M/DuGGDiehs/k7t5ojKFpZH9K1T9/ny2uuqh0NSW1jLoEYnbMVsxcscMSzRSfgi7ytSdyYFtpHQ1Y42aJi1YX0mUXwSAba0HyUGaAmAzA+8J4DD9hEdwbgxCBOcieP8WFce0fnmZEthj9TdTGvvn7chOpXUzgIz1jliXKLnArhY4OsLIzvfy7lQK+TyAI5f/hBCFelUfT32P0FElZJ44YM1Oh2DjUDaOFU3QXSncRyBXhXRPx7vzu8vDYjOt35eJXHgfkSvwaYHtsVm7JbDckRf+R1ogUBG8HtmchGeB+2Kee4LTNrfPhmAEB7HcHBh3Nru0B12lLg7H3ui279gkABkD64C+4pYnWBvhep2RBePK1ddwuICKTBjnSPGjeh+F3lfENF9NULeajxvohTeJ8TOPH/M8ogedAM2g7AWuRqIrSHTra7bJISnqbvfotbHzVJYKJamfOKOXNFPykSJXiG12K3sDgO43zC0IHyPNeOOB4Q9l+rBvxtkv43I8kkE2KLafJaFhL+QN8a2zMahHIJo7reZFxvQE3bXUFojekoKzFjniPGJ7nSCDQfr3CiGeszmABupuvrjVnJZcOq8S/0NZATCBI1qtcxnsYHCWBwuqrPB3G7JEZ0/G77wGERvcD963PwtpMCMdY64GqKHbK1J3eWIjg3wQdubYt+M55zhu1hNRA+Sf1IZrdxY0sbY4MiJrAMyBwkjuh0DiN98vsKaWlZ4EKK3uB8dukhHaNuMiq3Y14mN6zjL0W9TVVFRaSBKdBWVW5CD34+uoqLSROrvR/94hOgbsyoqKiOKTPSPx2+vX36g31/tvpDQ6xRr7Ki0e8HpFlXW29+KPQx2sqdUsEDcc5rWn4iO5vSdGNmf6H9WJFCjbPXL1asqa3xZkLpDsXfAXthPMj2lCdEvER3N6ZXosf5RiF7b4gcmjHT4EuxS9TY+SX23YGE/2YLo54fSAFq7U6LH+kr05thK9PKe0oboK1RrRIkuYRecWrRReEwNdoWMTfS1PVGJvgcsqqCBRyX9oxG99JisHvf1yjy4El3CXthPuhP9/d6u633WxbiE/i0QvSJtaER0FmEp0UsOSRB9NZeOTfSVqjWiRJew8z5Touft6UH0BKUF7IX95NaJHn6RN11BA49K+tsSXea1El20Z2eiw/GJ/nLc1F2JnitIuKNIT4mOdxUVjkr09/uH6Tr6twLVVaJEl7BlV21EdOArWYstHeg2Wa4XYUP8sYTst030n5/ef/l+hG/GfTz+wVe4ezv9GfN6SjLuzkR/vw+faCtV0MCjkv5RiZ5w6oGJbg9fTXTsBiV6wuRJPh4fPn7zdAyif/nxfv/lx9tPT6c0w7x+mqw6mWdeglvxhAoaeFTSv3qiA8riNyc6Rip5UksB0VNkvFGimxOvXvHtbjsS/cH9mXOqcSL6Oajbu+zTFTTwqKR/fUR3VLEPj9qU6Gh0CZCqiR7+EYXgqNsm+grVGqkj+imBh3NEnzZOWyUVNPCopL8N0bO8vgai25rWEp0juRJ9NKKfsgwU0QsraOBRSf9WiA6YLpmDlegM9sJ+sgnRX49yeQ0R/fVkzrObo5eRXYlO3SHtnx+4irgdEn0+tDXREbsh/EStS2KDg+KJLgPcNtGnda9C1VVSRfTpjrrfOQX2y6p7UeZ+20SPvbuU6KBETxcOS/RwpWs3oq+toIFHJf2uRC/ndSuih9zGHN+H6OA0GhI9HEJvm+jm5a5YdY0o0SXsAxLdKkiulrCTokRf2lPaEP0wc/S1FTTwqKR/nUT3v3piCd+J6J7GCaJDlugOpoboCO+2iX6cOfraChp4VNJXopdhy/oJatcQ3TNciV5u3Fl0jl6gr0Qvwxa0mxAd6SnRRUmovt4Vq64RJbqEfViigyNXKTYrSvS1PaVNRL/XOXpWfxiiey8XER0KiM4QQ4keFx6e6GtUa0SJLmFvSvQwmBv7EwER0cHrxCAsNqfH8Lqe6BDoKdFFUaIXeVTSH4/oUmQMOY6IDhmiF2QLUWVK9BY9pQ17w99eU6LH+sMT3YZs4xgt/fiPi+34B4HAHexrkRoU/P61RIeZvkr0YpFV499eK0etESW6hH0NREf155hcSPTLtv+nRC8SWVV/e61Af3iitxUlelx4eKLrb68V6G/74Ik27lDsHbAX9pOt5uj622sZ/YEjugtmeHktnbyn0nqEjStvHdHnCYNGdLSzSPqo1ogSXcJWoivRlegVFTTwqKSvRFeiZ1SPTXTusYtK9Fhfia5Ez6gOQHT04w0J1bWiRJewlehKdCV6RQWFUqUMvuMtkkxll72hi5ToF98MSPTlYlLHs3aUG3ddRB9VlOjXQ/T95dBEXzx8XYX09Ldib4u9v0hEv7fs2vM21STRr34UGLVTK3aMvb+sz/I7WqBE7wWr2Nti7y9K9MNKwSktOeuhCdMVuw/4QXrmDRAdiqVOvQ67rqqMYwp8lz5mWDL2xO5I9MX9JNNXKs1orloj/Yne5JIlr73zTS1dib4AXIkuYS/sJ5m+okQnig08Kmkr0ZdhL5BBib72CzMbEH3/J8wo0TM+25bo+YOukOhrazw+0Q/whBklesZnIBZkjj0w0aXDl2CXqrchOn/84Yl+gCfMKNEzPmtK9LBMiS5C8LiDEv0AT5hRomd8dnVEB/s/xqnFBvTH4cXYN0r0AzxhRokuuKoX0YH5DLhgKV7E84cAACAASURBVHapKNGr+koboq9QrREluoQtu6qc6AJIhuiAPivRCwuV6IthleiCq7JET/RumeiADveM25DouNoQp/hLRFatguhHvh+9M9Gv6aYWJXoMUkZ02IzoltqU6BipjOjg33iip8h4i0RfqVojSnQJW3ZVZ6IDITpg+qSlHdGhKdH9sUr0pqo1okSXsGVXdSQ6oC0AWiz7OIedO6oF0ZGJSvRCk8/yoql7VnubZ8ZFrupKdOCJbnmX49pSoltqR0QHa11B5WhIyhEdQd020d/vH6br6N8KVFeJEl3CLud1D6IDJjpg/idcLWEnJUV0H+0bEj2EunGi//z0/st3/WZcUvvqic69JFwtYSdFib60rzQh+sfjw8dvnpToSe3rJToo0U2glikclejm7aenVwBN3VPa10p02IzoArVrie4ZrkQvN261ao0o0SXsPYgO9MHPSnQleitRokvYmxIdyokOYqNVEJ3h9RKiB3pKdEkE1fMNLWfRy2sp7WGI7v0zItGhF9HJIHKLRDeviOEZ1ZWiRJew9yE6VBA9hCklOs/reqJDoKdEF0VWfb/H96gmVVeJEl3C7kJ0aa7LEx3c53ZEF3ldS3RQorN2lBtn5RkvuivRGe3bJDosJ3qK13VEByV6w8W493udo6e0Byc6WIJLHOeI7kvml6CWVFM0IzoYJXrTiP5QqLpclOgS9qhEF9oix+Qios+OU6KjXUUiq759hUP/mqoSfT3RW4sSPS48ONFfg5W4hOpKUaJL2Gu8mnCHYu+AvbCfZPrKeqLrdfQy7WEj+iUMRgk6Kzh1JyVe4VIFExmBmNIiogNoRKe7iqSPao0o0SVsJboSPd1XlOhEsYFHJW0luhI9o6pEz8Mq0amLlOhKdGZXkSjRizwqaSvRlegZVSV6HlaJTl2kRFeiM7uKRIle5FFJW4muRM+oDkj0TlJcY8XOZXbXqa/ySaaq3v5W7IGwE32lgr3lqioqKqOKEl1F5QZEia6icgOiRFdRuQE5yGJclXLt8lpHWXC6RSdzGH8r9gGw0/2ksKd2Ua2RqssPdVfAai4/LDS+1JK6k0GXftqbrNgCdh/w1ZfXcv2kwozmqjWiRBf2DEuYUbGV6NWqNaJEF/YUGA/RRuExixyTP0iJLmF37CezvN/bfD56frMzo8Li9qJEF/Yo0TfGHpvoJWY0V60RJbqwR4m+MbYSvVq1RpTowh4lOoHujz0+0V80de8kRyN66TE1jqkYTZToDPj5pWM/8fJ+//D65cfzt8gGo0R38vH4kFeKjS+1pO5klOgxtBJd2uPk/een91++v/1KI3pChiJ6xGslegPs4Yl+6sMfv3lSoidlBKLLvA4LcoNEiWPEShpg14sSXdzj5e2np1cATd0jmQa/09/bX/69z3//8ffPLjo5Cu5OhX9XcFhgfKkldSezF9GB+3DbRF9d45ZEz5nRXLVGjkD0rw+niP7lx/v9w+m/ef309Pb1m3llVy8D40stqTuZdkQXQLJEByX6vG81+PmlYz/xcpqjp82osLi9HIHoPz1dUvfnu6n05K+phJ/rBMaXWlJ3MrsQHYwSndm3Gvz80rGfIHmWp59K9Inop7cz0V/upp+cg09P84688aWW1J3MQYgekGtzomOkRkTnyXwlRJ+/BavX0SMJif78bQrlU0Q/FNGzvG5IdPe8ReCInnHoSjKKZ2EqiQ7C9pUTPWtGc9Ua2ZPo04z85fNM9C8/TnP1aWL+fJCIXs7r/kQHpJBwh4SdEyCHLyc60AMhOkSJ3lS1RvYk+vSdwb/6y0z086r79OuSv/P4oER3RHdPeu5GdAe6juiI1b2JXnOKGxJdU/duclVEx09JZ4luuS67Q8JOCkd0awm2LoOdIDrNTOIj2xA9UbplROdX5JTo64wvtaTuZG6R6OAPJ7mFHQRKiY6juED0EOq6iK7fjOthfKkldSdzHKJHL32JDvKnWqKHfwm2XxfR+e9/KNHXGV9qSd3J7ED0gMzzDy7FRE9+UbSW6GBf1xHdwdw40ec5uqbuHYwvtaTuZNoT3XuigOgQEj2kvOgOHlvUprxeSPRj//ba9hGdFyX6OuNLLak7mYMTXXSrEl0o3ZTo/6+m7u2NL7Wk7mR2Jzp6cnhE9MTjxEuJzvO6nugD/JrqdkSfEvdT1v72VefoHYwvtaTuZA5EdEgRPYRRoseFWxH94/Fuuk31Ge7KjeNFic4YX2pJ3cl0JHpMmAuLeI5LRAd/qKFQmX6S4nUd0WH+p0S/yPnWtWf+2zKiyWtVa0SJLuzZhujRdbRCogPilSG1SU7PMbmC6ID+KdEvcib6y5cfrGlK9LXGl1pSdzJbEP1CAMBEh3Ki++m7ozxLGBKCGxAdrN1K9D/jiC4+LEWJvs74UkvqTmYLovcQJXpcqETHsEr0eE9Xfyv2ttgd+8ks+pNMPWXYiH4Jg1GCnhLDbtqMnmAjB9mg2S6iuxo1ovdhrxKdMb7UkrqTUaIr0Yv7SZko0dcZX2pJ3cko0ZXoxf2kTJTo64wvtaTuZJToSvTiflImRyF6qZgq7f5EXyUyxGUHcRF6V6IPR/RVYkQI1o5y41ar1kh3Mo4qSvTrIfr+okQ/rCjRlejtRInOSdjF9zGiI6xib4u9v9ws0ZNcVqIrdlPs/UWJrkRXbCX6QtVqC3Lzw5B1Vcp8tU2IXmF3lf3OMQmfpVXSxx6YMNLhS7BL1fv7ZLGY5OGVZjRXrZGLIxpcVKy7zpj0WLE766+PFtm/mOgFbaREb4NdZcPi6+jp7qJEzztCid5BlOgSdnU/Keouocmv5+56DTe1KNEN3gViQeZYJfpq7FLoDYk+/ZRg8hQLTa4/y2JYJXqspERfjX1rRJ9uSU+dYqHJ9WdZDKtEj5XiamVeXxfRWYSlRC855EqIbl7u0qdYaHLtOVbAKtFjJSX6auwbI/oV/ZqqEp1s9CM6LGru1YSBdkQH9Feiu75/82ZvGdFrjVutWiNKdEFpb6IvaG8lelyoRMewSvRYaQ3RUyZn9lstdNrFLd+O6MLJLyR67rBrIfqLpu5xtUp0Ed7MRLdnDvH+WuxCASNyfTXRVw9+JTXGhRsS/f3+4fXLj2f2SbBKdCV6BG880bF2R6Iju1cRHejmrRH956f3X76//UojOq5WiR55BKng+743I3p0g3o1NrK0J9F51d2J/vH48PGbJyU6rXYgomd5LRNdGC1kosNla3uiQyOicyS/EaJPP7L4CqCpO6lWiR55xO6hRAdMn7Qo0ePCLYlebfJa1RpRogtKuxDdvgREty8CZhY7JzzRIVBpQPQERZXo9ao1okQXlJo+Mw6ZzBeXEh0g0RGU6HHhlkT/eDz11rsKk9eq1ogSXVDai+iwNdEptRcTHRxUS6Lz7K1Q3ZDoH493p9dn9qeTlehKdHfSIa0PQHRweA2JHkFdCdEvd6/pqjutVokeecQzGuYnOktEF1yiRI8LNyT65e41/cIMrVaJHnmkkugV2LII1K4lumf4rRL9un42WYleVtCL6KBEr1XdMqInRIl+G0T351BIdMBEDylvPNsphIQtCIT/E5/SZFxIdLBn7l7ZUyoq3J/o+oQZrloleuQREszBbXYhOsPrJUSnejdOdPOsz4yLq71RosuRsZLonF+U6HHhhkTXJ8xw1SrR6ZmwRMeUD9N68AwkteT6icjrWqKDVVGi50WJrkSfycLRO0N094drSbnLVbeW6Bf6KtGLRYmuRG8nMnYBk8uJfhlxlOj85TX2MroS/cBEX+PVhMmKvQN2dT8p6i6cye/3+oUZUu3xib5JRMcXzLOpu7zPuGhKsPFmm4gORiN6sDeQV/2uO6lWiT7vUKJfF9H1u+60WiX6vEOJfl1E14hOq1WizzuU6FdFdJ2jB9Uq0ecdSvSrILquugvVKtHnHUr0qyB6RpToSnQluhKdmtxHKrGrbCk+m4qdK31SdNyB/K3Yu2Knu0sFe8tVVVRURhWB6Oc7W6dn0vAX5VRUVIYSJbqKyg2IEl1F5QakgujLFxTyyw1VyhWWVNtdgd+3YQ7ib8U+CDbfJSp6E1/MEn1BZy21oPTyQ9XFNXuZqlS7Dr8/0TvCKjaD3Qd83eW1dMdUomP1KyR6dHW8FvbAZJQOX4Jdqn7DRGceEq1EZ1Q7Sg3Ra1u8RH/BCSrRJeyKPljRMTt9Yabq/OpgleiCY9K7lOjLsHjsii/B1dqgRFeipx2T3tWP6MDo5CtRokvYFX2womO2YO/LlLO/fnoqUF0nSvSUY9K7lOjLsHjsmyT6y2Vu/vb1W1Z1pSjRU45J7+pAdHBvEOoq0ZfIsYnuftwFPa5Cic6odpS4gohyV0N0IJsy43LYEH8sMaeW6DWnOAjR9fJaWrWjLCF6gVEVRA9uaVWiJwuT2BV9sKJjrif6x+Ocsr9oRE+qdpQdiO5JtgvR8Z3ly7C99eSvqPo+96MfnOh2cv6CVuOU6IxqTt7vz99EePsK8OA+VTiGL+lCdEKyM8t3JLpw8guJXnTYbRLdmOfp6zJ3Raqr5KqJ/vH4MCVF7/cP0yRo/lTjGL6kN9FBiR4gFhSOSvQ1qjVy1USfljjef36alzTnTzWO4Uu6Eh0YohcyeBnRHa+jZ0thpSw2YjRLdPnYno+SUqIT2Ksl+illh09PcxifP9U4hi/JEj1h3AKiz1t9ie7rXkd0juRKdOns9aaWQtWMzO5DEb3SMWRrA6ID4Bf8YQeiY6RGROd5q0RXomdUMzLNyk8uPM/Rf3qaP9U4hmx1JjrhuBLdo5YVKtELLLhWok/r7FOu7lfdizP3No97ZmCl/Up0CbWsUIleYMHVEn2NY45FdPuSAl9A9Ijay4juGX4woq98rnu6YyrRsfpNEz22sojosA3RIfy/jOjuECW6KEr0pfjXT3RgiQ6JjtCe6G5caUn0EOqWia5PmClT7SgHInpIedFkCVvQbkL0NT/J5M7cvbKnVFQ4JNFXqtaIEl1yzK5EhyTRhZOvIDrDa5noIBI9GBBqiU4GESV6tWqNKNElx2xKdGCJDizRxaBeSnSe1/VEByV6qSjRl+In3LH44dsBCnERet+S6CAQXSaAEp0UKtExrBKdQyEuQu/7Eh3k1XclelyoRMewSnQOhbgIvS8gujciIgwkOJ4iOpPB54ieZHIV0cH9U6IXiRJ9Kf6gRA8jIxCis5IiOkByEAlPSYm+UHoS/ePx1KTo7mklOqPKn9IIRL8QgHwzJiUB0XEZ0FrICaM9OSYXER3mbSV6pg+WmTzJx+Pd6fVZHyWVVOVPaQSi9xAlelx4eKJfnpCg34xLq/Kn1IboC48rgVXsjbEr+mBFx9SIjtWHJXrPiD5HxnAmnk7eE2n9uYowMkKHiA4a0Yv6YJnJZ9E5eoEqf0pKdFupEv34RF+hWiNKdBGFuAi9K9GV6Lk+WGbyWtUaUaKLKMRF6F2JrkTP9cEyk/XutVJV/pSU6LZSJfqxiT7LaZr+UKi6WJToIgpxEXpXoivRc32wzORZXvQHHHKq/Ckp0W2lSvTjE/3tK32QoRKdUeVPqQ3RFx5XAqvYG2NX9MGKjtmE6M/wjRYcwRFK9NUyNmFGxa7ogxUdswHRXyH6jbAjOEKJvlrGJsyo2BV9sKJjrie6rroXqvKnpERXbIpd0QcrOmabOfoK1RpRoqcc017GJsyo2BV9sKJjKtGxuhI9hlXsjbEr+mBFx1SiY3Ulegyr2BtjV/TBio6pRMfqSvQYVrE3xq7ogxUds8FinP5SS5kqf0pKdMWm2BV9sKJjKtGxuhI9hlXsjbEr+mBFx1SiY3Ulegyr2BtjV/TBio45INFLxVRp12FX4idOqVQx75j2MjZhRsVeLYaFqTQjlq2Jfl2iRFdsB32Q/q1E7yBKdMV20Afp3xLRN/4K7HWJEl2xHfRB+vf6LH9DC0aRJNGLR4HxOrViM9AH6d9K9A5ypUTPHzQoGW+a6C9Tzv6KHz1xjFVJU6VeolyHGBzHn1JKsRxljVczsEr0DtgswIIrPxU9cqVxZub59JSZb1nVlVJ3nZG/npjUzijXIYbH8ae0G9ELjpE7NSQ++ZJUFY3IKFu3ALvkkP5Er+9ZJYc0urw2yeuxfqlFiV7gTHysQNc6ogN+6010YM5iKTZEh/AASvSjfTNOiV7gTHzswEQHggSriZ448dsl+sfjnLK/aERPHsef0lZEj/i8hujEFvBs8w95zZrVjujQlOj+WCV6IPPk/AWtxinRmeP4U7oSooMnOhgo4MNKwoiPe3bWFWID88e+mHrsdK1x4cGJPj0F9iR3RaqrRImecky6pBfRZ1qDfd+C6JTay4nOkVyJXnN2SnTmOP6UrpboPpOXTe5AdOhF9AXZgqyqRM/DKtEFx5Atkdd5ogdgAtHnfTOjASi9OxKdpfYCosMaopcbfj1EP+r96DdF9HJej010idqVRAcH1ZLo5YVK9AILlOgcCnEReu9BdMtjm7IDJnpIecfDCITFzogSfWmPVKJjbSU6KcgQHWqIHsLUEX3JjyymYZTooijRleiWVojozjq0Zcge8Lo+uieJTk1heF1OdF8b1VOii6JEV6K3kxzRwb+vJDrMr0r0QlGiH5foa7yagVXsjbHre9ZGRD/ojyzeFNH7R3QXOtEUPEjQ5dSd7ouwQwuyIbs0ooON5RrR1xN9pWqNKNFFFOIi9K5EV6Ln+mCZyWtVa0SJLqIQF6F3JboSPdcHy0xeq1ojSnQRhbgIvSvRlei5Plhm8lrVGlGiiyjERehdia5Ez/XBMpPXqtaIEl1EIS5C70p0JXquD5aZvFa1RpToIgpxEXpXoivRc32wzGRetZPUYdeZUqK89OSKPVWx83j+Vuztscubn+k2z5c934LiTmFaRUVlD3men/32fEfLlegqKtcj3FNdz6JEV1G5HlGiq6jcgtiU/dk/vvkswy3G3Q52T+lpt2Jvix3Iy2XPisU4fz3D1SDU5dS5bVtwPhb4SzKtZGjsnjIv7OKKLu1hXwC30PxuXJ9l8MJLVouMshfL8BU/978cG4Lzw1fh4hdvPINeXphUTduNvJ4/QUzzFOEjQ4oFvNnBVdk8dKR2NtIUOmKxDI3dU4C8zR/cxXTjGG5Hdn81Wmhz8K/LjceXtsn1cTB1AQG4912vo6ftXSqdrqPXG1IMq9gxdk8Z1SeKHWPvqVojQzt5/wY8VAVD+3tUbCLcgyQE1QrUJjK0k5XoMaxib4sdSPhNmYRqBWoDGdrJSvQYVrG3xQ7E/T5qXrUCtYEM7WQlegyr2Nti91A9r73aBXPjV87dgixdkEc75nXNWf38WVfd09gLJfxKVKoC10CuGfH1NVsSvaALSRDD1hnv6oe5M4DwKY1NT4j7k1901T1UdVdV526AroV75jtq48uflwP8BVl3IVCvo4vYPcXSy104Y29DtSXRNXbH9ODacb1P8GXz+Mp58XV01AEN/09+QQeLwAWFSdWkT8g18ZSi1YWgFUpEI/phsXuKRnRsq0b0pao1MjQZt2zAt7/8e5+/v9+fr5u8wvm3609vn55Oe75Ob2+/+rvTNx9Pqfu8NZX/xceH4goa2a3Y22LvqVojQzt5U6J/fTAfJ96+fPnxfv9gXj89Tbcqvc6fPn9/+/rt/PYruzWVv4AS/cqx91StkaGdvCnRf3o6r7TNdyKe3iYmG2ML5/3T/8vWaQwws0pRBY3sVuxtsfdUrZGhnbwp0U98PiXj52x9ej+9Td+DepiC+hTqz/svRL9svVzKiytoZLdib4tNZZrb/f3H88Qup5pARXczTQVo+X3+TNfZ8Wq8cR/teqe7caLOjBoZGjuUmb7nzVPIngP7JVk/R/SQ6PmIDrbRyLK7cZdI8Mq78avyxn0OTK3zidVG/xOfRGwEg96iP/nlqhbjnk8D/zRde7nLqsqo9DIAvgpDuwbZRw409EKOuxw3Khm3JvoUoCcKf/5unj+dk/V5Lj7znRA9O0fHl9PIB07C3b49A9hSn7i+IV9QK7m8hmDQW/RPfjEOW0IvKkyqsvqse0V3Mc2DaGZlGv7PIWDFE2Y0om+LHcq56U6Z2Sll/zjlZr/z+BCuugdEn8r/gn20EFuBRvQLwvVE9DPRf/m+lug9ZGgybkn0RfKeJHprGdrfo2JTaZK6d5GhnXxoos+Zfb8KeFjF3habSpvFuB4ytJMPTfTp8WGf2IA+rE8UO8beU7VGhnby/g14qAqG9veo2Huq1sjQTt6/AQ9VwdD+HhW7gypa0scr+26Zdt5CK7kG/AdyWcDY62tjO3n/BlxXwaW1XEP5izzRBTXjL4rilmdgJePxMvf8H0zwP/EJY4P7o3roLfqTX65p1b2JqiThFXVCaPEoxPlxybh/A66qwF4tLWgoViE0NOUTeuGau1BefB2dXCEHDDfGdfSUe3nn8Yf1IrpG9E2xe4pGdGKORvSFqjUyNBn3b8BDVTC0v0fF3lO1RoZ28v4NeKgKhvb3qNh7qtbI0E7evwEPVcHQ/h4Ve0/VGhnayfs34KEqGNrfo2JTeb//dr6J+S6vKqPahbR4ocYt6gDSIct2/KquGdzJYxMdr3TlV95R8xtAS1d2CyKf0NNA++1/MMH/xKf5WPRGl9PnIvsW/ckv17QYNz3V/Xw/y5rvuhd1g0oZnIxDE91fu6puOntDmzFuK76CQi//2Gtk8vWzkstrjtqjXl5LetQ5lQFKHIOOmm5iOnN81d1rM7hG9E2we4pGdGzrNUX03zyZ1zujt6kOhN1TRvWJYsfYVF4/PU1PJ4l+mEmJfljsnjKqTxQ7xg7k8nuq0V2LSvTDYveUUX2i2DH2nqo1MrST92/AQ1UwtL9Hxe6gapdkwsU29113Y8jKDdWJlnrsEmmdGTUyNHZPAVfHZTULUOtGDTUX0tVWv/LusUxiUYtbXws+pXdjbG453SBbuD/55ZoW45qo5iWlIyy793fEqNg9BY2wpVdGBS07AHi7+YtPfqBYdXlt5i36h/SMf4v+yS+GYDOOKitMqtoBJU+PRJtxvi8WjeiHxe4pGtEpAWW7bzGi1xtSDKvYMXZPGdUnih1j76laI0M7ef8GPFQFQ/t7VOw9VWtkaCfv34CHqmBof4+KvadqjQzt5P0b8FAVDO3vUbH3VK2RoZ28fwMeqoKh/T0qNifTD+kWqjKobkFWWnWHWYuozIeg5Vq0YXTVXcTuKW6F2S5jnxtIuP/Ir7r71jS+3UO7A+MBbaxfdfc4uurOwV6+ADvJ5+U3tRRIkVJwQKUZNTI0dk8B18XJRdHipjV4yA7sBrYmk79Qntk9Y4fXypFetI8ZD+IXH2wYt5cXJlXzxMhfR4+bCUVbJ29fp7tZNKIPhN1TNKJjW68oop/k5RTL1xF9hUU5WMWOsXvKqD5R7Bg7kvf7OyX6QNg9ZVSfKHaMzcgrKNHHwe4po/pEsWPsPVVrZGgn79+Ah6pgaH+Pit1e1S2xmXBtBvxqSbTaZpcFdTGuErungHvzS1nROly8zm7QB7fejeyVfTKvevl1M4g/pXfjxbPLTl2M60Z0qRdQNteIXl4TsXuK4x2+iJVtqYD2dj3ejtYJn/jrWKsvr3mzyXBz9Mtr59cC98ptFh/fj+ga0TfE7ika0SkB/avgqYJCQfUg/aSPao0MTcb9G/BQFQzt71Gx91StkaGdvH8DHqqCof09KvaeqjUytJP3b8BDVTC0v0fF3lO1RoZ28v4NeKgKhvb3qNh7qtbI0E7evwEPVcHQ/h4Vm8rHI8B0W8u6316ztzfYLbQK76+0xBdg/N0ubuV2Xiw1uuouYvcUQHWQFuUv+9DLLL6RXWPa5esZT6pw/ao7OIt11Z2Ffb47/X35sYro6JKZeEkwur7GfvKXaPQ6uojdU9x1TeOJnBHDbqK2NM4nsfnoUjf+v+A6ug0pqP8g/WNfR/cOl92baLNEkzil6ddUjXn9/F0j+jDYPUUjOm6/K4roF6Kb1y+/1V9THQW7p4zqE8WOsalMqftJXtY8YWaFRTlYxY6xe8qoPlHsGJvKx+Pd+f1FiT4Kdk8Z1SeKHWPvqVojQzt5/wY8VAVD+3tU7D6qdmHN+GUYuwgCTsMv09nVN7+c4m9umUGHdvJuDRgumS2uAHwboPU47n4Wsmo3t6dfnvWwkjG2JnBrYP4ThLvjTxgbLcVRUP8W/ckv17QY10oVwl7grnW49Xhe0LiAeyfo5TURW9jFu5fuLK/AXZBKXV4jA4CJ9nimp3wSXAmb2RpeQsteXjP2SMt0DOrfon/yCxkwGKsLC5Oq8R6eIJmmi45JageG1KlatmpE35roCSKuILrv7hrRNaJXq9bI0GQ8KNGzfB/VJ4odY++pWiNDO/nYRJf5PqpPFDvG3lO1RoZ2ctcGTFJbia7YSvSrwFaiK3YJ9p6qNTK0k5XoMaxib4sdyPOl0b8VqCZRbb/xi+l2N7rYAbOevXmFrq2DVwr2tJahsZsRneE7oC3bkvyC+9yY6NKVu5RqO4ADY1avIdgE8h/SBeSTxYYZKrxuZnf4/fRPfrmqVffn+Tda5u+8p1STqLbH2Iss7tqK/0R7SXgl0+DedOk9dWbUyNDYrYmOVT03UUtmjnMNRlvXV89cjwZakx1O+AvlUQF3HT36h/T8YBT/k184u7kTyBQmVVn92L2+JQXhj0FHzXevrXvwRCdRbB67E7i25ZVhE2lBdBUVlaOLTdmfg59ZVKKrqFyTvFxifdli3CvAp6f+NqmoqGwjLNHffnqaHju1tS0qKiqdREzd3azeq3ZcUFDsbbF7Sk+7FTvGLmwVaceLm8xTi5vP6oe+BNYXuwt6f6In94H4qQRWvnC3SirbsuLyWiE2mLLLaxz2KtW3r/EcPbgCu0Ig+jAuGTtiLwAvOGRXogc7eb4kjgRWT6yx9Fxr27LiCzP9+8lK1XiO3szYcFhviR3WdFvYJbFtX6IXHC0cv8AnxaG+SURfib1PyV0O3QAAIABJREFURGfn6LXYuUoR4MEIc1DsFizeYI5eWV6mFjx4Yp0pKezluGNGdIHoDSeO+NuB10PGjtgtfN9p4k+r6FAz+Spp45NIfE11AUxc3AAb4+FvIVccxhVOafvbr3+wqrLd5XMzjeh12BC8c2NAMfSRInpyjh5M5xMRvWyOLp946qaWChkwor9A+EMPBvOy4pv+8z6yk85EroSMHbGjmVvkahAYIGL3lOq5LqG2O9nwrFMRJtzHQaTmv4lktTymCRWU9JP5DsHSOXqziJ5QvZjCekTua8E9akr0MmwXyCPXShF9oMU4PG1zb5f+jnsad0AS5oxhNy+3Qhv/IWkM36u5/lyuOjjR2b1A3klxsAvQ6+Bk7IntmT6/Rb6Xh4IMdk9JVuBPCXAZIII7yjNHhlE7gJlxUC0IKmGX3LUriC7F+UzdCLSI6C3n6AlV2RQ27NgGpNSeswJUOCwZe2KHSWzM/HAIiJs+NnBPosd9wR7iO+5MTrBpIB8Q7O4w6gPubxiPhJzQgETXLo/d/JhQ1k8qIvqmRIeoPCCu+wxkhLWmAjmuzowaGRs7jNfx9JUMqpw9zOQ9YfJ8L9PbV/sGD8a830/rNR+Pf/AV7k5Fd25/0nhhj2Ohi9vuGHChwRa4HnQ5Z3qeltAExoMwZ4+jC6Wl+5oqY3Ix+0X6iwdQpe2IPjes3fzCrrrDbBMuBvSGXWncAQFMcIDJOmKpDI4dBvA80XkkEuplk6crqq9ffrzfP0zXXaa3t199/3h8mL4O/fE47fjy4+2np3l/2vjkeVmLgjOkQQQASGkQ0d1fOGAYzj8ROu6XwqJAqIZPozyiH4/o5/abR+rXO1EVDbLzZ9f7QjWy29sJtNEGJ2NH7MiHIVlj5gcKXEGC6PfncX56NsmJ86+XkX7+NPF9/ptL0saXlOPYMHcrHEAsMS15EQbwMISyYXWE2RQjdXmtonA50c2WRJ8admrKSZ7D29adajjTwc4lQ6TP0EI7o+U6k3fEQhkbW1yMI6vKVJUqcAYmTD5l6aecbu4I811NU3J3ytQR0XFHEY3nihM7YO7reECyuSNOb/zIwOODi/VchCFbUaziUVeuuhcR3Z7qdnP0eUz/+A03XtNQHZXjXhgyHQgAHXDHJmNPbHnVPXqXIjrTc9Imn3LyOKKfpG1ER/bD3CPQShxmrGc6F9GBwhjc+yH4Qz0OgoEgEdE3IbrV3I7o8yj+/vNfQsstAMQGsR3tKdGWActsNHDihdHBydgTWw7gYWQLG0fO6VMmz88bwXP0n86hfJ6qz38r5uhOw4/5tuPaiI7LPOEN62/Uo3xHQxGdxBwUxyOoYGDFe7oT3XJrS6K/zM03tfj7L/zda3IGZteG8Uew6wxujw/1GHZYMvbEpi4qiejRkMAYmDBZWnU/fUJEX77qHqmQ6G0wtWfSAjolBjuM6Pj0w4ju43g0/cSrfkVnwxcujeh1RDdmNdFfcPNJd6+FTqLWGuRrN6zSpAiCw8cmY1fsgK1xnA6JLg8JAXZPKY7ozGm5oIxIiZJCNqJH5+yITggfvOAAnyD62ojOD1CcbBfRX0g6xhF9dg1rTxBUbKORtB/c+YQWjEvGjthRvA6JHqXuoiYydHeiexp7Yx0xcZdxFAfX5YIQAQEMCuwOANyikKV+EOC7Ej0b0R0/NovoU74+i3D32uxWEE7JOtxrRzx35xNaMCwZe2JHvM5G9PyQsD/R7fQbLeY4Xoc8t6k7cJHRL/Y4d9kjIP6L6e3xdiS6J0gh0bGnlhH98uNsD+dLa8Ldaxf/hBXYUh+GUP/D9oBv19CCYcnYE7uc6N7jocKBIrq30fYJR2PHT5S341AfEsb5AwiMO8JHcf9nPEzQdNCR6EWpe1VEX5+6F6miVAmX4GHJRm48VltFg8YECttFxsbOEl0s8B4OCW8KutJKESrw/QZPwlEAx9M9u9sdR4hOQ46HwUSPCB80GM5x9kzdXYp7JKLbQXfO4sHFb+dRMjhhMf5QN0y7AWNgMvbELs7UC1L3CKufJCI6IpzndkrcQTQy0uUiH2gw38PU3b9ETbcL0Vl+ZFsHnWY/omNH2qjt+pGwCOfJjc5nHii4gbatjI1dzOsxiB5F0TKmkyU3CJAoTCHRUb6AcobNI3ot0bFP+hLdZUU+F/eOJ8Mi9TsK88ZnAxrRc9hi/i3TN6Jz+L596g78tnXfhXNhEm57jytFET1gPIKxOCje42TTRBHdUdy/hifTPXW3VWeJ7gF7E90z16dTYI30sdzxnIxZhmyiuscmY0/seqJnd/Qz2QvtrHFv9NSOxcSbXt/YHueh2UNwnOeIHrDdYx87oruDGhId3bGKVf0c3XqN+jpoQHwqYasaP5zKZqyWsbF9mM7RNyyQNbcgOq0i5rntDRVM94QhPA+YjokeRXSfkKIufAEahuiBj1YTHd+xilUBGw4oorsPOAuxjkb9di7QiF6EHXEzLPD9QnqPC7aO6Mx+/x9M8N9He7LboRJsMJGef8PpexhRwqarTd0FH7KlRal7pTQjOn8joiWppbffxm3FDNJuSEYG4oFZMmO9jI1dHMBrIno2OVwtfH9HnLUUZfoKL8an7vYc0AbWc2/RH/tCHM1yurywf0QPTrNF6m7cHavYIE9kN/KibeMbJG6m8LQQ08cmY0/sHkSv6hzLhA+BtoNYIyqZjgnj+jiKN5gB5USn04LuRE+dYY7ooTecEwpFVH1hHiU1e8Xl4742sMOsy82dQcbQ7N4eZO0cm4w9sYF8xLVlCxKau0d0F9ml1J3/FEd014E8uH+L/uSXVOounk2xavd+sk71hfkKbDgI+5DuqeuLaKZh6AcyLNdZXCNjYzeI6CZQ2I3o8x4XRY0h3SItcT9BOSHpie6tJnUP/U1M7pK6M6dYkLo7QOKXQhFUX5gbjn04NrSFvOt4ptMmsxbasxqbjD2xW6TuRijoKIkKKFlLeW6nhxjbs5T0OvdWk7pvT/T4vIuITk+zBdGZeG5syPapuTPReYpqmdDHRN11StmM1TI2NunVpLawgCG4eOieRKcq6dQdBYYwdac4ham774mo+1m3+deSsxH4mFAt8Ume6EU11qiiO1ZjS/xo4jet13CaTlMzg490vHcWDEvGrtgNIrpU0FHyFczdhfSUvMTYjru0R1ZEdGeIEVxTXlgV0edd+OTQWYpeC7i0muj2jlXJMDcEIgdfwLAdUaaBP7nMfXQydsSW4/UVEN33FX6SF0uM7TsS2LDvPx2b6PHJobMUfBZQqEXqLqhSphq8NXs9UEGONwHjNXXPYgP5yLFVGgoiTd+DNiC6UANymCO4QWk67cS4bwGKLQ5qpiiGwUQ3PtjHzDb+eI+6KdGpSlFEd+rdiR61gW8cPnoHvI4/OAuGJWNP7OIAfjCi816xpJ23U51F6EIOe17rpWHFbx05ossnh85ScmxEnw2ITgbhYDQN9rvzMOSTPa2xydgTuwfRqzrHMslHdI7n+QQeYc/9Bkivsqymkdz+HYTodFdAEOCBQ+3uRDcQepZUh5sLf7RtgkZefFZjk7EndshWMVOXiW7C900ienanJSnPb5rOuzgSYLvQ7mG4iI7TfEpvZM4uREcqZRHdqm8a0YOmcbvtWD23AbYpaEtr6Nhk7IndMqJH8/x+IlVwYSsY1FuqBGHPXSuE4Yhu/44W0aOTQ2cpOZCe5gqizz+/I/2aarINkA0umGPbGKaTZK6LjI3dkOhRMtBRhAosOcHF10SHEjqZSwjcal6UusMARGdPDp0l677oNJcT/f3efk9G+DXVoDKbNbnOhYxAEd3M6RdtFhdkxiZjT+weRDdyV2olUg3gfWb/I77ifkE+uQKEbXNBoDAh0fF8nTB7NmPPiE5UyiK6A1xJ9Bf4KzaiC7+mGg0qJijArQKR9sWpZAcJ6+1lbOyGRPepey+TvSSqmH1m6QZVEpzHpYcRmEEiOn9y6Cx530V0Wkr0f/DDpu7011TrmoPaTQ52QZ2E9bHJ2BP7yiK6was2eF5X3Kc8NupCfPg5INEzJ4fOMuVbf5qLie7n6PTXVEkl0jA8j6+oUWZLmGMMPmpwMvbEbhnREUP6mOyFZ4HdRTtBDdk9NngkAnNsorsC/uTQWYqOJafZgOjSr6mKTQDB3tku2xy+AQwA5jlYC4YlY0/sHkTfIqJjr9DxBU+nLUXjXhWmfPbV2JzAQHi4wT0rIDjDbIO3tiU6p1FIdKvdjuiTMD+yyDQGm0E5vyF2A6DmIUwfm4w9sRsQ3QQKQh9tKrgGX+/lBcCfXKG4UcEdNnekOPuvieio/Tw2dzKtiS6cZY7o8Wn2IzrlsnW5MxRHa2eaQWMCM1MxZnAy9sTuEdETXamV4AqC6lx/oJldWkif8qz0wTzULCK6HSe8kaxrygtrIjpzkiURPXDIeqILv6ZKuY6YHg/Sxu21+Rpvr7VgWDL2xO5BdJPuSi0ExA/GuP7gO4bvRmz3953OHW6RwGYIXoMnuac3juNmP6KT/eVEN5ZWTYie/DXVKFvyhpLxxiBLLOuNTdy8mg3z45KxJ/Y1ED3eNXcLmgBmw7sJCINCCY75OaJzL9Yn2xI94o8nUkIQdSy9CqVGFS2MxA0RmI6Gapu6G7wPJV6VZtTI2Ng9iF7VOZaJXIHrBcYP9OWCseNeFLzVEN1ib0n0+OTQWaYciJ3Wi+hhKA4MJbuNtwR71PhC11KVZtTI2NhAPg4Y0SEon3uB+x/0I1tgaFcGf6jrLrbHsZHdghuUUFJmG/qyR0SPlEqIbgw6zY5ED9qE8p5O4BHT0VBAh/HQ4e1lbOwuEX1DotOOaPM644K67wYB4wHvo3N0lPbTTm+7mnuTI/quc3TmJMGaBjxwiLhNRA886wZUTGmUrRtaOvsdNd3gZOyJPTrRo4iOU5OI1zQIoFJMARccfGAPIewwEBAevxj3sj3Rw73lRA881Yjo+EqbV0V+sbNrN4pSpjuf4gPtBz9moNIeMja2Bc/TN0zuox0ozG5IdKbcDe5kCkgDBelRQUT3UHMnJDB2MIlITuvvE9H5E08SHeNliU70WxHd38iG4d1QjKzyI5LLpPxwixsC24hGp8HJ2BO7ZUQPh4KOAvwHn+ZFuSHuKagg2DfDxYMEOURM3VH0cS/E0WuJnlIFWhRLKdGJZ9YTHd3IhlWtN+kgi2ZMNjFyEQmN2PxZjk7GntgNiY4H2j4meyGdOmb6vEkpGn2KGQwOw/e5QM+GGeCI7nN19ILi+mZEn4sjHnhKSY4NHbOe6P5GNlLP7DXnbjtK+hCOk04cTNzo6VN31yqyGatlbGwgH3Ft2YKE5j4RnSYWLjCb4H/iE4nolqKBHu5q4Z/8Qvtr6myShYJq936yThURHQ2YM0Nn0hta5JnuRij7yY0NVg2NDGOTsSe2j8ZSADdhQT5172WyFzbaubHL9g874LuuxAjqZjgyuj4HLiyjvshE8yCEBy+bR3T2VPeJ6PxiHA7k4F7tKGpQc8ypvPEzJkOUPNE5R7STsbFdqweslRNykeje0I0jOil0wzqTGfJMx7s9NtgOGMIYnuSe6HxY3z51JwqlRLcHdCc6eoNwk6IBKaDDmR/eMefLLa6RsbEjcAg2oqEgfo+Ivk9ED3YB7ge4DFGbnkvYTzzjwyQFhL/QLuSRNqm7RH/xgOjwIqJn7ahTFYlOo7NvHF+Qrh0QkBI9iS3SWAzxcqiPjk3VvqTPocMzO8H1IR+y508osIXTkqif4Ckj7VbuWBfRfZ/FJ4m2wxJWLV24MKI7pQqnbxfR8RIaHlOtvan6ADWDEj2FHc3RTVAQ81mavFdF9I5Ed6m2q8f1h5mqiOf4nKJ+YtP3EAZz2qftJg78/vx2Td1nrXKib5O6R7GFfnQtlawQe1iJLmNnI3pwAPeeyP/l2qmUmVxYAYq8xq7egKThNRnC+DDNwvhKiF3cgJdK3QVOs6c2ZOrOq0oOdRqW6WkcH5iU6AnsKKKHBUifBu54jAiw07XzUmV8XglcsCZH+AUcskPoJyIMxkIRnQ2CDruU0xURvaifwI6pu6BKfMV63QhDI1cnWqcvN6NGxsYOA3g+xMdDQoOI3oroUeDGwZ2UgQl4Hqy6h3E5gKH+cV2WazCSLZSf4fUTnb6HTScFHBlRiZ7AjgJ46PmUhgk1jAm34mpLpMx4rjhVsyMzmcX7Q0k/kaHA+BCP0gJZ/TwCJOboYjVM4WKiV83Rs3a0V40jeh0oOrVhydgTW86/pRAvr9IVNU5Xoqd57sE5XsoRncOhw1vSYBrR17XmCqKbQ0b0ZgJ+7B6WjD2xZfDsCMCkX3ZjLdGzHbKgUzNlQr8N7AZ2X4iTDaDh5yZEF2ot7SfbRfSPR/egOP5HFhtUR4+7jL5rIHI1XCd2MnUN3oOInk6gtyC6ZAFwn0K7gajIETSDzX6uYJlQ8SAR/flu+n8W4UcWJYjFTGcbsKFcK3Z5U5RF9BqGd4no4olURvQMmnBg9WJc+RBzwIg+PcjdXj0XfmRRqGFxbwf60l6uDHtBLi+rkrItiR6p5GOwK4GE1tqxp4JkNc3evZ/Uqk4kn3+1QfiRRXxUC8vlqx6t5LqwUxn7uk66MdHZK+dFdidy8DV9EtYC5LGzSptFdER04UcWl9WQq/y6yNgVm6G+tCeDHZYtlsIKkipQfkwuoi9sj9qIXo/dBXo10aUfWWwsGtFXYi+KQZQcjUSsoNSmkpPNYy8NyrtHdLNPRJ8k8yOLDUWxt8fuBK79ZAfsQJ4ve8JVNq+KF+NYoquoqBxdnufL4vYCmhU0JvjLa/yPLKqoqBxcXHwObz9FRH+/P39hRvyRRRUVlYNLCdFVVFRGF5uyPwffbK1Zt+u4oKDY14PdU3raPSp2IC+XPfJiXN7J4QUIiC8J4GcYoXso3fleLqjZw/Q21TS2d6CpK8hqulK++7A9iu65XP0O8PsTvQ+ofHmtvDCpGl7/l30u2cg1D/ddNknqrsRF31dwd6VEeKh28F9GsBdg3M1KSnQZO/o6THlBQhPbDZcGsP/diyG9ylpi9xh3g1h4l9yYRE9+1728UFDt3k+ovN/Dl99+BbjLq8qo4R2AYQnYIh+rZwWgh/mIrze1yNj9iU63LO3nWz0d532jcoCR3T2ldQU4/lwH0Z+/nZL3u1MGf5dVFVFdyuaOpSUoLjhl9IoOc3mK3qaaws7k33JBYeqOd+CkPEwTXaMygLHdPaVxBf5sjHBKfVJ3t7c4dccHpG5TnVbdzyvvK1bdPXsN+LA8Z3QWDBE4iOj2hMH4eKERPYW9XUS3TRUOzi51x7Hdf3M5TNWGIjr2w/VE9DZE9yN7NJG7KITjk/HaNh7QKK8RPYW9QUS/FOKoLYmhzWaccmh3T2lXAQmHu0R0xr2ZiB6oshXay2trUnczR+aCiG73uYHfE8NO7DWi57A3iei+0OVq4GbrTES3zX0dER1vX0VENx+P5wtr4WX0OqKDXZ5ES+YQEx2POGTBFjt0HhCU6DJ2f6JTlvppuCO4sY1EUnfjOc/YvUg+Hh9K1Nr4O0K5HqKf0/ZXWHcdHdxYj7I8T3sm7/MzdZQKIBDQ1D2FTdPtioLC1J2mr37YlrN2A+FmbPci2ZLoccoLfHFVYVJVGANCH0cOZfVzi3HnB8G9BEyvjeh+9o2DtndU1Elw5/BTe3x4pRk1Mjb2VhEd3J6I5ME6HGk9Y1daA7sXyVZE51vtqiL6xZVrFuO4lRpjXKQ2vh9wimQBBw1JY5OxK3bviE6LgG1gRPdQgQCbEJaRqfOduuHb1/Pzi6ZHDT9c8sy7U/Hvx+mm4JgVwsfizSO65GNIRPRYVYjo64mOEzuDzMIR3c3GcZJvBwQf9Y03dHAydsTuH9HJvqqI7qyriehTF3z79T+8f5huhH4/vZ164/T2+unUO7/8mDYz0iai86XXEdHvP38/L7ivWnX3jRwGZgTjC/yGX6q1gwCOCXVm1MjY2NsRHeY2SsR1O8qbsCXRYmzWHafe9/JtfpTRq18XtmEofFhCLGv8nTj2eoh+/g7sKV9ateqelEiLrr1LufvoZOyKvV3qzmXmXHAn7YjbGtmdkLdf/6PfnBl+4vXL3BWnDP7ThehhFBIcs0yErN3DHjt1Z1RxA2RlSUSPuoQdDG1I8EEbp+m+l5DTGpyMHbG7R3SwJcK6Cm1jNG8LJmjFEd380R/++geN6OcHkdqInp2kL/R3rq2uKKJ/PF7WOlYtxgXNTj4aGxq4/UjTYN67ZdthydgVu3NEJ0l4ah3ObyE9A4GZpsAdr6dO+I7m6D89TY8te77M0d++dpqjp4K5h90soicdLdsYtwlX4TT9mfL21d91R2m4qxZnbpa9QkS/mGxsfDCGcUQ7GRt7q4huyBgddD+DKT7rGdRyxAdZd5wfIk5W3acI9DuPD31X3XOHXU9EvzxK6jR6NiB6MJubSesCBCry40I0/Qu6y7Bk7Indheih3bYq11JkbobHc7LgjtfdKyL6WllQQckh10Z08/rlt6tT93jkJ2ttkpIgo5OxK3aH1D20+7wj0WLM4ippPTuyI+yeUl9BNm13sFuk7gkiIJcyMGybcBXOVy5ewqe7Vkd0rtldRAc8HPiIzvQTt6wzOhk7YndP3ectm5TzLes7FS12K7B+ieZwREcTjDzsVUR08/G3L030H60gekIM7QgMs6UDBydjV+wOET1cHEJz8FxLsa1nEzm70VnqKig2aIOIXupe0USmJdiInv8Bh5yQPmHoElsc0Y2P6LgjkTKN6Enso0T0cL09iOho8DgY0YvVryeit3iuOxjUyGh7bugzsQnPSddB/cnt8xYMS8ae2F2ILtntWgkiyqNVd4PW7ZgQdiSi19iiRCeqXPZg+0CUnMTZChMdnAXDkrErdofUXbK7MLckWX7M9AMRvWoesUHq7ksS7gU5dUcHlizGrfoBB34xjuTsxAb+RDR1L8XeInXH+XdI5KD9wn3GzeFMWFs3KazA9asa2KuI6E1+wAHPs92LiT8HIwH3kQwTdWbUyODYHSJ6EGHcYJvjOVfsh3Jid08pq6AqmjvYbSK6wIOCiG4iauFom5O6iI5ma3ay7SK6+wxuF+4TOKL7qG5M6IimMjb25hHdvwRdCc3McTMa25hhbd2kIqJXw15HRG/1XfcwcOPFOBc+APwBvpsY3ItmBWvBsGTsid2F6GFEt4Vu2A3jjGM/XYdjzDwG0ZfYcEVEb/FddznrQP/sgGDCHhNO4g3uM8OSsSt2h9Sd2m2HXJKsxc3r96B1OGMH72NF9Oq03cFuk7qfP7EkQhFQNpRyKNZu8l13Os57xs7puF2acTYwPYXwXCN6GrtnRHdMt4VR1s51RT9c22MOFtFhmQ3XE9EbftfdpnGewsE4A9xHZr9NACrNqJHBsXtG9LicazSpEY37R8LK3kRfFM5N6O8s4qqInvNswsqQSVyFLb7rPhtio7GdvhkzR3RjSY8W41wYt3HfvRAfjEvGjtgbRHTaA22z8p1Riuih3T2lIKIvhr2OiP63/vrd+Xmba77rjpr5wmq3ZHt+tem7zeSNz9Uv+x3n/Vhhjx6WjD2xWxI9xKbUt/XZGB0w3LiBGsUUnBbItTWWZMBbCXsdRG/xXHeSuPlMAyypSRcxpL8EQQINGoOTsSu2lH/Xp+4cth8UUP6VWnKNSwzQOvYk+tK03cFulLobPmEqW4yjEyye6I0e94xYjCI6GvU9f5FR9uwM2mmPqDSjRsbG7pm6ixGdoTSaeqESPz0/QkSHVZVfVURv9wMOBqVtFgWvrllmk5COojhett/AEeNit4voYdQNIgwZluOwbuiLa17e7p4iVbAmnJvQ31ngtRF93rUkouPEmo3obZ7rbjzVyQUaP49DS3bYfmMM/uCorhE9gd03oqPabHuJEd3gF9vDEBDuC11FDKXrar6eiN7kue7GoLHE5Xu4Y6BhyiA92oOM6zTegmHJ2BV7fUSXsVEBbilWgmiP0jdLeTkkthWBdmvr3SWi05heGtEx3zDxssflVYiqG+PJKG4julcwNmDbLmEVXTQgsWZcMnbEbhHRJWxSYpdN5kbDCdfci+yLbfnDRfQGsFcS0Rup4rQNhWZwfgIDtIdesnykhXSD/tJcxsZuQfTYQNZu3zyWzGjN1MVyfDDOxyh2T+EjaRtYJTpS9am3cRHADv94Bc4egWd1KJ23YQJA6HiNZHBs5Ma6gmhHjB2U+eZBGTxdZvGDOjjgAH8PoreYMHDnkkBvlbrT1erCJDy3GGd3rvnCjKN1GNEhiOh+09PZaWlEL8TeKKLbMRuFbjv9imfnLqWzIwlgsO2JDk0qvaaILvxSZRXRffj2Ed0O73g0crtpfPdjlu0sGtGT2EzcJRv5iC5jB6iYxj6Ce+5bTZLS+eMwdk8JK2gRzo3k77rCJRHdIFa0iejTDensD97URvR5CLUtj/oNkM5jDyCBCcd9WzA6GTtic+GLbuQjuoQdoYIP6watmRIi44iOjt45oreCvZKI3kLVr8j4Qd7vhqjByZDjUr5gQBibjD2xy3m9hOhAC13y7oKQH5PRlgfheLE10VvVd01Ef75E+5XPjDM+dbcjvtvrmY5WalBXDBJBqzc2GbtiB+Nl09Q9GoTdTMsF9WB1FQV3MiULsXsKBJ8aVUj9na1i19SdHBPva/MDDiSiz+HYsRuNXaT/0Q9gvIWauiex+0d033Y0os9Ed03sBnU0jpBJWL7aNhKlIQ1hryKit/kBB4OHdfrmWR0ixmEHzwBHJ2NX7MURvQBbPMyv9ZCQ4SfwbpSPa9yU6O14Hvib7lkX0Uv6SWVETy7GNSM6iuH0De1nj0MbKAM0JY5YLmNjr4joWezEYS6ih7wyvo2BFrMfekin2jpG9P79hEqTH3AIX89bEG4kEVAPw+njsGTsib2A6NLnCJstA4wLZCNnrKEW9xJgN9vAXgfRW/yAw+UF3Ov5s1uczad8skdwAAAgAElEQVQdNO23n8YmY1dsNyoGgVQs8MfLhjF2u/m5/6aTK0ti0boa5tJyZXG1rWB3S91NXeoeHFic8Jej2leS03ERPagYBwrwg6dG9Az2goge7ZCwozLLcwi+x4iSsDCZI8XU4l6CTniQiD7kdXSDo3K8O4ryzEe0puM+1JlRI9eB3W0xjpTaLydbpgct7a+zQHBciN1TOiUPTSK6gNw8oicX4xJHVaqCeEw+ojstHHiug4xdsesjejk2Kp7jGviPtOGEiC596CHRCTeEXR3ReehtI3qjm1qiA2jkTqpKkFdCxg7YeRqLGgm7hEHEfoiJG5OLoVtpi68UIG9tYa8kdW9xU4uJUzUcxoNvxuUzC03d09i5xFzUKF+Miy6hB5+ZSpgNU5ShNpD86S2H7bIYV+aYlql7k5taogNwr2Aiep7pw5OxI7Yc0bMFSyI6kIgOse5hInrzRb9riui9VaNvUQVladhhybgltng1rQF2KleTi1jsniKH3i1h61fBshFv4eW1PVSB2S5Bvy4ydsVmImkz7ESuVl7rZql7D9gKktUYcG0RfbFcGRkVu6eM6pP9/a1EV+zG2OtR5Cx2VJ9s5u/zTS2vDb4Cu2J/6rBRnbwz9pLqV9qdPHITonfwecInq2vbnuirf2Qxt3vZ2RyBMINiL3L5OrvTVS6FpdROE73HN+pln6yvrXjQbvJd9zY/srhuf+qw6yVjV+xrieg1RNeIHmAT2YLodUJuidybMMfE7gOeil5+a0V2tuQ4VvgKenhlH383wibS5kcWGwoav5tjo1rGxu6CLtpNv+e2qO4NiN7FK7v4myi1u47e5kcWS8oK0TSi57CXTsBLsNN7jhTRySfoE3c7RvTr+GZci0FwbDIeC3v5bao1CBnsJccVE71P2O3XluU3AbSK6H1UGzjniIQZFntVRC9FWFU9f1yBzBXsl14vhL6KiN5CDkkYxV6OveS4cqJ3kRvwtxJdsRtj1+hXyKIKSu0YGXtP1RoZ2smKHWMXKdZLXQUL7B4Ve0/VGhnayYodYxcpKtGbYe+pWiNDO1mxY+wiRSV6M+xAGvzIYsnuQjwINkd1clfsCLzJWnmh3WC4Ji3qA0mNxTIfnq1hiQj+TutXYdfZs8KOJj+ymN4L7rUACtB2nRk1Mjg2hKVSdTVXncrs9j/dwD06KIkt7ForRQYsFN7fCfXWA+vSZ8ZBeFST317rJIp9XdidwLUPCthEWhBdRUXl6NLgRxZVVFQOL+t/ZFFFRWVUUaKrqNyA6GKcYg+E3VN62t0Tu/DsalTjX9IMq/O/MeDLwX+YL9jYw+xP9Y58CawntnegqSvIarpSvvuwPYrumSAgxO/nk07X1bJV9PlJpoTPJeO45ml3cTxUjS/shk8CcB+QzeC/jOC+9mDNVKLL2EA+4tqyBQlNbDeY+We/7IVcgwZg16usJXaPAXCnT/E7+mSLiM6WFhcKqt37yZ6qNTI2GRV7U+yect0+UaIr9kDYPeW6fVI3Rw+y97AEbJFxs+9ZAehhxk3b+/7e7tjY/VN3umUT+ct8yi+j+EblAGO7k6e2SFztPeU2Uve3r9OzI83HI3z+7t4C1XABAAwtQTM9p4xe0WFu5QE2cMS42G4mDaauoHAxDu/Ay2zhwo9rVAaQs7u1zDV3lg0X49ze4sU4fMCaxbj3+wfzcuL28935O3TzW2SzRvQtsTWiY1yN6KV2pFSnr8C///w0fSX+tDm/LUetkbHJqNibYtfJ5TdLOlZQDLu/T2bVySWvX37MfJ/frAoUZRVLZeiOp9jbYteJEj1WPc3Kv/wwLNGxKrj7lG3+bdCztm3C5/fZpAv8Cdt8X1P3HPYmqbsvdNNwcEm8u47uk3jb3LZhCVIXnwB5q5FKot9C6v5+/22ao+eI7hZlom9VXBTCpQXjte1SDl2g08W4FLbr4mDqCsoX4y6FeMFNEkObzTjl2O6m4k+Bl6mznhg9ryaf3uDh8gvhd6fi3z/fxnX6NO17v4e/mKD+1otxjHszaXOgumQxjjJcI/ohsDWiY9Ml2Clsv/36H96f5p6fv0+LytMi0/Tp09PH45cfp83zb4Z/+fHx+O1E+QTRbyGi62KcYo+AzcnLnXn5NnfgV/+4BfsDws93E+2NC/31FayTYxF9Gu1KLq/NocaO6mign8HC1IJ8TRpHqjnya0SXsftHdBqO/aVyF8mNbSQ8X7O6cQxs7xNgtgJ5+/U/+s2Z4ScWv8xEnzL4Txein8aBU84OD3mi30JEt1+YOblk+qbM/Baogkvq0HTO0954htN5nRsaaGZoNSotrpHBsR0dwdQVFM7RySQP5efy9NxAuMnZ3U6QhTLsH/3hr3/QiP7205OP6OdnrbxeFqDSEZ2pouMc3cQ+jhzK6q+Zo5epakTfFFsjelFEP028v52/8WXn6D89vU7J6WWO/vb1YaL9qUTn6O1Va2RsMir2ptisvP/y3aWkl1X3j0eA33l8iFfd/+Xfu/E5emPVGhm64yn2tthr5eYX48pU0QzOX0dD6bdL6mzKRyfqVgfN9czgHW/w1J3s4y6jkytrTsG4tRUOqZ1POCvXiS7GlagyyzSWvGDfXMeIFAFp4NWEscnYFdtxiQyoBQWFi3G0CNgGRnQPFQgwtruRMNhr5SCLcZKPIbEYF6t2XIzTiL4ltkZ08UMP0Yher1ojY5NRsTfF7inX7ROrOq1Tfss8eAKN5mEGjmr0BX7DuOhtoz1O/uosrpGxsbeL6DC3USKBt+mcCVsSXV5t65Mw3W0EK9d3ExH9+cuP6XJj8ptxSYm06NV0aZI+Ohm7YrvsmEyRCgqq5+jcFJzL4kk74rYmdjfyAQSfO8ux5+iM6pI5+nwLS+a77nMtGtG3wdaInvjYXm4ios+01gdPKPahsXvKdftkVn398sf2e/+J21TdcI8TN0Mzt8uIgNZn3Qq7GyvABhBN3ZPY3SO6f2UW3EkcR6vuZo7iBrc6sbv6dBmJQTSil9qRUp2+Lvz6KXM/etgByEdDeovQb1x2iKYkY5OxK7ZjEpi6grI5OknCU5fQ/RbSMxCYaRr6hJl+bhDRmSp6zNGTjpbNi9tkUUQ/3+WnEf1I2BrR0yWN5SYi+nwLnz54QrEPjd1TrtsnVvX5m3ktefAEDtU++bZr6wZF9PO4H8Z1nH4Yjehp7C4RPbTbVkWuqaA8LJiMGd901j4QsZdKRRRtKDcR0c9fmHkoefAEk+KRy+SSkiCjk7ErtsuNwdQVyJqh3ecdiRYzzBZuPTuyc9jLz56D2CCiM1U0nqMniIBcysCwbbKE6EWqM3jQATSid8LWiJ4tbCm3EdHbqtbI2GRU7E2xe8p1+6QyovsATdfSwe4nl81wMEeBAgV4M3jHGy+ihxHGpl/8rIteaCHF1j5A6WkLn0iHa0QvtWO1akJMfH2mbJo+Ohm7Ytvc2ICpK8hqooGADsSpJuZaz9jFGUL4VacuHL9BRGeqaDZHL3WvaB3TEhrRydkNi60RvaS8mWhEd6qokdH23NBnYhOek66D+pPb5y0Ylow9sbsQXbLbtRJElPdEv1iFGpS1u/AkOUkcq0QvtSOjev6GTPJ+dC57sH0gSk7ibIWJDs6CYcnYFbtD6i7ZXZhbkiw/Zvpan6SS0cFTd1+ScC/IqTs6cGXq/jxRO/+FGY3om2FrRC/c1UZuJKK//kvzF9z1K7CKfVjsnnLdPrGqH3/n/zgxO3dTCxrbbWi2odq4z+B2ocQEh3cb+o0uxiWxu0T0IMLgFTX0EmSJKI7jZjS2MandC044MLJ6Zwu5jYh++T3K9IMn8Mq5ezHxZzqRYD+iEWBsMnbFBvsJTF1BVtPxfx6EDW1EtqW4tnNbxO7FZ506dIOIzlTRfI4u8KBgjm4iai0g+vt/+0MkOrZ5hteIrhFdI3qZ6rEi+vPl6TL5B0+EgRsvxrnwAeAP8N3E4F40K5jVnSN/dsNidyF6GNFtoRt2wzjj2E/X4Rgz1/kkd5gSvdSOhOr0A9InydyPLmcd6J8dEEzYY9BAgRRGJ2NX7A6pO7XbDrkkWYub1+/xAd2CulE7xK4+5cxxV5K6nz+xJEIRULaRcqg+opv5Onr2cc8a0bfD1ohes3+13EJEn+RM9Pz96Mb4/mB7h5mL7foMmqMbEhLmQQDcCpBZ1zmKzm5Y7J5Ej8sJj4WY4/My40Z27ILFPik4RoleasdqVZ8woEUa1y9yy+zRfpcJ1JlRI4Njl2bqUUFB6h6Xc40mNaJx/0j+uNQnJUnotaTuOc8mDAyZ1JXoGtE3w9aIXqmyTjSiO1XfzBdW26n25RU8/+1wYEefy37HeT9WmBWdo8jkkbFbEj3EptS39dkYHTDcuIEaxRScFoTYdVJ2gBK91I7VqiRx85kGWFKTLmJIfwmCBBo0BidjV2wp/65P3TlsPyig/Ct1bSUuMUDrWOSTwgy0P9G5Ktqn7oZPmMpW3ekESyO6N3lkbI3o9VorRCO6U3VNHh0IOEiAGwDcHN3Y/Y7ovvtUmlEjY2O3JHqajIjoIdtxMcrDYgwJOyPF2kr0UjtWq9rGt7TGKHgZHWyoDtM82nvcVp0ZNTI4drvUPZ1eo9QdADUTSStJ1mjCHiBiZ8+0VL0/0fmhq7QwqcrqL0ndEQ2Xpe5vX8/PdU8+eMJ2B7TcPo9YPr1Da/PYfmMM/uCorhE9gb1VRHftZSib8SAdRHSgQLgvVPY+jegrpJ7o7/cP048sZh48YdBY4iZ2uGOgYcogPdqDXG6ILBiWjF2x10d0GRsV4JZiJYj2KH2zlMcGl/tkUUjqJltHdBrTSyM65tsC980/sph/8ISvgYvoXsHYgG27hFV00YDEmnHJ2BG7RUSXsEmJWzYBEyZcLh27vNiWbxfRu+gukpuI6JOconr6fvTLB9/IKDSD8xMYoD0U7Hq81UK6QX9pLmNjtyB6bCBrt28eS2ZA7QWAOE4RIIBhqxSkznlK9FI7cqovX35k7kc3vsltxuayPjv0o2VZ45N2l/PRMAFQ2znqZHBs5Ma6gmhHjB2U+eZBGTxdZvGDOjjgAL/KJ1WZ5xZE56romrrT1eqy1H3tYtyJ55+zD55wtA4jOgQR3W96OjstjeiF2BtFdDtmo9Btp1/x7NyldHYkAQxW4ROodZ5G9FI70qovn55M9gkzLnz7iG6Hdzwaud00vvsxy3YWjehJbCbuko18RJexA1RMYx/BPfetJknp/HECduoMa113jRHdIFZsFNFfzpfTMotx4CfkiOkzCjbSBWwfvUlE9wrDk7EjdouILmEHx9KAbTueK0bt6RrVpW0LInp1OC+DXSc3EdHffrrE7/SDJ1DDe6a7vZ7paAJHojyJD1ZvbDJ2xS4N4FFBQUQnKqhl0WpcsOiCgjfJ1GLs/Okt8JtGdOaYEm1ryFmezwc+pB88YbsH+I6ChmZAYxfpf/SDywuMRvQMdv+I7tvOtixacvHJl59uoXHER/QYO3dyS9ymEb3Ujiaqflinb57VIWIcdnwXUqInsRdH9AJs8TA/BSQhww4EhqYDtMYSnywK53lY+bjiyW8iIhcUSieW90llRF83Ry9WtRHdfUZRgQf0Ycce6PqLEj2F3TeiR+rgos8c0XHKxQ4Q0XZBp17ss+LDAmaXE71LRO/fT/qoBouu6Foa8mXgMrIc54IV6Kp7Dtv6zbs4UxBG+AQ2LUITdLscZyN4PP+nx0bRPTXILA3naViHTiQoLTl8TaEU5wv6Se0cHVXZTdWv1thS5FC/RfI53A1pBqhET2GHns0THeXyGeywDOwi69zpyFVUp4KrcEeWR3SiXCttiJ6g/Vqi8zwv6ScHTt25w6OIDtEOE/QzCAsay9jYQD7i2rIF5URHR9jVdveR6KKIjqslI0vSJ7DOXdVEZ3mf+KSLcVg1XHzBw3kQgvLjN4SdqbUMjh1SDF2/NLxG8J7Adp/IJiBIkn7h6qNqaeASqLEia/d2C7tKhKoyn/jgzdUrZASsalE/2S2iC/ejxwfgyIAjug1JWaYPT8aO2GG85phGNVZEdEtFUgVNv8JxJoYRfbKe5zxshdAjmE8SfdlqSy0sjOh7zdHF+9FTSGh/QX+jusOScVNskdcNsOUhPH9kUoOLD0tkLdGztF+duifs7tlPVqgm7keXyyqHbBoeroeMDbGz4NyXFgqx03vktky3cpwtrFlnZ7Ddh7YyY7K1rk1ESvsJGYFKwVeqyrepRkWoVWt6Ph5VKw6rlbGxo1aPaouS+wKDkum1f3GZO9HNVEAHKLt030Y2IHpp6l7R7GVz9L1S98SDJ8TD2Ysvcq2gET2LHXq8IM42jOjc9bRc8wbpALR0kGNjB7HQxaZUML050RsuxiV+H72TlxVbsUuwOxO9G3hP7OZEV1FRGVUqFuNUVFRGFT74s5fXVFRURhWe6Oz96CoqKgeX84z79TR7/xbsqJnOd1xQUGzF3hu7p2xm90T017vTxkvA9KVEN6Qy8u3nebeJ9OzFF1/uLsAMewmsK3Zwdby8IKtJ7XYNkuqQ0c1htF2x3cbQSu0nZM38CYL/yU/GGNtl/JU/rGf8W/RPfqm6WrZQiCOS7hWOzzSJUzwR/ePxwZhoja1qgX7GvlwbdGQlMHbP3JoGnYDvapcPwHyfurGMjU2cSmrLFiQ0ObvBc0TqT5ZOtsSN2BgrwA6GE2KN5xr+n/iEBz8yfiE99Bb9yS/tvtQjSs9+QqQN0bvI2GRU7KvB7inbEf3+8/eXu9PG+WWZBaM2oGIrdgl2T9nQ7vd7mH4t9cuPxRaM2oCKrdgl2D1lf7uV6Iqt2Er0hao1MnTnUOzrwe4p+9utRFdsxV4COy9rl31VfH+7leiKrdgLiT6RXIleBavYir03dqUo0Stl6M6h2NeDLcj0/ZO3rw/m9cuP+RaQ6avkdyeG/+l0IevtV38y/WjhigpWiBJdsRW7FluSlzvz+p9+My/fJsq/THQ/sX5i+CWifz3tzt4BpkQfvHMo9vVgS/L26x9/9If/2Ynl6HkspzdL9J9Knt2gRB+8cyj29WBL8v7z3/s7f/rLn/7y/e3rKWf/dOL1VwAf0Yvm6Ur0wTuHYl8PtijPf3CK6f/jnbugdorhKKIr0WtgFVux98YW5TxDP/2dl+V+9X2akT9fIvqctivRi2EVW7H3xhblvOZ+Stnn+0U+HgF+5zxjP209KNGrYBVbsffG7ilb2/0S3rxWR3T0LAITPJlg3m/wYwrIYwzCB9T4B81UmlEjY2MD+YhryxYkNLHduB3ZB0+YsMWiJ1OYeV+ELZwU+Q/pAvIpsNuEj46ZC+1b9Ce/XNWDJ+5tw3xe/uCJpASdJPeULPooqnHJ2BXbdWswdQWFj5JK8DrZYmzjB9jMOQEOBgsfJeWg3D+kZ/xb9E9+8c7pJ76CnHv5w3NN4lXfvk4Pi9OIPhK2RnSN6AtgX06xfCXRu8jYZFTsq8HuKZva/X5/p0RXbMUWsHvKxna/ghJdsRWbx+4p+9utRFdsxVaiL1StkaE7h2JfD3ZP2d9uJbpiK7YSfaFqjQzdORT7erB7yv52K9EVW7GV6AtVa2TozqHY14PdU/a3W4mu2IqtRF+oWiNDdw7Fvh7snrK/3Up0xVZsJfpC1RoZunMo9vVg95T97VaiK7ZiK9EXqtbI0J1Dsa8Hu6dsZ/f7PXz57dfpFyYWW+DvRp4Om28ld88F8PeXkw2nZch97GY+xqQfVrBWxsYG8hHXli1IaCK73St3K3p0U3qghZ87MONAYDc9o7m98X/6Kb0bYZOnSxi9Hx3L8zfzMrH85W6xBdyDIxaLHRcyDytYKYNjewaZuoKsJqoAgiG6oNnidjRk0I69Ij00pv4JM3Os8EwHDH70J8zkPZtEEVvDqUw/MHH+kYnwgZUa0Y+LrRFdI3otbBOirzUrAavYir03dk/ZMHW/u7yvSd2XmlMAq9iKvTd2T9nO7o/H6eGQ5jl8xIwSXbEV+4qIvq1qjQzdORT7erB7yv52K9EVW7GV6AtVa2TozqHY14PdU/a3W4mu2IqtRF+oWiNDdw7Fvh7snrK/3Up0xVZsJfpC1RoZunMo9vVg95T97U6ofjzCl9+iL9KN2oCKrdgl2D1lf7tl1Y/Hb69ffqBfaxu1ARVbsUuwe8r+dsuq7z8/nYiOvhs/agMqtmKXYPeU/e3ORfRnjeiKfRvYPWV/u9NzdMA/vzpqAyq2Ypdg95T97dZVd8VW7Csi+hSep/vXVj54wj4uYn7Sh3GPmzDuERTxQwsutcQPpbg8smDoztEVG8hHXFu2IKHJ2A3MIyXihjTMPmNbF9vNnMu8sf7BEx5HHzzBwk73o0/z7Qqiv56b8rNfjAueLVIhRnyCyehk7IoN9hOYuoKsJrFbZnfAabl1qd30VHzNmWdFZXbP2OHjopBetI8ZD+IXb2I/sT7IuzcFIjeJ0zk/Xca8fv5eTvT3+4eoHo3oW2JrRNeIXgt7Ibp5Jd+ASVswH1KiukrGJqNiXw12T9nO7vlRUi8oFc9ZsOKpUzUydOdQ7OvB9hlnrwq2gP14vDu/v5QTPZqjN7EtkqE7h2JfD/ZyoucP3D8TSaTu0Rx9oTUlFii2Yu+NHfC1gvaDE13n6Ip9S9glRIdkqVx4ZKKb17vFqDUydOdQ7OvBvlWiv9/rHF2xbwj7Vom+RrVGhu4cin092Er0etUaGbpzKPb1YN8s0fXuNcW+JexbJfrlyrvej67YN4J9q0S/XF7TJ8wo9o1g3yrRNaIr9k1h3yrRdY6u2DeFfbNEX6FaI0N3DsW+Hmwler1qjQzdORT7erBvlujx3WtwOeD8H+xt/+AePAEQPVwCVQDu3Km/hu4cXbGBfDTUl+mChKbFPvse0INEQJK5jdFzRYxrXLCPfeDspvXD3IVA+pTejeyGPg+euFGiM0+YMfjxPunOgXqJrYjfPTgZu2J715m6gqymbUpM5GxDCmUGNzKui9bPPh0KkgXBJ2u37XuuO3p9OgZw40H84o0kfKWfDC2sJ3qpe1O9Qm4SpzN/cR2g/MET8d1rnUSxFfsI2J3Au3MnkPkRM9EoIRI9esKMiorK4eXj8RtXLBBdzABUVFQGlE6TTBUVlSOJEl1F5QakZoG+44JClXKFJfXadaaUY9dVeBh/K3YT7P2lyZW49RbAn5XJTI1C7T+rwq4Er8Ouq3Dwy4KKHWLvL0r0peBKdMUuxt5flOhLwZXoil2Mvb8kzNj0BxzWUiOhrkRn/K3Ym2LvL4lvxm36Aw5rqZFQV6Iz/lbsTbH3l5qvwHa0QInOYPf0t2Jvir2/JMzY9EcW11Ijoa5EZ/yt2Jti7y+p1F3n6O2w6yoculMrdowtysfjQ2FhVzOWqlZboERnsHv6W7E3xRZFiV5JjYS6Ep3xt2Jvii3K3kR///lJU/eW2HUVDt2pFTvGFuXj8fcBvhnz9hU+Pdm3ieivp9JXOBduYMZS1WoLlOgMdk9/K/am2KJ8PH758X7/MF3Lfv38fX47Ef31xPDputcregxzRzOWqlZboERnsHv6W7E3xRblnKU/302/k3Li9fz28fgHPz0x32TpZ8ZS1WoLlOgMdk9/K/am2KKcif5yNwXuKY5f3k5h/o/vzOXCVyuuK9GXgivRFbsYW5RLRP8WRvQH+3W111ZPeEqY8fEIX37rf3pNib4Gu67CoTu1YsfYokxz9Lev8Rx9mpy/nfL3DYj+8fjtVNnLRr+9tpYaCXUlOuNvxd4UWxRx1X366cNtVt3f/8bnP/lz8E/923p5rQV2XYVDd2rFjrH3F9mMf/LX/s3/G/7r/+2f/T/zqustUKIz2D39rdibYu8viYj+3/yXAPAv/gca0Vtg11U4dKdW7Bh7f0nN0R9edI7eCruuwqE7tWLH2MKuDZ8imUB/hU9P08JfgepqC5ToDHZPfyv2ptjCrqMQXb/r3gy7rsKhO7Vix9jCrkMQXR8l1RK7rsKhO7Vix9jCrkMQ/f/7T6as/f0XjegtsOsqHLpTK3aMLew6BNHf/6t/dfpy3F2B6noLlOgMdk9/K/am2MKuQxDd/D86R2+HXVfh0J1asWNsYdchiH6ao9PfVFeir8Cuq3DoTq3YMbawa3+ic7+PrkRfgV1X4dCdWrFjbGHX/kSf5PWuWHW1BUp0BrunvxV7U2xh1yGIrs+Ma4ldV+HQnVqxY2xh1yGIPi25A6BHVinRV2DXVTh0p1bsGFvYdQiiTzfEGvOs33Vvgl1X4dCdWrFjbGHXIYiud6+1xK6rcOhOrdgxtrDrEET/J3/tnz+9/vd6P3oT7LoKh+7Uih1jC7sOQfT3v/XXT5X/C/+hRvQW2HUVDt2pFTvGFnYdgug6R2+JXVfh0J1asWNsYdchiK6r7i2x6yoculMrdowt7FKiV1Mjoa5EZ/yt2JtiC7sOQXRN3Vti11U4dKdW7Bhb2HUIor//jXPt//T/kFddb4ESncHu6W/F3hRb2HUIov/jr/+a0YjeCruuwqE7tWLH2MKuQxD9cnlN5+htsOsqHLpTK3aMLew6BNHNy12x6moLlOgMdk9/K/am2MKu/Ymu96M3xq6rcOhOrdgxtrBrf6JPcr68dlekutoCJTqD3dPfir0ptrDrEETXy2stsesqHLpTK3aMLew6BNEvP8X+9itN3Vtg11U4dKdW7Bhb2HUIol8W456/laiutkCJzmD39Ldib4ot7Nqf6LoY1xi7rsKhO7Vix9jCrv2JvlK12gIlOoPd09+KvSm2sOsYRN/0RxZLxVRp12FXgtdh11XY3d+KvSm2sIvpDb0ksRi35Y8sqsQyaqdW7Bhb2HUMov/8RAuU6NvKqJ1asWNsYdchiL7pV2BVYhm1Uyt2jC3sOgTRN/0BB5VYRu3Uih1jC7tSRG88Cvz/7V3Pi+M2FDYd5lY6x1AKPYaltIWtj7nM0pZQaLaldA4LppTuwT0MmCkh/vdrWbKtpx9eKbFkeYfjOGEAAAa1SURBVPR9h8lI7/mz9PK+yJISe4Yg5h1mAB1bTWpw69wWUxJCj/sVWHe0Pv7uvl60ntz+9MHjDe6o3BaTIUfcjIs14/z49N/X/QnSusOM52a3O7cXrSe3P/2mkxrcOrfFtL7Q20R/1AKhLxRvcEfltpiSEHqSd4GF0BeKN7ijcltMaQj9BlfvFkDoBu6Q8QZ3VG6LCUJfSDIQui3e4I7KbTGtL/RUf70GoS8Ub3BH5baY1he6QDdNn77wDqFfye1Pv+mkBrfObTGlIfTm/kNR3D25uN7cAgjdwB0y3uCOym0xJSH0y69ffrY/Ntheu5nbn37TSQ1undtiSkLoVfFVc/8xsXvGQegLxRvcUbktpgSE3hRf/HKsH55rjOg3c/vTbzqpwa1zW0zrCx2r7gty+9NvOqnBrXNbTOsL/UZX7xZA6AbukPEGd1RuiwlCX0gyELot3uCOym0xJSH08x/933e4dL+V259+00kNbp3bYkpD6IeSfWFm5+B6ewsgdAN3yHiDOyq3xZSE0Ptfr0k/XoPQr+X2p990UoNb57aY0hB621Y7Z9cbWwChG7hDxhvcUbktpvWFbtxeCwRPbq+2uPv6d9HrAE/6hOIN7gW4LeKbc3RmccOtq+4hKgEOLThBKpZHcWXJxzUEbk3bpDMcQk8XELrdNQRyFbrTraQg9HCA0O2uIZCp0N1uDgmhhwOEbncNgUyFzp+99qlfr0Ho4QCh211DIFOhY0RfGxC63TUEMhU65uhrA0K3u4ZArkJ3coXQwwFCt7uGAIQOAMC24XHpDgDAVuGxGAcAwFbhsb22Ybzs2deF701d0TvIa0T95XTUjwGArSGPEZ2r1tgXCB3IAXnM0Yl2TSarM4QOvArkseo+apf9w16+/fH+Y9N9ju3mhM68/jn9XBQlu/01d/69LwHAxpCT0Csuaybh/bE9H45tc/c0J/TO63J6eO48B+d92TbGqT4AJA2b0M+H4u7P06u5dO8X48p2FPob/ki58+Os0Duv/tKd32mHOXc1r2N5EsgMNqFXZaeOnXo3qa2i13c3Po9CZ2Jl6p8f0bt/eqHXO8kZQgc2CIvQ2d5an+NDWl9O4+4Uy/njWENfKNhyXunkGRh9L7pLb1nobHQ2juh8X7F5eB6FXpWScySha2ESYSdVtCki3OQQ6Wm4wj9w/Ck9a/R4UUhPP37YMjvNKVESxoY/0zd43hjeWEPM26E9rscnck3sKvRuZBeDu5iuDjX0haLqxPLGyTMwxu011viaPzqSzbUr04jeb8Oxm11zoXd92B8l50hCV8M0hJ340MwX4SaH1JLL+cAKgeNP6RvpTPT0vCTsNKeGEjeyLvXhD5w3vEFKlR7zsT0aKr1S7v7KcBQ6K4t/2YOUO5OooS86yXTsnGdo8BOeD7u2Loq378RFeVH8cDqa2lJNE/oLX3WXnOM0XguTCLvs03xOmiLCPYEdL1XWxfeiImAXFPpqusKgp+elwU5zaujqdHBnCJ03okEEhpiP7dHqlHejR5XODo2j0JWc6T7qRA19oST02DlPQIUxTGyEmXD57W+SWpoM2BsoPd/+3+fhcypg/Cn95f10Inp6XpLtU06JkmSs+TwqZN7wBumgMZ/ao1ap70Zf9z6dPLcJXbndszo4jIGfk2/z8Beb4kDoV8AYJppfdUlTU4RbQncdQo6ILvTz4zfSfJaenv832amYu9JofNmL1ZWweWMWukHTfXs0v1I/nnZ/XTjuo9Mws4mfi9C7a16xUw2he8IUpppMAs8/KWOQCLfkcSjpMdGF3q9hjk/v04Uu2aWcGkqTseHLKisIvTYuAOpzdO3d6BlJ99fFNUKvWTY5jej9FAdCvwKGMNV0cKiOSmpqM0qNI7rQGSTZa0Kf7HJOSV2dlnnWEXptHpD1hmjvht11HTgKXV4K4R9yDktsYnsqgcW47UEPkzK2iMmVoutPCz3uYlyrCn2yE6GTnJK6Ogk9fN4Y2M3juUG9+rthdV0Jrl+BnTY3hh0cl+21Ut4YWXF7bYNQw0Q2zoY6mpqV8v3cy8lw6R51e4015+U7so8+2octTmanOSVKwjj4BM8b86W3Btqn2eOtrivAVejdR5ZImUp8doka+kLBNqWOqovRE1ChhqkyDBlKaolwS3bTF2YCx5/S1/JdAOjp+5Kw05waSsIoXoLnjS5UU8yVPs0eb3VdAXn8qAUAMgeEDgAZAEIHgAwAoQNABoDQASADQOgAkAEgdADIABA6AGQACB0AMgCEDgAZAEIHgAwAoQNABoDQASADQOgAkAH+B6LupAF0XQhGAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i smallZambiaDF\n",
    "gpairs(smallZambiaDF) # from library gpairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlations\n",
    "corr = smallZambiaDF.corr()\n",
    "# Heatmap\n",
    "plt.figure()\n",
    "sns.heatmap(corr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To Do: implement an equivalent to `ggpairs` in python, similar to `scatter_matrix_all` found here: http://photo.etangkk.com/Python/blog-04.asp but using seaborn's PairGrid functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are our observed variables like? Continuous (e.g. age or height), ordinal (e.g. Likert items), or categorical (e.g. gender) or even dichotomous/binary (e.g. yes/no questions). Is validity (accurately captures the theoretical construct) and reliability (consistent results across time/rater/items) already established or just assumed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleanup and Checking Assumptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SEM maximum likelihood estimator assumes normality of all endogenous variables, and implies they are continuous. But these assumptions can sometimes be relaxed.\n",
    "\n",
    "TODO: how to check?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we do a simple regression. We can use `scikit-learn` (more oriented towards machine learning, less support for detailed statistics output) or `statsmodels` (more statistics oriented, with R-like interface) or R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = smf.ols(formula='socemo ~ ece', data=smallZambiaDF).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept     1.613317\n",
      "ece[T.ECE]    0.110536\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(result.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 socemo   R-squared:                       0.013\n",
      "Model:                            OLS   Adj. R-squared:                  0.012\n",
      "Method:                 Least Squares   F-statistic:                     21.06\n",
      "Date:                Sun, 18 Nov 2018   Prob (F-statistic):           4.80e-06\n",
      "Time:                        23:22:31   Log-Likelihood:                -993.01\n",
      "No. Observations:                1613   AIC:                             1990.\n",
      "Df Residuals:                    1611   BIC:                             2001.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.6133      0.013    119.945      0.000       1.587       1.640\n",
      "ece[T.ECE]     0.1105      0.024      4.589      0.000       0.063       0.158\n",
      "==============================================================================\n",
      "Omnibus:                       23.699   Durbin-Watson:                   0.838\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.012\n",
      "Skew:                           0.225   Prob(JB):                     2.74e-05\n",
      "Kurtosis:                       2.667   Cond. No.                         2.42\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0c0f2e207c64b21bff689086e825485",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x23e368b1cf8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = smg.regressionplots.abline_plot(model_results=result)\n",
    "fig.axes[0].scatter(smallZambiaDF['ece'], smallZambiaDF['socemo'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is based on a categorical variable (ece), it is effectively a one-way ANOVA, so we can also print it as that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              sum_sq      df          F    PR(>F)\n",
      "ece         4.229228     1.0  21.060384  0.000005\n",
      "Residual  323.511946  1611.0        NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "table = sm.stats.anova_lm(result, typ=2) # Type 2 Anova DataFrame\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>childid</th>\n",
       "      <th>male</th>\n",
       "      <th>urban</th>\n",
       "      <th>reasoning</th>\n",
       "      <th>socemo</th>\n",
       "      <th>vocab</th>\n",
       "      <th>vocabsq</th>\n",
       "      <th>wealth</th>\n",
       "      <th>books</th>\n",
       "      <th>ece_No ECE</th>\n",
       "      <th>ece_ECE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>18.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No books in home</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No books in home</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>Female</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>19.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No books in home</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>12.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No books in home</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>25.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Books in home</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   childid    male  urban  reasoning  socemo  vocab  vocabsq  wealth  \\\n",
       "0      101    Male  Urban        3.0    1.05   18.0    324.0     3.0   \n",
       "1      102    Male  Urban        4.0    1.00   19.0    361.0     2.0   \n",
       "2      103  Female  Urban        4.0    1.80   19.0    361.0     3.0   \n",
       "3      104    Male  Urban        5.0    2.35   12.0    144.0     3.0   \n",
       "4      105    Male  Urban        5.0    1.50   25.0    625.0     2.0   \n",
       "\n",
       "              books  ece_No ECE  ece_ECE  \n",
       "0  No books in home           0        1  \n",
       "1  No books in home           1        0  \n",
       "2  No books in home           1        0  \n",
       "3  No books in home           1        0  \n",
       "4     Books in home           1        0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(smallZambiaDF, columns=['ece',]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: ECE, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smallZambiaDF['ece'].str.get_dummies()['ECE'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1613,), (1613, 1))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smallZambiaDF['socemo'].shape, smallZambiaDF['ece'].str.get_dummies()[['ECE']].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg = sklearn.linear_model.LinearRegression()\n",
    "linreg.fit(smallZambiaDF['ece'].str.get_dummies()[['ECE']], smallZambiaDF['socemo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.613316750660673 [0.11053562]\n"
     ]
    }
   ],
   "source": [
    "print(linreg.intercept_, linreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38a165f5dff8460ba85465d82c164013",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x16c688009e8>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.lmplot(x='ece_ECE', y='socemo', data=pd.get_dummies(smallZambiaDF), height=defaultfigheight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7793bc3562f84046927ec313b5f5c869",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure()\n",
    "residaxes = sns.residplot(x='ece_ECE', y='socemo', data=pd.get_dummies(smallZambiaDF))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating and plotting the simple linear model with R:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "lm(formula = socemo ~ ece, data = smallZambiaDF)\n",
      "\n",
      "Residuals:\n",
      "     Min       1Q   Median       3Q      Max \n",
      "-1.16887 -0.32385 -0.02385  0.32615  1.38668 \n",
      "\n",
      "Coefficients:\n",
      "            Estimate Std. Error t value Pr(>|t|)    \n",
      "(Intercept)  1.66858    0.01204 138.551  < 2e-16 ***\n",
      "ece.L        0.07816    0.01703   4.589  4.8e-06 ***\n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "Residual standard error: 0.4481 on 1611 degrees of freedom\n",
      "Multiple R-squared:  0.0129,\tAdjusted R-squared:  0.01229 \n",
      "F-statistic: 21.06 on 1 and 1611 DF,  p-value: 4.795e-06\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R -i smallZambiaDF\n",
    "model = lm(socemo ~ ece, data = smallZambiaDF)\n",
    "summary(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i smallZambiaDF\n",
    "plot(socemo ~ ece, data = smallZambiaDF)\n",
    "abline(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i smallZambiaDF\n",
    "xyplot(socemo ~ ece, data = smallZambiaDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the Model for SEM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparison let's use SEM to run the same linear regression model as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan 0.6-2 ended normally after 11 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                          3\n",
      "\n",
      "  Number of observations                          1613\n",
      "\n",
      "  Estimator                                         ML\n",
      "  Model Fit Test Statistic                       0.000\n",
      "  Degrees of freedom                                 0\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Expected\n",
      "  Information saturated (h1) model          Structured\n",
      "  Standard Errors                             Standard\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "  socemo ~                                            \n",
      "    reasoning        -0.007    0.004   -1.672    0.094\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "   .socemo            1.681    0.023   73.853    0.000\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "   .socemo            0.203    0.007   28.399    0.000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R -i smallZambiaDF\n",
    "semfit <- sem('socemo ~ reasoning', data=smallZambiaDF, meanstructure=TRUE)\n",
    "summary(semfit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummiedSmallZambiaDF = pd.get_dummies(smallZambiaDF, dtype=np.int8)\n",
    "# We use int8 instead of uint8 for the dummy variables, because R has no unsigned integers,\n",
    "# so we would get warnings when passing these new variables into R.\n",
    "# Further: the new dummy columns use the actual value as part of the column name,\n",
    "# and therefore might have spaces in the column name.\n",
    "# this is not great for specifying models, so let us replace the spaces with underscores:\n",
    "dummiedSmallZambiaDF.columns = dummiedSmallZambiaDF.columns.str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan 0.6-2 ended normally after 13 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                          3\n",
      "\n",
      "  Number of observations                          1613\n",
      "\n",
      "  Estimator                                         ML\n",
      "  Model Fit Test Statistic                       0.000\n",
      "  Degrees of freedom                                 0\n",
      "  Minimum Function Value               0.0000000000000\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Expected\n",
      "  Information saturated (h1) model          Structured\n",
      "  Standard Errors                             Standard\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "  socemo ~                                                              \n",
      "    ece_ECE           0.111    0.024    4.592    0.000    0.111    0.114\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .socemo            1.613    0.013  120.020    0.000    1.613    3.579\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .socemo            0.201    0.007   28.399    0.000    0.201    0.987\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R -i dummiedSmallZambiaDF\n",
    "semfit <- sem('socemo ~ ece_ECE', data=dummiedSmallZambiaDF, meanstructure=TRUE)\n",
    "summary(semfit, standardized=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan 0.6-2 ended normally after 13 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                          3\n",
      "\n",
      "  Number of observations                          1613\n",
      "\n",
      "  Estimator                                         ML      Robust\n",
      "  Model Fit Test Statistic                       0.000       0.000\n",
      "  Degrees of freedom                                 0           0\n",
      "  Minimum Function Value               0.0000000000000\n",
      "  Scaling correction factor                                     NA\n",
      "  Shift parameter                                     \n",
      "    for simple second-order correction (Mplus variant)\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Expected\n",
      "  Information saturated (h1) model          Structured\n",
      "  Standard Errors                           Robust.sem\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "  socemo ~                                                              \n",
      "    ece_ECE           0.111    0.024    4.595    0.000    0.111    0.114\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .socemo            1.613    0.013  119.945    0.000    1.613    3.579\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .socemo            0.201    0.006   31.106    0.000    0.201    0.987\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R -i dummiedSmallZambiaDF\n",
    "semfit <- sem('socemo ~ ece_ECE', data=dummiedSmallZambiaDF, estimator='MLMV', meanstructure=TRUE)\n",
    "summary(semfit, standardized=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gdAgQOgQIHQKEDgFChwChQ4DQIUDoECB0CBA6BAgdAoQOAUKHAKFDgNAhQOgQIHQIEDoECB0ChA4BQocAoUOA0CFA6BAgdAgQOgQIHQKEDgFChwChQ4DQIUDoECB0CBA6BAgdAoQOAUKHAKFDgNAhQOgQIHQIEDoECB0ChA4BQocAoUOA0CFA6BAgdAgQOgQIHQKEDgFChwChQ4DQIUDoECB0CBA6BAgdAoQOAUKHAKFDgNAhQOgQIHQIEDoECB0ChA4BQocAoUOA0CFA6BAgdAgQOgQIHQKEDgFChwChQ4DQIUDoECB0CBA6BAgdAoQOAUKHAKFDgNAhQOgQ8D8kBb+3/u5urwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i dummiedSmallZambiaDF\n",
    "semPaths(lm('socemo ~ ece_ECE', data=dummiedSmallZambiaDF), \"std\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:191: FutureWarning: from_items is deprecated. Please use DataFrame.from_dict(dict(items), ...) instead. DataFrame.from_dict(OrderedDict(items)) may be used to preserve the key order.\n",
      "  res = PandasDataFrame.from_items(items)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan 0.6-2 ended normally after 31 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                         10\n",
      "\n",
      "  Number of observations                          1613\n",
      "\n",
      "  Estimator                                         ML      Robust\n",
      "  Model Fit Test Statistic                       6.390       6.305\n",
      "  Degrees of freedom                                 1           1\n",
      "  P-value (Chi-square)                           0.011       0.012\n",
      "  Scaling correction factor                                  1.014\n",
      "  Shift parameter                                           -0.000\n",
      "    for simple second-order correction (Mplus variant)\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Expected\n",
      "  Information saturated (h1) model          Structured\n",
      "  Standard Errors                           Robust.sem\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "  socemo ~                                                              \n",
      "    ece_ECE           0.100    0.026    3.916    0.000    0.100    0.103\n",
      "    wealth            0.009    0.008    1.161    0.246    0.009    0.029\n",
      "  reasoning ~                                                           \n",
      "    ece_ECE           0.741    0.151    4.922    0.000    0.741    0.136\n",
      "    wealth            0.208    0.046    4.502    0.000    0.208    0.118\n",
      "    books_Bks_n_hm    0.296    0.148    2.007    0.045    0.296    0.052\n",
      "\n",
      "Covariances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      " .socemo ~~                                                             \n",
      "   .reasoning        -0.079    0.030   -2.677    0.007   -0.079   -0.072\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .socemo            1.589    0.025   64.749    0.000    1.589    3.525\n",
      "   .reasoning         3.523    0.140   25.208    0.000    3.523    1.398\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .socemo            0.200    0.006   31.148    0.000    0.200    0.986\n",
      "   .reasoning         6.022    0.173   34.896    0.000    6.022    0.948\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R -i dummiedSmallZambiaDF\n",
    "bigmodel <- '\n",
    "  socemo ~ ece_ECE + wealth\n",
    "  reasoning ~ ece_ECE + wealth + books_Books_in_home\n",
    "  socemo ~~ reasoning\n",
    "'\n",
    "semfit <- sem(bigmodel, data=dummiedSmallZambiaDF, estimator=\"MLMV\", meanstructure=TRUE)\n",
    "summary(semfit, standardized=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "semPaths(semfit, \"std\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "semPaths(semfit, \"std\", curvePivot=TRUE, intercepts=FALSE, layout=\"tree2\", rotation=2, nCharNodes=9, sizeMan=15,\n",
    "         groups=list(c(\"wealth\", \"ece_ECE\", \"books_Books_in_home\"), c(\"reasoning\", \"socemo\")),\n",
    "         color=c(\"lightblue\", \"purple\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         npar                          fmin \n",
      "                        8.000                         0.002 \n",
      "                        chisq                            df \n",
      "                        6.390                         1.000 \n",
      "                       pvalue                  chisq.scaled \n",
      "                        0.011                         6.305 \n",
      "                    df.scaled                 pvalue.scaled \n",
      "                        1.000                         0.012 \n",
      "         chisq.scaling.factor                baseline.chisq \n",
      "                        1.014                       123.037 \n",
      "                  baseline.df               baseline.pvalue \n",
      "                        7.000                         0.000 \n",
      "        baseline.chisq.scaled            baseline.df.scaled \n",
      "                      120.437                         7.000 \n",
      "       baseline.pvalue.scaled baseline.chisq.scaling.factor \n",
      "                        0.000                         1.023 \n",
      "                          cfi                           tli \n",
      "                        0.954                         0.675 \n",
      "                         nnfi                           rfi \n",
      "                        0.675                         0.636 \n",
      "                          nfi                          pnfi \n",
      "                        0.948                         0.135 \n",
      "                          ifi                           rni \n",
      "                        0.956                         0.954 \n",
      "                   cfi.scaled                    tli.scaled \n",
      "                        0.953                         0.673 \n",
      "                   cfi.robust                    tli.robust \n",
      "                           NA                            NA \n",
      "                  nnfi.scaled                   nnfi.robust \n",
      "                        0.673                            NA \n",
      "                   rfi.scaled                    nfi.scaled \n",
      "                        0.634                         0.948 \n",
      "                   ifi.scaled                    rni.scaled \n",
      "                        0.948                         0.953 \n",
      "                   rni.robust                          logl \n",
      "                           NA                     -4724.918 \n",
      "            unrestricted.logl                           aic \n",
      "                    -4721.722                      9465.835 \n",
      "                          bic                        ntotal \n",
      "                     9508.922                      1613.000 \n",
      "                         bic2                         rmsea \n",
      "                     9483.508                         0.058 \n",
      "               rmsea.ci.lower                rmsea.ci.upper \n",
      "                        0.022                         0.104 \n",
      "                 rmsea.pvalue                  rmsea.scaled \n",
      "                        0.302                         0.057 \n",
      "        rmsea.ci.lower.scaled         rmsea.ci.upper.scaled \n",
      "                        0.021                         0.103 \n",
      "          rmsea.pvalue.scaled                  rmsea.robust \n",
      "                        0.308                            NA \n",
      "        rmsea.ci.lower.robust         rmsea.ci.upper.robust \n",
      "                           NA                            NA \n",
      "          rmsea.pvalue.robust                           rmr \n",
      "                           NA                         0.004 \n",
      "                   rmr_nomean                          srmr \n",
      "                        0.004                         0.016 \n",
      "                 srmr_bentler           srmr_bentler_nomean \n",
      "                        0.016                         0.016 \n",
      "                  srmr_bollen            srmr_bollen_nomean \n",
      "                        0.016                         0.016 \n",
      "                   srmr_mplus             srmr_mplus_nomean \n",
      "                        0.016                         0.016 \n",
      "                        cn_05                         cn_01 \n",
      "                      970.642                      1675.747 \n",
      "                          gfi                          agfi \n",
      "                        0.996                         0.941 \n",
      "                         pgfi                           mfi \n",
      "                        0.066                         0.998 \n",
      "                         ecvi \n",
      "                        0.014 \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "fitMeasures(semfit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  cfi   tli rmsea  srmr \n",
      "0.954 0.675 0.058 0.016 \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "fitMeasures(semfit, c(\"cfi\", \"tli\", \"rmsea\", \"srmr\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try to do the same with OpenMX. Here, we need to first drop variables that we are not actually using in the model, otherwise OpenMX will complain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "smallZambiaDFForOMX = dummiedSmallZambiaDF[[\"wealth\", \"ece_ECE\", \"books_Books_in_home\", \"reasoning\", \"socemo\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MxModel 'zambia simple' \n",
      "type : RAM \n",
      "$matrices : 'A', 'S', 'F', and 'M' \n",
      "$algebras : NULL \n",
      "$constraints : NULL \n",
      "$intervals : NULL \n",
      "$latentVars : none\n",
      "$manifestVars : 'wealth', 'ece_ECE', 'books_Books_in_home', 'reasoning', and 'socemo' \n",
      "$data : 1613 x 5 \n",
      "$data means : NA\n",
      "$data type: 'raw' \n",
      "$submodels : NULL \n",
      "$expectation : MxExpectationRAM \n",
      "$fitfunction : MxFitFunctionML \n",
      "$compute : NULL \n",
      "$independent : FALSE \n",
      "$options :  \n",
      "$output : FALSE \n"
     ]
    }
   ],
   "source": [
    "%%R -i smallZambiaDFForOMX\n",
    "manifests <- names(smallZambiaDFForOMX)\n",
    "model <- mxModel(\"zambia simple\", type=\"RAM\",\n",
    "                manifestVars=manifests,\n",
    "                mxPath(from=c(\"wealth\", \"ece_ECE\"), to=\"socemo\", arrows=1, free=TRUE),\n",
    "                mxPath(from=c(\"wealth\", \"ece_ECE\", \"books_Books_in_home\"), to=\"reasoning\", arrows=1, free=TRUE),\n",
    "                mxPath(from=\"socemo\", to=\"reasoning\", arrows=2),\n",
    "                mxPath(from=manifests, arrows=2, values=.8), # needs starting values or it won't converge\n",
    "                mxPath(from=\"one\", to=manifests),\n",
    "                mxData(observed=smallZambiaDFForOMX, type=\"raw\"))\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "modelRun <- mxRun(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$status\n",
      "[1] TRUE\n",
      "\n",
      "$jacobian\n",
      "       zambia simple.A[4,1] zambia simple.A[5,1] zambia simple.A[4,2]\n",
      "cov1_1                  0.0                  0.0                  0.0\n",
      "cov2_1                  0.0                  0.0                  0.0\n",
      "cov3_1                  0.0                  0.0                  0.0\n",
      "cov4_1                  0.8                  0.0                  0.0\n",
      "cov5_1                  0.0                  0.8                  0.0\n",
      "cov2_2                  0.0                  0.0                  0.0\n",
      "cov3_2                  0.0                  0.0                  0.0\n",
      "cov4_2                  0.0                  0.0                  0.8\n",
      "cov5_2                  0.0                  0.0                  0.0\n",
      "cov3_3                  0.0                  0.0                  0.0\n",
      "cov4_3                  0.0                  0.0                  0.0\n",
      "cov5_3                  0.0                  0.0                  0.0\n",
      "cov4_4                  0.0                  0.0                  0.0\n",
      "cov5_4                  0.0                  0.0                  0.0\n",
      "cov5_5                  0.0                  0.0                  0.0\n",
      "mean1                   0.0                  0.0                  0.0\n",
      "mean2                   0.0                  0.0                  0.0\n",
      "mean3                   0.0                  0.0                  0.0\n",
      "mean4                   0.0                  0.0                  0.0\n",
      "mean5                   0.0                  0.0                  0.0\n",
      "       zambia simple.A[5,2] zambia simple.A[4,3] zambia simple.S[1,1]\n",
      "cov1_1                  0.0                  0.0                    1\n",
      "cov2_1                  0.0                  0.0                    0\n",
      "cov3_1                  0.0                  0.0                    0\n",
      "cov4_1                  0.0                  0.0                    0\n",
      "cov5_1                  0.0                  0.0                    0\n",
      "cov2_2                  0.0                  0.0                    0\n",
      "cov3_2                  0.0                  0.0                    0\n",
      "cov4_2                  0.0                  0.0                    0\n",
      "cov5_2                  0.8                  0.0                    0\n",
      "cov3_3                  0.0                  0.0                    0\n",
      "cov4_3                  0.0                  0.8                    0\n",
      "cov5_3                  0.0                  0.0                    0\n",
      "cov4_4                  0.0                  0.0                    0\n",
      "cov5_4                  0.0                  0.0                    0\n",
      "cov5_5                  0.0                  0.0                    0\n",
      "mean1                   0.0                  0.0                    0\n",
      "mean2                   0.0                  0.0                    0\n",
      "mean3                   0.0                  0.0                    0\n",
      "mean4                   0.0                  0.0                    0\n",
      "mean5                   0.0                  0.0                    0\n",
      "       zambia simple.S[2,2] zambia simple.S[3,3] zambia simple.S[4,4]\n",
      "cov1_1                    0                    0                    0\n",
      "cov2_1                    0                    0                    0\n",
      "cov3_1                    0                    0                    0\n",
      "cov4_1                    0                    0                    0\n",
      "cov5_1                    0                    0                    0\n",
      "cov2_2                    1                    0                    0\n",
      "cov3_2                    0                    0                    0\n",
      "cov4_2                    0                    0                    0\n",
      "cov5_2                    0                    0                    0\n",
      "cov3_3                    0                    1                    0\n",
      "cov4_3                    0                    0                    0\n",
      "cov5_3                    0                    0                    0\n",
      "cov4_4                    0                    0                    1\n",
      "cov5_4                    0                    0                    0\n",
      "cov5_5                    0                    0                    0\n",
      "mean1                     0                    0                    0\n",
      "mean2                     0                    0                    0\n",
      "mean3                     0                    0                    0\n",
      "mean4                     0                    0                    0\n",
      "mean5                     0                    0                    0\n",
      "       zambia simple.S[4,5] zambia simple.S[5,5] zambia simple.M[1,1]\n",
      "cov1_1                    0                    0                    0\n",
      "cov2_1                    0                    0                    0\n",
      "cov3_1                    0                    0                    0\n",
      "cov4_1                    0                    0                    0\n",
      "cov5_1                    0                    0                    0\n",
      "cov2_2                    0                    0                    0\n",
      "cov3_2                    0                    0                    0\n",
      "cov4_2                    0                    0                    0\n",
      "cov5_2                    0                    0                    0\n",
      "cov3_3                    0                    0                    0\n",
      "cov4_3                    0                    0                    0\n",
      "cov5_3                    0                    0                    0\n",
      "cov4_4                    0                    0                    0\n",
      "cov5_4                    1                    0                    0\n",
      "cov5_5                    0                    1                    0\n",
      "mean1                     0                    0                    1\n",
      "mean2                     0                    0                    0\n",
      "mean3                     0                    0                    0\n",
      "mean4                     0                    0                    0\n",
      "mean5                     0                    0                    0\n",
      "       zambia simple.M[1,2] zambia simple.M[1,3] zambia simple.M[1,4]\n",
      "cov1_1                    0                    0                    0\n",
      "cov2_1                    0                    0                    0\n",
      "cov3_1                    0                    0                    0\n",
      "cov4_1                    0                    0                    0\n",
      "cov5_1                    0                    0                    0\n",
      "cov2_2                    0                    0                    0\n",
      "cov3_2                    0                    0                    0\n",
      "cov4_2                    0                    0                    0\n",
      "cov5_2                    0                    0                    0\n",
      "cov3_3                    0                    0                    0\n",
      "cov4_3                    0                    0                    0\n",
      "cov5_3                    0                    0                    0\n",
      "cov4_4                    0                    0                    0\n",
      "cov5_4                    0                    0                    0\n",
      "cov5_5                    0                    0                    0\n",
      "mean1                     0                    0                    0\n",
      "mean2                     1                    0                    0\n",
      "mean3                     0                    1                    0\n",
      "mean4                     0                    0                    1\n",
      "mean5                     0                    0                    0\n",
      "       zambia simple.M[1,5]\n",
      "cov1_1                    0\n",
      "cov2_1                    0\n",
      "cov3_1                    0\n",
      "cov4_1                    0\n",
      "cov5_1                    0\n",
      "cov2_2                    0\n",
      "cov3_2                    0\n",
      "cov4_2                    0\n",
      "cov5_2                    0\n",
      "cov3_3                    0\n",
      "cov4_3                    0\n",
      "cov5_3                    0\n",
      "cov4_4                    0\n",
      "cov5_4                    0\n",
      "cov5_5                    0\n",
      "mean1                     0\n",
      "mean2                     0\n",
      "mean3                     0\n",
      "mean4                     0\n",
      "mean5                     1\n",
      "\n",
      "$non_identified_parameters\n",
      "[1] \"None\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "mxCheckIdentification(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of zambia simple \n",
      " \n",
      "free parameters:\n",
      "                   name matrix                 row                 col\n",
      "1  zambia simple.A[4,1]      A           reasoning              wealth\n",
      "2  zambia simple.A[5,1]      A              socemo              wealth\n",
      "3  zambia simple.A[4,2]      A           reasoning             ece_ECE\n",
      "4  zambia simple.A[5,2]      A              socemo             ece_ECE\n",
      "5  zambia simple.A[4,3]      A           reasoning books_Books_in_home\n",
      "6  zambia simple.S[1,1]      S              wealth              wealth\n",
      "7  zambia simple.S[2,2]      S             ece_ECE             ece_ECE\n",
      "8  zambia simple.S[3,3]      S books_Books_in_home books_Books_in_home\n",
      "9  zambia simple.S[4,4]      S           reasoning           reasoning\n",
      "10 zambia simple.S[4,5]      S           reasoning              socemo\n",
      "11 zambia simple.S[5,5]      S              socemo              socemo\n",
      "12 zambia simple.M[1,1]      M                   1              wealth\n",
      "13 zambia simple.M[1,2]      M                   1             ece_ECE\n",
      "14 zambia simple.M[1,3]      M                   1 books_Books_in_home\n",
      "15 zambia simple.M[1,4]      M                   1           reasoning\n",
      "16 zambia simple.M[1,5]      M                   1              socemo\n",
      "       Estimate   Std.Error A\n",
      "1   0.208256249 0.045763284  \n",
      "2   0.009257539 0.008323664  \n",
      "3   0.741469780 0.144594365  \n",
      "4   0.100492479 0.025700813  \n",
      "5   0.296275774 0.142460515  \n",
      "6   2.045906814 0.072042943  \n",
      "7   0.214596238 0.007556487  \n",
      "8   0.198934598 0.007005001  \n",
      "9   6.021752810 0.212049316  \n",
      "10 -0.079150620 0.027477928  \n",
      "11  0.200411594 0.007057006  \n",
      "12  2.957222635 0.035614481  \n",
      "13  0.311841230 0.011534387  \n",
      "14  0.274023513 0.011105512  \n",
      "15  3.523063821 0.141593354  \n",
      "16  1.589071982 0.025607614  \n",
      "\n",
      "Model Statistics: \n",
      "               |  Parameters  |  Degrees of Freedom  |  Fit (-2lnL units)\n",
      "       Model:             16                   8049              19249.93\n",
      "   Saturated:             20                   8045                    NA\n",
      "Independence:             10                   8055                    NA\n",
      "Number of observations/statistics: 1613/8065\n",
      "\n",
      "Information Criteria: \n",
      "      |  df Penalty  |  Parameters Penalty  |  Sample-Size Adjusted\n",
      "AIC:       3151.934               19281.93                 19282.28\n",
      "BIC:     -40198.781               19368.11                 19317.28\n",
      "To get additional fit indices, see help(mxRefModels)\n",
      "timestamp: 2018-11-20 01:04:09 \n",
      "Wall clock time: 0.07181001 secs \n",
      "optimizer:  CSOLNP \n",
      "OpenMx version number: 2.9.9 \n",
      "Need help?  See help(mxSummary) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "summary(modelRun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "semPaths(modelRun, \"std\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "semPaths(modelRun, \"std\", curvePivot=TRUE, intercepts=FALSE, layout=\"tree2\", rotation=2, nCharNodes=9, sizeMan=15,\n",
    "         groups=list(c(\"wealth\", \"ece_ECE\", \"books_Books_in_home\"), c(\"reasoning\", \"socemo\")),\n",
    "         color=c(\"lightblue\", \"purple\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's also another package for doing SEM in R, calles `sem`, but it seems older and less well supported/documented."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A more complex model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Research Question: Do ECE participation and book availability fully explain the relations between wealth and children's socio-emotional & reasoning skills in the Zambian context?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan 0.6-2 ended normally after 35 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                         15\n",
      "\n",
      "  Number of observations                          1613\n",
      "\n",
      "  Estimator                                         ML      Robust\n",
      "  Model Fit Test Statistic                     105.787      98.505\n",
      "  Degrees of freedom                                 3           3\n",
      "  P-value (Chi-square)                           0.000       0.000\n",
      "  Scaling correction factor                                  1.074\n",
      "    for the Yuan-Bentler correction (Mplus variant)\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Observed\n",
      "  Observed information based on                Hessian\n",
      "  Standard Errors                   Robust.huber.white\n",
      "\n",
      "Regressions:\n",
      "                        Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "  ece_ECE ~                                                                  \n",
      "    wealth                 0.114    0.008   14.928    0.000    0.114    0.351\n",
      "  books_Books_in_home ~                                                      \n",
      "    wealth                 0.051    0.008    6.560    0.000    0.051    0.162\n",
      "  socemo ~                                                                   \n",
      "    ece_ECE                0.093    0.025    3.721    0.000    0.093    0.096\n",
      "    books_Bks_n_hm         0.067    0.026    2.588    0.010    0.067    0.067\n",
      "  reasoning ~                                                                \n",
      "    ece_ECE                0.961    0.143    6.730    0.000    0.961    0.177\n",
      "    books_Bks_n_hm         0.320    0.148    2.159    0.031    0.320    0.057\n",
      "\n",
      "Covariances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      " .socemo ~~                                                             \n",
      "   .reasoning        -0.076    0.030   -2.574    0.010   -0.076   -0.069\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .ece_ECE          -0.025    0.023   -1.089    0.276   -0.025   -0.053\n",
      "   .books_Bks_n_hm    0.124    0.024    5.262    0.000    0.124    0.279\n",
      "   .socemo            1.600    0.014  112.146    0.000    1.600    3.555\n",
      "   .reasoning         4.064    0.076   53.429    0.000    4.064    1.616\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .ece_ECE           0.188    0.005   39.133    0.000    0.188    0.877\n",
      "   .books_Bks_n_hm    0.194    0.005   38.651    0.000    0.194    0.974\n",
      "   .socemo            0.200    0.006   31.190    0.000    0.200    0.986\n",
      "   .reasoning         6.099    0.175   34.873    0.000    6.099    0.964\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R -i dummiedSmallZambiaDF\n",
    "biggermodel <- '\n",
    "  ece_ECE ~ wealth\n",
    "  books_Books_in_home ~ wealth\n",
    "  socemo ~ ece_ECE + books_Books_in_home\n",
    "  reasoning ~ ece_ECE + books_Books_in_home\n",
    "  socemo ~~ reasoning\n",
    "'\n",
    "semfit2 <- sem(biggermodel, data=dummiedSmallZambiaDF, estimator=\"MLR\", meanstructure=TRUE)\n",
    "# WLS would correspond to the adf method, but does not run to completion with this dataset\n",
    "summary(semfit2, standardized=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  cfi   tli rmsea  srmr \n",
      "0.882 0.608 0.110 0.046 \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "fitMeasures(semfit2, c(\"cfi\", \"tli\", \"rmsea\", \"srmr\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "semPaths(semfit2, \"std\", curvePivot=TRUE, intercepts=FALSE, layout=\"tree2\", rotation=2, nCharNodes=9, sizeMan=15,\n",
    "         groups=list(c(\"wealth\", \"ece_ECE\", \"books_Books_in_home\"), c(\"reasoning\", \"socemo\")),\n",
    "         color=c(\"lightblue\", \"purple\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### High-level tools for SEM in R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "simsem's semtools, see https://github.com/simsem/semTools/wiki/Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "naive.chisq    k-factor   adj.chisq          df      pvalue \n",
      "      0.000       0.999       0.000       0.000       1.000 \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "chisqSmallN(semfit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bigsem, ctsem, gsem, lavaan.survey, regsem, semdiag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A caveat regarding fit measures: similar to the arbitraty cutoff for p-values indicating significance, the use of fit indices is controversial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usual indices:\n",
    "\n",
    "* chisquare, basic but can be unclear\n",
    "* RMSEA (root mean square error of approximation) based on chisquare but takes degrees of freedom (model parsimony) and sample size into account. Should be close to 0 ( < 0.05-0.08)\n",
    "* CFI/TLI (Bentler comparative fit index, Tucker Lewis index) measure relative fit vs an \"independence model\" (with covariances 0). Should be close to 1 ( > 0.90-0.95)\n",
    "* SRMR (standardized root mean square residual) measures the difference between observed and predicted correlations. Should be close to 0 ( < 0.06-0.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  cfi   tli rmsea  srmr \n",
      "0.954 0.675 0.058 0.013 \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "fitMeasures(semfit, c(\"cfi\", \"tli\", \"rmsea\", \"srmr\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latent Variables and Mediation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simpler model to show mediation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan 0.6-2 ended normally after 21 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                          7\n",
      "\n",
      "  Number of observations                          1613\n",
      "\n",
      "  Estimator                                         ML      Robust\n",
      "  Model Fit Test Statistic                       0.000       0.000\n",
      "  Degrees of freedom                                 0           0\n",
      "  Minimum Function Value               0.0000000000000\n",
      "  Scaling correction factor                                     NA\n",
      "    for the Yuan-Bentler correction (Mplus variant)\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Observed\n",
      "  Observed information based on                Hessian\n",
      "  Standard Errors                   Robust.huber.white\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "  ece_ECE ~                                                             \n",
      "    wealth     (a)    0.114    0.008   14.928    0.000    0.114    0.351\n",
      "  socemo ~                                                              \n",
      "    wealth     (c)    0.009    0.008    1.166    0.244    0.009    0.029\n",
      "    ece_ECE    (b)    0.100    0.026    3.920    0.000    0.100    0.103\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .ece_ECE          -0.025    0.023   -1.089    0.276   -0.025   -0.053\n",
      "   .socemo            1.589    0.024   64.924    0.000    1.589    3.525\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .ece_ECE           0.188    0.005   39.133    0.000    0.188    0.877\n",
      "   .socemo            0.200    0.006   31.147    0.000    0.200    0.986\n",
      "\n",
      "Defined Parameters:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "    indirect          0.011    0.003    3.815    0.000    0.011    0.036\n",
      "    total             0.021    0.007    2.772    0.006    0.021    0.066\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R -i dummiedSmallZambiaDF\n",
    "simplermodel <- '\n",
    "  # Here we use a and b and c to keep track of the parameter estimations,\n",
    "  # so that we can then use it to estimate the indirect and total effects.\n",
    "  # ece is the supposed Mediator\n",
    "  ece_ECE ~ a*wealth\n",
    "  socemo ~ c*wealth + b*ece_ECE\n",
    "  # also estimate indirect effect:\n",
    "  indirect := a*b\n",
    "  # total effect:\n",
    "  total := c + (a*b)\n",
    "'\n",
    "semfit3 <- sem(simplermodel, data=dummiedSmallZambiaDF, estimator=\"MLR\", meanstructure=TRUE)\n",
    "summary(semfit3, standardized=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  cfi   tli rmsea  srmr \n",
      "    1     1     0     0 \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "fitMeasures(semfit3, c(\"cfi\", \"tli\", \"rmsea\", \"srmr\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "semPaths(semfit3, \"std\", curvePivot=TRUE, intercepts=FALSE, layout=\"tree2\", rotation=2, nCharNodes=9, sizeMan=15,\n",
    "         groups=list(c(\"wealth\", \"ece_ECE\"), c(\"socemo\")),\n",
    "         color=c(\"lightblue\", \"purple\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other packages that should be useful for mediation analysis, for example to compare results: `psych` and `MBESS`, see here http://nickmichalak.com/blog_entries/2018/nrg01/nrg01.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing two different models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The semtools package is helpful for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-squared test(s) results, ADJUSTED for missing data:\n",
      "\n",
      "Browne (1984) residual-based test statistic:\n",
      "\n",
      " chisq     df pvalue \n",
      "97.915  3.000  0.000 \n",
      "\n",
      "\n",
      "Satorra-Bentler (2001) scaled test statistic:\n",
      "\n",
      "   chisq.naive scaling.factor   chisq.scaled             df         pvalue \n",
      "       105.787          0.001     178767.164          3.000          0.000 \n",
      "\n",
      "\n",
      "Chi-squared test results, UNADJUSTED for missing data:\n",
      "\n",
      "lavaan 0.6-2 ended normally after 35 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                         17\n",
      "\n",
      "  Number of observations                          1613\n",
      "\n",
      "  Estimator                                         ML\n",
      "  Model Fit Test Statistic                     105.787\n",
      "  Degrees of freedom                                 3\n",
      "  P-value (Chi-square)                           0.000\n",
      "\n",
      "\n",
      "Parameter Estimates, with SEs (and tests/CIs) ADJUSTED for missing data:\n",
      "\n",
      "                   lhs op                 rhs    est    se       z pvalue\n",
      "1              ece_ECE  ~              wealth  0.114 0.008  14.943  0.000\n",
      "2  books_Books_in_home  ~              wealth  0.051 0.008   6.599  0.000\n",
      "3               socemo  ~             ece_ECE  0.093 0.024   3.925  0.000\n",
      "4               socemo  ~ books_Books_in_home  0.067 0.025   2.709  0.007\n",
      "5            reasoning  ~             ece_ECE  0.961 0.131   7.315  0.000\n",
      "6            reasoning  ~ books_Books_in_home  0.320 0.138   2.321  0.020\n",
      "7               socemo ~~           reasoning -0.076 0.028  -2.706  0.007\n",
      "8              ece_ECE ~~             ece_ECE  0.188 0.008  24.811  0.000\n",
      "9  books_Books_in_home ~~ books_Books_in_home  0.194 0.008  24.842  0.000\n",
      "10              socemo ~~              socemo  0.200 0.007  28.354  0.000\n",
      "11           reasoning ~~           reasoning  6.099 0.223  27.342  0.000\n",
      "12              wealth ~~              wealth  2.046 0.074  27.537  0.000\n",
      "13             ece_ECE ~1                     -0.025 0.025  -0.988  0.323\n",
      "14 books_Books_in_home ~1                      0.124 0.025   4.943  0.000\n",
      "15              socemo ~1                      1.600 0.016 102.991  0.000\n",
      "16           reasoning ~1                      4.064 0.086  47.308  0.000\n",
      "17              wealth ~1                      2.957 0.036  83.034  0.000\n",
      "   ci.lower ci.upper std.lv std.all std.nox\n",
      "1     0.099    0.129  0.114   0.351   0.246\n",
      "2     0.036    0.066  0.051   0.162   0.113\n",
      "3     0.047    0.140  0.093   0.096   0.096\n",
      "4     0.019    0.116  0.067   0.067   0.067\n",
      "5     0.704    1.219  0.961   0.177   0.177\n",
      "6     0.050    0.591  0.320   0.057   0.057\n",
      "7    -0.131   -0.021 -0.076  -0.069  -0.069\n",
      "8     0.173    0.203  0.188   0.877   0.877\n",
      "9     0.178    0.209  0.194   0.974   0.974\n",
      "10    0.186    0.214  0.200   0.986   0.986\n",
      "11    5.662    6.537  6.099   0.964   0.964\n",
      "12    1.900    2.192  2.046   1.000   2.046\n",
      "13   -0.074    0.024 -0.025  -0.053  -0.053\n",
      "14    0.075    0.174  0.124   0.279   0.279\n",
      "15    1.570    1.631  1.600   3.555   3.555\n",
      "16    3.895    4.232  4.064   1.616   1.616\n",
      "17    2.887    3.027  2.957   2.067   2.957\n"
     ]
    }
   ],
   "source": [
    "%%R -i dummiedSmallZambiaDF\n",
    "twostagesemfit1 <- sem.2stage(model=biggermodel, data=dummiedSmallZambiaDF, estimator=\"MLR\", meanstructure=TRUE)\n",
    "summary(twostagesemfit1, standardized=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-squared test(s) results, ADJUSTED for missing data:\n",
      "\n",
      "Browne (1984) residual-based test statistic:\n",
      "\n",
      " chisq     df pvalue \n",
      "77.505  1.000  0.000 \n",
      "\n",
      "\n",
      "Satorra-Bentler (2001) scaled test statistic:\n",
      "\n",
      "   chisq.naive scaling.factor   chisq.scaled             df         pvalue \n",
      "        83.543          0.001     160732.251          1.000          0.000 \n",
      "\n",
      "\n",
      "Chi-squared test results, UNADJUSTED for missing data:\n",
      "\n",
      "lavaan 0.6-2 ended normally after 38 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                         19\n",
      "\n",
      "  Number of observations                          1613\n",
      "\n",
      "  Estimator                                         ML\n",
      "  Model Fit Test Statistic                      83.543\n",
      "  Degrees of freedom                                 1\n",
      "  P-value (Chi-square)                           0.000\n",
      "\n",
      "\n",
      "Parameter Estimates, with SEs (and tests/CIs) ADJUSTED for missing data:\n",
      "\n",
      "                   lhs op                 rhs    est    se      z pvalue\n",
      "1              ece_ECE  ~              wealth  0.114 0.008 15.072  0.000\n",
      "2  books_Books_in_home  ~              wealth  0.051 0.008  6.602  0.000\n",
      "3               socemo  ~              wealth  0.008 0.009  0.898  0.369\n",
      "4               socemo  ~             ece_ECE  0.086 0.026  3.334  0.001\n",
      "5               socemo  ~ books_Books_in_home  0.066 0.025  2.597  0.009\n",
      "6            reasoning  ~              wealth  0.209 0.047  4.472  0.000\n",
      "7            reasoning  ~             ece_ECE  0.747 0.141  5.305  0.000\n",
      "8            reasoning  ~ books_Books_in_home  0.270 0.139  1.947  0.051\n",
      "9               socemo ~~           reasoning -0.079 0.027 -2.881  0.004\n",
      "10             ece_ECE ~~             ece_ECE  0.188 0.008 24.832  0.000\n",
      "11 books_Books_in_home ~~ books_Books_in_home  0.194 0.008 24.832  0.000\n",
      "12              socemo ~~              socemo  0.200 0.007 28.399  0.000\n",
      "13           reasoning ~~           reasoning  6.022 0.212 28.399  0.000\n",
      "14              wealth ~~              wealth  2.046 0.072 28.399  0.000\n",
      "15             ece_ECE ~1                     -0.025 0.025 -0.995  0.320\n",
      "16 books_Books_in_home ~1                      0.124 0.025  4.945  0.000\n",
      "17              socemo ~1                      1.581 0.026 61.422  0.000\n",
      "18           reasoning ~1                      3.526 0.141 24.952  0.000\n",
      "19              wealth ~1                      2.957 0.036 83.034  0.000\n",
      "   ci.lower ci.upper std.lv std.all std.nox\n",
      "1     0.099    0.129  0.114   0.351   0.246\n",
      "2     0.036    0.066  0.051   0.162   0.113\n",
      "3    -0.009    0.024  0.008   0.024   0.017\n",
      "4     0.035    0.136  0.086   0.088   0.088\n",
      "5     0.016    0.115  0.066   0.065   0.065\n",
      "6     0.117    0.300  0.209   0.119   0.083\n",
      "7     0.471    1.023  0.747   0.138   0.138\n",
      "8    -0.002    0.542  0.270   0.048   0.048\n",
      "9    -0.132   -0.025 -0.079  -0.072  -0.072\n",
      "10    0.173    0.203  0.188   0.877   0.877\n",
      "11    0.178    0.209  0.194   0.974   0.974\n",
      "12    0.186    0.213  0.200   0.985   0.985\n",
      "13    5.606    6.437  6.022   0.951   0.951\n",
      "14    1.905    2.187  2.046   1.000   2.046\n",
      "15   -0.073    0.024 -0.025  -0.053  -0.053\n",
      "16    0.075    0.174  0.124   0.279   0.279\n",
      "17    1.530    1.631  1.581   3.511   3.511\n",
      "18    3.249    3.803  3.526   1.401   1.401\n",
      "19    2.887    3.027  2.957   2.067   2.957\n"
     ]
    }
   ],
   "source": [
    "%%R -i dummiedSmallZambiaDF\n",
    "biggerermodel <- '\n",
    "  ece_ECE ~ wealth\n",
    "  books_Books_in_home ~ wealth\n",
    "  socemo ~ wealth + ece_ECE + books_Books_in_home\n",
    "  reasoning ~ wealth + ece_ECE + books_Books_in_home\n",
    "  socemo ~~ reasoning\n",
    "'\n",
    "twostagesemfit2 <- sem.2stage(model=biggerermodel, data=dummiedSmallZambiaDF, estimator=\"MLR\", meanstructure=TRUE)\n",
    "summary(twostagesemfit2, standardized=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference test for Browne (1984) residual-based statistics:\n",
      "\n",
      " chisq     df pvalue \n",
      "20.411  2.000  0.000 \n",
      "\n",
      "\n",
      "Satorra-Bentler (2001) scaled difference test:\n",
      "\n",
      "   chisq.naive scaling.factor   chisq.scaled             DF         pvalue \n",
      "        22.244          0.001      35434.111          2.000          0.000 \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "anova(twostagesemfit1, twostagesemfit2) # equivalent to \"likelihood-ratio test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   lhs op                 rhs group\n",
      "12              wealth ~~              wealth     1\n",
      "18             ece_ECE ~~ books_Books_in_home     1\n",
      "19             ece_ECE ~~              socemo     1\n",
      "20             ece_ECE ~~           reasoning     1\n",
      "21 books_Books_in_home ~~              socemo     1\n",
      "22 books_Books_in_home ~~           reasoning     1\n",
      "23             ece_ECE  ~ books_Books_in_home     1\n",
      "24             ece_ECE  ~              socemo     1\n",
      "25             ece_ECE  ~           reasoning     1\n",
      "26 books_Books_in_home  ~             ece_ECE     1\n",
      "27 books_Books_in_home  ~              socemo     1\n",
      "28 books_Books_in_home  ~           reasoning     1\n",
      "30              socemo  ~              wealth     1\n",
      "32           reasoning  ~              wealth     1\n",
      "33              wealth  ~             ece_ECE     1\n",
      "34              wealth  ~ books_Books_in_home     1\n",
      "35              wealth  ~              socemo     1\n",
      "36              wealth  ~           reasoning     1\n",
      "                                     mi                   epc target.epc\n",
      "12  0.000000000000000000000000000003003 -0.000000000000001431    0.20459\n",
      "18 81.416605542162955089224851690232754  0.042884997752392018    0.02066\n",
      "19  1.544749222765177121274859928234946 -0.017232019584356661    0.02085\n",
      "20 21.625988933431568028709079953841865 -0.356307742032899999    0.11651\n",
      "21  1.544738483979728682626841873570811 -0.039922711700388476    0.02008\n",
      "22 21.626245636034973074401932535693049 -0.825492627898089082    0.11218\n",
      "23 81.416625996730118686173227615654469  0.221397869134911884    0.10386\n",
      "24  0.619563202904253484959440356760751  0.053795614873202932    0.10291\n",
      "25  9.314088537604437334493923117406666 -0.037919599570400378    0.01842\n",
      "26 81.416636979079456182262219954282045  0.227984502302336800    0.09628\n",
      "27 14.613812036635893676361774851102382  0.529474396339441045    0.09908\n",
      "28 13.254150786988219579143333248794079  0.071090656370003724    0.01773\n",
      "30  1.544754756539778339785584648780059  0.010424279832891337    0.03147\n",
      "32 21.626012906364280752313788980245590  0.215543337637906313    0.17583\n",
      "33  0.000000005867903821677934663066101  0.000135862687082167    0.30877\n",
      "34  0.000000008669718146489592254175238  0.000371547803528799    0.32069\n",
      "35  0.855398203706040693994339108030545  0.079649234055344187    0.31774\n",
      "36 20.934768677467971542682789731770754  0.071299881188622113    0.05687\n",
      "                  std.epc std.target.epc significant.mi high.power decision.pow\n",
      "12 -0.0000000000000006995            0.1          FALSE      FALSE            I\n",
      "18  0.2075578170512830112            0.1           TRUE       TRUE        EPC:M\n",
      "19 -0.0826332446876676813            0.1          FALSE      FALSE            I\n",
      "20 -0.3058234554311530795            0.1           TRUE      FALSE            M\n",
      "21 -0.1988357948464972713            0.1          FALSE      FALSE            I\n",
      "22 -0.7358929489158683168            0.1           TRUE      FALSE            M\n",
      "23  0.2131657988650298707            0.1           TRUE       TRUE        EPC:M\n",
      "24  0.0522764444171291212            0.1          FALSE      FALSE            I\n",
      "25 -0.2058713928429005968            0.1           TRUE      FALSE            M\n",
      "26  0.2367888435868654440            0.1           TRUE       TRUE        EPC:M\n",
      "27  0.5343921132592205359            0.1           TRUE      FALSE            M\n",
      "28  0.4008673589843934626            0.1           TRUE      FALSE            M\n",
      "30  0.0331221683287136609            0.1          FALSE       TRUE           NM\n",
      "32  0.1225841182785387951            0.1           TRUE       TRUE        EPC:M\n",
      "33  0.0000440015864972028            0.1          FALSE      FALSE            I\n",
      "34  0.0001158582390606253            0.1          FALSE      FALSE            I\n",
      "35  0.0250673777159869729            0.1          FALSE       TRUE           NM\n",
      "36  0.1253687229667201364            0.1           TRUE       TRUE        EPC:M\n",
      "     se.epc lower.epc upper.epc lower.std.epc upper.std.epc decision.ci\n",
      "12 0.825817 -1.358348  1.358348      -0.66393       0.66393           I\n",
      "18 0.004753  0.035067  0.050703       0.16972       0.24539           M\n",
      "19 0.013865 -0.040037  0.005573      -0.19199       0.02673           I\n",
      "20 0.076619 -0.482335 -0.230280      -0.41399      -0.19765           M\n",
      "21 0.032121 -0.092757  0.012912      -0.46198       0.06431           I\n",
      "22 0.177510 -1.117470 -0.533515      -0.99618      -0.47561           M\n",
      "23 0.024537  0.181039  0.261757       0.17431       0.25202           M\n",
      "24 0.068345 -0.058621  0.166212      -0.05697       0.16152           I\n",
      "25 0.012425 -0.058357 -0.017482      -0.31683      -0.09491           I\n",
      "26 0.025267  0.186424  0.269545       0.19362       0.27995           M\n",
      "27 0.138504  0.301655  0.757294       0.30446       0.76433           M\n",
      "28 0.019527  0.038972  0.103210       0.21975       0.58198           M\n",
      "30 0.008387 -0.003371  0.024220      -0.01071       0.07696          NM\n",
      "32 0.046350  0.139305  0.291782       0.07923       0.16594           I\n",
      "33 1.773612 -2.917197  2.917469      -0.94479       0.94487           I\n",
      "34 3.990361 -6.563189  6.563932      -2.04657       2.04680           I\n",
      "35 0.086119 -0.062003  0.221302      -0.01951       0.06965          NM\n",
      "36 0.015583  0.045668  0.096932       0.08030       0.17044           I\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "miPowerFit(semfit2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-group analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare a model across groups in the data, for example male vs. female. See http://lavaan.ugent.be/tutorial/groups.html for how to specify path constraints across groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan 0.6-2 ended normally after 66 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                         30\n",
      "\n",
      "  Number of observations per group         \n",
      "  1                                                803\n",
      "  0                                                810\n",
      "\n",
      "  Estimator                                         ML      Robust\n",
      "  Model Fit Test Statistic                     112.355     104.373\n",
      "  Degrees of freedom                                 6           6\n",
      "  P-value (Chi-square)                           0.000       0.000\n",
      "  Scaling correction factor                                  1.076\n",
      "    for the Yuan-Bentler correction (Mplus variant)\n",
      "\n",
      "Chi-square for each group:\n",
      "\n",
      "  1                                             70.945      65.905\n",
      "  0                                             41.410      38.468\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Observed\n",
      "  Observed information based on                Hessian\n",
      "  Standard Errors                   Robust.huber.white\n",
      "\n",
      "\n",
      "Group 1 [1]:\n",
      "\n",
      "Regressions:\n",
      "                        Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "  ece_ECE ~                                                                  \n",
      "    wealth                 0.118    0.011   11.214    0.000    0.118    0.372\n",
      "  books_Books_in_home ~                                                      \n",
      "    wealth                 0.038    0.011    3.490    0.000    0.038    0.126\n",
      "  socemo ~                                                                   \n",
      "    ece_ECE                0.076    0.036    2.104    0.035    0.076    0.078\n",
      "    books_Bks_n_hm         0.050    0.038    1.321    0.186    0.050    0.049\n",
      "  reasoning ~                                                                \n",
      "    ece_ECE                0.862    0.207    4.165    0.000    0.862    0.155\n",
      "    books_Bks_n_hm         0.353    0.220    1.604    0.109    0.353    0.061\n",
      "\n",
      "Covariances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      " .socemo ~~                                                             \n",
      "   .reasoning        -0.079    0.042   -1.891    0.059   -0.079   -0.070\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .ece_ECE          -0.038    0.031   -1.205    0.228   -0.038   -0.082\n",
      "   .books_Bks_n_hm    0.152    0.034    4.508    0.000    0.152    0.346\n",
      "   .socemo            1.567    0.020   78.464    0.000    1.567    3.497\n",
      "   .reasoning         4.155    0.110   37.657    0.000    4.155    1.622\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .ece_ECE           0.184    0.007   26.400    0.000    0.184    0.861\n",
      "   .books_Bks_n_hm    0.191    0.007   26.100    0.000    0.191    0.984\n",
      "   .socemo            0.199    0.009   21.330    0.000    0.199    0.991\n",
      "   .reasoning         6.375    0.257   24.797    0.000    6.375    0.971\n",
      "\n",
      "\n",
      "Group 2 [0]:\n",
      "\n",
      "Regressions:\n",
      "                        Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "  ece_ECE ~                                                                  \n",
      "    wealth                 0.109    0.011    9.902    0.000    0.109    0.330\n",
      "  books_Books_in_home ~                                                      \n",
      "    wealth                 0.064    0.011    5.859    0.000    0.064    0.199\n",
      "  socemo ~                                                                   \n",
      "    ece_ECE                0.111    0.035    3.208    0.001    0.111    0.115\n",
      "    books_Bks_n_hm         0.081    0.036    2.257    0.024    0.081    0.081\n",
      "  reasoning ~                                                                \n",
      "    ece_ECE                1.056    0.198    5.347    0.000    1.056    0.199\n",
      "    books_Bks_n_hm         0.302    0.201    1.503    0.133    0.302    0.055\n",
      "\n",
      "Covariances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      " .socemo ~~                                                             \n",
      "   .reasoning        -0.068    0.041   -1.667    0.096   -0.068   -0.064\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .ece_ECE          -0.010    0.033   -0.320    0.749   -0.010   -0.023\n",
      "   .books_Bks_n_hm    0.094    0.033    2.855    0.004    0.094    0.208\n",
      "   .socemo            1.634    0.020   80.741    0.000    1.634    3.647\n",
      "   .reasoning         3.971    0.104   38.032    0.000    3.971    1.610\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .ece_ECE           0.192    0.007   28.994    0.000    0.192    0.891\n",
      "   .books_Bks_n_hm    0.195    0.007   28.525    0.000    0.195    0.961\n",
      "   .socemo            0.196    0.009   22.897    0.000    0.196    0.979\n",
      "   .reasoning         5.813    0.235   24.713    0.000    5.813    0.956\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R -i dummiedSmallZambiaDF\n",
    "multigroupsemfit <- sem(model=biggermodel, data=dummiedSmallZambiaDF, estimator=\"MLR\", meanstructure=TRUE,\n",
    "                        group=\"male_Male\")\n",
    "summary(multigroupsemfit, standardized=TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measurement Models, Confirmatory Factor Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SEM can be used not only to relate measured variables, as above, but also to relate indicators (items, or full \"scales\" that combine items) to theorized \"latent\" variables that cannot be directly observed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the extended version of the zambia dataset here which includes several more variables that are directly observed items. Initially, we'll use 5 items each for a socioemotional scale and a task-orientation scale. Each item is rated on a scale from 1 (never) to 4 (always)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>childid</th>\n",
       "      <th>male</th>\n",
       "      <th>urban</th>\n",
       "      <th>reas1</th>\n",
       "      <th>reas2</th>\n",
       "      <th>reas3</th>\n",
       "      <th>reas4</th>\n",
       "      <th>reas5</th>\n",
       "      <th>reas6</th>\n",
       "      <th>reas7</th>\n",
       "      <th>...</th>\n",
       "      <th>to12</th>\n",
       "      <th>to13</th>\n",
       "      <th>ece</th>\n",
       "      <th>reasoning</th>\n",
       "      <th>socemo</th>\n",
       "      <th>vocab</th>\n",
       "      <th>vocabsq</th>\n",
       "      <th>taskorient</th>\n",
       "      <th>wealth</th>\n",
       "      <th>books</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>ECE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>18.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>3.615385</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No books in home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>No ECE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>3.769231</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No books in home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>Female</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>No ECE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>19.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>3.769231</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No books in home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No ECE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>12.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>3.692308</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No books in home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>Correctly answered</td>\n",
       "      <td>Incorrect</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No ECE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>25.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>3.461539</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Books in home</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   childid    male  urban               reas1               reas2  \\\n",
       "0      101    Male  Urban  Correctly answered  Correctly answered   \n",
       "1      102    Male  Urban  Correctly answered           Incorrect   \n",
       "2      103  Female  Urban  Correctly answered  Correctly answered   \n",
       "3      104    Male  Urban  Correctly answered  Correctly answered   \n",
       "4      105    Male  Urban  Correctly answered  Correctly answered   \n",
       "\n",
       "                reas3               reas4      reas5               reas6  \\\n",
       "0  Correctly answered           Incorrect  Incorrect           Incorrect   \n",
       "1  Correctly answered  Correctly answered  Incorrect           Incorrect   \n",
       "2  Correctly answered           Incorrect  Incorrect  Correctly answered   \n",
       "3  Correctly answered           Incorrect  Incorrect           Incorrect   \n",
       "4  Correctly answered           Incorrect  Incorrect  Correctly answered   \n",
       "\n",
       "                reas7        ...        to12 to13     ece reasoning socemo  \\\n",
       "0           Incorrect        ...         2.0  4.0     ECE       3.0   1.05   \n",
       "1  Correctly answered        ...         4.0  4.0  No ECE       4.0   1.00   \n",
       "2           Incorrect        ...         4.0  4.0  No ECE       4.0   1.80   \n",
       "3  Correctly answered        ...         4.0  3.0  No ECE       5.0   2.35   \n",
       "4           Incorrect        ...         4.0  3.0  No ECE       5.0   1.50   \n",
       "\n",
       "  vocab vocabsq taskorient wealth             books  \n",
       "0  18.0   324.0   3.615385    3.0  No books in home  \n",
       "1  19.0   361.0   3.769231    2.0  No books in home  \n",
       "2  19.0   361.0   3.769231    3.0  No books in home  \n",
       "3  12.0   144.0   3.692308    3.0  No books in home  \n",
       "4  25.0   625.0   3.461539    2.0     Books in home  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measureZambiaDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1623 entries, 0 to 1622\n",
      "Data columns (total 84 columns):\n",
      "childid       1623 non-null int16\n",
      "male          1623 non-null category\n",
      "urban         1623 non-null category\n",
      "reas1         1623 non-null category\n",
      "reas2         1623 non-null category\n",
      "reas3         1623 non-null category\n",
      "reas4         1623 non-null category\n",
      "reas5         1623 non-null category\n",
      "reas6         1623 non-null category\n",
      "reas7         1623 non-null category\n",
      "reas8         1623 non-null category\n",
      "reas9         1623 non-null category\n",
      "reas10        1623 non-null category\n",
      "se1           1551 non-null category\n",
      "se2           1461 non-null category\n",
      "se3           1512 non-null category\n",
      "se4           1587 non-null category\n",
      "se5           1482 non-null category\n",
      "se6           1539 non-null category\n",
      "se7           1562 non-null category\n",
      "se8           1567 non-null category\n",
      "se9           1550 non-null category\n",
      "se10          1569 non-null category\n",
      "se11          1571 non-null category\n",
      "se12          1516 non-null category\n",
      "se13          1560 non-null category\n",
      "se14          1532 non-null category\n",
      "se15          1559 non-null category\n",
      "se16          1476 non-null category\n",
      "se17          1581 non-null category\n",
      "se18          1484 non-null category\n",
      "se19          1573 non-null category\n",
      "se20          1569 non-null category\n",
      "voc1          1623 non-null float32\n",
      "voc2          1623 non-null float32\n",
      "voc3          1623 non-null float32\n",
      "voc4          1623 non-null float32\n",
      "voc5          1623 non-null float32\n",
      "voc6          1623 non-null float32\n",
      "voc7          1623 non-null float32\n",
      "voc8          1623 non-null float32\n",
      "voc9          1623 non-null float32\n",
      "voc10         1623 non-null float32\n",
      "voc11         1623 non-null float32\n",
      "voc12         1623 non-null float32\n",
      "voc13         1623 non-null float32\n",
      "voc14         1623 non-null float32\n",
      "voc15         1623 non-null float32\n",
      "voc16         1623 non-null float32\n",
      "voc17         1623 non-null float32\n",
      "voc18         1623 non-null float32\n",
      "voc19         1623 non-null float32\n",
      "voc20         1623 non-null float32\n",
      "voc21         1623 non-null float32\n",
      "voc22         1623 non-null float32\n",
      "voc23         1623 non-null float32\n",
      "voc24         1623 non-null float32\n",
      "voc25         1623 non-null float32\n",
      "voc26         1623 non-null float32\n",
      "voc27         1623 non-null float32\n",
      "voc28         1623 non-null float32\n",
      "voc29         1623 non-null float32\n",
      "voc30         1623 non-null float32\n",
      "to1           1589 non-null float32\n",
      "to2           1585 non-null float32\n",
      "to3           1573 non-null float32\n",
      "to4           1587 non-null float32\n",
      "to5           1588 non-null float32\n",
      "to6           1591 non-null float32\n",
      "to7           1563 non-null float32\n",
      "to8           1572 non-null float32\n",
      "to9           1552 non-null float32\n",
      "to10          1551 non-null float32\n",
      "to11          1547 non-null float32\n",
      "to12          1548 non-null float32\n",
      "to13          1566 non-null float32\n",
      "ece           1623 non-null category\n",
      "reasoning     1623 non-null float32\n",
      "socemo        1613 non-null float32\n",
      "vocab         1623 non-null float32\n",
      "vocabsq       1623 non-null float32\n",
      "taskorient    1597 non-null float32\n",
      "wealth        1623 non-null float32\n",
      "books         1623 non-null category\n",
      "dtypes: category(34), float32(49), int16(1)\n",
      "memory usage: 385.5 KB\n"
     ]
    }
   ],
   "source": [
    "measureZambiaDF.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>se1</th>\n",
       "      <th>se2</th>\n",
       "      <th>to1</th>\n",
       "      <th>to2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1551</td>\n",
       "      <td>1461</td>\n",
       "      <td>1589.000000</td>\n",
       "      <td>1585.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>648</td>\n",
       "      <td>704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.111391</td>\n",
       "      <td>3.076341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.971020</td>\n",
       "      <td>0.950069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              se1        se2          to1          to2\n",
       "count        1551       1461  1589.000000  1585.000000\n",
       "unique          4          4          NaN          NaN\n",
       "top     Sometimes  Sometimes          NaN          NaN\n",
       "freq          648        704          NaN          NaN\n",
       "mean          NaN        NaN     3.111391     3.076341\n",
       "std           NaN        NaN     0.971020     0.950069\n",
       "min           NaN        NaN     1.000000     1.000000\n",
       "25%           NaN        NaN     2.000000     3.000000\n",
       "50%           NaN        NaN     3.000000     3.000000\n",
       "75%           NaN        NaN     4.000000     4.000000\n",
       "max           NaN        NaN     4.000000     4.000000"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measureZambiaDF.describe(include=\"all\")[[\"se1\", \"se2\", \"to1\", \"to2\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The se variables are not yet numerically coded, so we have to fix that, and prepare dummy-coding if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Never, Always, Sometimes, Usually, NaN]\n",
       "Categories (4, object): [Never < Sometimes < Usually < Always]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measureZambiaDF[\"se1\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for colnum in range(1,6):\n",
    "    measureZambiaDF[\"se\" + str(colnum) + \"c\"] = measureZambiaDF[\"se\" + str(colnum)].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  3,  1,  2, -1], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measureZambiaDF[\"se1c\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check correlations to make sure our assumptions, i.e. that indicators within a factor are correlated, is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlations\n",
    "corr = measureZambiaDF[[\"se1c\", \"se2c\", \"se3c\", \"se4c\", \"se5c\", \"to1\", \"to2\", \"to3\", \"to4\", \"to5\", \"socemo\", \"taskorient\"]].corr()\n",
    "# Heatmap\n",
    "plt.figure()\n",
    "sns.heatmap(corr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>se1c</th>\n",
       "      <th>se2c</th>\n",
       "      <th>se3c</th>\n",
       "      <th>se4c</th>\n",
       "      <th>se5c</th>\n",
       "      <th>to1</th>\n",
       "      <th>to2</th>\n",
       "      <th>to3</th>\n",
       "      <th>to4</th>\n",
       "      <th>to5</th>\n",
       "      <th>socemo</th>\n",
       "      <th>taskorient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>se1c</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.448828</td>\n",
       "      <td>0.397943</td>\n",
       "      <td>0.315406</td>\n",
       "      <td>0.299793</td>\n",
       "      <td>0.057045</td>\n",
       "      <td>0.055181</td>\n",
       "      <td>0.092964</td>\n",
       "      <td>0.057652</td>\n",
       "      <td>0.004773</td>\n",
       "      <td>0.529947</td>\n",
       "      <td>0.048885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>se2c</th>\n",
       "      <td>0.448828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.362351</td>\n",
       "      <td>0.215676</td>\n",
       "      <td>0.299680</td>\n",
       "      <td>0.034002</td>\n",
       "      <td>0.053028</td>\n",
       "      <td>0.043206</td>\n",
       "      <td>-0.022127</td>\n",
       "      <td>-0.008994</td>\n",
       "      <td>0.386499</td>\n",
       "      <td>0.006681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>se3c</th>\n",
       "      <td>0.397943</td>\n",
       "      <td>0.362351</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.317611</td>\n",
       "      <td>0.332186</td>\n",
       "      <td>0.041432</td>\n",
       "      <td>0.049954</td>\n",
       "      <td>0.101046</td>\n",
       "      <td>0.055205</td>\n",
       "      <td>0.030859</td>\n",
       "      <td>0.525709</td>\n",
       "      <td>0.045439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>se4c</th>\n",
       "      <td>0.315406</td>\n",
       "      <td>0.215676</td>\n",
       "      <td>0.317611</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.283498</td>\n",
       "      <td>0.033503</td>\n",
       "      <td>0.065899</td>\n",
       "      <td>0.055018</td>\n",
       "      <td>0.037609</td>\n",
       "      <td>0.045378</td>\n",
       "      <td>0.501649</td>\n",
       "      <td>0.027030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>se5c</th>\n",
       "      <td>0.299793</td>\n",
       "      <td>0.299680</td>\n",
       "      <td>0.332186</td>\n",
       "      <td>0.283498</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.037358</td>\n",
       "      <td>0.061677</td>\n",
       "      <td>0.090891</td>\n",
       "      <td>0.032899</td>\n",
       "      <td>0.062674</td>\n",
       "      <td>0.521056</td>\n",
       "      <td>0.053759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to1</th>\n",
       "      <td>0.057045</td>\n",
       "      <td>0.034002</td>\n",
       "      <td>0.041432</td>\n",
       "      <td>0.033503</td>\n",
       "      <td>0.037358</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.689873</td>\n",
       "      <td>0.579799</td>\n",
       "      <td>0.443605</td>\n",
       "      <td>0.475216</td>\n",
       "      <td>0.086210</td>\n",
       "      <td>0.776598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to2</th>\n",
       "      <td>0.055181</td>\n",
       "      <td>0.053028</td>\n",
       "      <td>0.049954</td>\n",
       "      <td>0.065899</td>\n",
       "      <td>0.061677</td>\n",
       "      <td>0.689873</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.604540</td>\n",
       "      <td>0.448777</td>\n",
       "      <td>0.448261</td>\n",
       "      <td>0.124137</td>\n",
       "      <td>0.759687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to3</th>\n",
       "      <td>0.092964</td>\n",
       "      <td>0.043206</td>\n",
       "      <td>0.101046</td>\n",
       "      <td>0.055018</td>\n",
       "      <td>0.090891</td>\n",
       "      <td>0.579799</td>\n",
       "      <td>0.604540</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.444270</td>\n",
       "      <td>0.430099</td>\n",
       "      <td>0.151745</td>\n",
       "      <td>0.725361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to4</th>\n",
       "      <td>0.057652</td>\n",
       "      <td>-0.022127</td>\n",
       "      <td>0.055205</td>\n",
       "      <td>0.037609</td>\n",
       "      <td>0.032899</td>\n",
       "      <td>0.443605</td>\n",
       "      <td>0.448777</td>\n",
       "      <td>0.444270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.518978</td>\n",
       "      <td>0.106936</td>\n",
       "      <td>0.657686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to5</th>\n",
       "      <td>0.004773</td>\n",
       "      <td>-0.008994</td>\n",
       "      <td>0.030859</td>\n",
       "      <td>0.045378</td>\n",
       "      <td>0.062674</td>\n",
       "      <td>0.475216</td>\n",
       "      <td>0.448261</td>\n",
       "      <td>0.430099</td>\n",
       "      <td>0.518978</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108867</td>\n",
       "      <td>0.673800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>socemo</th>\n",
       "      <td>0.529947</td>\n",
       "      <td>0.386499</td>\n",
       "      <td>0.525709</td>\n",
       "      <td>0.501649</td>\n",
       "      <td>0.521056</td>\n",
       "      <td>0.086210</td>\n",
       "      <td>0.124137</td>\n",
       "      <td>0.151745</td>\n",
       "      <td>0.106936</td>\n",
       "      <td>0.108867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.115544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taskorient</th>\n",
       "      <td>0.048885</td>\n",
       "      <td>0.006681</td>\n",
       "      <td>0.045439</td>\n",
       "      <td>0.027030</td>\n",
       "      <td>0.053759</td>\n",
       "      <td>0.776598</td>\n",
       "      <td>0.759687</td>\n",
       "      <td>0.725361</td>\n",
       "      <td>0.657686</td>\n",
       "      <td>0.673800</td>\n",
       "      <td>0.115544</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                se1c      se2c      se3c      se4c      se5c       to1  \\\n",
       "se1c        1.000000  0.448828  0.397943  0.315406  0.299793  0.057045   \n",
       "se2c        0.448828  1.000000  0.362351  0.215676  0.299680  0.034002   \n",
       "se3c        0.397943  0.362351  1.000000  0.317611  0.332186  0.041432   \n",
       "se4c        0.315406  0.215676  0.317611  1.000000  0.283498  0.033503   \n",
       "se5c        0.299793  0.299680  0.332186  0.283498  1.000000  0.037358   \n",
       "to1         0.057045  0.034002  0.041432  0.033503  0.037358  1.000000   \n",
       "to2         0.055181  0.053028  0.049954  0.065899  0.061677  0.689873   \n",
       "to3         0.092964  0.043206  0.101046  0.055018  0.090891  0.579799   \n",
       "to4         0.057652 -0.022127  0.055205  0.037609  0.032899  0.443605   \n",
       "to5         0.004773 -0.008994  0.030859  0.045378  0.062674  0.475216   \n",
       "socemo      0.529947  0.386499  0.525709  0.501649  0.521056  0.086210   \n",
       "taskorient  0.048885  0.006681  0.045439  0.027030  0.053759  0.776598   \n",
       "\n",
       "                 to2       to3       to4       to5    socemo  taskorient  \n",
       "se1c        0.055181  0.092964  0.057652  0.004773  0.529947    0.048885  \n",
       "se2c        0.053028  0.043206 -0.022127 -0.008994  0.386499    0.006681  \n",
       "se3c        0.049954  0.101046  0.055205  0.030859  0.525709    0.045439  \n",
       "se4c        0.065899  0.055018  0.037609  0.045378  0.501649    0.027030  \n",
       "se5c        0.061677  0.090891  0.032899  0.062674  0.521056    0.053759  \n",
       "to1         0.689873  0.579799  0.443605  0.475216  0.086210    0.776598  \n",
       "to2         1.000000  0.604540  0.448777  0.448261  0.124137    0.759687  \n",
       "to3         0.604540  1.000000  0.444270  0.430099  0.151745    0.725361  \n",
       "to4         0.448777  0.444270  1.000000  0.518978  0.106936    0.657686  \n",
       "to5         0.448261  0.430099  0.518978  1.000000  0.108867    0.673800  \n",
       "socemo      0.124137  0.151745  0.106936  0.108867  1.000000    0.115544  \n",
       "taskorient  0.759687  0.725361  0.657686  0.673800  0.115544    1.000000  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se1\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se2\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se3\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se4\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se5\". Fall back to string conversion. The error is: Converting pandas \"Category\" series to R factor is only possible when categories are strings.\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se6\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se7\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se8\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se9\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se10\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se11\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se12\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se13\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se14\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se15\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se16\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se17\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se18\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se19\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n",
      "C:\\Users\\sr876\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\rpy2-2.9.4-py3.7-win-amd64.egg\\rpy2\\robjects\\pandas2ri.py:67: UserWarning: Error while trying to convert the column \"se20\". Fall back to string conversion. The error is: Error in as.character.factor(x) : malformed factor\n",
      "\n",
      "  (name, str(e)))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan 0.6-2 ended normally after 29 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                         21\n",
      "\n",
      "                                                  Used       Total\n",
      "  Number of observations                          1547        1623\n",
      "\n",
      "  Estimator                                         ML\n",
      "  Model Fit Test Statistic                     219.773\n",
      "  Degrees of freedom                                34\n",
      "  P-value (Chi-square)                           0.000\n",
      "\n",
      "Model test baseline model:\n",
      "\n",
      "  Minimum Function Test Statistic             4175.115\n",
      "  Degrees of freedom                                45\n",
      "  P-value                                        0.000\n",
      "\n",
      "User model versus baseline model:\n",
      "\n",
      "  Comparative Fit Index (CFI)                    0.955\n",
      "  Tucker-Lewis Index (TLI)                       0.940\n",
      "\n",
      "Loglikelihood and Information Criteria:\n",
      "\n",
      "  Loglikelihood user model (H0)             -19814.396\n",
      "  Loglikelihood unrestricted model (H1)     -19704.510\n",
      "\n",
      "  Number of free parameters                         21\n",
      "  Akaike (AIC)                               39670.792\n",
      "  Bayesian (BIC)                             39783.018\n",
      "  Sample-size adjusted Bayesian (BIC)        39716.306\n",
      "\n",
      "Root Mean Square Error of Approximation:\n",
      "\n",
      "  RMSEA                                          0.059\n",
      "  90 Percent Confidence Interval          0.052  0.067\n",
      "  P-value RMSEA <= 0.05                          0.018\n",
      "\n",
      "Standardized Root Mean Square Residual:\n",
      "\n",
      "  SRMR                                           0.034\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Expected\n",
      "  Information saturated (h1) model          Structured\n",
      "  Standard Errors                             Standard\n",
      "\n",
      "Latent Variables:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "  to_latent =~                                        \n",
      "    to1               1.000                           \n",
      "    to2               0.984    0.030   32.415    0.000\n",
      "    to3               0.864    0.030   28.936    0.000\n",
      "    to4               0.620    0.027   23.135    0.000\n",
      "    to5               0.655    0.028   23.500    0.000\n",
      "  se_latent =~                                        \n",
      "    se1c              1.000                           \n",
      "    se2c              0.996    0.063   15.798    0.000\n",
      "    se3c              1.034    0.064   16.161    0.000\n",
      "    se4c              0.645    0.049   13.223    0.000\n",
      "    se5c              1.220    0.085   14.332    0.000\n",
      "\n",
      "Covariances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "  to_latent ~~                                        \n",
      "    se_latent         0.057    0.016    3.489    0.000\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "   .to1               0.324    0.017   18.665    0.000\n",
      "   .to2               0.301    0.016   18.284    0.000\n",
      "   .to3               0.411    0.018   22.537    0.000\n",
      "   .to4               0.431    0.017   25.277    0.000\n",
      "   .to5               0.461    0.018   25.161    0.000\n",
      "   .se1c              0.539    0.028   19.129    0.000\n",
      "   .se2c              0.761    0.035   21.731    0.000\n",
      "   .se3c              0.712    0.034   20.787    0.000\n",
      "   .se4c              0.645    0.026   24.863    0.000\n",
      "   .se5c              1.749    0.073   23.896    0.000\n",
      "    to_latent         0.621    0.034   18.044    0.000\n",
      "    se_latent         0.388    0.034   11.426    0.000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R -i measureZambiaDF\n",
    "model <- '\n",
    "    to_latent  =~ to1 + to2 + to3 + to4 + to5 \n",
    "    se_latent  =~ se1c + se2c + se3c + se4c + se5c \n",
    "'\n",
    "fit <- cfa(model, data=measureZambiaDF)\n",
    "summary(fit, fit.measures=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "semPaths(fit, \"std\", curvePivot=TRUE, intercepts=FALSE, layout=\"tree2\", rotation=2, nCharNodes=9, sizeMan=5, sizeLat=10,\n",
    "         groups=\"latents\",\n",
    "         color=c(\"lightblue\", \"purple\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: compare against models (similar to above) that use one latent factor instead of two, or that load some indicators (e.g. se4c) to both."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining structural and measurement models for Structural Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test whether ECE partially explains the relation between wealth and children's socioemotional and task orientation skills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we need dummy coding again here, to use ECE\n",
    "dummiedMeasureZambiaDF = pd.get_dummies(measureZambiaDF, dtype=np.int8)\n",
    "# We use int8 instead of uint8 for the dummy variables, because R has no unsigned integers,\n",
    "# so we would get warnings when passing these new variables into R.\n",
    "# Further: the new dummy columns use the actual value as part of the column name,\n",
    "# and therefore might have spaces in the column name.\n",
    "# this is not great for specifying models, so let us replace the spaces with underscores:\n",
    "dummiedMeasureZambiaDF.columns = dummiedMeasureZambiaDF.columns.str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan 0.6-2 ended normally after 52 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                         38\n",
      "\n",
      "                                                  Used       Total\n",
      "  Number of observations                          1547        1623\n",
      "\n",
      "  Estimator                                         ML      Robust\n",
      "  Model Fit Test Statistic                     242.490     224.444\n",
      "  Degrees of freedom                                50          50\n",
      "  P-value (Chi-square)                           0.000       0.000\n",
      "  Scaling correction factor                                  1.080\n",
      "    for the Yuan-Bentler correction (Mplus variant)\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Observed\n",
      "  Observed information based on                Hessian\n",
      "  Standard Errors                   Robust.huber.white\n",
      "\n",
      "Latent Variables:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "  to_latent =~                                                          \n",
      "    to1               1.000                               0.788    0.811\n",
      "    to2               0.984    0.028   35.744    0.000    0.775    0.817\n",
      "    to3               0.862    0.031   27.367    0.000    0.679    0.727\n",
      "    to4               0.621    0.033   18.963    0.000    0.490    0.598\n",
      "    to5               0.654    0.034   19.294    0.000    0.516    0.605\n",
      "  se_latent =~                                                          \n",
      "    se1c              1.000                               0.627    0.651\n",
      "    se2c              0.990    0.059   16.805    0.000    0.620    0.579\n",
      "    se3c              1.022    0.072   14.228    0.000    0.640    0.603\n",
      "    se4c              0.641    0.057   11.225    0.000    0.401    0.447\n",
      "    se5c              1.212    0.100   12.078    0.000    0.760    0.498\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "  ece_ECE ~                                                             \n",
      "    wealth            0.115    0.008   14.756    0.000    0.115    0.353\n",
      "  se_latent ~                                                           \n",
      "    ece_ECE           0.125    0.044    2.855    0.004    0.199    0.093\n",
      "    wealth            0.028    0.014    2.013    0.044    0.045    0.064\n",
      "  to_latent ~                                                           \n",
      "    ece_ECE           0.171    0.048    3.540    0.000    0.217    0.101\n",
      "    wealth            0.103    0.016    6.432    0.000    0.131    0.188\n",
      "\n",
      "Covariances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      " .to_latent ~~                                                          \n",
      "   .se_latent         0.042    0.015    2.732    0.006    0.089    0.089\n",
      "\n",
      "Intercepts:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .to1               2.743    0.054   51.115    0.000    2.743    2.822\n",
      "   .to2               2.717    0.052   51.998    0.000    2.717    2.861\n",
      "   .to3               2.702    0.047   57.230    0.000    2.702    2.891\n",
      "   .to4               3.258    0.040   81.464    0.000    3.258    3.981\n",
      "   .to5               3.072    0.042   73.536    0.000    3.072    3.601\n",
      "   .se1c              1.433    0.045   31.959    0.000    1.433    1.488\n",
      "   .se2c              1.167    0.045   25.922    0.000    1.167    1.090\n",
      "   .se3c              1.376    0.047   29.391    0.000    1.376    1.296\n",
      "   .se4c              1.582    0.034   46.726    0.000    1.582    1.762\n",
      "   .se5c              1.689    0.058   29.171    0.000    1.689    1.107\n",
      "   .ece_ECE          -0.026    0.023   -1.134    0.257   -0.026   -0.056\n",
      "   .to_latent         0.000                               0.000    0.000\n",
      "   .se_latent         0.000                               0.000    0.000\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .to1               0.323    0.022   14.525    0.000    0.323    0.342\n",
      "   .to2               0.301    0.022   13.493    0.000    0.301    0.333\n",
      "   .to3               0.412    0.023   17.755    0.000    0.412    0.472\n",
      "   .to4               0.430    0.023   19.102    0.000    0.430    0.642\n",
      "   .to5               0.461    0.023   20.300    0.000    0.461    0.634\n",
      "   .se1c              0.534    0.035   15.350    0.000    0.534    0.576\n",
      "   .se2c              0.762    0.048   15.995    0.000    0.762    0.665\n",
      "   .se3c              0.717    0.042   17.175    0.000    0.717    0.636\n",
      "   .se4c              0.645    0.028   23.178    0.000    0.645    0.800\n",
      "   .se5c              1.749    0.076   22.919    0.000    1.749    0.752\n",
      "   .ece_ECE           0.188    0.005   38.582    0.000    0.188    0.876\n",
      "   .to_latent         0.585    0.032   18.120    0.000    0.941    0.941\n",
      "   .se_latent         0.386    0.040    9.545    0.000    0.983    0.983\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R -i dummiedMeasureZambiaDF\n",
    "fullsrmodel <- '\n",
    "    to_latent =~ to1 + to2 + to3 + to4 + to5 \n",
    "    se_latent =~ se1c + se2c + se3c + se4c + se5c \n",
    "    ece_ECE   ~ wealth\n",
    "    se_latent ~ ece_ECE + wealth\n",
    "    to_latent ~ ece_ECE + wealth\n",
    "'\n",
    "srsemfit <- sem(model=fullsrmodel, data=dummiedMeasureZambiaDF, estimator=\"MLR\", meanstructure=TRUE)\n",
    "summary(srsemfit, standardized=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "semPaths(srsemfit, \"std\", curvePivot=TRUE, intercepts=FALSE, layout=\"tree2\", rotation=2, nCharNodes=9, sizeMan=5, sizeLat=10,\n",
    "         groups=\"latents\",\n",
    "         color=c(\"lightblue\", \"purple\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Presenting the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notes on Aspects Skipped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Power calculations. Is the number of subjects/observations large enough for the number of parameters/connections to be estimated. In general, SEM needs large sample sizes above 100.\n",
    "* ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For reference: some models from tutorials with built-in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "model <- ' visual  =~ x1 + x2 + x3 \n",
    "              textual =~ x4 + x5 + x6\n",
    "              speed   =~ x7 + x8 + x9 '\n",
    "fit <- cfa(model, data=HolzingerSwineford1939)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan 0.6-2 ended normally after 35 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                         21\n",
      "\n",
      "  Number of observations                           301\n",
      "\n",
      "  Estimator                                         ML\n",
      "  Model Fit Test Statistic                      85.306\n",
      "  Degrees of freedom                                24\n",
      "  P-value (Chi-square)                           0.000\n",
      "\n",
      "Model test baseline model:\n",
      "\n",
      "  Minimum Function Test Statistic              918.852\n",
      "  Degrees of freedom                                36\n",
      "  P-value                                        0.000\n",
      "\n",
      "User model versus baseline model:\n",
      "\n",
      "  Comparative Fit Index (CFI)                    0.931\n",
      "  Tucker-Lewis Index (TLI)                       0.896\n",
      "\n",
      "Loglikelihood and Information Criteria:\n",
      "\n",
      "  Loglikelihood user model (H0)              -3737.745\n",
      "  Loglikelihood unrestricted model (H1)      -3695.092\n",
      "\n",
      "  Number of free parameters                         21\n",
      "  Akaike (AIC)                                7517.490\n",
      "  Bayesian (BIC)                              7595.339\n",
      "  Sample-size adjusted Bayesian (BIC)         7528.739\n",
      "\n",
      "Root Mean Square Error of Approximation:\n",
      "\n",
      "  RMSEA                                          0.092\n",
      "  90 Percent Confidence Interval          0.071  0.114\n",
      "  P-value RMSEA <= 0.05                          0.001\n",
      "\n",
      "Standardized Root Mean Square Residual:\n",
      "\n",
      "  SRMR                                           0.065\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Expected\n",
      "  Information saturated (h1) model          Structured\n",
      "  Standard Errors                             Standard\n",
      "\n",
      "Latent Variables:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "  visual =~                                           \n",
      "    x1                1.000                           \n",
      "    x2                0.554    0.100    5.554    0.000\n",
      "    x3                0.729    0.109    6.685    0.000\n",
      "  textual =~                                          \n",
      "    x4                1.000                           \n",
      "    x5                1.113    0.065   17.014    0.000\n",
      "    x6                0.926    0.055   16.703    0.000\n",
      "  speed =~                                            \n",
      "    x7                1.000                           \n",
      "    x8                1.180    0.165    7.152    0.000\n",
      "    x9                1.082    0.151    7.155    0.000\n",
      "\n",
      "Covariances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "  visual ~~                                           \n",
      "    textual           0.408    0.074    5.552    0.000\n",
      "    speed             0.262    0.056    4.660    0.000\n",
      "  textual ~~                                          \n",
      "    speed             0.173    0.049    3.518    0.000\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)\n",
      "   .x1                0.549    0.114    4.833    0.000\n",
      "   .x2                1.134    0.102   11.146    0.000\n",
      "   .x3                0.844    0.091    9.317    0.000\n",
      "   .x4                0.371    0.048    7.779    0.000\n",
      "   .x5                0.446    0.058    7.642    0.000\n",
      "   .x6                0.356    0.043    8.277    0.000\n",
      "   .x7                0.799    0.081    9.823    0.000\n",
      "   .x8                0.488    0.074    6.573    0.000\n",
      "   .x9                0.566    0.071    8.003    0.000\n",
      "    visual            0.809    0.145    5.564    0.000\n",
      "    textual           0.979    0.112    8.737    0.000\n",
      "    speed             0.384    0.086    4.451    0.000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "summary(fit, fit.measures=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lavaan 0.6-2 ended normally after 10 iterations\n",
      "\n",
      "  Optimization method                           NLMINB\n",
      "  Number of free parameters                          2\n",
      "\n",
      "  Number of observations                            75\n",
      "\n",
      "  Estimator                                         ML\n",
      "  Model Fit Test Statistic                       0.000\n",
      "  Degrees of freedom                                 0\n",
      "\n",
      "Parameter Estimates:\n",
      "\n",
      "  Information                                 Expected\n",
      "  Information saturated (h1) model          Structured\n",
      "  Standard Errors                             Standard\n",
      "\n",
      "Regressions:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "  x1 ~                                                                  \n",
      "    y3                0.073    0.024    2.999    0.003    0.073    0.327\n",
      "\n",
      "Variances:\n",
      "                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all\n",
      "   .x1                0.473    0.077    6.124    0.000    0.473    0.893\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "model <- '\n",
    "  x1 ~ y3\n",
    "  # measurement model\n",
    "  #  ind60 =~ x1 + x2 + x3\n",
    "  #  dem60 =~ y1 + y2 + y3 + y4\n",
    "  #  dem65 =~ y5 + y6 + y7 + y8\n",
    "  # regressions\n",
    "  #  dem60 ~ ind60\n",
    "  #  dem65 ~ ind60 + dem60\n",
    "'\n",
    "fit <- sem(model, data=PoliticalDemocracy)\n",
    "summary(fit, standardized=TRUE)"
   ]
  }
 ],
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