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    "# Linear Mixed Effects Models\n",
    "\n",
    "With linear mixed effects models, we wish to model a linear\n",
    "relationship for data points with inputs of varying type, categorized\n",
    "into subgroups, and associated to a real-valued output.\n",
    "\n",
    "We demonstrate with an example in Edward. A webpage version is available \n",
    "[here](http://edwardlib.org/tutorials/linear-mixed-effects-models)."
   ]
  },
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   "source": [
    "%matplotlib inline\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import edward as ed\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from edward.models import Normal\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "ed.set_seed(42)"
   ]
  },
  {
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     "worksheet-0"
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   "source": [
    "## Data\n",
    "\n",
    "We use the `InstEval` data set from the popular\n",
    "[lme4 R package](http://lme4.r-forge.r-project.org) (Bates, Mächler, Bolker, & Walker, 2015), located\n",
    "[here](https://github.com/blei-lab/edward/blob/master/examples/data/insteval.csv).\n",
    "It is a data set of instructor evaluation ratings, where the inputs\n",
    "(covariates) include categories such as `students` and\n",
    "`departments`, and our response variable of interest is the instructor\n",
    "evaluation rating."
   ]
  },
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    {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>s</th>\n",
       "      <th>d</th>\n",
       "      <th>studage</th>\n",
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      "text/plain": [
       "       Unnamed: 0     s     d  studage  lectage  service  dept  y  dcodes  \\\n",
       "66702       66703  2714   474        8        5        1     1  4     248   \n",
       "51671       51672  2074   102        8        1        1     1  2      55   \n",
       "35762       35763  1456   139        6        4        0    12  2      73   \n",
       "43777       43778  1772  2096        8        3        0    10  4    1092   \n",
       "4788         4789   178   554        6        6        1     6  5     290   \n",
       "\n",
       "       deptcodes  \n",
       "66702          0  \n",
       "51671          0  \n",
       "35762         11  \n",
       "43777          9  \n",
       "4788           5  "
      ]
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# s - students - 1:2972\n",
    "# d - instructors - codes that need to be remapped\n",
    "# dept also needs to be remapped\n",
    "data = pd.read_csv('../examples/data/insteval.csv')\n",
    "data['dcodes'] = data['d'].astype('category').cat.codes\n",
    "data['deptcodes'] = data['dept'].astype('category').cat.codes\n",
    "data['s'] = data['s'] - 1\n",
    "\n",
    "train = data.sample(frac=0.8)\n",
    "test = data.drop(train.index)\n",
    "\n",
    "train.head()"
   ]
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   "source": [
    "In the code, we denote:\n",
    "+ `students` as `s`\n",
    "+ `instructors` as `d`\n",
    "+ `departments` as `dept`\n",
    "+ `service` as `service`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "s_train = train['s'].values.astype(int)\n",
    "d_train = train['dcodes'].values.astype(int)\n",
    "dept_train = train['deptcodes'].values.astype(int)\n",
    "y_train = train['y'].values.astype(float)\n",
    "service_train = train['service'].values.astype(int)\n",
    "n_obs_train = train.shape[0]\n",
    "\n",
    "s_test = test['s'].values.astype(int)\n",
    "d_test = test['dcodes'].values.astype(int)\n",
    "dept_test = test['deptcodes'].values.astype(int)\n",
    "y_test = test['y'].values.astype(float)\n",
    "service_test = test['service'].values.astype(int)\n",
    "n_obs_test = test.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "n_s = 2972  # number of students\n",
    "n_d = 1128  # number of instructors\n",
    "n_dept = 14  # number of departments\n",
    "n_obs = train.shape[0]  # number of observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Model\n",
    "\n",
    "With linear regression, one makes an independence assumption where\n",
    "each data point regresses with a constant slope among\n",
    "each other. In our setting, the observations come from\n",
    "groups which may have varying slopes and intercepts. Thus we'd like to\n",
    "build a model that can capture this behavior (Gelman & Hill, 2006).\n",
    "\n",
    "For examples of this phenomena:\n",
    "+ The observations from a single student are not independent of\n",
    "each other. Rather, some students may systematically give low (or\n",
    "high) lecture ratings.\n",
    "+ The observations from a single teacher are not independent of\n",
    "each other. We expect good teachers to get generally good ratings and\n",
    "bad teachers to get generally bad ratings.\n",
    "+ The observations from a single department are not independent of\n",
    "each other. One department may generally have dry material and thus be\n",
    "rated lower than others.\n",
    "\n",
    "\n",
    "Typical linear regression takes the form\n",
    "\n",
    "\\begin{equation*}\n",
    "\\mathbf{y} = \\mathbf{X}\\beta + \\epsilon,\n",
    "\\end{equation*}\n",
    "\n",
    "where $\\mathbf{X}$ corresponds to fixed effects with coefficients\n",
    "$\\beta$ and $\\epsilon$ corresponds to random noise,\n",
    "$\\epsilon\\sim\\mathcal{N}(\\mathbf{0}, \\mathbf{I})$.\n",
    "\n",
    "In a linear mixed effects model, we add an additional term\n",
    "$\\mathbf{Z}\\eta$, where $\\mathbf{Z}$ corresponds to random effects\n",
    "with coefficients $\\eta$. The model takes the form\n",
    "\n",
    "\\begin{align*}\n",
    "\\eta &\\sim \\mathcal{N}(\\mathbf{0}, \\sigma^2 \\mathbf{I}), \\\\\n",
    "\\mathbf{y} &= \\mathbf{X}\\beta + \\mathbf{Z}\\eta + \\epsilon.\n",
    "\\end{align*}\n",
    "\n",
    "Given data, the goal is to infer $\\beta$, $\\eta$, and $\\sigma^2$,\n",
    "where $\\beta$ are model parameters (\"fixed effects\"), $\\eta$ are\n",
    "latent variables (\"random effects\"), and $\\sigma^2$ is a variance\n",
    "component parameter.\n",
    "\n",
    "Because the random effects have mean 0, the data's mean is captured by\n",
    "$\\mathbf{X}\\beta$. The random effects component $\\mathbf{Z}\\eta$\n",
    "captures variations in the data (e.g.  Instructor \\#54 is rated 1.4\n",
    "points higher than the mean).\n",
    "\n",
    "A natural question is the difference between fixed and random effects.\n",
    "A fixed effect is an effect that is constant for a given population. A\n",
    "random effect is an effect that varies for a given population (i.e.,\n",
    "it may be constant within subpopulations but varies within the overall\n",
    "population). We illustrate below in our example:\n",
    "\n",
    "+ Select `service` as the fixed effect. It is a binary covariate\n",
    "corresponding to whether the lecture belongs to the lecturer's main\n",
    "department. No matter how much additional data we collect, it\n",
    "can only take on the values in $0$ and $1$.\n",
    "+ Select the categorical values of `students`, `teachers`,\n",
    "and `departments` as the random effects. Given more\n",
    "observations from the population of instructor evaluation ratings, we\n",
    "may be looking at new students, teachers, or departments.\n",
    "\n",
    "In the syntax of R's lme4 package (Bates et al., 2015), the model\n",
    "can be summarized as\n",
    "\n",
    "```\n",
    "y ~ 1 + (1|students) + (1|instructor) + (1|dept) + service\n",
    "```\n",
    "where `1` denotes an intercept term,`(1|x)` denotes a\n",
    "random effect for `x`, and `x` denotes a fixed effect."
   ]
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  {
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   "execution_count": 6,
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   "source": [
    "# Set up placeholders for the data inputs.\n",
    "s_ph = tf.placeholder(tf.int32, [None])\n",
    "d_ph = tf.placeholder(tf.int32, [None])\n",
    "dept_ph = tf.placeholder(tf.int32, [None])\n",
    "service_ph = tf.placeholder(tf.float32, [None])\n",
    "\n",
    "# Set up fixed effects.\n",
    "mu = tf.Variable(tf.random_normal([]))\n",
    "service = tf.Variable(tf.random_normal([]))\n",
    "\n",
    "sigma_s = tf.sqrt(tf.exp(tf.Variable(tf.random_normal([]))))\n",
    "sigma_d = tf.sqrt(tf.exp(tf.Variable(tf.random_normal([]))))\n",
    "sigma_dept = tf.sqrt(tf.exp(tf.Variable(tf.random_normal([]))))\n",
    "\n",
    "# Set up random effects.\n",
    "eta_s = Normal(loc=tf.zeros(n_s), scale=sigma_s * tf.ones(n_s))\n",
    "eta_d = Normal(loc=tf.zeros(n_d), scale=sigma_d * tf.ones(n_d))\n",
    "eta_dept = Normal(loc=tf.zeros(n_dept), scale=sigma_dept * tf.ones(n_dept))\n",
    "\n",
    "yhat = tf.gather(eta_s, s_ph) + \\\n",
    "    tf.gather(eta_d, d_ph) + \\\n",
    "    tf.gather(eta_dept, dept_ph) + \\\n",
    "    mu + service * service_ph\n",
    "y = Normal(loc=yhat, scale=tf.ones(n_obs))"
   ]
  },
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   "source": [
    "## Inference\n",
    "\n",
    "Given data, we aim to infer the model's fixed and random effects.\n",
    "In this analysis, we use variational inference with the\n",
    "$\\text{KL}(q\\|p)$ divergence measure. We specify fully factorized\n",
    "normal approximations for the random effects and pass in all training\n",
    "data for inference. Under the algorithm, the fixed effects will be\n",
    "estimated under a variational EM scheme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    "collapsed": false,
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   "source": [
    "q_eta_s = Normal(\n",
    "    loc=tf.Variable(tf.random_normal([n_s])),\n",
    "    scale=tf.nn.softplus(tf.Variable(tf.random_normal([n_s]))))\n",
    "q_eta_d = Normal(\n",
    "    loc=tf.Variable(tf.random_normal([n_d])),\n",
    "    scale=tf.nn.softplus(tf.Variable(tf.random_normal([n_d]))))\n",
    "q_eta_dept = Normal(\n",
    "    loc=tf.Variable(tf.random_normal([n_dept])),\n",
    "    scale=tf.nn.softplus(tf.Variable(tf.random_normal([n_dept]))))\n",
    "\n",
    "latent_vars = {\n",
    "    eta_s: q_eta_s,\n",
    "    eta_d: q_eta_d,\n",
    "    eta_dept: q_eta_dept}\n",
    "data = {\n",
    "    y: y_train, \n",
    "    s_ph: s_train,\n",
    "    d_ph: d_train,\n",
    "    dept_ph: dept_train,\n",
    "    service_ph: service_train}\n",
    "inference = ed.KLqp(latent_vars, data)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "One way to critique the fitted model is a residual plot, i.e., a\n",
    "plot of the difference between the predicted value and the observed\n",
    "value for each data point. Below we manually run inference,\n",
    "initializing the algorithm and performing individual updates within a\n",
    "loop. We form residual plots as the algorithm progresses. This helps\n",
    "us examine how the algorithm proceeds to infer the random and fixed\n",
    "effects from data.\n",
    "\n",
    "To form residuals, we first make predictions on test data. We do this\n",
    "by copying `yhat` defined in the model and replacing its\n",
    "dependence on random effects with their inferred means. During the\n",
    "algorithm, we evaluate the predictions, feeding in test inputs.\n",
    "\n",
    "We have also fit the same model (`y ~ service + (1|dept) + (1|s) + (1|d)`, \n",
    "fit on the entire `InstEval` dataset, specifically) in `lme4`.  We \n",
    "have saved the random effect estimates and will compare them to our \n",
    "learned parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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   "source": [
    "yhat_test = ed.copy(yhat, {\n",
    "    eta_s: q_eta_s.mean(),\n",
    "    eta_d: q_eta_d.mean(),\n",
    "    eta_dept: q_eta_dept.mean()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "    1/10000 [  0%]                                ETA: 17377s | Loss: 1378558.375"
     ]
    },
    {
     "data": {
      "image/png": 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4+Ki4uFglJSXy8/OrsLyoqKhuqgYAwI1VG85NmzZVmzZtZLVaFRAQoEaNGunA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\n51dheVFRkYtLBQDg3FDrfmc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11857d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 2000/10000 [ 20%] ██████                         ETA: 40s | Loss: 97301.945"
     ]
    },
    {
     "data": {
      "image/png": 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Ir776Kp588kk88sgjGDlyJLZu3YqTJ0/Wel/Lli2xfft2HD58GAcOHMC///1vrF27FmvX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\nzkRERBLDcCYiIpIYhjMREZHE/D9UdCyPpurdZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a7d9050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 4000/10000 [ 40%] ████████████                   ETA: 27s | Loss: 97092.609"
     ]
    },
    {
     "data": {
      "image/png": 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46KGHUFBQgJCQEOTm5iI0NBSBgYHIzs5GTU0NtFotSktL4e/v32ABPj4+FumI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a7c7090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 6000/10000 [ 60%] ██████████████████             ETA: 17s | Loss: 97104.344"
     ]
    },
    {
     "data": {
      "image/png": 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FF1/Eo48+igceeACjR4/Gli1bcOzYsQa/17ZtW2zbtg0HDx7Evn378N///hdr167F2rVr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a7eaa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8000/10000 [ 80%] ████████████████████████       ETA: 8s | Loss: 97135.047"
     ]
    },
    {
     "data": {
      "image/png": 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GDRqERx99FKNHj8b27dtx+vTpGr/XqlUr7Ny5E0ePHsWhQ4fw73//G+vXr8f69evRvn37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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11857d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [100%] ██████████████████████████████ Elapsed: 43s | Loss: 97130.734\n"
     ]
    },
    {
     "data": {
      "image/png": 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JkyapoqLCcb+fn59KSkpUWlqqgIAAp/bi4uK6qRoAAA9WYzg3bdpUrVu3ltVq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\nZAhnAABMhnAGAMBk/j8l0jCWX3VEDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8c0e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inference.initialize(n_print=2000, n_iter=10000)\n",
    "tf.global_variables_initializer().run()\n",
    "\n",
    "for _ in range(inference.n_iter):\n",
    "  # Update and print progress of algorithm.\n",
    "  info_dict = inference.update()\n",
    "  inference.print_progress(info_dict)\n",
    "\n",
    "  t = info_dict['t']\n",
    "  if t == 1 or t % inference.n_print == 0:\n",
    "    # Make predictions on test data.\n",
    "    yhat_vals = yhat_test.eval(feed_dict={\n",
    "        s_ph: s_test,\n",
    "        d_ph: d_test,\n",
    "        dept_ph: dept_test,\n",
    "        service_ph: service_test})\n",
    "\n",
    "    # Form residual plot.\n",
    "    plt.title(\"Residuals for Predicted Ratings on Test Set\")\n",
    "    plt.xlim(-4, 4)\n",
    "    plt.ylim(0, 800)\n",
    "    plt.hist(yhat_vals - y_test, 75)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Criticism\n",
    "\n",
    "Above, we described a method for diagnosing the fit of the model via\n",
    "residual plots. See the residual plot at the end of the algorithm.\n",
    "\n",
    "The residuals appear normally distributed with mean 0. This is a good\n",
    "sanity check for the model.\n",
    "\n",
    "We can also compare our learned parameters to those estimated by R's\n",
    "`lme4`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "student_effects_lme4 = pd.read_csv('../examples/data/insteval_student_ranefs_r.csv')\n",
    "instructor_effects_lme4 = pd.read_csv('../examples/data/insteval_instructor_ranefs_r.csv')\n",
    "dept_effects_lme4 = pd.read_csv('../examples/data/insteval_dept_ranefs_r.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "student_effects_edward = q_eta_s.mean().eval()\n",
    "instructor_effects_edward = q_eta_d.mean().eval()\n",
    "dept_effects_edward = q_eta_dept.mean().eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pb86DvlKKhx56iIMHD+LxePjYxz5Gfn5+4vFnn32WP/3pT6SnpwPwT//0TxQW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fnVqzXd3TAwAK5rcfKkzf/+bxDbNqsJa9f6rFrls2aNS1aWZnjYorrap6YmSVWVTzRqsWePubBJ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\n7yu0hooKM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cZa/CFipUglsinFrCz3+e6b2WEuFVhTS9yx2OzudDN/nvk6Q0MWHR0W3d0O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\ntLTM+nuqrKykt7eXiYkJXNfl0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cbf61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Student Effects Comparison\")\n",
    "plt.xlim(-1, 1)\n",
    "plt.ylim(-1, 1)\n",
    "plt.xlabel(\"Student Effects from lme4\")\n",
    "plt.ylabel(\"Student Effects from edward\")\n",
    "plt.scatter(student_effects_lme4[\"(Intercept)\"], \n",
    "            student_effects_edward, \n",
    "            alpha = 0.25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hQXcaDufsh5gZkwb9N954A4C+vj5aWlpYunQplmWxZcsW5s6dK0FfCCGmITo1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\n4ja8urr1C/Q2mzExYm+4FjGibzDscq50utmPYuf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3Oxfc7wTnz9vs2eOxtGQhpaKtTR8D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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ca87350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Instructor Effects Comparison\")\n",
    "plt.xlim(-1.5, 1.5)\n",
    "plt.ylim(-1.5, 1.5)\n",
    "plt.xlabel(\"Instructor Effects from lme4\")\n",
    "plt.ylabel(\"Instructor Effects from edward\")\n",
    "plt.scatter(instructor_effects_lme4[\"(Intercept)\"], \n",
    "            instructor_effects_edward, \n",
    "            alpha = 0.25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great!  Our estimates for both student and instructor effects seem to\n",
    "match those from `lme4` closely.  We have set up a slightly different \n",
    "model here (for example, our overall mean is regularized, as are our\n",
    "variances for student, department, and instructor effects, which is not\n",
    "true of `lme4`s model), and we have a different inference method, so we \n",
    "should not expect to find exactly the same parameters as `lme4`.  But \n",
    "it is reassuring that they match up closely!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Add in the intercept from R and edward\n",
    "dept_effects_and_intercept_lme4 = 3.28259 + dept_effects_lme4[\"(Intercept)\"]\n",
    "dept_effects_and_intercept_edward = mu.eval() + dept_effects_edward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CkyZNwtSpU3H79m3Mnj0b//jHP2Bra4sGDRpg586dSEpKgrOzM3799Vds27atxNaZ8uh0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\n/oq9e/dCqVTC1dUV4eHh/CD7kCr692L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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cd272d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Departmental Effects Comparison\")\n",
    "plt.xlim(3.0, 3.5)\n",
    "plt.ylim(3.0, 3.5)\n",
    "plt.xlabel(\"Department Effects from lme4\")\n",
    "plt.ylabel(\"Department Effects from edward\")\n",
    "plt.scatter(dept_effects_and_intercept_lme4, \n",
    "            dept_effects_and_intercept_edward,\n",
    "            s = 0.01*train.dept.value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our department effects do not match up nearly as well with those from `lme4`.  \n",
    "There are likely several reasons for this:\n",
    "  *  We regularize the overal mean, while `lme4` doesn't, which causes the\n",
    "  edward model to put some of the intercept into the department effects, \n",
    "  which are allowed to vary more widely since we learn a variance\n",
    "  *  We are using 80% of the data to train the edward model, while our `lme4`\n",
    "  estimate uses the whole `InstEval` data set\n",
    "  *  The department effects are the weakest in the model and difficult to \n",
    "  estimate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ],
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Acknowledgments\n",
    "\n",
    "We thank Mayank Agrawal for writing the initial version of this\n",
    "tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  },
  "name": "linear_mixed_effects_models.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 1
}