notebooks/default_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "**B**ayesian **I**nferential **R**egression for **D**ifferential **M**icrobiome **A**nalysis (BIRDMAn) is a WIP tool for flexible differential abundance analysis through Bayesian inference. BIRDMAn is unique in that it has been designed to support custom statistical modelling. Other tools implement specific models designed for general use cases. BIRDMAn, on the other hand, makes use of the [Stan](https://mc-stan.org/) probabilistic programming language for model specification. The overall goal of this software is to allow users to specify their own statistical models to address their individual experimental design/questions.\n",
    "\n",
    "BIRDMAn also includes several default models for those who do not wish to tinker with custom Stan models. In this demo notebook, we'll walkthrough fitting the default Negative Binomial model to some example data. For more information see Jamie's [blogpost](https://mortonjt.github.io/probable-bug-bytes/probable-bug-bytes/differential-abundance/) that inspired this project.\n",
    "\n",
    "**NOTE:** BIRDMAn is still in development and things are likely to change."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick primer on Bayesian inference\n",
    "\n",
    "Bayesian inference is a technique to learn about a given population through Bayes' Theorem. Given data, $D$, and parameter(s), $\\theta$, Bayes' Theorem applies as follows:\n",
    "\n",
    "$$ P(\\theta|D) = \\frac{P(D|\\theta)P(\\theta)}{P(D)} $$\n",
    "\n",
    "The component parts of this formula are usually described with the following terms:\n",
    "\n",
    "* $P(\\theta)$: *prior* - our \"current\" knowledge of $\\theta$ independent of our data\n",
    "* $P(D|\\theta)$: *likelihood* - how likely is the data given our knowledge of the parameters?\n",
    "* $P(\\theta|D)$: *posterior* - the \"updated\" knowledge of $\\theta$ after seeing the data\n",
    "* $P(D)$: *evidence* - the probability of the data over all values of $\\theta$\n",
    "\n",
    "Our goal is to hone in on values of $\\theta$ that best fit the data according to some statistical model we specify. Importantly, we do so by providing both the *data generative model* and the *prior distribution* of the parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling from the posterior distribution\n",
    "\n",
    "One of the biggest hurdles to Bayesian inference is that computing $P(\\theta|D)$ is *hard* and more often than not unattainable via analytic methods. To address this, scientists often use *Markov Chain Monte Carlo* (MCMC) methods. In a nutshell, MCMC uses dependent sampling of a distribution to attempt convergence around some parameter estimate.\n",
    "\n",
    "The core of BIRDMAn is using Stan to perform multivariate regression on count data to determine which biological features are changing with respect to your covariates of interest. We wrap a lot of this integration into an easy-to-use Python package that is both flexible and extensible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example usage\n",
    "\n",
    "## Dataset\n",
    "\n",
    "We will be using data from the study **\"Responses of gut microbiota to diet composition and weight loss in lean and obese mice\" (Qiita ID: 107)**. This study looks at the effect of weight loss and diet composition on the gut microbiome.\n",
    "\n",
    "We will first process and explore the raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/grahman/miniconda3/envs/birdman-demo/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from birdman import __version__\n",
    "print(__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.style.use(\"./plot.mplstyle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1932 x 29 <class 'biom.table.Table'> with 9055 nonzero entries (16% dense)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import biom\n",
    "\n",
    "raw_tbl = biom.load_table(\"../data/lean_obese_mice/44773_otu_table.biom\")\n",
    "raw_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>107.CM04</th>\n",
       "      <th>107.CM10</th>\n",
       "      <th>107.CM19</th>\n",
       "      <th>107.CM23</th>\n",
       "      <th>107.CM28</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>206494</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261725</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276620</th>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296165</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389067</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        107.CM04  107.CM10  107.CM19  107.CM23  107.CM28\n",
       "206494       1.0       3.0      14.0       5.0       1.0\n",
       "261725       0.0       0.0       0.0       0.0       0.0\n",
       "276620       2.0       9.0       2.0       2.0       0.0\n",
       "296165       2.0       0.0       0.0       0.0       0.0\n",
       "389067       0.0       0.0       0.0       0.0       0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_tbl_df = raw_tbl.to_dataframe()\n",
    "raw_tbl_df.iloc[:5, :5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this demo we are going to use a small subset of the OTUs present in this table based on prevalence (present in at least 20 samples). This is done primarily to let the models run quickly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77, 29)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev = raw_tbl_df.clip(upper=1).sum(axis=1)\n",
    "filt_tbl_df = raw_tbl_df.loc[prev[prev >= 20].index, :]\n",
    "filt_tbl_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BIRDMAn expects tables to be input in BIOM format so we save our filtered table in this format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77 x 29 <class 'biom.table.Table'> with 1789 nonzero entries (80% dense)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt_tbl = biom.table.Table(\n",
    "    filt_tbl_df.values,\n",
    "    sample_ids=filt_tbl_df.columns,\n",
    "    observation_ids=filt_tbl_df.index\n",
    ")\n",
    "filt_tbl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring metadata\n",
    "\n",
    "Given the title of this study, we are going to be focused on weight loss and diet as covariates in our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adiponectin</th>\n",
       "      <th>altitude</th>\n",
       "      <th>anonymized_name</th>\n",
       "      <th>b_cell_function_measure</th>\n",
       "      <th>body_habitat</th>\n",
       "      <th>body_product</th>\n",
       "      <th>body_site</th>\n",
       "      <th>body_weight</th>\n",
       "      <th>cd11c</th>\n",
       "      <th>collection_date</th>\n",
       "      <th>...</th>\n",
       "      <th>saa3</th>\n",
       "      <th>sample_type</th>\n",
       "      <th>slc25a</th>\n",
       "      <th>t3</th>\n",
       "      <th>t4</th>\n",
       "      <th>title</th>\n",
       "      <th>tnfa</th>\n",
       "      <th>total_ee</th>\n",
       "      <th>total_number_of_synapses</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107.CM01</th>\n",
       "      <td>44.03</td>\n",
       "      <td>0</td>\n",
       "      <td>sample01</td>\n",
       "      <td>154.5</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>30.6</td>\n",
       "      <td>0.60</td>\n",
       "      <td>11/8/11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08</td>\n",
       "      <td>XXQIITAXX</td>\n",
       "      <td>0.85</td>\n",
       "      <td>72.86</td>\n",
       "      <td>45.06</td>\n",
       "      <td>Leptin levels correlate with the composition o...</td>\n",
       "      <td>1.51</td>\n",
       "      <td>10.60</td>\n",
       "      <td>23.29</td>\n",
       "      <td>Weight Reduced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.CM02</th>\n",
       "      <td>44.16</td>\n",
       "      <td>0</td>\n",
       "      <td>sample09</td>\n",
       "      <td>200.6</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.86</td>\n",
       "      <td>11/8/11</td>\n",
       "      <td>...</td>\n",
       "      <td>1.62</td>\n",
       "      <td>XXQIITAXX</td>\n",
       "      <td>1.07</td>\n",
       "      <td>13.60</td>\n",
       "      <td>33.96</td>\n",
       "      <td>Leptin levels correlate with the composition o...</td>\n",
       "      <td>1.30</td>\n",
       "      <td>13.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ad Lib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.CM03</th>\n",
       "      <td>39.17</td>\n",
       "      <td>0</td>\n",
       "      <td>sample02</td>\n",
       "      <td>133.3</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>29.3</td>\n",
       "      <td>1.01</td>\n",
       "      <td>11/8/11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10</td>\n",
       "      <td>XXQIITAXX</td>\n",
       "      <td>1.14</td>\n",
       "      <td>7.76</td>\n",
       "      <td>45.12</td>\n",
       "      <td>Leptin levels correlate with the composition o...</td>\n",
       "      <td>1.82</td>\n",
       "      <td>10.23</td>\n",
       "      <td>18.40</td>\n",
       "      <td>Weight Reduced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.CM04</th>\n",
       "      <td>23.55</td>\n",
       "      <td>0</td>\n",
       "      <td>sample03</td>\n",
       "      <td>280.7</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>36.1</td>\n",
       "      <td>0.53</td>\n",
       "      <td>11/8/11</td>\n",
       "      <td>...</td>\n",
       "      <td>2.24</td>\n",
       "      <td>XXQIITAXX</td>\n",
       "      <td>1.01</td>\n",
       "      <td>15.30</td>\n",
       "      <td>57.87</td>\n",
       "      <td>Leptin levels correlate with the composition o...</td>\n",
       "      <td>1.17</td>\n",
       "      <td>11.70</td>\n",
       "      <td>19.06</td>\n",
       "      <td>Weight Reduced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.CM05</th>\n",
       "      <td>28.33</td>\n",
       "      <td>0</td>\n",
       "      <td>sample10</td>\n",
       "      <td>472.9</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>52.9</td>\n",
       "      <td>1.14</td>\n",
       "      <td>11/8/11</td>\n",
       "      <td>...</td>\n",
       "      <td>3.83</td>\n",
       "      <td>XXQIITAXX</td>\n",
       "      <td>1.55</td>\n",
       "      <td>33.46</td>\n",
       "      <td>52.03</td>\n",
       "      <td>Leptin levels correlate with the composition o...</td>\n",
       "      <td>0.62</td>\n",
       "      <td>16.06</td>\n",
       "      <td>26.44</td>\n",
       "      <td>Ad Lib</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             adiponectin  altitude anonymized_name  b_cell_function_measure  \\\n",
       "sample_name                                                                   \n",
       "107.CM01           44.03         0        sample01                    154.5   \n",
       "107.CM02           44.16         0        sample09                    200.6   \n",
       "107.CM03           39.17         0        sample02                    133.3   \n",
       "107.CM04           23.55         0        sample03                    280.7   \n",
       "107.CM05           28.33         0        sample10                    472.9   \n",
       "\n",
       "             body_habitat  body_product     body_site  body_weight  cd11c  \\\n",
       "sample_name                                                                 \n",
       "107.CM01     UBERON:feces  UBERON:feces  UBERON:feces         30.6   0.60   \n",
       "107.CM02     UBERON:feces  UBERON:feces  UBERON:feces         52.0   1.86   \n",
       "107.CM03     UBERON:feces  UBERON:feces  UBERON:feces         29.3   1.01   \n",
       "107.CM04     UBERON:feces  UBERON:feces  UBERON:feces         36.1   0.53   \n",
       "107.CM05     UBERON:feces  UBERON:feces  UBERON:feces         52.9   1.14   \n",
       "\n",
       "            collection_date  ...  saa3  sample_type slc25a     t3     t4  \\\n",
       "sample_name                  ...                                           \n",
       "107.CM01            11/8/11  ...  0.08    XXQIITAXX   0.85  72.86  45.06   \n",
       "107.CM02            11/8/11  ...  1.62    XXQIITAXX   1.07  13.60  33.96   \n",
       "107.CM03            11/8/11  ...  0.10    XXQIITAXX   1.14   7.76  45.12   \n",
       "107.CM04            11/8/11  ...  2.24    XXQIITAXX   1.01  15.30  57.87   \n",
       "107.CM05            11/8/11  ...  3.83    XXQIITAXX   1.55  33.46  52.03   \n",
       "\n",
       "                                                         title  tnfa total_ee  \\\n",
       "sample_name                                                                     \n",
       "107.CM01     Leptin levels correlate with the composition o...  1.51    10.60   \n",
       "107.CM02     Leptin levels correlate with the composition o...  1.30    13.80   \n",
       "107.CM03     Leptin levels correlate with the composition o...  1.82    10.23   \n",
       "107.CM04     Leptin levels correlate with the composition o...  1.17    11.70   \n",
       "107.CM05     Leptin levels correlate with the composition o...  0.62    16.06   \n",
       "\n",
       "            total_number_of_synapses       treatment  \n",
       "sample_name                                           \n",
       "107.CM01                       23.29  Weight Reduced  \n",
       "107.CM02                         NaN          Ad Lib  \n",
       "107.CM03                       18.40  Weight Reduced  \n",
       "107.CM04                       19.06  Weight Reduced  \n",
       "107.CM05                       26.44          Ad Lib  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "metadata = pd.read_csv(\n",
    "    \"../data/lean_obese_mice/107_20180101-113755.txt\",\n",
    "    sep=\"\\t\",\n",
    "    index_col=0\n",
    ")\n",
    "metadata.index = metadata.index.astype(str)\n",
    "metadata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diet     treatment     \n",
       "Control  Ad Lib            8\n",
       "         Weight Reduced    8\n",
       "DIO      Ad Lib            7\n",
       "         Weight Reduced    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.groupby([\"diet\", \"treatment\"]).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will consider diet, treatment, and their interaction in our regression model. Importantly, we will specify that we want to keep `Control` and `Ad Lib` as the respective reference values (same way as in Songbird)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running BIRDMAn\n",
    "\n",
    "Now it's time to run BIRDMAn! A crucial step is determining which data generative model to use. We have designed BIRDMAn to accept Stan files containing model specification. However, we recognize that many users will not need or want to write their own Stan code. For this reason, we have included a number of default models that can be fit without writing any Stan code. In this notebook we make use of the negative binomial distribution as it is [common for overdispersed count data](https://esajournals.onlinelibrary.wiley.com/doi/full/10.1890/10-1831.1). We also include the multinomial distribution (same as in Songbird) and are planning to include the Poisson log-normal distribution.\n",
    "\n",
    "### Negative binomial model\n",
    "\n",
    "The negative binomial model can be thought of as an extension of the Poisson model that takes overdispersion into account.\n",
    "\n",
    "For a given sample, $i$ and microbe $j$, the associated feature table entry is modeled as:\n",
    "\n",
    "$$ y_{ij} \\sim \\textrm{NB}(\\mu_{ij}, \\phi_j)$$\n",
    "\n",
    "where $\\mu_{ij}$ is the expected abundance and $\\phi_j$ represents the dispersion parameter associated with microbe $j$. We then model $\\mu_{ij}$ as a function of the sampling depth of sample $i$ and the probability of $ij$.\n",
    "\n",
    "$$ \\mu_{ij} = n_{i}p_{ij} $$\n",
    "\n",
    "The probability of microbe $j$ in sample $i$ is modeled by the inverse additive log-ratio (ALR).\n",
    "\n",
    "$$ p_{ij} = \\textrm{alr}^{-1}(x_i\\beta) $$\n",
    "\n",
    "$x_i$ represents your specified covariates and $\\beta$ represents their associated coefficients (differentials). In the negative binomial model we are primarily interested in specifying covariates and estimating which values of $\\beta$ are most plausible given our data.\n",
    "\n",
    "Finally, as mentioned above we have to integrate our prior knowledge of our parameters $\\beta$ and $\\phi$. By default we use a normally distributed $\\beta$ with a mean of 0 and user-specified standard deviation (defaults to 5) and a lognormally distributed reciprocal $\\phi$ with location 0 and user-specified scale parameter (defaults to 5).\n",
    "\n",
    "$$ \\beta \\sim \\mathcal{N}(0, B_p) $$\n",
    "\n",
    "$$ \\frac{1}{\\phi} \\sim \\textrm{Lognormal}(0, s) $$\n",
    "\n",
    "For more details on this model see Jamie's excellent [blogpost](https://mortonjt.github.io/probable-bug-bytes/probable-bug-bytes/differential-abundance/).\n",
    "\n",
    "### Input parameters\n",
    "\n",
    "Every BIRDMAn model takes several parameters that I will outline here:\n",
    "\n",
    "* `table`: BIOM table of features x samples\n",
    "* `formula`: Formula with which to fit model (same as in Songbird)\n",
    "* `metadata`: DataFrame with columns specified in `formula`\n",
    "* `num_iter`: Number of iterations of MCMC sampling to run *per chain*\n",
    "* `chains`: Number of chains to run\n",
    "\n",
    "Each BIRDMAn model also expects the priors to be input by the user. For the negative binomial model these are referred to as `beta_prior` and `inv_disp_sd`.\n",
    "\n",
    "To save on computation time we will only specify 100 iterations per chain. In an actual analysis you will likely need to increase this number (defaults to 500)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from birdman import NegativeBinomial\n",
    "\n",
    "nb = NegativeBinomial(\n",
    "    table=filt_tbl,\n",
    "    formula=\"C(diet, Treatment('Control'))*C(treatment, Treatment('Ad Lib'))\",\n",
    "    metadata=metadata,\n",
    "    num_iter=100,\n",
    "    chains=4,\n",
    "    beta_prior=3.0,\n",
    "    inv_disp_sd=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Stan model must be compiled before fitting. This should only have to be done once *per model type*. So the second time you use the default `NegativeBinomial` model it will not have to compile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb.compile_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll fit the model to our data. This should take ~1.5 minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "chain 1 |\u001b[33m          \u001b[0m| 00:00 Status\n",
      "chain 2 |\u001b[33m          \u001b[0m| 00:00 Status\u001b[A\n",
      "\n",
      "chain 3 |\u001b[33m          \u001b[0m| 00:00 Status\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "chain 1 |\u001b[33m██▌       \u001b[0m| 00:00 Status\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "chain 3 |\u001b[33m██▌       \u001b[0m| 00:00 Status\u001b[A\u001b[A\n",
      "chain 2 |\u001b[33m██▌       \u001b[0m| 00:00 Status\u001b[A\n",
      "\n",
      "\n",
      "chain 4 |\u001b[33m██▌       \u001b[0m| 00:00 Status\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "chain 4 |\u001b[33m█████     \u001b[0m| 00:36 Iteration:   1 / 200 [  0%]  (Warmup)\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "chain 3 |\u001b[33m█████     \u001b[0m| 00:36 Iteration:   1 / 200 [  0%]  (Warmup)\u001b[A\u001b[A\n",
      "chain 2 |\u001b[33m█████     \u001b[0m| 00:36 Iteration:   1 / 200 [  0%]  (Warmup)\u001b[A\n",
      "\n",
      "\n",
      "chain 4 |\u001b[34m███████▌  \u001b[0m| 00:36 Iteration: 100 / 200 [ 50%]  (Warmup)\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "chain 3 |\u001b[34m███████▌  \u001b[0m| 00:37 Iteration: 100 / 200 [ 50%]  (Warmup)\u001b[A\u001b[A\n",
      "chain 1 |\u001b[34m███████▌  \u001b[0m| 00:37 Iteration: 100 / 200 [ 50%]  (Warmup)\u001b[A\n",
      "\n",
      "\n",
      "chain 4 |\u001b[34m██████████\u001b[0m| 01:14 Iteration: 101 / 200 [ 50%]  (Sampling)\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "chain 3 |\u001b[34m██████████\u001b[0m| 01:14 Iteration: 101 / 200 [ 50%]  (Sampling)\u001b[A\u001b[A\n",
      "chain 1 |\u001b[34m██████████\u001b[0m| 01:16 Sampling completed                     \u001b[A\n",
      "chain 2 |\u001b[34m██████████\u001b[0m| 01:16 Sampling completed                     \n",
      "chain 3 |\u001b[34m██████████\u001b[0m| 01:16 Sampling completed                     \n",
      "chain 4 |\u001b[34m██████████\u001b[0m| 01:16 Sampling completed                     "
     ]
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     "output_type": "stream",
     "text": [
      "                                                                                                                                                                                                                                                                                                                                "
     ]
    },
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 257 ms, sys: 55.3 ms, total: 313 ms\n",
      "Wall time: 1min 16s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import logging\n",
    "\n",
    "cmdstanpy_logger = logging.getLogger(\"cmdstanpy\")\n",
    "cmdstanpy_logger.disabled = True\n",
    "\n",
    "nb.fit_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data structure for model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BIRDMAn makes heavy use of the `arviz` package for downstream analysis (see [documentation](https://arviz-devs.github.io/arviz/index.html)). We provide a wrapper function `to_inference` to make this relatively easy.\n",
    "\n",
    "This function wraps much of the functionality of `xarray` for multi-dimensional arrays (see [documentation](http://xarray.pydata.org/en/stable/))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "inference = nb.to_inference()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnosing our fitted model\n",
    "\n",
    "As with Songbird (or any regression/ML procedure) we want to diagnose our model to make sure we are extracting useful signal and not simply overfitting predictive power. There are a number of ways to do this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LOO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we look at the Pareto-smoothed Importance Sampling Leave-One-Out Cross-Validation (PSIS-LOO-CV). This is a mouthful but the core of it is that we want to cross-validate our model to make sure we are not overfitting. This can be fairly computationally expensive for Bayesian models so we use an estimation developed by Aki Vehtari and others. This way we can estimate model performance *from the existing sample draws*.\n",
    "\n",
    "See these two papers for more information:\n",
    "\n",
    "* https://arxiv.org/abs/1507.04544\n",
    "* https://arxiv.org/abs/1507.02646\n",
    "\n",
    "**Note:** This function requires that you calculated the `log likelihood` values in your Stan code (done by the default Negative Binomial model) and passed them to the inference object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Computed from 400 posterior samples and 2233 observations log-likelihood matrix.\n",
       "\n",
       "         Estimate       SE\n",
       "elpd_loo -8057.05   104.66\n",
       "p_loo      318.31        -\n",
       "\n",
       "There has been a warning during the calculation. Please check the results.\n",
       "------\n",
       "\n",
       "Pareto k diagnostic values:\n",
       "                         Count   Pct.\n",
       "(-Inf, 0.5]   (good)     2005   89.8%\n",
       " (0.5, 0.7]   (ok)        159    7.1%\n",
       "   (0.7, 1]   (bad)        59    2.6%\n",
       "   (1, Inf)   (very bad)   10    0.4%"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import birdman.diagnostics as diag\n",
    "\n",
    "diag.loo(inference, pointwise=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log likelihood is calculated for each entry in your input table - essentially figuring out how likely is the table value given the estimated parameters. We want to maximize the `elpd_loo` entry. In this demonstration we also calculate the *pointwise* predictive accuracy. We want to see the majority of the Pareto k diagnostic values < 0.7. If there are a lot of values above 0.7 this is indicative of a relatively poor model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\hat{R}$ convergence\n",
    "\n",
    "Another diagnostic tool is making sure your chains are converging. This can be done by checking the $\\hat{R}$ values of your fitted parameters. In a nutshell, these values should be extremely close to 1 to ensure convergence.\n",
    "\n",
    "See [this link](https://arviz-devs.github.io/arviz/api/generated/arviz.rhat.html) for more details. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   ()\n",
       "Data variables:\n",
       "    inv_disp  float64 1.04\n",
       "    beta_var  float64 1.041</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-32006a34-f257-4619-9948-b81a38d8d79e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-32006a34-f257-4619-9948-b81a38d8d79e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dea6117f-8217-4142-b3dc-6b4c7861c613' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dea6117f-8217-4142-b3dc-6b4c7861c613' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e9e30b6b-3e28-4307-8dba-c41ebb9a8bd4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e9e30b6b-3e28-4307-8dba-c41ebb9a8bd4' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>inv_disp</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.04</div><input id='attrs-aead5092-54d7-434d-8dd5-89ba9a0d2dc5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aead5092-54d7-434d-8dd5-89ba9a0d2dc5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91ed2253-46df-4825-884e-6e753b0d892b' class='xr-var-data-in' type='checkbox'><label for='data-91ed2253-46df-4825-884e-6e753b0d892b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.04000651)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_var</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.041</div><input id='attrs-e8826884-dd65-46ea-9fb6-124fe68f1d04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8826884-dd65-46ea-9fb6-124fe68f1d04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac457de8-5c78-4ee8-a278-1f7da70c38ff' class='xr-var-data-in' type='checkbox'><label for='data-ac457de8-5c78-4ee8-a278-1f7da70c38ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.04149709)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc436ef7-281a-4eb1-9b26-2d70f7020ef4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dc436ef7-281a-4eb1-9b26-2d70f7020ef4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   ()\n",
       "Data variables:\n",
       "    inv_disp  float64 1.04\n",
       "    beta_var  float64 1.041"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diag.rhat(inference).max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that across both our covariate coefficients and overdispersion parameters our chains have converged well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Posterior predictive check\n",
    "\n",
    "We can do some more diagnosing by performing a *Posterior Predictive Check*. This procedure essentially uses the parameter distributions we've estimated and tries to predict our original values. We've provided an easy-to-use visualization function that performs this for you. This figure plots the individual table entries (samples x features) and how our model predictions fall. The black line represents the true value, the light gray vertical lines represent the middle 95% interval of values, and the dark gray dots represent the median values from all chains/iterations. We want to see the dark gray lines more-or-less follow the same shape as the black line. It is also preferable to have small credible intervals but predicting microbiome data is very difficult so it is expected that these intervals will be fairly large.\n",
    "\n",
    "Note that this requires posterior predictive values to have been calculated in Stan and provided to `to_inference`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import birdman.visualization as viz\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "viz.plot_posterior_predictive_checks(inference);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model seems to do an okay job of predicting counts from metadata."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing to a null model\n",
    "\n",
    "Similarly to Songbird we can compare our fitted regression model to a \"null\" model with only an intercept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "from birdman import NegativeBinomial\n",
    "\n",
    "nb_null = NegativeBinomial(\n",
    "    table=filt_tbl,\n",
    "    formula=\"1\",\n",
    "    metadata=metadata,\n",
    "    num_iter=100,\n",
    "    chains=4\n",
    ")\n",
    "nb_null.compile_model()\n",
    "nb_null.fit_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "inference_null = nb_null.to_inference()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at use `birdman.diagnostics.loo` again to see how our predictive power is with this null model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Computed from 400 posterior samples and 2233 observations log-likelihood matrix.\n",
       "\n",
       "         Estimate       SE\n",
       "elpd_loo -8204.70   105.93\n",
       "p_loo      144.88        -\n",
       "\n",
       "There has been a warning during the calculation. Please check the results.\n",
       "------\n",
       "\n",
       "Pareto k diagnostic values:\n",
       "                         Count   Pct.\n",
       "(-Inf, 0.5]   (good)     2206   98.8%\n",
       " (0.5, 0.7]   (ok)         20    0.9%\n",
       "   (0.7, 1]   (bad)         6    0.3%\n",
       "   (1, Inf)   (very bad)    1    0.0%"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diag.loo(inference_null, pointwise=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a lower value of `elpd_loo`, indicating that this null model has less predictive power than our regression model.\n",
    "\n",
    "Another thing we can do is use the `arviz.compare` function to compare multiple models. This function takes a dictionary in the form of `{\"model_1\": InferenceObject, ...}` and outputs a table where the models are ranked from \"best\" at the top to \"worst\" at the bottom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>rank</th>\n",
       "      <th>loo</th>\n",
       "      <th>p_loo</th>\n",
       "      <th>d_loo</th>\n",
       "      <th>weight</th>\n",
       "      <th>se</th>\n",
       "      <th>dse</th>\n",
       "      <th>warning</th>\n",
       "      <th>loo_scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>0</td>\n",
       "      <td>-8057.047377</td>\n",
       "      <td>318.309078</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.794809</td>\n",
       "      <td>104.656839</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>null</th>\n",
       "      <td>1</td>\n",
       "      <td>-8204.699362</td>\n",
       "      <td>144.884394</td>\n",
       "      <td>147.651985</td>\n",
       "      <td>0.205191</td>\n",
       "      <td>105.932653</td>\n",
       "      <td>25.500515</td>\n",
       "      <td>True</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rank          loo       p_loo       d_loo    weight          se  \\\n",
       "model     0 -8057.047377  318.309078    0.000000  0.794809  104.656839   \n",
       "null      1 -8204.699362  144.884394  147.651985  0.205191  105.932653   \n",
       "\n",
       "             dse  warning loo_scale  \n",
       "model   0.000000     True       log  \n",
       "null   25.500515     True       log  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import arviz as az\n",
    "\n",
    "az.compare({\"null\": inference_null, \"model\": inference})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that indeed our regression model is at the top. We can also compare the difference in `elpd` relative to the standard error to get a rough idea of how much better this model is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4108202236071692"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(-8057.047377 - -8204.699362) / 104.656839"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like this model is about 1.4 SE above the null. Not bad!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing differentials\n",
    "\n",
    "We are now ready to use our fitted parameters for further analysis.\n",
    "\n",
    "We can now plot our parameter estimates similarly to how we would do a rank-plot in Qurro. However, since in the Bayesian framework each parameter has a distribution, we also include the standard deviation of these parameter estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from birdman.transform import posterior_alr_to_clr\n",
    "\n",
    "inference.posterior = posterior_alr_to_clr(\n",
    "    inference.posterior,\n",
    "    alr_params=[\"beta_var\"],\n",
    "    dim_replacement={\"feature_alr\": \"feature\"},\n",
    "    new_labels=nb.feature_names\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Intercept', 'C(diet, Treatment('Control'))[T.DIO]',\n",
       "       'C(treatment, Treatment('Ad Lib'))[T.Weight Reduced]',\n",
       "       'C(diet, Treatment('Control'))[T.DIO]:C(treatment, Treatment('Ad Lib'))[T.Weight Reduced]'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eUBFTJozOUHUAuUsYBgAAAACQAfPnz4+6uroevbetcV2kHv+fAa4IYGgQhgEAAAAADLBUKhULFizo1Zrvfff/RSqVGqCKAIYOd4YBAAAAwG64q4t0efrpp2Pt2rW9WrNmzZp45pln4vDDDx+gqgCGBmEYAAAAQI4R4MDg09jY2Kd1GzduTG8hAEOQNokAAAAAAANszJgxfVo3duzY9BYCMAQJwwAAAAAABtjk1x4aI8aM79WaysrKmDJlygBVBDB0CMMAAAAAAAZQR0dH/MevnophR1T3at3cuXMjkUgMUFUAQ4c7wwAAAGAf5m6o9PLrCQyEa+95Jn7+yOooOfbk2PzE/dHWuG6va6qqqmLevHkZqA4g9zkZBgAAAAAwQH6w9Pn49u9rIyIiv2hUVMy5KgrGTNjjmqqqqqipqYmSkpJMlAiQ85wMAwAAYJ/kBA8Ag91vHlsbV/72qS5jhWMnxsSz5kfzw7+O5LJFkUrWd84lisvjs+eeE5ddfIEgDCCNhGEAAAAAAGn20HMvxfl3PhYdHbvO5ReNijHTT4/SaXOitWFNtLckI394cRSWVca8C2ZFSYkf6ABIJ2EYAAAAAEAaPbGmKT55619je6q92/lEXl6kOjoiLz8Rw8YdmOHqAIYed4YBAAAAAKTJ2satcdYP/hLJbW27fc8F7zo0gxUBIAwDAAAAAEiT8+98LF5Kbtvt/AXVh8Z7Xr9/BisCQJtEAAAAAHLWqoYtMePaxd3OPXjRzJhU5m4m0mt149bdzv3btIPi0zNfE6s37v49AKSfMAwAAAAAGHI62lPRWr862rdtjvyiUVFYfkBa9sjLT3T73hOPnBCXv/d1kZeX19/SAeglYRgAAAAAMGQ0NzdF49LbI7lsYaSSDZ3jieKyuCFxTlx28QVRWlra5z2KjzohSo49OfKLRnWOv/mQsrh+zlGRyBeEAWSDO8MAAAAAgCGhtrY2Tpw5PZqW3NYlxIqISCUb4vprro5jjjkmamtr+7xH05Lb4oWb50XrxhciImLqhNHx3/92bAwv7P7EGAADTxgGAAAAAOS8pqamqK6ujpXLn9/j++rq6qK6ujqampqiNdUeW7enomlra7yU3BbPrnwxjp81e697tDWui/V3Xhr7DW+PWz76pigdUZjOTwWAXtImEQAAACBNVjVsiRnXLu527sGLZsakspEZrgh42fz586Ourq5H762rq4uD3vPpGDP99C7jjUtvj6a9BGEva2tcF29p+XPsV/LeXtcKQHo5GQYAAAAA5LRUKhULFizo1ZrkskXR0Z7qfN3RnorksoW92uOnP/pBpFKpvb8RgAHlZBgAAADAIOJ0GaTf008/HWvXru3VmlSyPlob1sSwcQdGRERr/epd7gjbmzVr1sQzzzwThx9+eK/WAZBeToYBAAAAADmtsbGxT+vaW5KvfLxtc5/22LhxY5/WAZA+wjAAAAAAIKeVlJT2aV3+8OJXPi4a1ac9xo4d26d1AKSPMAwAAAAAyFnt7R3xo6dbI1Fc1qt1ieLyKCyr7HxdWH5Ar/eorKyMKVOm9GoNAOnnzjAAAAAAICe1t3fEF+96Iu7469ooPuqEaFpyW4/XfuRjn4hPnvPWKEzkR0F+XgwryI9v558dN3z1yz3eY+7cuZFIJPpSOgBpJAwDAACAPlrVsCVmXLu427kHL5oZk8pGZrgiAF7W0dERl/36ifjx/66MiIiSY0+OzU/cH22N6/a6tqqqKr525SVRUlLSZfxLl1wYv/7p7VFXV9ejPebNm9e34gFIK20SAQAAAICc0tHREZfd9WT86E8rO8fyi0ZFxZyromDMhD2uraqqipqaml2CsIiI0tLSqKmpiQMPPqTPewCQeU6GAQAAMCQ51QWQmzo6OuJLv34ybv3Til3mCsdOjIlnzY/mh38dyWWLIpWs75xLFJfHZ889Jy67+II9hliTJ0+OuxcvjelnXNjnPQDILGEYAAAAAJATOjo64orfPBW3/HHXIOxlw0eOjvzpp0fptDnR2rAm2luSkT+8OArLKmPeBbOipGTvPwxRUlIaY/q5BwCZIwwDAAAAss5JPaC/Ojo64srfPhU3P7R8t+8ZVpAfX/nnI+KCn/4t8vITMWzcgf16Zjr2AGDgCcMAAADoFaEFAIPRN373XPz0r6t3Oz8skR8LznhDvGZ8cQarAmAwEIYBAADAEJeOgFNICmRSR3sqWutXR/u2zZFfNCoKyw/oURA2c0pFrGrYksFKARgMhGEAAAAAwD6hubkpGpfeHsllCyOVbOgcTxSXRfFRJ0TJsSdHftGoLmsKE3nxnTOOiZlTKzJdLgCDhDAMAAAAyAlOp0Fuq62tjRNnzY6m5c/vMpdKNkTTktti8xP3R8Wcq6Jw7MSI2BGEffvDb4jjp+6X6XIBGESEYQAAAGSc0AKA3mhqaorq6upY2U0Q9mptjeti/Z2XxsSzboyikaPipg+/IWYdLggDGOrys10AAAAAAMCezJ8/P+rq6nr03rbGdZH866/jWx86JmYLwgAIJ8MAAAAAgEEslUrFggULerUm/5n7451Txw9QRbxsUtnIWH7NSdkuA2CvnAwDAAAAAAalLdvb4r/u+F2sXbu2V+vq178QzzzzzABVBcC+xskwAAAAACCjOtpT0Vq/Otq3bY78olFRWH5Al/mV9Vvih39cHnc8vCo2PPe3Pj1j48aN6SgVgBwgDAMAAAAAMqK5uSkal94eyWULI5Vs6BxPFJfFDflnx6w5H4mfP94Q9z+9Pjo6dszlF43q07PGjh2bjpIByAHCMAAAgCFkVcOWmHHt4m7nHrxoZkwqG5nhigAYKmpra+PEWbOjafnzu8ylkg1x/Ve/HDcu+F5UzLkqCsdO7JwrLD8gEsVlXcKzvamsrIwpU6akpW4A9n3uDAMAAAAABlRTU1NUV1fHym6CsFdra1wX6++8NNq3bekcy8tPRPFRJ/TqeXPnzo1EItGnWgHIPU6GAQBADnMKCAAYDObPnx91dXU9em9b47pofviuGDP99M6xkmNPjs1P3B9tjev2ur6qqirmzZvX51oByD1OhgEAAAAAPdbRnortG1ZEy+qnYvuGFdHRntr9ezs64vFVG+P6G7/Vq2ckly3qsu/YMWNi3tdujsoDD97juqqqqqipqYmSkpJePQ+A3OZkGAAAOcdpKGBf4u8sYF/R3NwUjUtvj+SyhV3u70oUl8UNiXPisosviNLS0tje1h7/+3x93PfUi3Hf39fH8/94Oprq1/fqWalkfbQ2rInXH/G6OOstB8f7jqqMEcMS8ZHjH4rpZ1wYyWWLIpWsf1UN5fHZc3fUIAgDYGfCMAAAAABgj2pra+PEWbOjqZs7v1LJhrj+mqvjth/dGrM/Pz+WNQ6PTdvaOufbt23u0zMvrz4oPnnqjMjLy+scKykpjTHTT4/SaXOitWFNtLckI394cRSWVca8C2ZFSYkfIABgV8IwAABg0HNyBgCyp6mpKaqrq2NlN0HYq724ekX85MpPxcSzboz8olf+25xfNKpPz51xxMFdgrBXy8tPxLBxB/ZpXwCGHmEYAABAhgj1ANgXzZ8/P+rq6nr03rbGddH88F0xZvrpnWOF5QdEorisS2vFvamsrIwpU6b0ulYA6I4wDAAAGFACIADYd7W1tcU3b/pOr9Ykly2K0mlzIi8/ERE7TnEVH3VCNC25rcd7zJ07NxKJRK+eCwC7IwwDAAAAgCGioz0VrfWro33b5sgvGhWF5Qfs8p729o54dFVjLHrihfjZfX+KDS++0KtnpJL10dqwpksbw7FvfF+0Pf372PzSmr2ur6qqinnz5vXqmQCwJ8IwAAAAAMhxzc1N0bj09kguW9ilXWGiuCxuSJwT/3HR+fFcY3vc/fgLseiJdbGuuSUiIlo21Pfpee0tyRhdVBBvnzI+Zh++X7zj0Ip46dPHxPGzZu/x7rGqqqqoqamJkpKSPj0XALojDAMAgG5o7cdg5c8mAL1VW1sbJ86aHU3dhFCpZENcf83V8c0F34txp14ZhWMndpnPLxrVp2f+14enxWmz3xLDCvI7x0onT467Fy+N6WdcGMlliyKVfCVoSxSXx2fPPScuu/gCQRgAaScMAwAAAIAc1dTUFNXV1Xs8jRURsX3jC7H+zktj4lk3Rn7RKz9YUVh+QCSKy7qcJtubysrK+FD1tEgk8neZKykpjTHTT4/SaXOitWFNtLckI394cRSWVca8C2ZFSYkf6gAg/Xb9LxIAAAAAkBPmz58fdXV1PXpvW+O6aH74ri5jefmJKD7qhF49c+7cuZFIJPb4nrz8RAwbd2AMP+DwGDbuwMjL3/P7AaA/hGEAAAAAkINSqVR8Z8GCXq1JLlsUHe2pLmMlx54cBWMm9Gh9VVVVzJs3r1fPBICBJgwDAAAAgByzcfP2uOT7i+KFtWt7tS6VrI/WhjURETEskR/HT62Ir314Wvz5wd/FgQcfsse1VVVVUVNT484vAAYdd4YBAAAAQI54oWlr/L8Hno/b/7wyNj7/VJ/2OHbCsPjoB4+K4w+riJLhhZ3jdy9eGtPPuDCSyxZFKlnfOZ4oLo/PnntOXHbxBYIwAAYlYRgAADAkrGrYEjOuXdzt3IMXzYxJZSMzXBEA9F5Heypa61dH+7bNkV80KgrLD4iIiNoNyVjwh9r45aNrojXVERER+UWj+vSMaz40LQ4/vHKX8ZKS0hgz/fQonTYnWhvWRHtLMvKHF0dhWWXMu2BWlJT4bykAg5MwDAAAAAAGuebmpmhcenskly2MVLKhczxRXBbvffaDsfGQWZG3U/hVWH5AJIrLurx/byorK2PKlCl7fE9efiKGjTuwd58AAGSRMAwAAAAABrHa2to4cdbsaFr+/C5zqWRDPHbX/4uCMb+JijlXReHYiZ1zefmJKD7qhGhacluPnzV37txIJBJpqZuBNalsZCy/5qRslwGwTxCGAUAWaNXFYOXPJgDAwNldi8M9aWpqiurq6ljZTRD2am2N62L9nZfGxLNujPyiV75mKzn25Nj8xP3R1rhur8+qqqqKefPm7f0TAYB9jDAMAAAAAAbQnloc3pA4Jy67+IIoLS3tsqZxy/Z4bHVTfPUrV0ddXV2PntPWuC6aH74rxkw/vXOsonxsfOybP4rv/cfHY9WK5btdW1VVFTU1NVFSUtK7Tw4A9gHCMAAAAAAYIHtrcXj9NVfHL+/8cXz9Bz+N+vyx8djqxnhsVWMsr98SHe2pWPPzH/Xqeclli6J02pyYVF4cc98+OU59wwExvDARp0x/KKafcWEkly2KVLK+8/2J4vL47Lk7AjlBGAC5ShgGAAAAAHsxkC0On6+ri1Ped9IuLQ5b61d3OUnWE6lkfVzw5tFx9vvfHgWJ/M7xkpLSGDP99CidNidaG9ZEe0sy8ocXR2FZZcy7YFaUlGiHDUDuEoYBADDouLsMABgs+tLi8GVf//oN/Wpx2L5tc59qPmbCsC5B2Kvl5Sdi2LgD+7QvuWdS2chYfs1J2S4DYMAJwwAAYID0N9QTCgJAdvWkxeGv7vxx1NTUxAEHHRz/eDEZT6xpiifWNsXjqzbG/3ztG7163sstDvPyExERkV80qk91jx07tk/rACBXCcMAAEgrAQ4AkAt62uKwrq4uXv/mt8WEs+ZHqmBE5/j2DSu63M3VE6lkfbQ2rOk8uVVYfkAkist61SqxsrIypkyZ0qvnAkCu6/68NAAAAAAMYfPnz+9xi8Mt9Wuj/n9/1WWsry0O21uSnR/n5Sei4k3v6dX6uXPnRiKR6NOzASBXORkGAAAAAK+SSqXi29/5Tq/WpKvF4dGvqYy3vbkqjjpgTPzTpDExMqbHG96wpEfBXFVVVcybN69PzwWAXCYMAwAAACDndbSnorV+dbRv2xz5RaOisPyArvMdHfGP9cm496kX42f3/zHWvfBCr/ZPV4vD31562k4nu0ZETU1NHD9r9h5bNlZVVUVNTU2UlJT0qm4AGAqEYQAAAAAMansLsvakubkpGpfeHsllC7sEU4nisvh6/tlx0ukfiz+u2hr3/v3FWFG/JSIiWlav61OdO7c4LD7qhGhacluP1++uxeHkyZPj7sVLY/oZF0Zy2aIud5Elisvjs+eeE5ddfIEgDAB2QxgGAAAAwKC0pyDrhsSOAKi0tHS362tra+PEWbOjqZsTValkQ3z9q1+Obyz4XlTMuSoKx07snOtri8P84cUxrCA/DptYEkfsXxKTqy+Oyz+6NFavWL7XtXtrcVhSUhpjpp8epdPmRGvDmmhvSUb+8OIoLKuMeRfMipKSkX2qGQCGAmEYAABAD6xq2BIzrl3c7dyDF82MSWW+CQmQTnsLsq6/5ur41Z0/jpqampg8eXK0t3fEpm1t0by1NZpbWmPt+oY4873vjBdWr9jjc9oa18X6Oy+NiWfdGPlFO/4u70uLw7KKCXHvFf8Sh04ojcJEfuf42++/L60tDvPyE52tGAGAnhGGAQAAeyQEAiDTmpqaorq6eo8BUkREXV1dvO5NM6Lqk9+MrVEUHR2vzDUuvT2a9hKEvaytcV00P3xXjJl+ekT0rcXhZz9zTryucuwu41ocAkD2CcMAANIgHWGBwAEAyEV9ue/rqq9eF3V1dT3af1vDC7F2yS86g6yXn5lctrBXdSaXLYrSaXMiL3/HnV0lx54cm5+4P9oa935/mBaHADC4CcMAAAAASLve3PfVmmqPh5dvjN8/sz4W/31d/O4bN/XqWTsHWa31q3vV4jAiIpWsj9aGNTF8/IHxhoPGRvXhh8VrP1QTZ855nxaHALCPE4YBAAAAkFY9ue/r5z+5LT53/S3x9OaRseQfL8WmbW0REbF9w4o+B1kvB03t2zb3qe5PTdsvPn3arCgvLuoc0+IQAPZ9wjAAAAAA0qan932tWP58XPDROTHxrBsjv+iVNoF9DbLaW5KdH+cXjerTHqdMm9olCIvQ4hAAckF+tgsAAAAAIHfMnz+/x/d9tTWui+aH7+oy1tcgK394cefHheUHRKK4rFfrKysrY8qUKbudf7nF4fADDo9h4w7sbMkIAAx+ToYBAAAA5KiO9lS01q+O9m2bI79oVBSWHzCg6zdt3RY3fuvbvXrGzvd9vRxk9aZVYnnFxPjJhR+IsaOGR8mIgigZXhjzR54XV1zxpR7vMXfu3EgkBFwAkIuEYQAAAAA5prm5KRqX3h7JZQu7hEqJ4rK4IbHjnqvS0tJ+r29v74gn1zbHA//YEA/+Y0M89PCyqF+/rle17nzfV15+IoqPOiGaltzW4z3mfebseNuU/bqMfe5zn41bb/1hj06pVVVVxbx583pVNwCw7xCGAQAAAOSQ2traOHHW7Gjq5s6uVLIhrr/m6vjVnT+OmpqamDx5cp/W33brD2P2+TfGE5tGxMYtrZ3z27ckd1nTEy/f9zVqWCLe+tpx8ebqi+PKjz0UK/Zy71jE7oOs0tLSqKmpieNnzd7j/WVVVVVRU1MTJSUlfaodABj83BkGAAAAkCOampqiurp6j+FPRERdXV0cP2t2PFa3Nuo2JKP2//559Lk1MfOdew6PIiJeXLMyfnLlp6J+Y1OX8b7e9/Uvb50aP/74m+PRy6pjwRnHxkePPyLuv+/eOPDgQ/a4bm9B1uTJk+PuxUuj9K0fjkRxeZe5RHF5nH/JpfHoo492GwoCALnDyTAAAACAHDF//vwetQWMiFi5/Pl4+5kXxZjpp3eONS69PZpW7P00VkREW+O6aH74ri7r+3LfV2VlZXztEyfucl/Xy0HW9DMujOSyRZFK1nfOJYrL47Pn7mjXuLcTXSUlpTFm+ulROm1OtDasifaWZOQPL47CssqYd8GsKCkZ2eNaAYB9kzAMAAAAIAds294aN37r271ak1y2KEqnzYm8/ER0tKciuWxhn9dH9O2+r7lz5+4ShL0snUFWXn6i814yAGBoEYYBAAAA7MNW1m+JOx5eGT/8nyVRv35dr9amkvXR2rAmho07MFrrV/fqRNfO619WcuzJsfXJ38X2jS/sdf3u7vvamSALAOgPYRgA0GerGrbEjGsXdzv34EUzY1KZljMAAH3V0Z6K1vrV0b5tc+QXjYrC8gM651paU1Hz1Itxx19WxtLndrQPbGnY2KfntLckd/zvts19Xj8skR9vPGRszHjt+Jjx2nEx7JwHYtZe7i7b231fAADpIgwDAAAAGESam5uicentkVy2sMtJrURxWVze8vHY7y3/HAufbY7GLa1d1uUXjerT8/KHF/dr/TWnHxenzX5LjBj26laHpWm57wsAIB2EYQAAAABptqdTXXtSW1sbJ86aHU3dnKhKJRviB9+4Ngpu/WFUzLkqCsdO7DJfWH5AJIrLetXqMFFcHoVllX1eX1lZGWe8+y3d3vmVzvu+AAD6QxgGAAAAkCZ7OtV1Q2LHaajS0tJu1zY1NUX1XloLRkS0Na6L9XdeGhPPujHyi14JlPLyE1F81AnRtOS2Htf7+fM+Hef/e/WO9RHxtcJPx7VfuarH6+fOndttEPZq7vsCALJNGAYAAACQBns71XX9NVfHr+78cfz6twujYOzEWFG/OVbUb4kVDTv+94E7vhMr6+p69Ky2xnXR/PBdMWb66V3GS449ObY8cX+0Nq7b6x5VVVXxxX8/P0pKhneOfeGi8+NnP7kt6npQR1VVVcybN69H9QIAZJMwDAAAAGAnvW1z2NNTXXV1dXHUW96+y6mujvZUrPnjr3tVY3LZoiidNify8neczHrd/iXxL286Io78+OJ433tO3GMtVVVVUVNTs8udXaWlpVFTUxPHz5rdp/VAz0wqGxnLrzkp22UADBnCMAAAACCn9PW+roi+tTns6OiIq796XY9OU0V0f6qrtX51r+7qiohIJetjWHJdnFY9Lf7ljQfGEZWv1HX34qUx/YwLI7lsUaSS9a/6PMrjs+fu+Dx2F2RNnjy5X+sBAAYbYRgAMOStatgSM65d3O3cgxfNjEllLncHgEzJdJD1aj1tc/jz3/xPJAvHxWOrG+PRlY2xbGV9LPvGTb36PHc+1dW+bXOv1r9swZypcfw7jtxlvKSkNMZMPz1Kp82J1oY10d6SjPzhxVFYVhnzLpgVJSV7/vqmv+sBAAYTYRgAAACQdZkKsmpqamLy5Mm7vKc3bQ7fOH1mlzaH2zes6NOprtaGNTFs3IEREZFfNKpX6182oWLcHufz8hOdz+iL/q4HABgM8rNdAAAAAJA7OtpTsX3DimhZ/VRs37AiOtpTe11TW1sbJ86cHk1LbtslVHo5yDrmmGOitra22/W9CbJmza6Op1e+GH9/oTn+t64+7n3qxfj5X1fHWed/qddtDl/W11Nd7S3Jzo8Lyw+IRHFZr9ZXVlbGlClT+vRsAIChxMkwAAAAICKy06KwN0HW7OrquO+BP0UMGxGbWtpiU0tbNLe0xs3f7Pl9Xcufr4vjPnx+l/u6OtpTseaOH/bwM93h1W0O+3qqq3Dk6DiwbGQcVL7jn8df+rf49Q9u6PH6uXPnRiKR6NOzAQCGEmEYAAAADHHZaFG4rS0VLyW3x5ev+mqPg6zn6+rimH/53K5B1o9v7uFnusPO93W11q/uV5vDl0919WaPCRP3j39882MxfFhh51jTzC/FMX/4dY9+PaqqqmLevHm9qhkAYKgShgEAAEAO6Ouprv7etbVxY2PMnt2zk13/dNzb4ph5/x0b2wpiU0vbjiDrvxf07BP8P+kOsiL63+YwLz8RxUedEE1Lbuvx2nPO/lSXICwiorS0NGpqauL4WbP3+OtZVVUVNTU1UVJS0qe6AQCGGmEYAAxhqxq2xIxrF3c79+BFM2NS2cgMVwQA9FZ/TnX1pkXhPx33tnjrhd+P1kRRbN2eiq2tqdiyPRUv/uG2aHq+Zye7Nr+0Nv52z+2dJ7sGQ5AVEX1uc1g2dmxMm1oR/zRpTEw+9Yr4zKkPxfLn9/xrGbHnU12TJ0+OuxcvjelnXBjJZYsilazvnEsUl8dnz93xeyoIAwDoOWEYABklfAEA6F5fTnb19FTXPffcE6X7TYoV9Ztjef2Wzv+990ff7HGLws0vrY0//ebWXVoUJpct7PknGV1PdmUzyMofXtz5cV/bHP7t62dFQcEr31qZeu+9aTnVVVJSGmOmnx6l0+ZEa8OaaG9JRv7w4igsq4x5F8yKkhJfMwMA9IYwDADIKgEpAP3V1/aA6Vrf3z36erKrN6e6Dn/TjJhw5o2RX/TKf1c72lOx5oFf9bjOiPS3KMxWkJUoLo/CssqIiBhRmIiSEUVRMP19UXfPD3q8xzlnf6pLEBaR/lNdefmJzhNwAAD0nTAMAACAfVJ/2gOmY3069ujJya6f/+S2+PaPfhHDy/ePDZu2xYbkttiwaVvc/cNv9PhUV+vGddH88F1dTnUNhhaF/Q2y8vIiRo8sio7j3hur7rulx3uc95mz4z/+410xenhhDCvIj4iIpk+/MY455g89+jXdU5tDp7oAAAYfYRgAQ06unETKlc8DAPqip+0Ba2pqYvLkyWlf39s9Dj6kKpItbdHc0hpNW1ujuaU1XljfEJ8+dXa8uGblHj/XFcufj5Pfc0JMPOuVk10d7alYc89P97huZzuf6hoMLQrz8hNRfNQJ0bTkth6v/fTZn4p//8LsGD28IEYNK4j8/LxoajoujjnmwR4HWV+65MIoKS7qMl5aWho1NTVpaXMY4VQXAMBgIgwDAABgn9Kb9oDV1dXx6KOPdgkuerN+dnV1/O7BP0V+0ahIbmuLLdvbIrmtLTbUb4xz51TvNciqq6uLw944IyacOb9Li8KIiMalt0fTXta/rK2x68mudJzqylaLwrHjJ8RN57wn9isdGeNHD4vCeW+K49/6xx4HWVd98aIoKRnRZTxdQVa62xwCg9OkspGx/JqTsl0GABkkDAMAAGCfMn/+/B63B6yrq4tpHz4/pp74kc6xv9/9/R6vf76uLo467XNd2gtG9C7Iat34wi4tCjvaU5FctrBH61/26pNd6TjV1Zcga8y4/eIrZ1XHqOHDYsSwRIwclogfb/9EfG/+V3u8x+fOPSfef8ykV42MHlRBljaHAAC5RxgGAABA1nS0p6K1fnW0b9sc+UWjorD8gD2+f1V9Mm74xk29esYzv/9lJA97b+TlJ3a0F/z9L3u1fuf2gv0NsiL6f7KrP6e6iosK4uBxI+Og8lHxj/d/KO790Td7vP7z5306znpr17aRb7jiklj8m5/2666twRhkaXMI7InTZQD7FmEYAAAAGdfc3BSNS2+P5LKFXUKhRHFZ3JDYEXyUlpZGqr0jHlvdGL/7+/q4/+n18djfHo+NL73Yq2e9OkRKR3vBdOzR35NdfTnVVTFhYiz72pkxvmRE5OXlRURE00mT45iH7u5XkJWuFoWCLAAABoowDGAvVjVsiRnXLu527sGLZsakMm1SAAB6o7a2Nk6cNTuauglOUsmGuP6aq+PHP7o1TrjwG/FY0/Co37y9c76/IVI62gumY4++nuwaX14WB+5fEuNHF8U/TjwtHrrz2z1e+5lzzo6K0q5fuw7Gu7YEWQAApJswDAAAgD7pbYvDiIimpqaorq7eY/ASEbFu9Yq49fJPxsSzboz8olcCnP60B0zH+nTtUVh+QBSMLo+2TfV7WNFVZWVlLLv+rEgkdrRabPrAlDjm4Xv6daorYnC2KAQAgHQShgFAHzgxCMBQ1tMWh92ZP39+j8KbiIi2xnXR/PBdMWb66Z1jfWkPmCguj8KyyrSsj4gYMX5Sr4Os8ftNjIWXnRZjRw2PkhEFUVxUEF8pPi8uv/zyHu8xd+7cziAsIn2nuiK0KAQAILcJw3rp+eefj2XLlsXatWsjmUzGxIkT46CDDoq3vOUtUVhYmO3yAAAABlRPWhz+6s4fR01NTVQeeHA8tz4Zf3+hOZ5ZtymeWtsYP7vuxl49L7lsUZROmxN5+TtCoLz8RBQfdUI0Lbmtx3u865R/jXe/67Ad6/MiFq7/17j7lp7Xcc7Zn4oLL54Vo4oKYlRRIooKEnHlyN4FWed++uz4pwPLuozNmzcvbrnlln6d7Epne8IIQRYAALlJGNZDP/vZz+L666+PP/7xj93Ol5WVxWmnnRZXXnlljBs3LsPVAcDQ5qQeQGb0tMVhXV1dHPnmt8V+Z86PjsIRnePbN6yI1l6cpoqISCXro7VhTZeApuTYk2P73xfH1vq1e11fVVUVt8+/sksYdMYbroxjHvxtj0Ooq7940S4no9IRZKXrZJf2hAAAsGf52S5gsEsmk3H66afHqaeeutsgLCKioaEhvv3tb8cRRxwR99xzTwYrBAAAyIzetDjcWr82Nv75V13G2rdt7tNz21uSUVSQH8dPrYir3n9E/PHy98bj//tAHHjwIXtct7sQ6eUQqq/r07VHxCsnu0rf+uFIFJd3mUsUl8f5l1wajz76aEyePHmPz4l45VTX8AMOj2HjDuw8TQcAAEOdk2F7kEql4rTTTou77767y/j48ePj6KOPjtLS0qitrY1HH300Ojo6IiLixRdfjPe9731x3333xVvf+tZslA0AAJB2qVQqvvOdBb1as3OLw/yiUX169ldOe3Oc/q7pMWLYq8KdMf1rD5iO9oLpalHoZBdAz00qGxnLrzkp22UAsI8Rhu3BxRdf3CUIKywsjOuvvz4++clPxrBhwzrHn3rqqfj4xz/eeXJs27Zt8f73vz8ef/zxmDhxYsbrBgAA2JuO9lS01q+O9m2bI79oVBSWH9Dt+7ZuT8X9T78YN//2wXjhhb23JXy1nVscFpYfEIniskglG3q8R2VlZZx54vRIJHY95dTfECkdIVQ6gyz3dQEAwMAQhu1GXV1dzJ8/v8vYT3/603jf+963y3sPP/zwuP/+++Od73xnZyBWX18fV1xxRXznO9/JSL0AAMDQ0tMwa2fNzU3RuPT2SC5b2CWUShSXxQ2JHSeZRhaPjiXPvRS/WbY27nlyXWzenoqW1Sv7VGd7S7Lz47z8RBQfdUI0Lbmtx+vnzp3bbRD2av0NkdIRQgmyAABg8BKG7cYVV1wRra2tna/POuusboOwl40YMSJuvvnmOPLII2P79u0REfG9730vLrrooqiqqhrwegEAgH1HX4OsiJ6FWaWlpd2ura2tjRNnzY6m5c/vMpdKNsT111wd3//BzbH/6VfH5qJxXeb72uIwf3hxFBXkx6H7jY6pE0bHge+4MK755NJYu2r5XtdWVVXFvHnz+vRcAACAlwnDurF169b42c9+1mXs3//93/e67tBDD433v//9ceedd0ZERFtbW/z4xz+OL37xiwNSJwAAsG/pT5AV0bMw61d3/jhqampi8uTJXeabmpqiuro6Vnaz9tUaX1wdyZsvjoln3Rj5Ra+0+OtLi8Pyionx+6s/FJMrSiKRn9c5fuLi++L4WbP3WEtVVVXU1NTs9b4tAACAvRGGdeOee+6JLVu2dL6eNm1aTJ06tUdrP/KRj3SGYRERv/jFL4RhQFqsatgSM65d3O3cgxfNjEllQ+Nidb8OAOyr+hNkRfQ8zKqrq4uZ75wVN/96cWzPHx6NW7bHxi2t8fPv3hB1dXU9qrWtcV00P3xXjJl+eudYX1oczvvM2XHohF3DvcmTJ8fdi5fG9DMujOSyRZFK1nfOJYrL47Pn7ggGBWEAAEA6CMO6sWjRoi6v3/GOd/R47YwZM6KgoCDa2toiIuLRRx+NF198Mfbbb790lgjsYwZDgDMYagCAoao3Qdbbj58V3/3V/ZE3bGRs2d4WW7anYsu2VPzie/N7HGatWrE8PvDpL3aGWR3tqVhz1497VXNy2aIonTYn8vJfua+r5NiTY/MT90db47q9rt9bi8OSktIYM/30KJ02J1ob1kR7SzLyhxdHYVllzLtgVpSU+NoEAABID2FYN5544okur6dNm9bjtaNGjYojjzwyHn300c6xJ598UhgGAABD2Pz5PQ+y1qxcHv9y3mVdTmV1tKdizS9+1KtnvjrMaq1f3av2hhERqWR9tDasiWHjDoyIiLEjC+Ok4w6LN7z/t/HZs05NW4vDvPxE5zMAAAAGgjCsG3//+9+7vH7Na17Tq/WTJ0/uEoY99dRTcfzxx6eltv5obm6OxsbGbJcB+5xNzVujOG/bbuaaojF/+z6xRzpqSIfB8HkMhj0GQw3p2GMw1JCOPQZDDenao78Gy+fh93Tw1JCOPQZDDenYo6/rU6lU3HbbbTFmzJi91viy/LqlMeot7+s8lbW9cU2MLmiP6MUeEakoaloZw8r2j22pTb16/svK8pJR/boxMfvw/eLYg8uiMJEfERE//c2iOOVzX4nNT/0h2rc0vlL3yDHx0TPPiPPO/kSMHj16j///Y1/+PR1sewyGGtKxx2CoIR17DIYa0rHHYKhhsOwxGGpIxx6DoYZ07QG5rC9fswF7l9fR0dGR7SIGk4aGhigvL+8ylkwmY9SoUT3e4/zzz4/rr7++8/V5550X8+fPT1uNPfXkk0/GEUcc0fn6nHPOiYqKiozXAQAAAADA3l1++eXZLgFykpNhO9n5JxdHjhzZqyAsInYJnJqamvpbVqxfvz42bNjQqzXPPfdcv58LAAAAAACwLxOG7SSZTHZ5PWLEiF7vsfOaTZs29aumiIibbroprrjiin7vAwAAAAAAMJTkZ7uAwWbnMGz48OG93mPnMGznPQEAAAAAAMgMYdhe5OXlZWQNAAAAAAAA6adN4k6Ki4u7vN66dWuv99h5zc579sU555wTp556aq/WPPfcc/H+97+/388GAAAAAADYVwnDdjJYw7CKioqoqKjo1x7/+q//Gocddli/a4F9ydrGrTFnwR+7nbtz7rTYf8ze7wXMlT0GQw3p2GMw1JCOPQZDDenYYzDUkI49BkMN6dqjvwbL5+H3dPDUkI49Xl7f0Z6K1o3romP7lsgbNjIKx06In5791j2ub25ujve85z2xatWqvdY5etyEOH7e9bG1ozCat7ZFc0trNLe0RtNffh2bHr5rr+s79zn2fVFy7Mmv1PBw/9ZHRBQV5Me0/Tri3psujfUvrNnt2kmTJsWtt94aBx100G7f8+zq9XHK574Sm5/6Q7Rvaewczx85Jj565hlx3tmfiNGjR+92fZffj8YXo2Pb5sgrGhWFY/bb6+/Hznt0Z1/8s5nNGtKxx2CoIR17DIYa0rHHYKghHXsMhhoGyx6DoYZ07dFfufJ5ADD0CMN2Ulpa2uX1li1bYvPmzTFq1Kge77F+/four8eMGZOO0vqtpKRk0NQCmbKpfVgkO4q6nRtdUhpjxowcMnsMhhrSscdgqCEdewyGGtKxx2CoIR17DIYa0rVHfw2Wz8Pv6eCpIR17rGncGquX/CKSyxZGKtnQOZ4oLovv5Z8Tl118wS5fh0dEtLd3xOX/eV08/vjje60xIqKxsTF+dcdtMWb66f83kh8d7QWx9k+/iVSysUd7RERs+tNvI+/oD0RefiI62lP9Wr9fSVH827SD40NvOjDGjhoWT77nuJh+xoWRXLYoUsn6zjWJ4vL47Lk7fi1KSkr2uP/E9mGR/4ZTo/joD0Rrw5pob0lG/vDiKCyrjPMumBWTynr45yovIsYe0jm+PfatP1fp2GMw1JCOPQZDDenYYzDUkI49BkMN6dhjMNQwWPYYDDVE7Pj+0hP/+YG9vm8gDZZfCwDoLWHYTsrLy2Ps2LGxcePGzrGVK1f26kTVihUrurx+7Wtfm7b6AABgX1JbWxsnzpodTcuf32UulWyI66+5On5154/jnnvuicKxE+Ox1U3x+OrG+Nvqpnhi9cZ45qZv9+p5yWWLonTanMjLT0RERGv96i4BXE+kkvXR2rAmho07sM/rDylois+eenyceOTEGFbwylXNJSWlMWb66VE6bc4uQda8C2ZFSUnPvwGYl5+IYeMO7FVtAAAAQ5EwrBuHHXZYPPTQQ52vn3vuuV6FYXV1dbvsB5m2qmFLzLh2cbdzD140c68/MQwAsLOO9lS01q+O9m2bI79oVBSWH7DH9zc1NUV1dXWs7CYIe7W6urp43ZveFvudOT/yi175GmX7hhX9CrIiItq3be7V+pe1tyT7tf7qEw+Jtx5dudt5QRYAAEDmCMO6ccQRR3QJw/74xz/Ge9/73h6t3bx5c/ztb3/bZT8AAOir3oZQ6d6jubkpGpfe3m2bwxsSu29z+PWv37DLD4rtzvaNL0Tzw3e9qsVh/4OsiIj8op63O3+1/OHF/VpfVlbWp3UAAACknzCsG+9+97vjv//7vztf//73v+/x2gcffDDa2to6Xx999NGx3377pbM8AAD2MX0NovoaQqVzj560Ofz5T26L675/Z7SOqojnX9ocdRs2R936pnjga9/o0ef5sp1bHPY3yIqIKCw/IBLFZb06YTZizPh4+5teHwWJgsh7bVn8/BfjY0vjhh6vr6ysjClTpvSqZgAAAAaOMKwb73rXu2LEiBGxdevWiNhxMuzpp5+OqVOn7nXtzTff3OX1P//zPw9EiQAA7AP6E0T19K6tmpqamDx5ctr3aE21x9oX6+P4WbP32uZwxfLn4/QPvDcmnnVjZ5vDHS0O6/e4bteaurY47EuQNbp8v7jg1HdE+egRMWZkYYwZOSzuSM2Nb1//nz3e45LPnxuXfnJ65+vXrPlMXH755T1eP3fu3EgkEj1+PwAAAANLGNaNkSNHximnnBK33npr59hXv/rV+MEPfrDHdc8++2z88pe/7HxdUFAQH/rQhwasTgAABq++BlEdHR2xdkNDvLMHIVRdXV1Me9vxcdn3fxMjRo3usseW5Ka4+uMnx4a1K/e6xz8d97Z4y4Xfi+15RZHc1habWtpiW1t7NC69vdv6u9PWuK5Lm8N0tDjMy09E8VEnRNOS23q8/sJ5n47Pv6vrnb1HXfbvcc+v7uhRy8aqqqqYN29el7F58+bFLbfc0uf1AAAAZJcwbDe+9KUvxU9+8pNobW2NiB0nvv75n/85Tj755G7f39LSEh/5yEdi+/btnWMf+9jHdvtTugAA7Bv60uKwqakpqqurexRmHTXt7THjou/H1hgWTVtbo3lra7z04I97HEJtWLsyvnj1tV3u2oqIHUHWXoKwl21+aW38+Tc/6rJHR3sqkssW9mj9y17d5rCvLQ4TI4rjtRXFceQBpfH6ytI45MNfjo+8/4/xfD+CqNLS0qipqdnrKbeqqqqoqamJkpKStK4HAAAgu4Rhu/Hy/5G+7rrrOsdOOeWUuP766+OTn/xkDBs2rHP873//e3z84x+Phx56qHOsvLy8V61UAAAYXPrS4rCjoyNWNWyNSy69ukeniCIikhvWxEN3/bAziOpvCJWuPVrrV/eqPWFE1zaHfWlxOH7CxHhq/kejdGRRl/F70xBETZ48Oe5evDSmn3FhJJct6tLCMVFcHp89d8fv6UCtByB7JpWNjOXXnJTtMgCALBKG7cE111wTTz75ZCxcuOMbCa2trXHuuefGVVddFcccc0yMHj066urq4pFHHomOjo7OdcOGDYtf/vKXMXHixGyVDgBAP/S0xeFtP/9NNBeWxd9WN8Vjqxvj8TVNsTHZEmtu/X6vnvfqIKq/IVRE/4Os96JhigAAWe1JREFUiP63OexLi8Nzzzl7lyAsIn1BVElJaYyZfnqUTpsTrQ1ror0lGfnDi6OwrDLmXTArSkpGDuh6AAAAskMYtgeJRCLuvPPO+PjHPx533HFH5/j69etj0aJF3a6pqKiIW265JWbMmJGpMgEA2I2BbnE4Y+Y7Y+JZN0Z+0SshSH+DqHTctZWOPfra5vCIQybGPx15QFSNHxXj33tpXPDhpbFqxfK9rtvbXVvpDKLy8hOdoV9f9Hc9APsWJ8sAYN8nDNuL4uLi+MlPfhKnnHJKfO1rX4s//elP3b6vrKwsTjvttLjiiiti/PjxGa4SACA39SXMiuhbi8OXXfu163vc4rCtcV00P3xXl7u2+htE9TWEyh9e/MrHadijL20OKysr454vnR6JRKJz7A3335fWu7YEUQAAAPSWMKyHTjnllDjllFPi+eefj0ceeSTWrl0bmzdvjgkTJsRBBx0U06dP73KPGAAAfdefMKunLQ7v+OVvoq14v3hufbLzn2fXNcYjX/9mr2rd+a6t/gZRfQmhikrHx5GHT438xI4v7zv2OzwaSsfFtqaXerzHmHH7xVc/+q4oGVkUxUUFUVxUELd2zI1vXfefPd5j7ty5XYKwCHdtAQBdOWkHQDYIw3rpkEMOiUMOOSTbZQAADHp9PdXV0zCrpqYmJk+e3GW+Ny0Op7191xaH2zes6PddW30Js8aM2y/+86zqGFM8PEpHFMYdqU/Gd66/psfr/+P8c+PSz8/sMnblpnPj8ssv7/Eenz/v03HGW6q6jE394r/Hwl/c0aOTcntqc+iuLQAAALJJGAYAQFr151RXb8KsY97yjnjfFT+M7fnDY/O2VGze1hbPLPxBrM1ii8OIHW38Rh91QjQuua3H6z9/3qfjzLe+EuwdfdnFUfOrO/sVQs2bNy9uueWWfu1RWloaNTU1aWtzqMUhAAAA2ZCf7QIAABh8OtpTsX3DimhZ/VRs37AiOtpTPVpXW1sbJ86cHk1LbtvlZNTLp7qOOeaYqK2tjVR7R6zeuCWWPvdS/Ph/V8Z/Lvx7zProv/f4vq7m9avj17d9L/5U1xCPr2mK2vXN8eKff9urzzO5bFGXz62vLQ4r9yuPk14/MS45YWrc/onj4vGf3RBVVVV7XxjdB1Evh1AHHrznjgR7CqHSsUfEK20OS9/64UgUl3eZSxSXx/mXXBqPPvroLqf0AAAAYLBwMgwAIEesatgSM65d3O3cgxfNjElle29Fl6lTXUe86W2x/0fmR6pgROd4R3sq1tT8dK81vtqr7+tqrV+dlRaHE/evjD9fe+Yud2X190RVOu7aStd9XdocAgAAsC8ThgEA5JhM39XV0pqKVQ1b4qqrvtzjU10tDWuj/n9/1aVFYX/DrHS1OCw+6oRo6kWLw7M/NXeXICwiPUFUOkKodAZZ2hwCAACwLxKGAd1Kx+kCADIrU6e6jnvb8fFv1/4k1m3Ni+UvbYm1TVujPZWKNbd8r1f1vvpUV0T/7+vqa4vDMWPGxJEHj43XVBTH5PHFMfHUK+LTpzwUK/byaxGx+7u2XpauICodIZQgCwCyZ1LZyFh+zUnZLgMAhixhGABADujrqa6XXf/1G3p8quultSvje9/5VlpPdUX0PczKH14cEX1vcfjEDR+JgoKuXxZPve/efrU43JkgCgAAALJHGAYAsI/rzamuGe94Z1z6vV/HxrbCWNO4NdY2bo01Dcl45Gs39uqZ6T7VFdG3MGt0+X7x2Q+8PUpGFcWoooK4e9NH4iff+VqP15/9qbm7BGER6btrCwAAAMg+YRgAwD5u/vz5PT7V9cLqFXHJVdd2OdW1fcOKrJ/qKh81LA4sHxn7v3tO/Oln3+nx+gvnfTouOvHwztcnH3Zp/Lnmlz369chUi0MAAAAgu4RhAAD7sFQqFQsWLOjVmsFyqqu8YmIsuvy0OGT86Bg9vDAiIpo+fEQc80hNn8Os0tLSqKmp0eIQAAAA6CQMgzRb1bAlZly7uNu5By+aGZPK/BQ5AHvW0Z6K1vrV0b5tc+QXjYrC8gO6fd+qhi2x4K4/xNq1a3u1f7pPdUXsCIxGH3VCNC65rcfr533m7Hj9pLIuY+kIs7Q4BAAAAF5NGAYAkEY9DbK609zcFI1Lb4/ksoVdTlglisvihsSOAKclryj+528vxK8fWxvLVjVGy+qn+lRnf091jRw7Pj5ywluiar+SOLh8ZBw8blSUXPiWOO5Nf+x3i8J0hFlaHAIAAAAvE4YBAPyfgQ6ySktLd7u+trY2Tpw1O5q6OQ2VSjbE9ddcHQu++4MY88EvRcGYiZ1z6TrVVXzUCdHUi1NdF3/u3Lj0g/+0y3i6WhSmK8zS4hAAIH0mlY2M5declO0yAKDXhGEAwJCXiSDrV3f+OGpqamLy5Mm7vKepqSmqq6v3GCBFRGx+aU1su+PSmHjWjZFftCMM6suprmEl42L6G46MSWXFsf+YEbH/mBEx+pQp8elTlsaqFcv3un6gT3W9mjALAAAA6C9hGDBg3J8G7AsyFWTV1dXFm2fMjE/f+PPoKBwRW7enYmtrKlpaU/HnX/x3j1oLRkS0Na6L5ofvijHTT4+Ivp3q+uIF58WlZ791l/HD779vUJ3qAgBwEgkASAdhGAAwZPUmyHrjW98Rp//n7bEtb3g0t7TGppa2aN7aGs/dc3Os72GQVf/Cqpg/f35nkBWxozXjmt/9old1J5ctitJpcyIvPxERESXHnhybn7g/2hrX7XWtU10AAADAUCMMAwCGrPnz5/f4RNbGdavjtu99Z5cgq/7h/+nVM3cOslrrV/eqxWFERCpZH60NazqDptdXTYy5N90W3774Y7Fm5fLdrnOqCwAAABiKhGEAwKDQ0Z6K1vrV0b5tc+QXjYrC8gMGbI/lL22OxX9fF1/9+jd7tf9ABFnt2zb3av3L9h+Rin+bfWi85/UTo2p8cUREvH/aQ051AQAAAOxEGAYAZFVzc1M0Lr09kssWdgmWEsVlcUNiR4BTWlrarz3O/9xn46mX2uIPz26IB/6xIVbUb4ntG1bElsYNvao1XUFWe0uy8+P8olF92uOWT82M173utV3GnOoCAAAA2JUwDAahVQ1bYsa1i7ude/CimTGpzDczgfTq76msvq6vra2NE2fNjqZu7uxKJRvi+muujl/d+eOoqamJyZMn93mPG7/z3aiYc1UUjp3YOZfNICt/eHHnx4XlB0SiuKxXJ8wqKytj6tSpu513qgsAAADgFcIwABjC+nsqqz/rm5qaorq6OlZ2E2K9Wl1dXcyeXR33L/lTJIpGxebtbbFle1ts3paKDfUN8elTq+PFNSv3uEdb47pYf+elMfGsGyO/aMcPFGQryBo5dnycNOOYGFU0LEYMS8TwwkQsfeFDce+Pet6yce7cuZFIJHYZn1Q2MpZfc1KP9wEAAAAYCoRhADBE9fdUVl/Wt6baoz65PTZs2hbXf/UrUVdX16Nan3++Lo4+7XMxZvrpXcYbl94eTXsJwl7W1rgumh++q3OPvgRZxWUV8bET3xJjiodHyfDCKBlREL/d/NH40f9v787jq6zu/IF/k5sEhJhAAigGAYO7topaR8VqVaRuo61tbdXWrQvV2tLFdmqnVjt2sZ22il1mmLZ2maoddar2ZwVRSt11qoLWBamEIIsoAgmENST394clcgkh9yY325P3+/XKq57nec55DvAtCfnknPOzH2Q9xle/8Nm46sJ/yrhWf9y34rDH7s3q96O6ujqmTJmS9fsAAAAA+jthGNCr2TISukYuq7ImTZoUc+bMicGlu8bmLc2xaUtTvLlydZw08eSs+h9y1PFxxBf+K+q2FEXd+saIeGtbxaW//kVOc26YOyPKjz4nCgpTLWM0zJ3e4TEKClNReuipUf/IzVn3/8rnL4+rzj4k49op+349Hpvxh04FWeXl5TFz5sw4sZ3f0+rq6pg5c2aUlZVlPWcA6E+Ssko6Kb8OAIDeorCnJwAAdE66uSk2r1gUG5e8GJtXLIp0c1O7faZOnZr1qqyampoYe8ZnYtzX7o0DvjEjDv23++OI877UbhC21bo3l8bc6be0BGEREY0rl+S0IisioqlhZTSuWpq3MQoKIo4+86NROXLPrPq2F2SNHrtXu/13FmSNGzcu7p39aJQfe36kSisz7qVKK+NLV14Vc+bMafPsNAAAAAB2zMowAOijcjmvK51Ox5LVG+LZJXUxp3ZlfPdHP87tXXOmx65HfSgKClOdXpEVEdG8aV1O/bdq3tjw9n93cIx3j94lzjvz0Dh272FRWTogFpw9u9MrsrYGWRM+9uVomDsjmhpWttxLlVbG5z/71p9Heyu6ysrKY8iEc6P86HOicdXSaN7YEIUDS6O4oiqmXDExysqshgUAAADIlTAMAHpQurkpGlcuieZN66JwwOAorhyVVb9szuv6/c3/HZd865extLksnl1cFyvXbY6IeGsVWf2bOc1z64qqkmGjO7Uiq2TY6IiIKBwwOKf+WxUOLH37vzs4xtUfPDIOPLCqpd0bg6yCwlTL7xUAAAAAnSMMA4AekMuqru1le97XssWL4ropH42RF90YhQPeDmI6uyorH6u6iitHRaq0IqdQLVVaGcUVb4dYA4fvGUW7VsaWtSt30itTVVVV7Lfffq2uC7IA6EnOhwIAgK4lDAOAbpbNqq67brslps+4L8pGjIrX6jfEa/UbY1ndW//7/359Y9bnfW2pWx5rnro7hkw4t+VaZ1dl5WNVV0FhKsrGnxqrH7456/6fufTT8eUrJ8bgkqIYNCAVJanCuHbQ5+Lqq6/OeozJkydHKpVq874gCwBICiErAMDbhGEA0I2yXdVVU1MTBx357larutLNTbH0vttzeuf253V1dlVWR/oPGb57/OTS02O38kExrHRADN91QBRsnhBHHPF4VsFedXV1XPv1r7RaoTVlypT4zW9+k/UYU6ZMyXrOAAAAACSDMAyAfquj53V1dIx0Oh3XfPffO7WqKx/ndRUUpqL00FOj/pHsV2VdeMkn4lOfnhAlRYVRUlQY/1Vwadz4/W9n3f+Ln70szj58uxVXg0ti5syZceLEk3caDlZXV8fMmTN3eGZXeXl5p8cAAHqHpKxkSsqvAwAgSYRhAPRJnQmyOnNeVy5jDBhUGn9bWh9P1a6Opxetjqdr34y//fQ/cvp1br+qKx/ndY0bPjj2//jkuL3m4Vix7NV2+1ZXV8f1134tI0j6t699Oe6549ZOr8gaN25c3Dv70ZjwsS9Hw9wZ0dTw9vlfqdLK+Pxn3/q93FmIlY8xAIDOEwIBANBbCcMA6FM6G2Rle17XzJkzY9y4cR0e4z9/cVMM/9C/RZTt3nJv84pFnV7V1dHzuj5x0sFx6rvfFe8YVR5lA4sjIuKL7/5zh1dU5XNFVllZeQyZcG6UH31ONK5aGs0bG6JwYGkUV1TFlCsmttoasavGAID+TJAFAECSFfb0BAAgWwsWLIjTTpgQ9Y/c3CpU2hpCHXbYYbFgwYId9s/lvK73nDQx/vK32njk72/GAy++Hvc8tyzueHpJTHvgb3H0cSe2O8b6N5fF0lu/Hs2b1rdcy8eqrq3ndeWiqqoqvnnBpJiw97CWICzi7RVV5ceeH6nSyow+qdLK+NKVV8WcOXPaDAU72397BYWpKBk2OgaOOjBKho1uWQ2Xi3yMAQAAAECyWBkGQJ+QS5B17HtOiu/89k/RVLRLrNm4JdZubIy1G7fEX37/s6zP61qyqDbed9nXM87rioioe/TWqM9ia8GI1md+dXRVV+HA0hhQVBh7DNklRpYPjNpTz4nHb//PrPtPnjw5Uqkdh0KdXVFlRRYAAAAAvZ0wDIA+YerUqVkHWcuXLIovfOO7GUFWurkpls68I6d3bn9eV7q5KRrmTu/wGFtXdeWyVeJuu+8Rz/3ooqjcdWAUFBRERET9OQfEYU/P7PR5XdvauqKqozrbHwAAAAC6im0SAegR6eam2LxiUWxc8mJsXrEo0s1NbT5bv35TTP3Jf+Q0fsPcGRljNq5c0uHzuvI1RkFhKkoPPTWn/p+57NMxrGyXliAs4u3zukaP3WunfbM5rwsAAAAAks7KMAC61Zo19VH36K3RMHd6RrCUKq2IG1KXxTe+ekWUl5fHyoZN8cBLr8d9L7wesx57OlatWJ7Te7aGUFtXK+XjvK6OjjGwaUMcu+/wOHz00NjvnH+Lyz74eCxc2LlVXVvP65rwsS9Hw9wZ0dSwsuVeqrQyPv/Zt34vBWEAAPSUPSsGRe11p/f0NAAAhGEAdJ8FCxbEaRNPjvodnPvV1LAqfnTdt+K/f/vbeNelP4iX1g2K5vRb9zatX9uh920bZHXmvK7OjnH3l94b7zj4oJb2/ffPjBMnnrzT88+yWdXlvC4AAAAAaJ9tEgHIWS5bHG5VX18fkyZN2mkAFBGxYtmrMfOHn4stG9e3XMtHkLX1vK5cpEoro6SyKnYpTkXF4JIYU713lJQNy2mMqqqqOPCA/TOubV3VVX7s+ZEqrWz1zi9deVXMmTMnxo0bl9U7tp7XNXDUgVEybHTLGWcAAAAAgJVhAOQg2y0Od+SGG26Impr2twaMiNhStzzWPHV3DJlwbkS8HWTlcl5XSdmweMeB+0fZoAGx68Di2HVgUcw94yPx0O9/lvUY/3rFZ+Oab/xzxnld/9b02bj66quzHmPy5MmRSrUOp6zqAgAAAIDuIQwDICvZbHF41223xL3TZ0RJxR7xyhsNb3+8Xh/3/vDHOb2vYe6MKD/6nCgoTEVBYSpKDz016h+5Oev+X7/ic3HVF0/IuFZ/6nfisP+bkVUoV11dHV/6whcygrCIiClTpsRvfvObrMdo68yvrbau6gIAAAAAuoYwDIB2ZbvFYU1NTbzjn46L3S+6MQoHvL2yafOKRbFl7cqc3tnUsDIaVy1tCYrKjjgzNr7w59i0+rV2+7YVQpWXl8fMmZ07rysfYwAAAAAA3UcYBttZvGp9vPv7s3d47+GvnBB7Vti6jL4t3dwUjSuXRPOmdVE4YHAUV45qt8/UqVOz3uKwcbstDiMimjet69Bcmzc2xNjKQfHeg3aP9x68e+x62UMxsZ1Qrr0Qaut5XRM+9uVomDsjmhreDulSpZXx+c++td3jzkKsfIwBAAAAAHQPYRhAH9ORMCsit/O+0ul01K5cH39duCqeqFkRP/3hjTnNcdstDiMiCgcMzqn/Vr/45PFx+nHv2marwqF5CaHycV6XM78AAAAAoG8QhgFkqaMhVL7GyCXM2l42533dduvv4vLv/yoWNe4af61dHSvWboqIt7Y43Lymc1scFleOilRpRca821NVVRWnHnt4qzO78hlC5eO8Lmd+AQAAAEDvJgwDaEdnQqh8jZFNmHXXbbfEzJkzY9y4cRn3sz3va8mi2vj65HNj5HbnfXVmi8OtCgpTUXroqVH/yM1Z9588eXKkUqk27wuhAACg6+xZMShqrzu9p6cBAJAXhT09AYDebMGCBXHaCROi/pGbW61q2hpCHXbYYbFgwYIuGyPbMKumpiZOOGli3DdnYdw9d2nc9MjC+Pf75sUZk6/M+ryvLf8472tbHd3iMLVLaexZsUu8Z7/h8Ylj94offfOrUTV6bFZ9q6urY8qUKR16LwAAAADAtqwMA2hDLiHUpEmT4om/PhWDBu8ajU3NsbmpORqb0rFq9eo4/aSJsXhRbbtjHHP8ifHNX90TRQMHv9V/Szoam5pjxn//JOswa/Gi2vjw566KIRPOjYi3tmVc+qf/yarvVtuf99WRLQ53232PePnGj0fpLiUZ14//8wNx4sSTd/p7Wl1dHTNnzmz33C8AgM6y8gUAAPoHYRhAG6ZOnZp1CFVTUxP7nPXZlhBqq7pHb436doKwrd5Y+mp89d++lzFGurkplt59S9ZzjsgMsxpXLskpxIpofd5XR7Y4/Mxln24VhEVEjBs3Lu6d/WhM+NiXo2HujGhqePssslRpZXz+s29tGSkIA6A3EpywI+oiefyZAgAkjzAMYAeamppi2rRpOfXZfkVVurkpGuZO79QYnQ2zOnveV0FBxAG7l8U7L7s8flP7SCxfsqjdvu1tcVhWVh5DJpwb5UefE42rlkbzxoYoHFgaxRVVMeWKiVFWNqjNvgBAcggcAACA7iIMA9iBefPmxbJly3Lqs/2KqnysyupsmNXR877Of/cBccZ73hWHjxkaZQOLIyLik0fOyusWhwWFqZZfJwAAAABAVyns6QkA9EZ1dXUd6rc1hIrofJAV0fEwq3BgaUS8fd5XLqqqquL7nzg1TthvREsQFvH2Foflx54fqdLKjD6p0sr40pVXxZw5c2LcuHEdmjMAAAAAQFewMgxgB1KdDKEiOh9kRbwdZuWywixVWhnDR42NYbvuEpWlA2LhpA/FU3/IfsvHyZMnRyqV2uE9WxwCAAAAAH2NMAxgO2+s2RhXP7S6QyFUcUVVS7sjQdaA8mFxyEEHRElJcZSkCqKkqDBenPiBmHvXz7Me4+tXfC6uufrUlnb9Rw6Mw+beHzU1Ne32be+8r61scQgAPcdZWwAAALkRhgFs49WV6+Ojv3wyXl21PkoPPTXqH7k5676f/NSn4tLPHRfFqcJ/fBTET4ouix9891tZj/GvX/pcXPWF92Rcq//QAXHYYbOyDrO++IXPZ1wrLy+PmTNn5vW8LwAAAACAvsKZYQD/MG/5mvjAfz4Wr65aHxERZUecGUVDds+qb3V1dXzv6q/GO0cNiQNGlsXeI0pjTOXg+Pq/XBHV1dVZj7GjVVlbw6zRY/dqt39bYZbzvgAAAACA/srKMICIeHrRqrj4V3+NNRu3tFwrHDA4Rpxzbbxx21WxpW55m313FkLla1XW1jBrwse+HA1zZ0RTw8qWe6nSyvj8Zy+Lb3z1ip2u6nLeFwAAAADQHwnDgH7vLy+/EZ/+3dOxsbG51b3ioSNj5EVTY81Tf+xwCJWPICsif2GW874AAAAAgP5EGAb0a398dll88X/mxpbmdJvP7LfnbvH3AZ0LofK5KkuYBQAAAACQPWEY0G/dOWdpXP/A/Ei3nYPFkXtVxDfPPChOnfpwXkIoQRYA9G97VgyK2utO7+lpAAAA9CvCMKBfSDc3RePKJdG8aV0UDhgcxZWj4kf3z99pn4kHjIifnHdYrFi7qZtmCQDQNwj1ehd/HgAAsHPCMCDR1qypj7pHb42GudOjqWFVy/VUaUWUHnpqlB1xZhQOGNyq39njq+J7H3xnFKcKu3O6ANAr+UZ7svjzBAAA+hvf5QUSa8GCBXHaCROi/pGbM4KwiIimhlVR/8jN8dqvp0Tj6tcy7l0yYa/4wYcOEYQBAAAAACSA7/QCiVRfXx+TJk2KV2sX7vS5LXXL443brormTesjIuJLJ+8bV51xQBQWFnTHNAEAAAAA6GK2SQT6hB2d+bUz3/7eD6OmpiarsbfULY+1T98dN37vW/Gxo8fmYbYAQG9ke0AAAID+SRgG9Go7O/PrhtRl8Y2vXhFlZWVRu3J9/HXhqnhy4ap4suaNeOLHP83pPamXZ8V5R/4m39MHAAAAAKCHCcOAXmvBggVx2sSTo34HWx02NayKH133rfjFTb+K0ed/J9aWVLbc27xiUaszwtqz8o3l8fLLL8eBBx7Y6XkDANA9rPYDAACy4cwwoMulm5ti84pFsXHJi7F5xaJINze12yfbM7/WvLE05v3qX1rO/IqIaN60rkPzXL16dYf6AQAAAADQe1kZBnSZbLY4LC8vb9XvjbUb40tf+3ZOZ36teeruGDLh3IiIKBwwuEPzHTp0aIf6AQAAAADQewnDgC6RzRaHd912S0yfcV9sGTwinn51dTyzaHU8vWh1LHpzbSz97S9zel/D3BlRfvQ5UVCYiuLKUZEqrchpq8SqqqrYb7/9cnonAAAAAAC9nzAMyLtstzisqamJg//p3bH7hTdG4YBBLdcbVy7J+cyvpoaV0bhqaZQMGx0FhakoPfTUqH/k5qz7T548OVKpVE7vBAAAAACg9xOGATuVbm6KxpVLonnTuigcMDiKK0e122fq1KlZb3HYuDpzi8OIjp/5ld7UEAftURbvGlsRB595VVxx/mOxqJ1ALiKiuro6pkyZ0qF3AkBX2rNiUNRed3pPTwMAAAD6NGEYsEMdOe9r7cbGeKZ2Vfzoxp/m9K5ttziM6PiZX3d9YVL802GHtLTHP3B/nDjx5J2uUKuuro6ZM2dGWVlZh94JAAAAAEDvJgwDWsn2vK+f33pnrCwcGs+8WhdzXl0dL7++Nja9sSjqV76R0/u23eIwIjp85tcRhxyccW3cuHFx7+xHY8LHvhwNc2dEU8PKlnup0sr4/GffCvUEYQB0FSu7AAAAoOcJw4AMuZz39d73vjdGXpR53ldHtzhs3tjQ8t/5PPOrrKw8hkw4N8qPPicaVy2N5o0NUTiwNIorqmLKFROjrGzQDkYDAAAAACAphGFAhlzO+9pS1/q8r45ucThgUFkcPmZoHD5maBw2emjsffkR8d7jH89qLtmc+VVQmGpZeQYAAAAAQP8hDANaNDU1xbRp03Lqs/15Xx3Z4nDE7iPjpR9fHIMGlGRcnzlzpjO/AAAAAADoFGEYEBER9esb4yf/++dYtmxZTv22P++rI1scXn7Zpa2CsAhnfgEAb3P+GgAAAB0lDIOESzc3RePKJdG8aV0UDhgcxZWj3r6XTsczr66OW55cHPc8tyzqa5/v0Du2Pe8rImLMcR+MhS//JdauWNpu3/a2OHTmF5AN3yQHAAAAoC3CMEioNWvqo+7RW6Nh7vSMLQtTpRXxvfTkOHDih+PuF+vj5dfXttzr6Hlf+47ePY47cnQcNvqt877GVA6KmkvekdctDp35BQAAAABARwjDIIEWLFgQp008Oep3EEQ1NayKn/7gu1H0i1/FiHOujeKhI1vudeS8rz2qqmL2tedFKpXKuG6LQwAAAAAAegNhGCRMfX19TJo0aacrsiIittQtjzduuypGXnRjFA54a6vBjpz39enJk1sFYVvZ4hAAiLCVKQAAAD2rsKcnAOTX1KlTo6amJqtnt9QtjzVP3Z1xreyIM6NoyO5Z9W/vvK+ttm5xOHDUgVEybHQUFO44PAMAAAAAgHwThkGCNDU1xbRp03Lq0zB3RqSbm1raI4dXxJdv+O+oGj12p/1yOe8LAAAAAAB6im0SIUHmzZsXy5Yty6lPU8PK2LJ6aUw65vA475/GxAn7DY+iVGGcf/xjzvsCAAAAAKDPE4ZBgtTV1XWo308/sF+875QjM6457wsAAAAAgCQQhkGCrG0u6VC/fUe3fUbY1vO+AAAAAACgL3JmGCTE8vqN8W8P10WqtCKnflVVVbHffvt10awAAAAAAKBnWRkGCVC3fnN87JdPxrI1m6P00FOj/pGbs+47efLkSKVSXTg7gN5vz4pBUXvd6T09DQAAAAC6gDAM+rh1m7bERb/6a/z9jYaIiCg74sxY9/ys2FK3vN2+1dXVMWXKlK6eIgDkTEAJAAAA5IttEqEP27ylOT79u6dj7uK6lmuFAwbHiHOujaIhbZ8DFvFWEDZz5swoKyvr4lkCAAAAAEDPsTIM+qim5nR88ba58fDf32x1r3joyBh50dRY89Qfo2HujGhqWNlyL1VaGZ//7GXxja9eIQgDegUrgJLHnykAAADQmwjDoA9Kp9NxzR9fiHuee63NZ/YaOTwWTTg3yo8+JxpXLY3mjQ1ROLA0iiuqYsoVE6OsbFA3zhgAAAAAAHqGbRKhD7r+gb/Hfz+xqM37e5QPjB+ec0hERBQUpqJk2OgYOOrAKBk2OgoKU901TQAAAAAA6HFWhkEfc8fTS+LGWX9v837F4JL47cf/KQYUyboBoDNs9wgAAADJIAyDPmbqToKwwSWp+PXF74q9R5TG4lXru3FWAEBvJdQDAACgvxOGQS+Wbm6KxpVLonnTuigcMDiKK0e1uc1hSaow/uuCI+Kdo4Z07ySBPs03yQEAAABIOmEY9EJr1tRH3aO3RsPc6dHUsKrleqq0IkoPPTXKjjgzCgcMbrleWBAx9SOHxoS9h/XEdAEAAAAAoNcShkEvs2DBgjht4slRX7uw1b2mhlVR/8jNse75WTHinGujeOjIiIj49vvfEae+Y2R3TxV6hJVMAAAAAEAuhGHQi9TX18ekSZPi1R0EYdvaUrc83rjtqhh50Y3x1bPGx7lHju6mGQK9iWAQAAAAANpX2NMTAN42derUqKmpyerZLXXLY58VD8Wlx4/r4lkBAAAAAEDfZWUYdJF0c1M0rlwSzZvWReGAwVFcOWrHz6XTMW/52nhw3vK47vqf5PSOl/9yZzQ33xCpVCofUwYAAAAAgMQRhkGerVlTH3WP3hoNc6dHU8Oqluup0oq4IXVZfOOrV8SW1MB45JU346H5b8bDf18Rb6zdFJtXLIoNdStyetfSpUvj5ZdfjgMPPDD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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in nb.colnames[1:]:\n",
    "    ax = viz.plot_parameter_estimates(\n",
    "        inference,\n",
    "        parameter=\"beta_var\",\n",
    "        coords={\"covariate\": col}\n",
    "    )\n",
    "    ax.set_title(\":\\n\".join(col.split(\":\")))\n",
    "    ax.axhline(y=0, color=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use these differentials in the same ways as Songbird. Let's focus on the diet differentials - first we have to sort the features by their means across all chains and draws. This is fairly straightforward using `xarray`-style indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;beta_var&#x27; (feature: 77)&gt;\n",
       "array([-5.73694892, -2.65988109, -2.4219365 , -2.23708606, -2.00739175,\n",
       "       -1.9573763 , -1.69342822, -1.53772343, -1.33882338, -1.09196751,\n",
       "       -1.05590138, -0.86744068, -0.83328258, -0.82724836, -0.81196281,\n",
       "       -0.7135843 , -0.65096482, -0.6316501 , -0.58691858, -0.56759943,\n",
       "       -0.52819507, -0.52580754, -0.51376801, -0.43769074, -0.35128673,\n",
       "       -0.34720449, -0.34082285, -0.30568045, -0.29715427, -0.24836883,\n",
       "       -0.24346562, -0.23557318, -0.18048565, -0.02537629, -0.00989158,\n",
       "        0.02788562,  0.09380669,  0.142793  ,  0.16447638,  0.18803242,\n",
       "        0.1936418 ,  0.19895287,  0.20065998,  0.28541747,  0.29322137,\n",
       "        0.30317563,  0.31384448,  0.36595709,  0.37729278,  0.37940998,\n",
       "        0.42116538,  0.53334287,  0.54078781,  0.57996696,  0.58657954,\n",
       "        0.64947155,  0.66508215,  0.67095088,  0.67743187,  0.69672586,\n",
       "        0.70374091,  0.76906979,  0.77883749,  0.97571642,  1.07582325,\n",
       "        1.20113236,  1.31475314,  1.43184984,  1.44930872,  1.46225464,\n",
       "        1.51979415,  1.53214966,  1.78738834,  1.93900751,  2.16822761,\n",
       "        2.49023848,  2.67052281])\n",
       "Coordinates:\n",
       "  * feature    (feature) object &#x27;230759&#x27; &#x27;193831&#x27; ... &#x27;1684221&#x27; &#x27;258522&#x27;\n",
       "    covariate  &lt;U36 &quot;C(diet, Treatment(&#x27;Control&#x27;))[T.DIO]&quot;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'beta_var'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>feature</span>: 77</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b96065d4-3ddb-42bd-9f0f-1efbb78f939f' class='xr-array-in' type='checkbox' checked><label for='section-b96065d4-3ddb-42bd-9f0f-1efbb78f939f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-5.737 -2.66 -2.422 -2.237 -2.007 ... 1.787 1.939 2.168 2.49 2.671</span></div><div class='xr-array-data'><pre>array([-5.73694892, -2.65988109, -2.4219365 , -2.23708606, -2.00739175,\n",
       "       -1.9573763 , -1.69342822, -1.53772343, -1.33882338, -1.09196751,\n",
       "       -1.05590138, -0.86744068, -0.83328258, -0.82724836, -0.81196281,\n",
       "       -0.7135843 , -0.65096482, -0.6316501 , -0.58691858, -0.56759943,\n",
       "       -0.52819507, -0.52580754, -0.51376801, -0.43769074, -0.35128673,\n",
       "       -0.34720449, -0.34082285, -0.30568045, -0.29715427, -0.24836883,\n",
       "       -0.24346562, -0.23557318, -0.18048565, -0.02537629, -0.00989158,\n",
       "        0.02788562,  0.09380669,  0.142793  ,  0.16447638,  0.18803242,\n",
       "        0.1936418 ,  0.19895287,  0.20065998,  0.28541747,  0.29322137,\n",
       "        0.30317563,  0.31384448,  0.36595709,  0.37729278,  0.37940998,\n",
       "        0.42116538,  0.53334287,  0.54078781,  0.57996696,  0.58657954,\n",
       "        0.64947155,  0.66508215,  0.67095088,  0.67743187,  0.69672586,\n",
       "        0.70374091,  0.76906979,  0.77883749,  0.97571642,  1.07582325,\n",
       "        1.20113236,  1.31475314,  1.43184984,  1.44930872,  1.46225464,\n",
       "        1.51979415,  1.53214966,  1.78738834,  1.93900751,  2.16822761,\n",
       "        2.49023848,  2.67052281])</pre></div></div></li><li class='xr-section-item'><input id='section-7e8434f2-5052-469a-bf5d-f37ef83dbdc1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e8434f2-5052-469a-bf5d-f37ef83dbdc1' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>feature</span></div><div class='xr-var-dims'>(feature)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;230759&#x27; &#x27;193831&#x27; ... &#x27;258522&#x27;</div><input id='attrs-4174ac77-e97e-4185-9690-0903c7598a8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4174ac77-e97e-4185-9690-0903c7598a8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9dd2df7-eb8d-4d93-9357-e7650d77fded' class='xr-var-data-in' type='checkbox'><label for='data-b9dd2df7-eb8d-4d93-9357-e7650d77fded' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;230759&#x27;, &#x27;193831&#x27;, &#x27;269172&#x27;, &#x27;192698&#x27;, &#x27;261208&#x27;, &#x27;4311539&#x27;, &#x27;259564&#x27;,\n",
       "       &#x27;258165&#x27;, &#x27;350664&#x27;, &#x27;305143&#x27;, &#x27;273388&#x27;, &#x27;1802717&#x27;, &#x27;4430921&#x27;, &#x27;812475&#x27;,\n",
       "       &#x27;192878&#x27;, &#x27;192557&#x27;, &#x27;167749&#x27;, &#x27;192180&#x27;, &#x27;302316&#x27;, &#x27;203713&#x27;, &#x27;193358&#x27;,\n",
       "       &#x27;194662&#x27;, &#x27;194087&#x27;, &#x27;229455&#x27;, &#x27;194656&#x27;, &#x27;192930&#x27;, &#x27;193418&#x27;, &#x27;262166&#x27;,\n",
       "       &#x27;289974&#x27;, &#x27;204093&#x27;, &#x27;3571630&#x27;, &#x27;177855&#x27;, &#x27;193064&#x27;, &#x27;261511&#x27;, &#x27;196194&#x27;,\n",
       "       &#x27;143690&#x27;, &#x27;4324038&#x27;, &#x27;197600&#x27;, &#x27;135956&#x27;, &#x27;276466&#x27;, &#x27;259173&#x27;, &#x27;259922&#x27;,\n",
       "       &#x27;261325&#x27;, &#x27;325289&#x27;, &#x27;208571&#x27;, &#x27;270396&#x27;, &#x27;4393466&#x27;, &#x27;266587&#x27;, &#x27;4465746&#x27;,\n",
       "       &#x27;239571&#x27;, &#x27;212228&#x27;, &#x27;298529&#x27;, &#x27;4392243&#x27;, &#x27;199070&#x27;, &#x27;106860&#x27;, &#x27;192462&#x27;,\n",
       "       &#x27;288680&#x27;, &#x27;174573&#x27;, &#x27;258717&#x27;, &#x27;195427&#x27;, &#x27;262807&#x27;, &#x27;4315660&#x27;, &#x27;399245&#x27;,\n",
       "       &#x27;193463&#x27;, &#x27;192368&#x27;, &#x27;193160&#x27;, &#x27;196689&#x27;, &#x27;260859&#x27;, &#x27;258177&#x27;, &#x27;196697&#x27;,\n",
       "       &#x27;263933&#x27;, &#x27;206578&#x27;, &#x27;213533&#x27;, &#x27;4374042&#x27;, &#x27;3694599&#x27;, &#x27;1684221&#x27;, &#x27;258522&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>covariate</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U36</div><div class='xr-var-preview xr-preview'>&quot;C(diet, Treatment(&#x27;Control&#x27;))[T...</div><input id='attrs-dfe7b651-a6fc-4155-a26b-81684aa94a30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dfe7b651-a6fc-4155-a26b-81684aa94a30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69046cf6-a1c6-4bae-a2e7-aaeefa3f783a' class='xr-var-data-in' type='checkbox'><label for='data-69046cf6-a1c6-4bae-a2e7-aaeefa3f783a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&quot;C(diet, Treatment(&#x27;Control&#x27;))[T.DIO]&quot;, dtype=&#x27;&lt;U36&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-37f159c5-2d55-44d9-8c9f-32b5bde924d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-37f159c5-2d55-44d9-8c9f-32b5bde924d6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'beta_var' (feature: 77)>\n",
       "array([-5.73694892, -2.65988109, -2.4219365 , -2.23708606, -2.00739175,\n",
       "       -1.9573763 , -1.69342822, -1.53772343, -1.33882338, -1.09196751,\n",
       "       -1.05590138, -0.86744068, -0.83328258, -0.82724836, -0.81196281,\n",
       "       -0.7135843 , -0.65096482, -0.6316501 , -0.58691858, -0.56759943,\n",
       "       -0.52819507, -0.52580754, -0.51376801, -0.43769074, -0.35128673,\n",
       "       -0.34720449, -0.34082285, -0.30568045, -0.29715427, -0.24836883,\n",
       "       -0.24346562, -0.23557318, -0.18048565, -0.02537629, -0.00989158,\n",
       "        0.02788562,  0.09380669,  0.142793  ,  0.16447638,  0.18803242,\n",
       "        0.1936418 ,  0.19895287,  0.20065998,  0.28541747,  0.29322137,\n",
       "        0.30317563,  0.31384448,  0.36595709,  0.37729278,  0.37940998,\n",
       "        0.42116538,  0.53334287,  0.54078781,  0.57996696,  0.58657954,\n",
       "        0.64947155,  0.66508215,  0.67095088,  0.67743187,  0.69672586,\n",
       "        0.70374091,  0.76906979,  0.77883749,  0.97571642,  1.07582325,\n",
       "        1.20113236,  1.31475314,  1.43184984,  1.44930872,  1.46225464,\n",
       "        1.51979415,  1.53214966,  1.78738834,  1.93900751,  2.16822761,\n",
       "        2.49023848,  2.67052281])\n",
       "Coordinates:\n",
       "  * feature    (feature) object '230759' '193831' ... '1684221' '258522'\n",
       "    covariate  <U36 \"C(diet, Treatment('Control'))[T.DIO]\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diet_diffs = inference.posterior[\"beta_var\"].sel({\"covariate\": \"C(diet, Treatment('Control'))[T.DIO]\"})\n",
    "diet_diffs = diet_diffs.stack(mcmc_sample=(\"chain\", \"draw\"))\n",
    "diet_diffs_means = diet_diffs.mean(dim=\"mcmc_sample\")\n",
    "diet_diffs_means = diet_diffs_means.sortby(diet_diffs_means)\n",
    "diet_diffs_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting log-ratios\n",
    "\n",
    "Finally, we'll calculate log-ratios using autoselected OTUs. We'll take the top and bottom 5 OTUs to use as our numerator and denominator respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def log_ratio(table, top_feats, bot_feats):\n",
    "    num_df = table.loc[:, top_feats].sum(axis=1).to_frame()# + 1\n",
    "    num_df.columns = [\"num\"]\n",
    "    num_df = num_df[num_df[\"num\"] > 0]\n",
    "    denom_df = table.loc[:, bot_feats].sum(axis=1).to_frame()# + 1\n",
    "    denom_df.columns = [\"denom\"]\n",
    "    denom_df = denom_df[denom_df[\"denom\"] > 0]\n",
    "    lr_df = num_df.join(denom_df, how=\"inner\")\n",
    "    lr_df[\"log_ratio\"] = np.log(lr_df[\"num\"]/lr_df[\"denom\"])\n",
    "    return lr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 59)\n"
     ]
    },
    {
     "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>num</th>\n",
       "      <th>denom</th>\n",
       "      <th>log_ratio</th>\n",
       "      <th>adiponectin</th>\n",
       "      <th>altitude</th>\n",
       "      <th>anonymized_name</th>\n",
       "      <th>b_cell_function_measure</th>\n",
       "      <th>body_habitat</th>\n",
       "      <th>body_product</th>\n",
       "      <th>body_site</th>\n",
       "      <th>...</th>\n",
       "      <th>saa3</th>\n",
       "      <th>sample_type</th>\n",
       "      <th>slc25a</th>\n",
       "      <th>t3</th>\n",
       "      <th>t4</th>\n",
       "      <th>title</th>\n",
       "      <th>tnfa</th>\n",
       "      <th>total_ee</th>\n",
       "      <th>total_number_of_synapses</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107.CM04</th>\n",
       "      <td>415.0</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>-1.101019</td>\n",
       "      <td>23.55</td>\n",
       "      <td>0</td>\n",
       "      <td>sample03</td>\n",
       "      <td>280.7</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>...</td>\n",
       "      <td>2.24</td>\n",
       "      <td>XXQIITAXX</td>\n",
       "      <td>1.01</td>\n",
       "      <td>15.30</td>\n",
       "      <td>57.87</td>\n",
       "      <td>Leptin levels correlate with the composition o...</td>\n",
       "      <td>1.17</td>\n",
       "      <td>11.70</td>\n",
       "      <td>19.06</td>\n",
       "      <td>Weight Reduced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.CM10</th>\n",
       "      <td>1889.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>2.136631</td>\n",
       "      <td>31.19</td>\n",
       "      <td>0</td>\n",
       "      <td>sample11</td>\n",
       "      <td>222.5</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>...</td>\n",
       "      <td>1.05</td>\n",
       "      <td>XXQIITAXX</td>\n",
       "      <td>0.87</td>\n",
       "      <td>38.90</td>\n",
       "      <td>56.13</td>\n",
       "      <td>Leptin levels correlate with the composition o...</td>\n",
       "      <td>2.22</td>\n",
       "      <td>13.93</td>\n",
       "      <td>15.82</td>\n",
       "      <td>Ad Lib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.CM19</th>\n",
       "      <td>1148.0</td>\n",
       "      <td>50976.0</td>\n",
       "      <td>-3.793334</td>\n",
       "      <td>22.85</td>\n",
       "      <td>0</td>\n",
       "      <td>sample25</td>\n",
       "      <td>93.7</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>XXQIITAXX</td>\n",
       "      <td>0.66</td>\n",
       "      <td>8.80</td>\n",
       "      <td>50.95</td>\n",
       "      <td>Leptin levels correlate with the composition o...</td>\n",
       "      <td>1.67</td>\n",
       "      <td>12.28</td>\n",
       "      <td>14.27</td>\n",
       "      <td>Ad Lib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.CM23</th>\n",
       "      <td>167.0</td>\n",
       "      <td>37841.0</td>\n",
       "      <td>-5.423155</td>\n",
       "      <td>14.35</td>\n",
       "      <td>0</td>\n",
       "      <td>sample27</td>\n",
       "      <td>140.0</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>XXQIITAXX</td>\n",
       "      <td>0.59</td>\n",
       "      <td>54.04</td>\n",
       "      <td>42.76</td>\n",
       "      <td>Leptin levels correlate with the composition o...</td>\n",
       "      <td>2.07</td>\n",
       "      <td>12.43</td>\n",
       "      <td>20.92</td>\n",
       "      <td>Ad Lib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107.CM28</th>\n",
       "      <td>20.0</td>\n",
       "      <td>1205.0</td>\n",
       "      <td>-4.098503</td>\n",
       "      <td>40.49</td>\n",
       "      <td>0</td>\n",
       "      <td>sample21</td>\n",
       "      <td>183.8</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>UBERON:feces</td>\n",
       "      <td>...</td>\n",
       "      <td>0.15</td>\n",
       "      <td>XXQIITAXX</td>\n",
       "      <td>0.49</td>\n",
       "      <td>13.42</td>\n",
       "      <td>43.39</td>\n",
       "      <td>Leptin levels correlate with the composition o...</td>\n",
       "      <td>0.80</td>\n",
       "      <td>7.37</td>\n",
       "      <td>30.64</td>\n",
       "      <td>Weight Reduced</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             num    denom  log_ratio  adiponectin  altitude anonymized_name  \\\n",
       "107.CM04   415.0   1248.0  -1.101019        23.55         0        sample03   \n",
       "107.CM10  1889.0    223.0   2.136631        31.19         0        sample11   \n",
       "107.CM19  1148.0  50976.0  -3.793334        22.85         0        sample25   \n",
       "107.CM23   167.0  37841.0  -5.423155        14.35         0        sample27   \n",
       "107.CM28    20.0   1205.0  -4.098503        40.49         0        sample21   \n",
       "\n",
       "          b_cell_function_measure  body_habitat  body_product     body_site  \\\n",
       "107.CM04                    280.7  UBERON:feces  UBERON:feces  UBERON:feces   \n",
       "107.CM10                    222.5  UBERON:feces  UBERON:feces  UBERON:feces   \n",
       "107.CM19                     93.7  UBERON:feces  UBERON:feces  UBERON:feces   \n",
       "107.CM23                    140.0  UBERON:feces  UBERON:feces  UBERON:feces   \n",
       "107.CM28                    183.8  UBERON:feces  UBERON:feces  UBERON:feces   \n",
       "\n",
       "          ...  saa3  sample_type slc25a     t3     t4  \\\n",
       "107.CM04  ...  2.24    XXQIITAXX   1.01  15.30  57.87   \n",
       "107.CM10  ...  1.05    XXQIITAXX   0.87  38.90  56.13   \n",
       "107.CM19  ...  0.00    XXQIITAXX   0.66   8.80  50.95   \n",
       "107.CM23  ...  0.01    XXQIITAXX   0.59  54.04  42.76   \n",
       "107.CM28  ...  0.15    XXQIITAXX   0.49  13.42  43.39   \n",
       "\n",
       "                                                      title  tnfa  total_ee  \\\n",
       "107.CM04  Leptin levels correlate with the composition o...  1.17     11.70   \n",
       "107.CM10  Leptin levels correlate with the composition o...  2.22     13.93   \n",
       "107.CM19  Leptin levels correlate with the composition o...  1.67     12.28   \n",
       "107.CM23  Leptin levels correlate with the composition o...  2.07     12.43   \n",
       "107.CM28  Leptin levels correlate with the composition o...  0.80      7.37   \n",
       "\n",
       "          total_number_of_synapses       treatment  \n",
       "107.CM04                     19.06  Weight Reduced  \n",
       "107.CM10                     15.82          Ad Lib  \n",
       "107.CM19                     14.27          Ad Lib  \n",
       "107.CM23                     20.92          Ad Lib  \n",
       "107.CM28                     30.64  Weight Reduced  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_otus = diet_diffs_means[:5].coords[\"feature\"].values\n",
    "top_otus = diet_diffs_means[-5:].coords[\"feature\"].values\n",
    "\n",
    "lr_df = log_ratio(filt_tbl_df.T, top_otus, bottom_otus).join(metadata, how=\"inner\")\n",
    "print(lr_df.shape)\n",
    "lr_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now plot these log-ratios and compare them by diet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "args = {\n",
    "    \"data\": lr_df,\n",
    "    \"x\": \"diet\",\n",
    "    \"y\": \"log_ratio\"\n",
    "}\n",
    "\n",
    "sns.boxplot(**args)\n",
    "sns.stripplot(**args, linewidth=2, size=10, jitter=0.3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=188.0, pvalue=2.1589841841116642e-05)\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as ss\n",
    "\n",
    "dio_samples = lr_df.query(\"diet == 'DIO'\")[\"log_ratio\"]\n",
    "control_samples = lr_df.query(\"diet == 'Control'\")[\"log_ratio\"]\n",
    "\n",
    "print(ss.mannwhitneyu(dio_samples, control_samples))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, this log-ratio separates the samples well by diet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical tests\n",
    "\n",
    "We plan to implement several statistical tests to help guide users in their analysis in determining covariates/features of interest. One such option is the Hotelling $t^2$-test on individual covariates to see if they are centered around 0 (in ALR coordinates)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(434.73755654521096, 1.1102230246251565e-16)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from birdman.stats import hotelling_ttest\n",
    "\n",
    "hotelling_ttest(inference, {\"covariate\": \"C(diet, Treatment('Control'))[T.DIO]\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarizing\n",
    "\n",
    "We may want to save the differentials in tabular format for downstream analysis. We can summarize each feature by taking the mean of the posterior distribution. We can do this easily with the `summarize_posterior` function in BIRDMAn. The means are calculated by default but we can also calculate the standard deviation just as easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Intercept_mean</th>\n",
       "      <th>C(diet, Treatment('Control'))[T.DIO]_mean</th>\n",
       "      <th>C(treatment, Treatment('Ad Lib'))[T.Weight Reduced]_mean</th>\n",
       "      <th>C(diet, Treatment('Control'))[T.DIO]:C(treatment, Treatment('Ad Lib'))[T.Weight Reduced]_mean</th>\n",
       "      <th>Intercept_std</th>\n",
       "      <th>C(diet, Treatment('Control'))[T.DIO]_std</th>\n",
       "      <th>C(treatment, Treatment('Ad Lib'))[T.Weight Reduced]_std</th>\n",
       "      <th>C(diet, Treatment('Control'))[T.DIO]:C(treatment, Treatment('Ad Lib'))[T.Weight Reduced]_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193358</th>\n",
       "      <td>5.973868</td>\n",
       "      <td>-0.528195</td>\n",
       "      <td>0.457452</td>\n",
       "      <td>-0.288822</td>\n",
       "      <td>0.045675</td>\n",
       "      <td>0.071277</td>\n",
       "      <td>0.064878</td>\n",
       "      <td>0.091214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4465746</th>\n",
       "      <td>2.359666</td>\n",
       "      <td>0.377293</td>\n",
       "      <td>0.065117</td>\n",
       "      <td>0.286920</td>\n",
       "      <td>0.442848</td>\n",
       "      <td>0.728221</td>\n",
       "      <td>0.600781</td>\n",
       "      <td>0.949312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4430921</th>\n",
       "      <td>-1.083593</td>\n",
       "      <td>-0.833283</td>\n",
       "      <td>-0.088493</td>\n",
       "      <td>1.865766</td>\n",
       "      <td>0.464448</td>\n",
       "      <td>0.726940</td>\n",
       "      <td>0.696268</td>\n",
       "      <td>0.942773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260859</th>\n",
       "      <td>-1.792018</td>\n",
       "      <td>1.431850</td>\n",
       "      <td>-0.494344</td>\n",
       "      <td>-0.055308</td>\n",
       "      <td>0.366695</td>\n",
       "      <td>0.499573</td>\n",
       "      <td>0.637038</td>\n",
       "      <td>0.771301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192557</th>\n",
       "      <td>-0.081276</td>\n",
       "      <td>-0.713584</td>\n",
       "      <td>-0.824521</td>\n",
       "      <td>0.643660</td>\n",
       "      <td>0.307847</td>\n",
       "      <td>0.495731</td>\n",
       "      <td>0.499313</td>\n",
       "      <td>0.686688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Intercept_mean  C(diet, Treatment('Control'))[T.DIO]_mean  \\\n",
       "feature                                                              \n",
       "193358         5.973868                                  -0.528195   \n",
       "4465746        2.359666                                   0.377293   \n",
       "4430921       -1.083593                                  -0.833283   \n",
       "260859        -1.792018                                   1.431850   \n",
       "192557        -0.081276                                  -0.713584   \n",
       "\n",
       "         C(treatment, Treatment('Ad Lib'))[T.Weight Reduced]_mean  \\\n",
       "feature                                                             \n",
       "193358                                            0.457452          \n",
       "4465746                                           0.065117          \n",
       "4430921                                          -0.088493          \n",
       "260859                                           -0.494344          \n",
       "192557                                           -0.824521          \n",
       "\n",
       "         C(diet, Treatment('Control'))[T.DIO]:C(treatment, Treatment('Ad Lib'))[T.Weight Reduced]_mean  \\\n",
       "feature                                                                                                  \n",
       "193358                                           -0.288822                                               \n",
       "4465746                                           0.286920                                               \n",
       "4430921                                           1.865766                                               \n",
       "260859                                           -0.055308                                               \n",
       "192557                                            0.643660                                               \n",
       "\n",
       "         Intercept_std  C(diet, Treatment('Control'))[T.DIO]_std  \\\n",
       "feature                                                            \n",
       "193358        0.045675                                  0.071277   \n",
       "4465746       0.442848                                  0.728221   \n",
       "4430921       0.464448                                  0.726940   \n",
       "260859        0.366695                                  0.499573   \n",
       "192557        0.307847                                  0.495731   \n",
       "\n",
       "         C(treatment, Treatment('Ad Lib'))[T.Weight Reduced]_std  \\\n",
       "feature                                                            \n",
       "193358                                            0.064878         \n",
       "4465746                                           0.600781         \n",
       "4430921                                           0.696268         \n",
       "260859                                            0.637038         \n",
       "192557                                            0.499313         \n",
       "\n",
       "         C(diet, Treatment('Control'))[T.DIO]:C(treatment, Treatment('Ad Lib'))[T.Weight Reduced]_std  \n",
       "feature                                                                                                \n",
       "193358                                            0.091214                                             \n",
       "4465746                                           0.949312                                             \n",
       "4430921                                           0.942773                                             \n",
       "260859                                            0.771301                                             \n",
       "192557                                            0.686688                                             "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from birdman.summary import summarize_posterior\n",
    "\n",
    "summ_means = summarize_posterior(inference.posterior, \"beta_var\")\n",
    "summ_means.columns = [x + \"_mean\" for x in summ_means.columns]\n",
    "summ_stds = summarize_posterior(inference.posterior, \"beta_var\", estimator=\"std\")\n",
    "summ_stds.columns = [x + \"_std\" for x in summ_stds.columns]\n",
    "\n",
    "summ_df = pd.concat([summ_means, summ_stds], axis=1)\n",
    "summ_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrapping up\n",
    "\n",
    "We hope you learned a bit about using BIRDMAn for differential abundance! There's definitely a lot to digest here and we only scratched the surface of the possibilities. You may also want to view the traceplots for individual parameters to see if the chains have converged or view the [effective sample size](https://mc-stan.org/docs/2_26/reference-manual/effective-sample-size-section.html).\n",
    "\n",
    "Check out the other notebook in this repository for an example of how to write and fit your own Stan model using BIRDMAn!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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