logistic_bambi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz as az\n",
    "import bambi as bmb\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.style.use(\"arviz-darkgrid\")\n",
    "SEED=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "diseased_means = np.array([2.80549926, 2.79666096, 2.9869277,  3.962205])\n",
    "\n",
    "diseased_cov = np.array([[ 0.19818925,  0.00432564, -0.02196359,  0.01666092],\n",
    " [ 0.00432564,  0.21659711, -0.02382195, -0.00258357],\n",
    " [-0.02196359, -0.02382195,  0.07842352,  0.02014519],\n",
    " [ 0.01666092, -0.00258357,  0.02014519,  0.0301144 ]])\n",
    "\n",
    "healthy_means = np.array([2.3076398,  3.33769212, 3.19855357, 3.90669097])\n",
    "\n",
    "healthy_cov = np.array([[ 0.08162375,  0.03669294,  0.01955666,  0.02087333],\n",
    " [ 0.03669294,  0.23478904, -0.02114343, -0.01991113],\n",
    " [ 0.01955666, -0.02114343,  0.04190113,  0.01566071],\n",
    " [ 0.02087333, -0.01991113,  0.01566071,  0.02848467]])\n",
    "\n",
    "genes = ['A','B','C','D']\n",
    "\n",
    "rng = np.random.default_rng(SEED)\n",
    "\n",
    "diseased_samples = pd.DataFrame(rng.multivariate_normal(diseased_means, diseased_cov, 20), columns=genes).assign(Dx=True)\n",
    "healthy_samples = pd.DataFrame(rng.multivariate_normal(healthy_means, healthy_cov, 20), columns=genes).assign(Dx=False)\n",
    "patients = pd.concat([diseased_samples,healthy_samples], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tomas/anaconda3/envs/bmb/lib/python3.8/site-packages/seaborn/axisgrid.py:64: UserWarning: This figure was using constrained_layout, but that is incompatible with subplots_adjust and/or tight_layout; disabling constrained_layout.\n",
      "  self.fig.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f24b29f38e0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1119x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(patients, hue='Dx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2.523901\n",
       "B    3.082775\n",
       "C    3.108742\n",
       "D    3.912432\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_means = patients[genes].mean(0)\n",
    "cntr_patients = patients.copy()\n",
    "cntr_patients[genes] = cntr_patients[genes] - patients_means\n",
    "patients_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>Dx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.582543</td>\n",
       "      <td>-0.575459</td>\n",
       "      <td>-0.135688</td>\n",
       "      <td>-0.121367</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.330818</td>\n",
       "      <td>-0.728639</td>\n",
       "      <td>0.054388</td>\n",
       "      <td>0.202846</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.335780</td>\n",
       "      <td>-0.494823</td>\n",
       "      <td>-0.134017</td>\n",
       "      <td>0.128997</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.332867</td>\n",
       "      <td>0.079791</td>\n",
       "      <td>-0.094507</td>\n",
       "      <td>0.171785</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.182319</td>\n",
       "      <td>-0.220569</td>\n",
       "      <td>0.106031</td>\n",
       "      <td>0.091745</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D    Dx\n",
       "0  0.582543 -0.575459 -0.135688 -0.121367  True\n",
       "1  0.330818 -0.728639  0.054388  0.202846  True\n",
       "2  0.335780 -0.494823 -0.134017  0.128997  True\n",
       "3  0.332867  0.079791 -0.094507  0.171785  True\n",
       "4  0.182319 -0.220569  0.106031  0.091745  True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cntr_patients.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Modeling the probability that Dx==1\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 2 jobs)\n",
      "NUTS: [D, C, B, A]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='44000' class='' max='44000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [44000/44000 00:24<00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 26 seconds.\n"
     ]
    }
   ],
   "source": [
    "bmb_model = bmb.Model('Dx ~ 0 + ' + ' + '.join(genes), cntr_patients, family=\"bernoulli\")\n",
    "\n",
    "# Fit the model using 10000 on each of 4 chains\n",
    "bmb_idata = bmb_model.fit(draws=10000, chains=4, random_seed=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "bmb_model.predict(bmb_idata, kind='pps')\n",
    "\n",
    "# compute pointwise LOO\n",
    "loo = az.loo(bmb_idata, pointwise=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_khat(loo.pareto_k)\n",
    "sorted_kappas = np.sort(loo.pareto_k.values.ravel())\n",
    "\n",
    "# find observation where the kappa value exceeds the threshold\n",
    "threshold = 0.5\n",
    "ax.axhline(threshold, ls=\"--\", color=\"orange\")\n",
    "influential_observations = cntr_patients.reset_index()[loo.pareto_k.values >= threshold].index\n",
    "\n",
    "for x in influential_observations:\n",
    "    y = loo.pareto_k.values[x]\n",
    "    ax.text(x, y + 0.01, str(x), ha=\"center\", va=\"baseline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>Dx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.458574</td>\n",
       "      <td>4.284597</td>\n",
       "      <td>2.865789</td>\n",
       "      <td>3.848123</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.681139</td>\n",
       "      <td>2.607368</td>\n",
       "      <td>2.673873</td>\n",
       "      <td>4.054039</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D    Dx\n",
       "6  2.458574  4.284597  2.865789  3.848123  True\n",
       "8  3.681139  2.607368  2.673873  4.054039  True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers = patients[loo.pareto_k.values>=threshold].index\n",
    "patients[loo.pareto_k.values>=threshold]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cntr_patients_f = cntr_patients.drop(index=outliers).reset_index(drop=True)\n",
    "cntr_patients_f[genes] -= cntr_patients_f[genes].mean(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymc3 as pm\n",
    "import theano.tensor as tt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Modeling the probability that Dx==1\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 2 jobs)\n",
      "NUTS: [D, C, B, A]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='44000' class='' max='44000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [44000/44000 00:24<00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 25 seconds.\n"
     ]
    }
   ],
   "source": [
    "bmb_model = bmb.Model('Dx ~ 0 + ' + ' + '.join(genes), cntr_patients_f, family=\"bernoulli\")\n",
    "bmb_idata = bmb_model.fit(draws=10000, chains=4, random_seed=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior_mus = np.zeros(len(genes))\n",
    "prior_sigmas = np.array([prior['sigma'] for prior in bmb_model.scaler.priors.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 2 jobs)\n",
      "NUTS: [slopes]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
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       "                border: none;\n",
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       "                background-size: auto;\n",
       "            }\n",
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       "                background: #F44336;\n",
       "            }\n",
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       "      100.00% [44000/44000 00:22<00:00 Sampling 4 chains, 0 divergences]\n",
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       "    "
      ],
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       "<IPython.core.display.HTML object>"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 23 seconds.\n"
     ]
    }
   ],
   "source": [
    "coords = {'gene': genes, 'sample': cntr_patients_f.index}\n",
    "with pm.Model(coords=coords) as pm_model_1:\n",
    "    data = pm.Data('data', cntr_patients_f[genes].values, dims=('sample', 'gene'))\n",
    "    slopes = pm.Normal('slopes', prior_mus, prior_sigmas.flatten(), dims='gene')\n",
    "    logit_p = pm.Deterministic(\"logit_p\", pm.math.dot(data, slopes))\n",
    "    p = pm.Deterministic('p', pm.math.invlogit(logit_p))\n",
    "    _ = pm.Bernoulli('Dx', p=p, observed=cntr_patients_f.Dx.values)\n",
    "    \n",
    "    pm_idata_1 = pm.sample(draws=10000, chains=4, return_inferencedata=True, random_seed=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 2 jobs)\n",
      "NUTS: [slopes]\n"
     ]
    },
    {
     "data": {
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       "                border: none;\n",
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       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
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       "      100.00% [44000/44000 00:25<00:00 Sampling 4 chains, 0 divergences]\n",
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       "    "
      ],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 26 seconds.\n"
     ]
    }
   ],
   "source": [
    "X = cntr_patients_f[genes].values\n",
    "with pm.Model(coords=coords) as pm_model_2:\n",
    "    slopes = pm.Normal('slopes', prior_mus, prior_sigmas.flatten(), dims='gene')\n",
    "    logit_p = pm.Deterministic(\"logit_p\", pm.math.dot(X, slopes))\n",
    "    p = pm.Deterministic('p', pm.math.invlogit(logit_p))\n",
    "    _ = pm.Bernoulli('Dx', p=p, observed=cntr_patients_f.Dx.values)\n",
    "    \n",
    "    pm_idata_2 = pm.sample(draws=10000, chains=4, return_inferencedata=True, random_seed=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 2 jobs)\n",
      "NUTS: [D, C, B, A]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
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       "            /* Turns off some styling */\n",
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       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
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       "      100.00% [44000/44000 00:20<00:00 Sampling 4 chains, 0 divergences]\n",
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       "    "
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      ]
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     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 20 seconds.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as pm_model_3:\n",
    "    bA = pm.Normal(\"A\", mu=0, sigma=prior_sigmas[0])[None]\n",
    "    bB = pm.Normal(\"B\", mu=0, sigma=prior_sigmas[1])[None]\n",
    "    bC = pm.Normal(\"C\", mu=0, sigma=prior_sigmas[2])[None]\n",
    "    bD = pm.Normal(\"D\", mu=0, sigma=prior_sigmas[3])[None]\n",
    "    \n",
    "    b = tt.concatenate([bA, bB, bC, bD])\n",
    "    logit_p = pm.math.dot(X, b)\n",
    "    p = pm.math.invlogit(logit_p)\n",
    "    pm.Bernoulli(\"Dx\", p=p, observed=cntr_patients_f.Dx.values)\n",
    "    pm_idata_3 = pm.sample(draws=10000, chains=4, return_inferencedata=True, random_seed=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 2 jobs)\n",
      "NUTS: [D, C, B, A]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
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       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
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       "      100.00% [44000/44000 00:35<00:00 Sampling 4 chains, 0 divergences]\n",
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       "    "
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 36 seconds.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as pm_model_4:\n",
    "    bA = pm.Normal(\"A\", mu=0, sigma=prior_sigmas[0])\n",
    "    bB = pm.Normal(\"B\", mu=0, sigma=prior_sigmas[1])\n",
    "    bC = pm.Normal(\"C\", mu=0, sigma=prior_sigmas[2])\n",
    "    bD = pm.Normal(\"D\", mu=0, sigma=prior_sigmas[3])\n",
    "    \n",
    "    logit_p = bA * X[:, 0] + bB * X[:, 1] + bC * X[:, 2] + bD * X[:, 3]\n",
    "    p = pm.math.invlogit(logit_p)\n",
    "    pm.Bernoulli(\"Dx\", p=p, observed=cntr_patients_f.Dx.values)\n",
    "    pm_idata_4 = pm.sample(draws=10000, chains=4, return_inferencedata=True, random_seed=SEED)"
   ]
  },
  {
   "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>6.496</td>\n",
       "      <td>2.545</td>\n",
       "      <td>1.977</td>\n",
       "      <td>11.359</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.012</td>\n",
       "      <td>24087.0</td>\n",
       "      <td>23537.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-4.444</td>\n",
       "      <td>2.138</td>\n",
       "      <td>-8.523</td>\n",
       "      <td>-0.650</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.011</td>\n",
       "      <td>23702.0</td>\n",
       "      <td>20285.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-14.140</td>\n",
       "      <td>5.300</td>\n",
       "      <td>-24.143</td>\n",
       "      <td>-4.531</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.027</td>\n",
       "      <td>21650.0</td>\n",
       "      <td>23638.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>5.348</td>\n",
       "      <td>7.206</td>\n",
       "      <td>-7.494</td>\n",
       "      <td>19.329</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.037</td>\n",
       "      <td>22881.0</td>\n",
       "      <td>22479.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "A   6.496  2.545   1.977   11.359      0.017    0.012   24087.0   23537.0   \n",
       "B  -4.444  2.138  -8.523   -0.650      0.014    0.011   23702.0   20285.0   \n",
       "C -14.140  5.300 -24.143   -4.531      0.036    0.027   21650.0   23638.0   \n",
       "D   5.348  7.206  -7.494   19.329      0.048    0.037   22881.0   22479.0   \n",
       "\n",
       "   r_hat  \n",
       "A    1.0  \n",
       "B    1.0  \n",
       "C    1.0  \n",
       "D    1.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(bmb_idata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>slopes[A]</th>\n",
       "      <td>5.114</td>\n",
       "      <td>1.792</td>\n",
       "      <td>1.772</td>\n",
       "      <td>8.454</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.007</td>\n",
       "      <td>39953.0</td>\n",
       "      <td>28236.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slopes[B]</th>\n",
       "      <td>-3.123</td>\n",
       "      <td>1.414</td>\n",
       "      <td>-5.784</td>\n",
       "      <td>-0.540</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>39603.0</td>\n",
       "      <td>29319.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slopes[C]</th>\n",
       "      <td>-6.026</td>\n",
       "      <td>2.493</td>\n",
       "      <td>-10.668</td>\n",
       "      <td>-1.321</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.009</td>\n",
       "      <td>40410.0</td>\n",
       "      <td>30231.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slopes[D]</th>\n",
       "      <td>0.481</td>\n",
       "      <td>2.954</td>\n",
       "      <td>-4.974</td>\n",
       "      <td>6.145</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.014</td>\n",
       "      <td>44160.0</td>\n",
       "      <td>31579.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "slopes[A]  5.114  1.792   1.772    8.454      0.009    0.007   39953.0   \n",
       "slopes[B] -3.123  1.414  -5.784   -0.540      0.007    0.005   39603.0   \n",
       "slopes[C] -6.026  2.493 -10.668   -1.321      0.012    0.009   40410.0   \n",
       "slopes[D]  0.481  2.954  -4.974    6.145      0.014    0.014   44160.0   \n",
       "\n",
       "           ess_tail  r_hat  \n",
       "slopes[A]   28236.0    1.0  \n",
       "slopes[B]   29319.0    1.0  \n",
       "slopes[C]   30231.0    1.0  \n",
       "slopes[D]   31579.0    1.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(pm_idata_1, var_names=[\"slopes\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'var names: \"[\\'A\\' \\'B\\' \\'C\\' \\'D\\'] are not present\" in dataset'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/bmb/lib/python3.8/site-packages/arviz/utils.py\u001b[0m in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m             \u001b[0mvar_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_subset_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter_items\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilter_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/bmb/lib/python3.8/site-packages/arviz/utils.py\u001b[0m in \u001b[0;36m_subset_list\u001b[0;34m(subset, whole_list, filter_items, warn)\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexisting_items\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{np.array(subset)[~existing_items]} are not present\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['A' 'B' 'C' 'D'] are not present\"",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-afc1fa33b1ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpm_idata_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"A\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"B\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"C\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"D\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/bmb/lib/python3.8/site-packages/arviz/stats/stats.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(data, var_names, filter_vars, group, fmt, kind, round_to, circ_var_names, stat_funcs, extend, hdi_prob, skipna, labeller, coords, index_origin, order)\u001b[0m\n\u001b[1;32m   1186\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1187\u001b[0m         \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"posterior\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1188\u001b[0;31m     \u001b[0mvar_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_var_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1189\u001b[0m     \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvar_names\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar_names\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1190\u001b[0m     \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_coords\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/bmb/lib/python3.8/site-packages/arviz/utils.py\u001b[0m in \u001b[0;36m_var_names\u001b[0;34m(var_names, data, filter_vars)\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\" \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"var names:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"{err}\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"in dataset\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mvar_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'var names: \"[\\'A\\' \\'B\\' \\'C\\' \\'D\\'] are not present\" in dataset'"
     ]
    }
   ],
   "source": [
    "az.summary(pm_idata_2, var_names=[\"slopes\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>6.472</td>\n",
       "      <td>2.524</td>\n",
       "      <td>2.035</td>\n",
       "      <td>11.357</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.012</td>\n",
       "      <td>25723.0</td>\n",
       "      <td>23100.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-4.440</td>\n",
       "      <td>2.119</td>\n",
       "      <td>-8.508</td>\n",
       "      <td>-0.678</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.011</td>\n",
       "      <td>23786.0</td>\n",
       "      <td>20007.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-14.105</td>\n",
       "      <td>5.264</td>\n",
       "      <td>-24.183</td>\n",
       "      <td>-4.646</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.027</td>\n",
       "      <td>22200.0</td>\n",
       "      <td>22314.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>5.382</td>\n",
       "      <td>7.170</td>\n",
       "      <td>-7.452</td>\n",
       "      <td>19.254</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.037</td>\n",
       "      <td>24107.0</td>\n",
       "      <td>21354.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "A   6.472  2.524   2.035   11.357      0.016    0.012   25723.0   23100.0   \n",
       "B  -4.440  2.119  -8.508   -0.678      0.014    0.011   23786.0   20007.0   \n",
       "C -14.105  5.264 -24.183   -4.646      0.036    0.027   22200.0   22314.0   \n",
       "D   5.382  7.170  -7.452   19.254      0.047    0.037   24107.0   21354.0   \n",
       "\n",
       "   r_hat  \n",
       "A    1.0  \n",
       "B    1.0  \n",
       "C    1.0  \n",
       "D    1.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(pm_idata_3, var_names=[\"A\", \"B\", \"C\", \"D\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>6.484</td>\n",
       "      <td>2.553</td>\n",
       "      <td>1.859</td>\n",
       "      <td>11.244</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.012</td>\n",
       "      <td>23791.0</td>\n",
       "      <td>22275.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-4.447</td>\n",
       "      <td>2.117</td>\n",
       "      <td>-8.478</td>\n",
       "      <td>-0.699</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.011</td>\n",
       "      <td>23569.0</td>\n",
       "      <td>20885.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-14.086</td>\n",
       "      <td>5.326</td>\n",
       "      <td>-24.362</td>\n",
       "      <td>-4.675</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.027</td>\n",
       "      <td>21709.0</td>\n",
       "      <td>23628.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>5.337</td>\n",
       "      <td>7.238</td>\n",
       "      <td>-7.361</td>\n",
       "      <td>19.722</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.037</td>\n",
       "      <td>23857.0</td>\n",
       "      <td>23001.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "A   6.484  2.553   1.859   11.244      0.017    0.012   23791.0   22275.0   \n",
       "B  -4.447  2.117  -8.478   -0.699      0.014    0.011   23569.0   20885.0   \n",
       "C -14.086  5.326 -24.362   -4.675      0.037    0.027   21709.0   23628.0   \n",
       "D   5.337  7.238  -7.361   19.722      0.047    0.037   23857.0   23001.0   \n",
       "\n",
       "   r_hat  \n",
       "A    1.0  \n",
       "B    1.0  \n",
       "C    1.0  \n",
       "D    1.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(pm_idata_4, var_names=[\"A\", \"B\", \"C\", \"D\"])"
   ]
  }
 ],
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