{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b2f8545bfe2a",
   "metadata": {},
   "source": [
    "# Logistic regression analyses: linear and cubic models\n",
    "\n",
    "This notebook consolidates the analyses previously split across two notebooks. The same lung SUV data are analysed on both the **plain** and **log-transformed** predictor scales using three regression models:\n",
    "\n",
    "1. [Standard (linear) logistic regression](#2-standard-linear-logistic-regression)\n",
    "2. [Cubic unconstrained (non-monotonic) logistic regression](#3-cubic-unconstrained-non-monotonic-logistic-regression)\n",
    "3. [Cubic constrained (monotonic) logistic regression](#4-cubic-constrained-monotonic-logistic-regression)\n",
    "\n",
    "The existing numerical and graphical outputs are retained from the source notebooks. Use **Restart Kernel and Run All Cells** to reproduce the complete analysis in the merged order. Each section also stores its main objects in a model-specific results dictionary for later comparison.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f6904454fdc",
   "metadata": {},
   "source": [
    "## 1. Shared setup and data\n",
    "\n",
    "The data-loading and predictor-transformation steps are common to all three models. Model-specific figures are written to separate subdirectories below `../results/logit_models/`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "737bb31e0d5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import sys\n",
    "\n",
    "project_root = Path.cwd().parent   # when notebook is in project/notebooks/\n",
    "src_dir = project_root / \"src\"\n",
    "\n",
    "if str(src_dir) not in sys.path: sys.path.insert(0, str(src_dir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b867066e59d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# our libs\n",
    "from irae_risk import data\n",
    "from irae_risk import logistic\n",
    "\n",
    "np.set_printoptions(precision=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86f652a20401",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = Path(\"../data/raw\")\n",
    "results_root = Path(\"../results/logit_models\")\n",
    "results_root.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "df = data.load_mat_data_to_dataframe(\n",
    "    suv_filename=data_path / \"suv_percentilesSLOthenUWM.mat\",\n",
    "    flags_filename=data_path / \"flags_combined.mat\",\n",
    "    nr_patients=58,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5956c35b9f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get data\n",
    "perc = 95\n",
    "organ = \"lung\"\n",
    "\n",
    "x0, y = data.get_data_from_dataframe(df, organ, perc)\n",
    "\n",
    "scales = [\"plain\", \"log\"]\n",
    "xs = [x0, np.log(x0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f8e352bec94c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Two-sided 95% confidence intervals used throughout the notebook\n",
    "alpha = 0.05\n",
    "probs = [alpha / 2, 1 - alpha / 2]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c8948cbea0",
   "metadata": {},
   "source": [
    "## 2. Standard (linear) logistic regression\n",
    "\n",
    "The log-odds are linear in the selected predictor scale,\n",
    "\n",
    "$$\n",
    "\\eta(t) = \\beta_0 + \\beta_1 t,\n",
    "\\qquad\n",
    "p(t) = \\operatorname{expit}\\!\\left(\\eta(t)\\right).\n",
    "$$\n",
    "\n",
    "The model is unconstrained (`degree=1`, `mono=False`). The analysis includes the fit, asymptotic parameter covariance and confidence intervals, goodness-of-fit measures, and normal/delta confidence bands for the fitted risk curve.\n",
    "\n",
    "References: [scikit-learn logistic regression documentation](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) and [discussion of the optimized objective](https://stats.stackexchange.com/questions/186830/what-is-scikit-learns-logisticregression-minimizing).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39cbb1957ccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_path = results_root / \"linear\"\n",
    "results_path.mkdir(parents=True, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e977ed6c50",
   "metadata": {},
   "source": [
    "### 2.1 Fit and parameter uncertainty\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "774797739c35",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parss = array([[-11.68387204638564 ,   5.686965443671553],\n",
      "       [ -7.527409035255565,  10.73815986880034 ]])\n"
     ]
    }
   ],
   "source": [
    "# unconstrained linear logistic-regression model\n",
    "logit = logistic.LogisticPolyRegression(degree=1, mono=False)\n",
    "\n",
    "# fits\n",
    "fit_results = [logit.fit(x, y, method=\"local\") for x in xs]\n",
    "if not all(result[\"success\"] for result in fit_results):\n",
    "    raise RuntimeError(\"At least one linear logistic-regression fit failed.\")\n",
    "\n",
    "parss = np.array([result[\"pars\"] for result in fit_results])\n",
    "print(f\"{parss = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82a3535b87ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cov_parss = array([[[12.32303701551929 , -6.712919128276373],\n",
      "        [-6.712919128276373,  3.818783925853122]],\n",
      "\n",
      "       [[ 5.050236216443688, -7.934551247510333],\n",
      "        [-7.934551247510333, 14.005977815082305]]])\n"
     ]
    }
   ],
   "source": [
    "# asymptotic covariance matrix of parameters\n",
    "cov_parss = np.array([logit.get_cov(x, y, pars) for x, pars in zip(xs, parss)])\n",
    "print(f\"{cov_parss = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a4a84bad7b8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pars_CIs = array([[[-18.56416558961523  ,   1.856860850283348 ],\n",
      "        [ -4.803578503156049 ,   9.517070037059757 ]],\n",
      "\n",
      "       [[-11.931983300748945 ,   3.4030806585808495],\n",
      "        [ -3.1228347697621865,  18.073239079019828 ]]])\n"
     ]
    }
   ],
   "source": [
    "# CI of params (assuming asymptotic distr of parameters)\n",
    "pars_CIs = np.array([\n",
    "    logit.get_pars_quantiles_normal(probs, pars, cov_pars)\n",
    "    for pars, cov_pars in zip(parss, cov_parss)\n",
    "])\n",
    "\n",
    "print(f\"{pars_CIs = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2e5269ad11c",
   "metadata": {},
   "outputs": [
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       "                value        LCL        UCL\n",
       "scale coef                                 \n",
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       "      b1     5.686965   1.856861   9.517070\n",
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   "source": [
    "# present table of results\n",
    "d = parss.shape[-1]\n",
    "index = pd.MultiIndex.from_tuples([(lab, f\"b{i}\") for lab in scales for i in range(d)], names = ['scale', 'coef'])\n",
    "pd.DataFrame({\"value\": parss.flatten(), \"LCL\": pars_CIs[:,0].flatten(), \"UCL\": pars_CIs[:,1].flatten()}, index = index)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5fc72adc9c57",
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       "      <th></th>\n",
       "      <th>LLF</th>\n",
       "      <th>AIC</th>\n",
       "      <th>BIC</th>\n",
       "      <th>A</th>\n",
       "      <th>chi2</th>\n",
       "      <th>p-value(chi2)</th>\n",
       "      <th>n</th>\n",
       "      <th>k</th>\n",
       "      <th>dof</th>\n",
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       "    <tr>\n",
       "      <th>scale</th>\n",
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       "      <td>56</td>\n",
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       "            LLF       AIC        BIC         A       chi2  p-value(chi2)   n  \\\n",
       "scale                                                                          \n",
       "plain -7.394195  18.78839  22.909276  0.965517  23.103221       0.999971  58   \n",
       "log   -6.795375  17.59075  21.711636  0.965517  19.362333       0.999999  58   \n",
       "\n",
       "       k  dof  \n",
       "scale          \n",
       "plain  2   56  \n",
       "log    2   56  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "# goodness of fit measures\n",
    "index = pd.Index(scales, name = 'scale')\n",
    "pd.DataFrame(\n",
    "    [logit.goodness_of_fit(x, y, pars) for x, pars in zip(xs, parss)],\n",
    "    index=index,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32deb7403fd0",
   "metadata": {},
   "source": [
    "### 2.2 Fitted risk curve and confidence bands\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5630ea114f77",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting results\n",
    "fig, axs = plt.subplots(ncols = len(scales), figsize = (6*len(scales), 5))\n",
    "\n",
    "quantile_methods = {\n",
    "    \"normal\": logit.get_model_quantiles_normal,\n",
    "    \"delta\": logit.get_model_quantiles_delta,\n",
    "}\n",
    "\n",
    "for ax, scale, x, pars, cov_pars in zip(axs, scales, xs, parss, cov_parss):\n",
    "\n",
    "    ax.set_title(f\"cond. prob. for AE: scale {scale}\")\n",
    "\n",
    "    if scale == \"plain\":\n",
    "        xlab = r\"$x = max_{visit} SUV(visit, p)$\"\n",
    "    else:\n",
    "        xlab = r\"$x = log(max_{visit} SUV(visit, p))$\"\n",
    "    \n",
    "    ax.set_xlabel(xlab)\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # plotting data points\n",
    "    for i, group_label in enumerate([\"NC\", \"AE\"]):\n",
    "        ax.scatter(x[y == i], y[y == i], label=group_label)\n",
    "\n",
    "    # plotting fitted model \n",
    "    xp = np.linspace(min(x), max(x), 100)\n",
    "    yp = logit.model(xp, pars)\n",
    "\n",
    "    ax.plot(xp, yp, label = \"logistic reg\")\n",
    "\n",
    "    for ci_label, color in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        # get result of model quantiles at probs\n",
    "        # pars and cov_pars are obtained via MLE method\n",
    "        res = quantile_methods[ci_label](xp, probs, pars, cov_pars)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *res, color=color, alpha=0.5, label=f\"CI:{ci_label}\")\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(1.02, 0.5), loc='center right')\n",
    "\n",
    "fig.savefig(results_path / \"logit_linear_fit.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9fb1cb06129",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_results = {\n",
    "    \"model\": logit,\n",
    "    \"fit_results\": fit_results,\n",
    "    \"parameters\": parss,\n",
    "    \"covariances\": cov_parss,\n",
    "    \"parameter_ci_wald\": pars_CIs,\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4971dd5fff15",
   "metadata": {},
   "source": [
    "## 3. Cubic unconstrained (non-monotonic) logistic regression\n",
    "\n",
    "The log-odds are represented by an unconstrained cubic polynomial,\n",
    "\n",
    "$$\n",
    "\\eta(t) = \\beta_0 + \\beta_1 t + \\beta_2 t^2 + \\beta_3 t^3.\n",
    "$$\n",
    "\n",
    "Because no derivative constraint is imposed (`degree=3`, `mono=False`), the fitted probability curve may be non-monotonic. The same uncertainty and goodness-of-fit calculations used for the linear model are repeated here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "98a60d53d9fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_path = results_root / \"cubic_unconstrained\"\n",
    "results_path.mkdir(parents=True, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50ae75ed9fc",
   "metadata": {},
   "source": [
    "### 3.1 Fit and parameter uncertainty\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "430ae4924260",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parss = array([[-197.43456924470524,  248.4905000484067 ,  -98.59976324737492,\n",
      "          12.18035307606018],\n",
      "       [ -75.33868079212766,  287.5173164621367 , -339.6550928412925 ,\n",
      "         124.51434906316405]])\n"
     ]
    }
   ],
   "source": [
    "# unconstrained cubic logistic-regression model\n",
    "logit = logistic.LogisticPolyRegression(degree=3, mono=False)\n",
    "\n",
    "# fits\n",
    "fit_results = [logit.fit(x, y, method=\"local\") for x in xs]\n",
    "if not all(result[\"success\"] for result in fit_results):\n",
    "    raise RuntimeError(\"At least one cubic logistic-regression fit failed.\")\n",
    "\n",
    "parss = np.array([result[\"pars\"] for result in fit_results])\n",
    "print(f\"{parss = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10268adb7723",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cov_parss = array([[[ 10939.684523680417  , -14010.64625271519   ,\n",
      "           5653.962064108637  ,   -708.3678994835518 ],\n",
      "        [-14010.646252715049  ,  18013.760997935155  ,\n",
      "          -7300.0142220177195 ,    918.9939676788543 ],\n",
      "        [  5653.962064108636  ,  -7300.014222017792  ,\n",
      "           2973.1108398363144 ,   -376.64839162543296],\n",
      "        [  -708.367899483575  ,    918.9939676788935 ,\n",
      "           -376.6483916254451 ,     48.14091024553155]],\n",
      "\n",
      "       [[  1998.7034862620897 ,  -7705.433239992382  ,\n",
      "           9196.923211179579  ,  -3384.606945488192  ],\n",
      "        [ -7705.433239992488  ,  29940.973099421117  ,\n",
      "         -35991.86768578202   ,  13336.386966402508  ],\n",
      "        [  9196.923211179825  , -35991.867685782476  ,\n",
      "          43591.81868900229   , -16286.64998423657   ],\n",
      "        [ -3384.606945488327  ,  13336.386966402852  ,\n",
      "         -16286.649984236778  ,   6149.469270513834  ]]])\n"
     ]
    }
   ],
   "source": [
    "# asymptotic covariance matrix of parameters\n",
    "cov_parss = np.array([logit.get_cov(x, y, pars) for x, pars in zip(xs, parss)])\n",
    "print(f\"{cov_parss = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "233e3b495e13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pars_CIs = array([[[-402.43297804122045  ,  -14.566758128199893 ,\n",
      "         -205.46922970337693  ,   -1.4185926293590985],\n",
      "        [   7.563839551809906 ,  511.54775822501324  ,\n",
      "            8.269703208627035 ,   25.779298781479454 ]],\n",
      "\n",
      "       [[-162.962519656067    ,  -51.62426932901457  ,\n",
      "         -748.8691888997823   ,  -29.183188235918408 ],\n",
      "        [  12.285158071811665 ,  626.6589022532879   ,\n",
      "           69.55900321719724  ,  278.21188636224645  ]]])\n"
     ]
    }
   ],
   "source": [
    "# CI of params (assuming asymptotic distr of parameters)\n",
    "pars_CIs = np.array([\n",
    "    logit.get_pars_quantiles_normal(probs, pars, cov_pars)\n",
    "    for pars, cov_pars in zip(parss, cov_parss)\n",
    "])\n",
    "print(f\"{pars_CIs = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "18a1b339bcd7",
   "metadata": {},
   "outputs": [
    {
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       "                 value         LCL         UCL\n",
       "scale coef                                    \n",
       "plain b0   -197.434569 -402.432978    7.563840\n",
       "      b1    248.490500  -14.566758  511.547758\n",
       "      b2    -98.599763 -205.469230    8.269703\n",
       "      b3     12.180353   -1.418593   25.779299\n",
       "log   b0    -75.338681 -162.962520   12.285158\n",
       "      b1    287.517316  -51.624269  626.658902\n",
       "      b2   -339.655093 -748.869189   69.559003\n",
       "      b3    124.514349  -29.183188  278.211886"
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   ],
   "source": [
    "# present table of results\n",
    "d = parss.shape[-1]\n",
    "index = pd.MultiIndex.from_tuples([(lab, f\"b{i}\") for lab in scales for i in range(d)], names = ['scale', 'coef'])\n",
    "pd.DataFrame({\"value\": parss.flatten(), \"LCL\": pars_CIs[:,0].flatten(), \"UCL\": pars_CIs[:,1].flatten()}, index = index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b51346c478d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>LLF</th>\n",
       "      <th>AIC</th>\n",
       "      <th>BIC</th>\n",
       "      <th>A</th>\n",
       "      <th>chi2</th>\n",
       "      <th>p-value(chi2)</th>\n",
       "      <th>n</th>\n",
       "      <th>k</th>\n",
       "      <th>dof</th>\n",
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       "    <tr>\n",
       "      <th>scale</th>\n",
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       "      <td>-3.188295</td>\n",
       "      <td>14.37659</td>\n",
       "      <td>22.618362</td>\n",
       "      <td>0.965517</td>\n",
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       "      <th>log</th>\n",
       "      <td>-3.491410</td>\n",
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       "      <td>0.965517</td>\n",
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       "      <td>1.0</td>\n",
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      "text/plain": [
       "            LLF       AIC        BIC         A      chi2  p-value(chi2)   n  \\\n",
       "scale                                                                         \n",
       "plain -3.188295  14.37659  22.618362  0.965517  6.022997            1.0  58   \n",
       "log   -3.491410  14.98282  23.224592  0.965517  6.754119            1.0  58   \n",
       "\n",
       "       k  dof  \n",
       "scale          \n",
       "plain  4   54  \n",
       "log    4   54  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# goodness of fit measures\n",
    "index = pd.Index(scales, name = 'scale')\n",
    "pd.DataFrame(\n",
    "    [logit.goodness_of_fit(x, y, pars) for x, pars in zip(xs, parss)],\n",
    "    index=index,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8029f7f521b1",
   "metadata": {},
   "source": [
    "### 3.2 Fitted risk curve and confidence bands\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "141a540cbb5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting results\n",
    "fig, axs = plt.subplots(ncols = len(scales), figsize = (6*len(scales), 5))\n",
    "\n",
    "quantile_methods = {\n",
    "    \"normal\": logit.get_model_quantiles_normal,\n",
    "    \"delta\": logit.get_model_quantiles_delta,\n",
    "}\n",
    "\n",
    "for ax, scale, x, pars, cov_pars in zip(axs, scales, xs, parss, cov_parss):\n",
    "\n",
    "    ax.set_title(f\"cond. prob. for AE: scale {scale}\")\n",
    "\n",
    "    if scale == \"plain\":\n",
    "        xlab = r\"$x = max_{visit} SUV(visit, p)$\"\n",
    "    else:\n",
    "        xlab = r\"$x = log(max_{visit} SUV(visit, p))$\"\n",
    "    \n",
    "    ax.set_xlabel(xlab)\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # plotting data points\n",
    "    for i, group_label in enumerate([\"NC\", \"AE\"]):\n",
    "        ax.scatter(x[y == i], y[y == i], label=group_label)\n",
    "\n",
    "    # plotting fitted model \n",
    "    xp = np.linspace(min(x), max(x), 100)\n",
    "    yp = logit.model(xp, pars)\n",
    "\n",
    "    ax.plot(xp, yp, label = \"logistic reg\")\n",
    "\n",
    "    for ci_label, color in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        # get result of model quantiles at probs\n",
    "        # pars and cov_pars are obtained via MLE method\n",
    "        res = quantile_methods[ci_label](xp, probs, pars, cov_pars)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *res, color=color, alpha=0.5, label=f\"CI:{ci_label}\")\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(1.02, 0.5), loc='center right')\n",
    "\n",
    "fig.savefig(results_path / \"logit_cubic_nonmono_fit.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f57a346797fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "cubic_unconstrained_results = {\n",
    "    \"model\": logit,\n",
    "    \"fit_results\": fit_results,\n",
    "    \"parameters\": parss,\n",
    "    \"covariances\": cov_parss,\n",
    "    \"parameter_ci_wald\": pars_CIs,\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b193fac8ac7",
   "metadata": {},
   "source": [
    "## 4. Cubic constrained (monotonic) logistic regression\n",
    "\n",
    "This model also uses cubic log-odds, but imposes the monotonicity condition\n",
    "\n",
    "$$\n",
    "\\eta'(t) \\ge 0\n",
    "$$\n",
    "\n",
    "over the fitted domain. It uses `degree=3`, `mono=True`, light L2 regularization, and differential-evolution fitting. In addition to the asymptotic analysis, this section compares confidence intervals obtained from normal parameter sampling, nonparametric bootstrap, stratified nonparametric bootstrap, and parametric bootstrap.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2bf9dda5f4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_path = results_root / \"cubic_monotonic\"\n",
    "results_path.mkdir(parents=True, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50d789015352",
   "metadata": {},
   "source": [
    "### 4.1 Fit and numerical diagnostics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "53ae4440fced",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "pars",
         "rawType": "object",
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         "4.7820695656078325",
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       "                                                    pars      cost  success  \\\n",
       "scale                                                                         \n",
       "plain  [-123.99418557059668, 2.47595505520157e-07, 5....  4.782070     True   \n",
       "log    [-40.7090649672341, 1.8141884669276415e-07, 15...  4.605007     True   \n",
       "\n",
       "                                                 message  nit  \n",
       "scale                                                          \n",
       "plain  CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*...    6  \n",
       "log    CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*...    4  "
      ]
     },
     "execution_count": 23,
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   "source": [
    "# constrained cubic logistic-regression model, light L2 regularization, monotonicity constraints\n",
    "logit = logistic.LogisticPolyRegression(degree=3, mono=True, lam=(0.0, 1e-6))\n",
    "\n",
    "# fits\n",
    "ress = [logit.fit(x, y, method=\"diff_evol\") for x in xs]\n",
    "if not all(result[\"success\"] for result in ress):\n",
    "    raise RuntimeError(\"At least one monotonic cubic logistic-regression fit failed.\")\n",
    "\n",
    "pd.DataFrame(ress, index = pd.Index(scales, name = 'scale'))"
   ]
  },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pars:\n"
     ]
    },
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       "                0             1          2          3\n",
       "scale                                                \n",
       "plain -123.994186  2.475955e-07   5.582725 -12.798303\n",
       "log    -40.709065  1.814188e-07  15.072501 -12.365150"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parss = np.array([r[\"pars\"] for r in ress])\n",
    "\n",
    "print(\"pars:\")\n",
    "pd.DataFrame(parss, index = pd.Index(scales, name = 'scale'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f9e98365ccd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------\n",
      "pars=[np.float64(-123.99418557059668), np.float64(2.47595505520157e-07), np.float64(5.5827254615371045), np.float64(-12.798302794816944)]\n",
      "beta=[np.float64(-123.99418557059668), np.float64(163.79655442781927), np.float64(-71.44941087708604), np.float64(10.388941192964891)]\n",
      "nllf = 4.76650004417451, jac = [np.float64(0.00024779752488302), np.float64(1.4199571923547813e-07), np.float64(-9.704355768996864e-06), np.float64(2.6486599065317362e-05)]\n",
      "------------------------\n",
      "pars=[np.float64(-40.7090649672341), np.float64(1.8141884669276415e-07), np.float64(15.072501048103728), np.float64(-12.365149922018979)]\n",
      "beta=[np.float64(-40.7090649672341), np.float64(152.896932594006), np.float64(-186.37373515959078), np.float64(75.72676261502933)]\n",
      "nllf = 4.602970192032294, jac = [np.float64(8.104634476746586e-05), np.float64(4.467874199070872e-08), np.float64(-3.015911893108252e-05), np.float64(2.469581043511304e-05)]\n"
     ]
    }
   ],
   "source": [
    "for x, pars in zip(xs, parss): \n",
    "    r = logit.get_nllf(x, y, pars, jac = True)\n",
    "    beta = logit.get_beta(pars)\n",
    "    print(\"------------------------\")\n",
    "    print(f\"pars={list(pars)}\")\n",
    "    print(f\"beta={list(beta)}\")\n",
    "    print(f\"nllf = {r[0]}, jac = {list(r[1])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "89a497ed6bfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cov_pars = array([[ 7.1358476229803528e+03,  1.1571431904237741e-05,\n",
      "        -2.1300381704591399e+02,  4.0639680894100161e+02],\n",
      "       [ 1.1571428647779103e-05,  1.7436768051892870e+00,\n",
      "        -2.7371111129141675e-07,  6.6747868034048048e-07],\n",
      "       [-2.1300381704581164e+02, -2.7371120877624224e-07,\n",
      "         6.6082586018799407e+00, -1.2310560563759298e+01],\n",
      "       [ 4.0639680894092703e+02,  6.6747886576960763e-07,\n",
      "        -1.2310560563762900e+01,  2.3274850332258524e+01]])\n",
      "cov_pars = array([[ 7.3706414704315068e+02,  3.0761756416374867e-06,\n",
      "        -1.9971747645266089e+02,  1.2773740053043487e+02],\n",
      "       [ 3.0761757768424807e-06,  4.0604852623957877e+00,\n",
      "        -6.4297645838829395e-07,  5.9036563841225757e-07],\n",
      "       [-1.9971747645264557e+02, -6.4297641522154753e-07,\n",
      "         5.9638988236222680e+01, -3.5994366552930011e+01],\n",
      "       [ 1.2773740053043078e+02,  5.9036561196492392e-07,\n",
      "        -3.5994366552931560e+01,  2.2491483563425167e+01]])\n"
     ]
    }
   ],
   "source": [
    "# asymptotic covariance matrix of parameters\n",
    "cov_parss = np.array([logit.get_cov(x, y, pars) for x, pars in zip(xs, parss)])\n",
    "\n",
    "for cov_pars in cov_parss: print(f\"{cov_pars = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "16a0f1a3981a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.8327110754113301e-04 3.7826421791423154e-01 1.7436768051892766e+00\n",
      " 7.1653518844254686e+03]\n",
      "[7.400760828205792e-03 4.060485262395757e+00 5.371269198897574e+00\n",
      " 8.138159488830727e+02]\n"
     ]
    }
   ],
   "source": [
    "# check eigenvalues of the symmetric covariance matrices\n",
    "for cov_pars in cov_parss:\n",
    "    print(np.linalg.eigvalsh(cov_pars))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2ee2e94167d",
   "metadata": {},
   "source": [
    "### 4.2 Wald parameter intervals and goodness of fit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ed2a1bcb6679",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pars_CI = array([[-289.5600781766135   ,   -2.588099952113567 ,\n",
      "           0.5443374955682732,  -22.253956083151394 ],\n",
      "       [  41.57170703542009  ,    2.5881004473045772,\n",
      "          10.621113427505936 ,   -3.342649506482495 ]])\n",
      "pars_CI = array([[-9.3919980849939066e+01, -3.9494538223048079e+00,\n",
      "        -6.3572333632729183e-02, -2.1660315758367279e+01],\n",
      "       [ 1.2501850915470861e+01,  3.9494541851425002e+00,\n",
      "         3.0208574429840183e+01, -3.0699840856706810e+00]])\n"
     ]
    }
   ],
   "source": [
    "# CI of params (assuming asymptotic distr of parameters) : Wald approximation\n",
    "pars_CIs = np.array([\n",
    "    logit.get_pars_quantiles_normal(probs, pars, cov_pars)\n",
    "    for pars, cov_pars in zip(parss, cov_parss)\n",
    "])\n",
    "\n",
    "for pars_CI in pars_CIs: print(f\"{pars_CI = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e74159b26dc8",
   "metadata": {},
   "outputs": [
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       "      <td>3.949454</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507250e+01</td>\n",
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       "      <td>30.208574</td>\n",
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       "      <td>log</td>\n",
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       "   scale coef         value         LCL        UCL\n",
       "0  plain   p0 -1.239942e+02 -289.560078  41.571707\n",
       "1  plain   p1  2.475955e-07   -2.588100   2.588100\n",
       "2  plain   p2  5.582725e+00    0.544337  10.621113\n",
       "3  plain   p3 -1.279830e+01  -22.253956  -3.342650\n",
       "4    log   p0 -4.070906e+01  -93.919981  12.501851\n",
       "5    log   p1  1.814188e-07   -3.949454   3.949454\n",
       "6    log   p2  1.507250e+01   -0.063572  30.208574\n",
       "7    log   p3 -1.236515e+01  -21.660316  -3.069984"
      ]
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     "execution_count": 29,
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   "source": [
    "# nr of parameters\n",
    "d = parss.shape[-1]\n",
    "\n",
    "# present table of results\n",
    "df_pars_CI_wald = pd.DataFrame({\n",
    "    \"scale\" :[lab for lab in scales for _ in range(d)],\n",
    "    \"coef\" : [ f\"p{i}\" for _ in scales for i in range(d)],\n",
    "    \"value\" : parss.flatten(), \n",
    "    \"LCL\" : pars_CIs[:,0].flatten(), \n",
    "    \"UCL\": pars_CIs[:,1].flatten()\n",
    "})\n",
    "\n",
    "df_pars_CI_wald"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2f192e86182d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "LLF",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "AIC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BIC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "A",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "chi2",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "p-value(chi2)",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "n",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "k",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "dof",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "50e5bdb7-9b8b-44f3-8dca-617519c98d59",
       "rows": [
        [
         "plain",
         "-4.76650004417451",
         "17.53300008834902",
         "25.774772130534696",
         "0.9655172413793104",
         "9.165001422619829",
         "0.9999999999992062",
         "58",
         "4",
         "54"
        ],
        [
         "log",
         "-4.602970192032294",
         "17.20594038406459",
         "25.447712426250263",
         "0.9482758620689655",
         "8.31672758415927",
         "0.9999999999999134",
         "58",
         "4",
         "54"
        ]
       ],
       "shape": {
        "columns": 9,
        "rows": 2
       }
      },
      "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>LLF</th>\n",
       "      <th>AIC</th>\n",
       "      <th>BIC</th>\n",
       "      <th>A</th>\n",
       "      <th>chi2</th>\n",
       "      <th>p-value(chi2)</th>\n",
       "      <th>n</th>\n",
       "      <th>k</th>\n",
       "      <th>dof</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scale</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>plain</th>\n",
       "      <td>-4.76650</td>\n",
       "      <td>17.53300</td>\n",
       "      <td>25.774772</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>9.165001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log</th>\n",
       "      <td>-4.60297</td>\n",
       "      <td>17.20594</td>\n",
       "      <td>25.447712</td>\n",
       "      <td>0.948276</td>\n",
       "      <td>8.316728</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LLF       AIC        BIC         A      chi2  p-value(chi2)   n  k  \\\n",
       "scale                                                                           \n",
       "plain -4.76650  17.53300  25.774772  0.965517  9.165001            1.0  58  4   \n",
       "log   -4.60297  17.20594  25.447712  0.948276  8.316728            1.0  58  4   \n",
       "\n",
       "       dof  \n",
       "scale       \n",
       "plain   54  \n",
       "log     54  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# goodness of fit measures\n",
    "pd.DataFrame(\n",
    "    [logit.goodness_of_fit(x, y, pars) for x, pars in zip(xs, parss)], \n",
    "    index = pd.Index(scales, name = 'scale')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad4c94ba24a",
   "metadata": {},
   "source": [
    "### 4.3 Fitted risk curve and asymptotic confidence bands\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "91ed23f07d87",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting results\n",
    "fig, axs = plt.subplots(ncols = len(scales), figsize = (6*len(scales), 5))\n",
    "\n",
    "quantile_methods = {\n",
    "    \"normal\": logit.get_model_quantiles_normal,\n",
    "    \"delta\": logit.get_model_quantiles_delta,\n",
    "}\n",
    "\n",
    "for ax, scale, x, pars, cov_pars in zip(axs, scales, xs, parss, cov_parss):\n",
    "\n",
    "    ax.set_title(f\"cond. prob. for AE: scale {scale}\")\n",
    "\n",
    "    if scale == \"plain\":\n",
    "        xlab = r\"$x = max_{visit} SUV(visit, p)$\"\n",
    "    else:\n",
    "        xlab = r\"$x = log(max_{visit} SUV(visit, p))$\"\n",
    "    \n",
    "    ax.set_xlabel(xlab)\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # plotting data points\n",
    "    for i, group_label in enumerate([\"NC\", \"AE\"]):\n",
    "        ax.scatter(x[y == i], y[y == i], label=group_label)\n",
    "\n",
    "    # plotting fitted model \n",
    "    dx = max(x) - min(x)\n",
    "    xp = np.linspace(min(x) - 0.1 * dx, max(x) + 0.1 * dx, 100)\n",
    "    yp = logit.model(xp, pars)\n",
    "\n",
    "    ax.plot(xp, yp, label = \"logistic reg\")\n",
    "\n",
    "    for ci_label, color in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        # get result of model quantiles at probs\n",
    "        # pars and cov_pars are obtained via MLE method\n",
    "        res = quantile_methods[ci_label](xp, probs, pars, cov_pars)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *res, color=color, alpha=0.5, label=f\"CI:{ci_label}\")\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(1.02, 0.5), loc='center right')\n",
    "\n",
    "fig.savefig(results_path / \"logit_mono_asymptotic_fit.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32da73e5cac3",
   "metadata": {},
   "source": [
    "### 4.4 Model confidence-interval comparison\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cfe79b472ce4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model CI:scale:plain,method:normal\n",
      "model CI:scale:plain,method:delta\n",
      "model CI:scale:plain,boots-method:normal\n",
      "model CI:scale:plain,boots-method:nonparam_boots\n",
      "model CI:scale:plain,boots-method:nonparam_stratified_boots\n",
      "model CI:scale:plain,boots-method:parametric_boots\n",
      "model CI:scale:log,method:normal\n",
      "model CI:scale:log,method:delta\n",
      "model CI:scale:log,boots-method:normal\n",
      "model CI:scale:log,boots-method:nonparam_boots\n",
      "model CI:scale:log,boots-method:nonparam_stratified_boots\n",
      "model CI:scale:log,boots-method:parametric_boots\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting results\n",
    "fig, axs = plt.subplots(ncols = len(scales), figsize = (6*len(scales), 5))\n",
    "\n",
    "boots_pars_results = {}\n",
    "quantile_methods = {\n",
    "    \"normal\": logit.get_model_quantiles_normal,\n",
    "    \"delta\": logit.get_model_quantiles_delta,\n",
    "}\n",
    "parameter_generators = {\n",
    "    \"normal\": logit.get_normal_pars,\n",
    "    \"nonparam_boots\": logit.get_nonparam_boots_pars,\n",
    "    \"nonparam_stratified_boots\": logit.get_nonparam_stratified_boots_pars,\n",
    "    \"parametric_boots\": logit.get_parametric_boots_pars,\n",
    "}\n",
    "n_parameter_samples = 10_000\n",
    "\n",
    "for ax, scale, x, pars, cov_pars in zip(axs, scales, xs, parss, cov_parss):\n",
    "\n",
    "    ax.set_title(f\"cond. prob. for AE: scale {scale}\")\n",
    "    \n",
    "    if scale == \"plain\":\n",
    "        xlab = r\"$x = max_{visit} SUV(visit, p)$\"\n",
    "    else:\n",
    "        xlab = r\"$x = log(max_{visit} SUV(visit, p))$\"\n",
    "    \n",
    "    ax.set_xlabel(xlab)\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # plotting data points\n",
    "    for i, group_label in enumerate([\"NC\", \"AE\"]):\n",
    "        ax.scatter(x[y == i], y[y == i], label=group_label)\n",
    "\n",
    "    # plotting fitted model\n",
    "    dx = max(x) - min(x)\n",
    "    xp = np.linspace(min(x) - 0.1 * dx, max(x) + 0.1 * dx, 100)\n",
    "    yp = logit.model(xp, pars)\n",
    "\n",
    "    ax.plot(xp, yp, label = \"logistic reg\")\n",
    "\n",
    "    for method, color in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        \n",
    "        print(f\"model CI:scale:{scale},method:{method}\")\n",
    "\n",
    "        # get result of model quantiles at probs\n",
    "        # pars and cov_pars are obtained via MLE method\n",
    "        res = quantile_methods[method](xp, probs, pars, cov_pars)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *res, color=color, alpha=0.5, label=f\"CI:{method}\")\n",
    "\n",
    "    # plot quantiles of models at sampled or bootstrapped parameters\n",
    "    for method, color in zip(\n",
    "        [\"normal\", \"nonparam_boots\", \"nonparam_stratified_boots\", \"parametric_boots\"],\n",
    "        [\"orange\", \"pink\", \"purple\", \"cyan\"]):\n",
    "    \n",
    "        print(f\"model CI:scale:{scale},boots-method:{method}\")\n",
    "\n",
    "        # generate sampled or bootstrapped parameters\n",
    "        bpars = parameter_generators[method](x, y, n_parameter_samples)\n",
    "    \n",
    "        boots_pars_results[(scale, method)] = bpars\n",
    "\n",
    "        # quantiles\n",
    "        quant = np.quantile([logit.model(xp, p) for p in bpars], probs, axis = 0)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *quant, color=color, alpha=0.5, label=f\"CI:boots, {method}\")\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(1.17, 0.5), loc='center right')\n",
    "\n",
    "fig.savefig(results_path / \"logit_mono_ci_comparison.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121501281f2e",
   "metadata": {},
   "source": [
    "### 4.5 Parameter confidence-interval comparison\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2ed7732fc9ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pars CI:scale:['plain', 'log'],method:Wald/delta\n",
      "model CI:scale:plain,boots-method:normal\n",
      "model CI:scale:plain,boots-method:nonparam_boots\n",
      "model CI:scale:plain,boots-method:nonparam_stratified_boots\n",
      "model CI:scale:plain,boots-method:parametric_boots\n",
      "model CI:scale:log,boots-method:normal\n",
      "model CI:scale:log,boots-method:nonparam_boots\n",
      "model CI:scale:log,boots-method:nonparam_stratified_boots\n",
      "model CI:scale:log,boots-method:parametric_boots\n"
     ]
    },
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scale</th>\n",
       "      <th>coef</th>\n",
       "      <th>value</th>\n",
       "      <th>LCL</th>\n",
       "      <th>UCL</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239942e+02</td>\n",
       "      <td>-289.560078</td>\n",
       "      <td>41.571707</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>2.475955e-07</td>\n",
       "      <td>-2.588100</td>\n",
       "      <td>2.588100</td>\n",
       "      <td>Wald/delta</td>\n",
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       "      <th>2</th>\n",
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       "      <td>p2</td>\n",
       "      <td>5.582725e+00</td>\n",
       "      <td>0.544337</td>\n",
       "      <td>10.621113</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.279830e+01</td>\n",
       "      <td>-22.253956</td>\n",
       "      <td>-3.342650</td>\n",
       "      <td>Wald/delta</td>\n",
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       "      <th>4</th>\n",
       "      <td>log</td>\n",
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       "      <td>-4.070906e+01</td>\n",
       "      <td>-93.919981</td>\n",
       "      <td>12.501851</td>\n",
       "      <td>Wald/delta</td>\n",
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       "      <th>5</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>1.814188e-07</td>\n",
       "      <td>-3.949454</td>\n",
       "      <td>3.949454</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507250e+01</td>\n",
       "      <td>-0.063572</td>\n",
       "      <td>30.208574</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.236515e+01</td>\n",
       "      <td>-21.660316</td>\n",
       "      <td>-3.069984</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239942e+02</td>\n",
       "      <td>-288.567041</td>\n",
       "      <td>47.253388</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>2.475955e-07</td>\n",
       "      <td>-2.619964</td>\n",
       "      <td>2.543069</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>5.582725e+00</td>\n",
       "      <td>0.415890</td>\n",
       "      <td>10.624566</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.279830e+01</td>\n",
       "      <td>-22.243914</td>\n",
       "      <td>-3.030966</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239942e+02</td>\n",
       "      <td>-662.077285</td>\n",
       "      <td>-20.931699</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>2.475955e-07</td>\n",
       "      <td>-2.420179</td>\n",
       "      <td>0.000387</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>5.582725e+00</td>\n",
       "      <td>-11.027820</td>\n",
       "      <td>17.817660</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.279830e+01</td>\n",
       "      <td>-32.838801</td>\n",
       "      <td>5.789172</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
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       "      <th>16</th>\n",
       "      <td>plain</td>\n",
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       "      <td>-1.239942e+02</td>\n",
       "      <td>-647.740990</td>\n",
       "      <td>-21.412504</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>2.475955e-07</td>\n",
       "      <td>-2.530518</td>\n",
       "      <td>0.210722</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>plain</td>\n",
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       "      <td>5.582725e+00</td>\n",
       "      <td>-11.034868</td>\n",
       "      <td>17.537221</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.279830e+01</td>\n",
       "      <td>-32.436639</td>\n",
       "      <td>5.801690</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239942e+02</td>\n",
       "      <td>-519.918596</td>\n",
       "      <td>-14.267928</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>2.475955e-07</td>\n",
       "      <td>-2.558559</td>\n",
       "      <td>0.017588</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>5.582725e+00</td>\n",
       "      <td>-17.294438</td>\n",
       "      <td>14.727337</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.279830e+01</td>\n",
       "      <td>-28.083489</td>\n",
       "      <td>26.411133</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-4.070906e+01</td>\n",
       "      <td>-93.563386</td>\n",
       "      <td>14.367493</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>1.814188e-07</td>\n",
       "      <td>-3.941758</td>\n",
       "      <td>3.915599</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507250e+01</td>\n",
       "      <td>-0.444823</td>\n",
       "      <td>30.275320</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.236515e+01</td>\n",
       "      <td>-21.647918</td>\n",
       "      <td>-2.748287</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-4.070906e+01</td>\n",
       "      <td>-362.341899</td>\n",
       "      <td>-14.457646</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>1.814188e-07</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507250e+01</td>\n",
       "      <td>-35.741173</td>\n",
       "      <td>78.600834</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.236515e+01</td>\n",
       "      <td>-44.062999</td>\n",
       "      <td>2.279963</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-4.070906e+01</td>\n",
       "      <td>-369.351128</td>\n",
       "      <td>-14.205809</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>1.814188e-07</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507250e+01</td>\n",
       "      <td>-35.750994</td>\n",
       "      <td>79.271857</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.236515e+01</td>\n",
       "      <td>-44.499963</td>\n",
       "      <td>2.280283</td>\n",
       "      <td>nonparam_stratified_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-4.070906e+01</td>\n",
       "      <td>-310.971797</td>\n",
       "      <td>-8.945695</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>1.814188e-07</td>\n",
       "      <td>-0.000220</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.507250e+01</td>\n",
       "      <td>-74.060051</td>\n",
       "      <td>48.619996</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-1.236515e+01</td>\n",
       "      <td>-34.976531</td>\n",
       "      <td>17.689025</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    scale coef         value         LCL        UCL                     method\n",
       "0   plain   p0 -1.239942e+02 -289.560078  41.571707                 Wald/delta\n",
       "1   plain   p1  2.475955e-07   -2.588100   2.588100                 Wald/delta\n",
       "2   plain   p2  5.582725e+00    0.544337  10.621113                 Wald/delta\n",
       "3   plain   p3 -1.279830e+01  -22.253956  -3.342650                 Wald/delta\n",
       "4     log   p0 -4.070906e+01  -93.919981  12.501851                 Wald/delta\n",
       "5     log   p1  1.814188e-07   -3.949454   3.949454                 Wald/delta\n",
       "6     log   p2  1.507250e+01   -0.063572  30.208574                 Wald/delta\n",
       "7     log   p3 -1.236515e+01  -21.660316  -3.069984                 Wald/delta\n",
       "8   plain   p0 -1.239942e+02 -288.567041  47.253388                     normal\n",
       "9   plain   p1  2.475955e-07   -2.619964   2.543069                     normal\n",
       "10  plain   p2  5.582725e+00    0.415890  10.624566                     normal\n",
       "11  plain   p3 -1.279830e+01  -22.243914  -3.030966                     normal\n",
       "12  plain   p0 -1.239942e+02 -662.077285 -20.931699             nonparam_boots\n",
       "13  plain   p1  2.475955e-07   -2.420179   0.000387             nonparam_boots\n",
       "14  plain   p2  5.582725e+00  -11.027820  17.817660             nonparam_boots\n",
       "15  plain   p3 -1.279830e+01  -32.838801   5.789172             nonparam_boots\n",
       "16  plain   p0 -1.239942e+02 -647.740990 -21.412504  nonparam_stratified_boots\n",
       "17  plain   p1  2.475955e-07   -2.530518   0.210722  nonparam_stratified_boots\n",
       "18  plain   p2  5.582725e+00  -11.034868  17.537221  nonparam_stratified_boots\n",
       "19  plain   p3 -1.279830e+01  -32.436639   5.801690  nonparam_stratified_boots\n",
       "20  plain   p0 -1.239942e+02 -519.918596 -14.267928           parametric_boots\n",
       "21  plain   p1  2.475955e-07   -2.558559   0.017588           parametric_boots\n",
       "22  plain   p2  5.582725e+00  -17.294438  14.727337           parametric_boots\n",
       "23  plain   p3 -1.279830e+01  -28.083489  26.411133           parametric_boots\n",
       "24    log   p0 -4.070906e+01  -93.563386  14.367493                     normal\n",
       "25    log   p1  1.814188e-07   -3.941758   3.915599                     normal\n",
       "26    log   p2  1.507250e+01   -0.444823  30.275320                     normal\n",
       "27    log   p3 -1.236515e+01  -21.647918  -2.748287                     normal\n",
       "28    log   p0 -4.070906e+01 -362.341899 -14.457646             nonparam_boots\n",
       "29    log   p1  1.814188e-07   -0.000013   0.000014             nonparam_boots\n",
       "30    log   p2  1.507250e+01  -35.741173  78.600834             nonparam_boots\n",
       "31    log   p3 -1.236515e+01  -44.062999   2.279963             nonparam_boots\n",
       "32    log   p0 -4.070906e+01 -369.351128 -14.205809  nonparam_stratified_boots\n",
       "33    log   p1  1.814188e-07   -0.000011   0.000012  nonparam_stratified_boots\n",
       "34    log   p2  1.507250e+01  -35.750994  79.271857  nonparam_stratified_boots\n",
       "35    log   p3 -1.236515e+01  -44.499963   2.280283  nonparam_stratified_boots\n",
       "36    log   p0 -4.070906e+01 -310.971797  -8.945695           parametric_boots\n",
       "37    log   p1  1.814188e-07   -0.000220   0.000241           parametric_boots\n",
       "38    log   p2  1.507250e+01  -74.060051  48.619996           parametric_boots\n",
       "39    log   p3 -1.236515e+01  -34.976531  17.689025           parametric_boots"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nr of parameters\n",
    "d = parss.shape[-1]\n",
    "\n",
    "# CI of params:\n",
    "#   - using Wald approximation \n",
    "#   - assuming asymptotic MLE distr of parameters \n",
    "#   - cov = hessian(nllf)^{-1}\n",
    "#   - this could be considered as delta method\n",
    "\n",
    "print(f\"pars CI:scale:{scales},method:Wald/delta\")\n",
    "lst_pars_CI = [df_pars_CI_wald.assign(method= \"Wald/delta\")]\n",
    "\n",
    "for scale, pars in zip(scales, parss):\n",
    "\n",
    "    # parameter quantiles from sampled or bootstrapped parameter sets\n",
    "    for method in [\"normal\", \"nonparam_boots\", \"nonparam_stratified_boots\", \"parametric_boots\"]:\n",
    "    \n",
    "        print(f\"model CI:scale:{scale},boots-method:{method}\")\n",
    "\n",
    "        # get sampled or bootstrapped parameters\n",
    "        bpars = boots_pars_results[(scale, method)]\n",
    "    \n",
    "        # quantiles\n",
    "        quant = np.quantile(bpars, probs, axis = 0)\n",
    "\n",
    "        df_tmp = pd.DataFrame({\n",
    "            \"method\": f\"{method}\",\n",
    "            \"scale\" : scale,\n",
    "            \"coef\" : [f\"p{i}\" for i in range(d)],\n",
    "            \"value\" : pars, \n",
    "            \"LCL\" : quant[0], \n",
    "            \"UCL\": quant[1]\n",
    "        })\n",
    "        \n",
    "        lst_pars_CI.append(df_tmp)\n",
    "\n",
    "df_pars_CI = pd.concat(lst_pars_CI, ignore_index=True)\n",
    "df_pars_CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a93d455317b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cubic_monotonic_results = {\n",
    "    \"model\": logit,\n",
    "    \"fit_results\": ress,\n",
    "    \"parameters\": parss,\n",
    "    \"covariances\": cov_parss,\n",
    "    \"parameter_ci_wald\": df_pars_CI_wald,\n",
    "    \"parameter_ci_comparison\": df_pars_CI,\n",
    "    \"bootstrap_parameters\": boots_pars_results,\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e64342b71314",
   "metadata": {},
   "source": [
    "## 5. Collected model results\n",
    "\n",
    "The principal fitted objects and uncertainty results from the three sections are collected below. This makes later cross-model comparisons possible without rerunning or reconstructing section-specific variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4fd30ddf0b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['linear', 'cubic_unconstrained', 'cubic_monotonic'])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_results = {\n",
    "    \"linear\": linear_results,\n",
    "    \"cubic_unconstrained\": cubic_unconstrained_results,\n",
    "    \"cubic_monotonic\": cubic_monotonic_results,\n",
    "}\n",
    "\n",
    "model_results.keys()\n"
   ]
  }
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