{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b2f8545bfe2a",
   "metadata": {},
   "source": [
    "# Logistic regression analyses: linear and cubic models\n",
    "\n",
    "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",
    "\n",
    "Each section discussed a separate logistic model.\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": null,
   "id": "86f652a20401",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = Path(\"../data/raw\")\n",
    "results_root = Path(\"../results/logit_all_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": [
      "thetas = 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",
    "thetas = np.array([result[\"theta\"] for result in fit_results])\n",
    "print(f\"{thetas = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82a3535b87ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cov_thetas = 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_thetas = np.array([logit.get_cov(x, y, theta) for x, theta in zip(xs, thetas)])\n",
    "print(f\"{cov_thetas = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a4a84bad7b8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta_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",
    "theta_CIs = np.array([\n",
    "    logit.get_theta_quantiles_normal(probs, theta, cov_theta)\n",
    "    for theta, cov_theta in zip(thetas, cov_thetas)\n",
    "])\n",
    "\n",
    "print(f\"{theta_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 = thetas.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\": thetas.flatten(), \"LCL\": theta_CIs[:,0].flatten(), \"UCL\": theta_CIs[:,1].flatten()}, index = index)"
   ]
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       "      <th>AIC</th>\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",
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     },
     "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, theta) for x, theta in zip(xs, thetas)],\n",
    "    index=index,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32deb7403fd0",
   "metadata": {},
   "source": [
    "### 2.2 Fitted risk curve and confidence bands\n",
    "\n",
    "**Coverage note.** The term *confidence band* is used in its standard graphical sense. The displayed limits are pointwise confidence intervals evaluated separately at each predictor value; they do not provide simultaneous coverage of the complete fitted curve.\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, theta, cov_theta in zip(axs, scales, xs, thetas, cov_thetas):\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, theta)\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",
    "        # theta and cov_theta are obtained via MLE method\n",
    "        res = quantile_methods[ci_label](xp, probs, theta, cov_theta)\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\": thetas,\n",
    "    \"covariances\": cov_thetas,\n",
    "    \"parameter_ci_wald\": theta_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": [
      "thetas = 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",
    "thetas = np.array([result[\"theta\"] for result in fit_results])\n",
    "print(f\"{thetas = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10268adb7723",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cov_thetas = 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_thetas = np.array([logit.get_cov(x, y, theta) for x, theta in zip(xs, thetas)])\n",
    "print(f\"{cov_thetas = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "233e3b495e13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta_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",
    "theta_CIs = np.array([\n",
    "    logit.get_theta_quantiles_normal(probs, theta, cov_theta)\n",
    "    for theta, cov_theta in zip(thetas, cov_thetas)\n",
    "])\n",
    "print(f\"{theta_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",
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       "      b2   -339.655093 -748.869189   69.559003\n",
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   "source": [
    "# present table of results\n",
    "d = thetas.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\": thetas.flatten(), \"LCL\": theta_CIs[:,0].flatten(), \"UCL\": theta_CIs[:,1].flatten()}, index = index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b51346c478d5",
   "metadata": {},
   "outputs": [
    {
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       "            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  "
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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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, theta) for x, theta in zip(xs, thetas)],\n",
    "    index=index,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8029f7f521b1",
   "metadata": {},
   "source": [
    "### 3.2 Fitted risk curve and confidence bands\n",
    "\n",
    "**Coverage note.** The displayed limits are pointwise confidence intervals evaluated separately at each predictor value; they are not simultaneous confidence bands for the complete curve.\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, theta, cov_theta in zip(axs, scales, xs, thetas, cov_thetas):\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, theta)\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",
    "        # theta and cov_theta are obtained via MLE method\n",
    "        res = quantile_methods[ci_label](xp, probs, theta, cov_theta)\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\": thetas,\n",
    "    \"covariances\": cov_thetas,\n",
    "    \"parameter_ci_wald\": theta_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": "theta",
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       "                                                   theta      cost  success  \\\n",
       "scale                                                                         \n",
       "plain  [-123.99588049463472, 8.136261499606275e-09, -...  4.782070     True   \n",
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       "\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,
     "metadata": {},
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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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     "text": [
      "theta:\n"
     ]
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       "                0             1          2          3\n",
       "scale                                                \n",
       "plain -123.995880  8.136261e-09  -5.582776  12.798399\n",
       "log    -40.708347 -7.866662e-08 -15.072306  12.365025"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thetas = np.array([r[\"theta\"] for r in ress])\n",
    "\n",
    "print(\"theta:\")\n",
    "pd.DataFrame(thetas, index = pd.Index(scales, name = 'scale'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f9e98365ccd",
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     "name": "stdout",
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     "text": [
      "------------------------\n",
      "theta=[np.float64(-123.99588049463472), np.float64(8.136261499606275e-09), np.float64(-5.582775975021626), np.float64(12.798399268945223)]\n",
      "beta=[np.float64(-123.99588049463472), np.float64(163.79902384733762), np.float64(-71.45059595740173), np.float64(10.389129195759555)]\n",
      "nllf = 4.766499621329538, jac = [np.float64(0.0002476834205430434), np.float64(4.666192356279928e-09), np.float64(1.2642474929006053e-05), np.float64(-2.2839372309180206e-05)]\n",
      "------------------------\n",
      "theta=[np.float64(-40.708346704022496), np.float64(-7.866662219776253e-08), np.float64(-15.072305606134556), np.float64(12.365025236540689)]\n",
      "beta=[np.float64(-40.708346704022496), np.float64(152.89384910028812), np.float64(-186.3694391927075), np.float64(75.72479876157172)]\n",
      "nllf = 4.602970259567916, jac = [np.float64(8.196480919309135e-05), np.float64(-1.9373223735371098e-08), np.float64(3.0272395048054623e-05), np.float64(-2.4832938072167493e-05)]\n"
     ]
    }
   ],
   "source": [
    "for x, theta in zip(xs, thetas): \n",
    "    r = logit.get_nllf(x, y, theta, jac = True)\n",
    "    beta = logit.get_beta(theta)\n",
    "    print(\"------------------------\")\n",
    "    print(f\"theta={list(theta)}\")\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_theta = array([[ 7.1359801661784222e+03,  3.8024841307254323e-07,\n",
      "         2.1300605487535861e+02, -4.0640133503304241e+02],\n",
      "       [ 3.8025084167143377e-07,  1.7436557485629514e+00,\n",
      "         8.9944349874513598e-09, -2.1933979020925664e-08],\n",
      "       [ 2.1300605487581566e+02,  8.9943479925788592e-09,\n",
      "         6.6082756789497745e+00, -1.2310598789829530e+01],\n",
      "       [-4.0640133503337347e+02, -2.1933829769145808e-08,\n",
      "        -1.2310598789813382e+01,  2.3274936580666971e+01]])\n",
      "cov_theta = array([[ 7.3703848784994398e+02, -1.3338731542745014e-06,\n",
      "         1.9971301969540536e+02, -1.2773431665139005e+02],\n",
      "       [-1.3338731865899040e-06,  4.0605520491826139e+00,\n",
      "        -2.7880425539463663e-07,  2.5599389944396778e-07],\n",
      "       [ 1.9971301969540539e+02, -2.7880424620517608e-07,\n",
      "         5.9638414173281937e+01, -3.5993954819799661e+01],\n",
      "       [-1.2773431665138993e+02,  2.5599389315028116e-07,\n",
      "        -3.5993954819799619e+01,  2.2491183741798793e+01]])\n"
     ]
    }
   ],
   "source": [
    "# asymptotic covariance matrix of parameters\n",
    "cov_thetas = np.array([logit.get_cov(x, y, theta) for x, theta in zip(xs, thetas)])\n",
    "\n",
    "for cov_theta in cov_thetas: print(f\"{cov_theta = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "16a0f1a3981a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.8326684667108744e-04 3.7826642278806055e-01 1.7436557485629471e+00\n",
      " 7.1654845287484050e+03]\n",
      "[7.4008325651474327e-03 4.0605520491826255e+00 5.3712172731484182e+00\n",
      " 8.1378946765931107e+02]\n"
     ]
    }
   ],
   "source": [
    "# check eigenvalues of the symmetric covariance matrices\n",
    "for cov_theta in cov_thetas:\n",
    "    print(np.linalg.eigvalsh(cov_theta))"
   ]
  },
  {
   "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": [
      "theta_CI = array([[-289.5633107267442   ,   -2.588084564587584 ,\n",
      "         -10.621170451090375 ,    3.3427284609644623],\n",
      "       [  41.571549737474726 ,    2.5880845808601065,\n",
      "          -0.5443814989528777,   22.254070076925984 ]])\n",
      "theta_CI = array([[-9.3918336370834396e+01, -3.9494865625300837e+00,\n",
      "        -3.0208306140561199e+01,  3.0699213547766284e+00],\n",
      "       [ 1.2501642962789383e+01,  3.9494864051968386e+00,\n",
      "         6.3694928292081343e-02,  2.1660129118304745e+01]])\n"
     ]
    }
   ],
   "source": [
    "# CI of params (assuming asymptotic distr of parameters) : Wald approximation\n",
    "theta_CIs = np.array([\n",
    "    logit.get_theta_quantiles_normal(probs, theta, cov_theta)\n",
    "    for theta, cov_theta in zip(thetas, cov_thetas)\n",
    "])\n",
    "\n",
    "for theta_CI in theta_CIs: print(f\"{theta_CI = }\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e74159b26dc8",
   "metadata": {},
   "outputs": [
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       "      <td>3.949486</td>\n",
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       "      <td>log</td>\n",
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       "      <td>-30.208306</td>\n",
       "      <td>0.063695</td>\n",
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       "      <td>log</td>\n",
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       "   scale coef         value         LCL        UCL\n",
       "0  plain   p0 -1.239959e+02 -289.563311  41.571550\n",
       "1  plain   p1  8.136261e-09   -2.588085   2.588085\n",
       "2  plain   p2 -5.582776e+00  -10.621170  -0.544381\n",
       "3  plain   p3  1.279840e+01    3.342728  22.254070\n",
       "4    log   p0 -4.070835e+01  -93.918336  12.501643\n",
       "5    log   p1 -7.866662e-08   -3.949487   3.949486\n",
       "6    log   p2 -1.507231e+01  -30.208306   0.063695\n",
       "7    log   p3  1.236503e+01    3.069921  21.660129"
      ]
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     "execution_count": 29,
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   ],
   "source": [
    "# nr of parameters\n",
    "d = thetas.shape[-1]\n",
    "\n",
    "# present table of results\n",
    "df_theta_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\" : thetas.flatten(), \n",
    "    \"LCL\" : theta_CIs[:,0].flatten(), \n",
    "    \"UCL\": theta_CIs[:,1].flatten()\n",
    "})\n",
    "\n",
    "df_theta_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": "4a178634-7cdb-41ac-b3e4-ab291b464e2f",
       "rows": [
        [
         "plain",
         "-4.766499621329538",
         "17.532999242659074",
         "25.774771284844753",
         "0.9655172413793104",
         "9.165024287490791",
         "0.9999999999992062",
         "58",
         "4",
         "54"
        ],
        [
         "log",
         "-4.602970259567916",
         "17.20594051913583",
         "25.44771256132151",
         "0.9482758620689655",
         "8.316698327073187",
         "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>17.532999</td>\n",
       "      <td>25.774771</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>9.165024</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>17.205941</td>\n",
       "      <td>25.447713</td>\n",
       "      <td>0.948276</td>\n",
       "      <td>8.316698</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  \\\n",
       "scale                                                                         \n",
       "plain -4.76650  17.532999  25.774771  0.965517  9.165024            1.0  58   \n",
       "log   -4.60297  17.205941  25.447713  0.948276  8.316698            1.0  58   \n",
       "\n",
       "       k  dof  \n",
       "scale          \n",
       "plain  4   54  \n",
       "log    4   54  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# goodness of fit measures\n",
    "pd.DataFrame(\n",
    "    [logit.goodness_of_fit(x, y, theta) for x, theta in zip(xs, thetas)], \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",
    "\n",
    "**Coverage note.** The displayed limits are pointwise confidence intervals evaluated separately at each predictor value; they are not simultaneous confidence bands for the complete curve.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "91ed23f07d87",
   "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, theta, cov_theta in zip(axs, scales, xs, thetas, cov_thetas):\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, theta)\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",
    "        # theta and cov_theta are obtained via MLE method\n",
    "        res = quantile_methods[ci_label](xp, probs, theta, cov_theta)\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",
    "\n",
    "**Coverage note.** All analytic and bootstrap envelopes compared below are pointwise in the predictor. Their nominal coverage applies at a fixed predictor value, not simultaneously over the complete curve.\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_theta_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_theta,\n",
    "    \"nonparam_boots\": logit.get_nonparam_boots_theta,\n",
    "    \"nonparam_stratified_boots\": logit.get_nonparam_stratified_boots_theta,\n",
    "    \"parametric_boots\": logit.get_parametric_boots_theta,\n",
    "}\n",
    "n_parameter_samples = 10_000\n",
    "\n",
    "for ax, scale, x, theta, cov_theta in zip(axs, scales, xs, thetas, cov_thetas):\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, theta)\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",
    "        # theta and cov_theta are obtained via MLE method\n",
    "        res = quantile_methods[method](xp, probs, theta, cov_theta)\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",
    "        btheta = parameter_generators[method](x, y, n_parameter_samples)\n",
    "    \n",
    "        boots_theta_results[(scale, method)] = btheta\n",
    "\n",
    "        # quantiles\n",
    "        quant = np.quantile([logit.model(xp, p) for p in btheta], 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": [
      "theta 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>-1.239959e+02</td>\n",
       "      <td>-289.563311</td>\n",
       "      <td>41.571550</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>8.136261e-09</td>\n",
       "      <td>-2.588085</td>\n",
       "      <td>2.588085</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>-5.582776e+00</td>\n",
       "      <td>-10.621170</td>\n",
       "      <td>-0.544381</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>1.279840e+01</td>\n",
       "      <td>3.342728</td>\n",
       "      <td>22.254070</td>\n",
       "      <td>Wald/delta</td>\n",
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       "      <th>4</th>\n",
       "      <td>log</td>\n",
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       "      <td>-4.070835e+01</td>\n",
       "      <td>-93.918336</td>\n",
       "      <td>12.501643</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>-7.866662e-08</td>\n",
       "      <td>-3.949487</td>\n",
       "      <td>3.949486</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>-1.507231e+01</td>\n",
       "      <td>-30.208306</td>\n",
       "      <td>0.063695</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>1.236503e+01</td>\n",
       "      <td>3.069921</td>\n",
       "      <td>21.660129</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239959e+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>8.136261e-09</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>-5.582776e+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>1.279840e+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>-1.239959e+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>8.136261e-09</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>-5.582776e+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>1.279840e+01</td>\n",
       "      <td>-32.838801</td>\n",
       "      <td>5.789172</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-1.239959e+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>8.136261e-09</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",
       "      <td>p2</td>\n",
       "      <td>-5.582776e+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>1.279840e+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>-1.239959e+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>8.136261e-09</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>-5.582776e+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>1.279840e+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>-4.070835e+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>-7.866662e-08</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>-1.507231e+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>1.236503e+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>-4.070835e+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>-7.866662e-08</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>-1.507231e+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>1.236503e+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>-4.070835e+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>-7.866662e-08</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>-1.507231e+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>1.236503e+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>-4.070835e+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>-7.866662e-08</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>-1.507231e+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>1.236503e+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 -1.239959e+02 -289.563311  41.571550                 Wald/delta\n",
       "1   plain   p1  8.136261e-09   -2.588085   2.588085                 Wald/delta\n",
       "2   plain   p2 -5.582776e+00  -10.621170  -0.544381                 Wald/delta\n",
       "3   plain   p3  1.279840e+01    3.342728  22.254070                 Wald/delta\n",
       "4     log   p0 -4.070835e+01  -93.918336  12.501643                 Wald/delta\n",
       "5     log   p1 -7.866662e-08   -3.949487   3.949486                 Wald/delta\n",
       "6     log   p2 -1.507231e+01  -30.208306   0.063695                 Wald/delta\n",
       "7     log   p3  1.236503e+01    3.069921  21.660129                 Wald/delta\n",
       "8   plain   p0 -1.239959e+02 -288.567041  47.253388                     normal\n",
       "9   plain   p1  8.136261e-09   -2.619964   2.543069                     normal\n",
       "10  plain   p2 -5.582776e+00    0.415890  10.624566                     normal\n",
       "11  plain   p3  1.279840e+01  -22.243914  -3.030966                     normal\n",
       "12  plain   p0 -1.239959e+02 -662.077285 -20.931699             nonparam_boots\n",
       "13  plain   p1  8.136261e-09   -2.420179   0.000387             nonparam_boots\n",
       "14  plain   p2 -5.582776e+00  -11.027820  17.817660             nonparam_boots\n",
       "15  plain   p3  1.279840e+01  -32.838801   5.789172             nonparam_boots\n",
       "16  plain   p0 -1.239959e+02 -647.740990 -21.412504  nonparam_stratified_boots\n",
       "17  plain   p1  8.136261e-09   -2.530518   0.210722  nonparam_stratified_boots\n",
       "18  plain   p2 -5.582776e+00  -11.034868  17.537221  nonparam_stratified_boots\n",
       "19  plain   p3  1.279840e+01  -32.436639   5.801690  nonparam_stratified_boots\n",
       "20  plain   p0 -1.239959e+02 -519.918596 -14.267928           parametric_boots\n",
       "21  plain   p1  8.136261e-09   -2.558559   0.017588           parametric_boots\n",
       "22  plain   p2 -5.582776e+00  -17.294438  14.727337           parametric_boots\n",
       "23  plain   p3  1.279840e+01  -28.083489  26.411133           parametric_boots\n",
       "24    log   p0 -4.070835e+01  -93.563386  14.367493                     normal\n",
       "25    log   p1 -7.866662e-08   -3.941758   3.915599                     normal\n",
       "26    log   p2 -1.507231e+01   -0.444823  30.275320                     normal\n",
       "27    log   p3  1.236503e+01  -21.647918  -2.748287                     normal\n",
       "28    log   p0 -4.070835e+01 -362.341899 -14.457646             nonparam_boots\n",
       "29    log   p1 -7.866662e-08   -0.000013   0.000014             nonparam_boots\n",
       "30    log   p2 -1.507231e+01  -35.741173  78.600834             nonparam_boots\n",
       "31    log   p3  1.236503e+01  -44.062999   2.279963             nonparam_boots\n",
       "32    log   p0 -4.070835e+01 -369.351128 -14.205809  nonparam_stratified_boots\n",
       "33    log   p1 -7.866662e-08   -0.000011   0.000012  nonparam_stratified_boots\n",
       "34    log   p2 -1.507231e+01  -35.750994  79.271857  nonparam_stratified_boots\n",
       "35    log   p3  1.236503e+01  -44.499963   2.280283  nonparam_stratified_boots\n",
       "36    log   p0 -4.070835e+01 -310.971797  -8.945695           parametric_boots\n",
       "37    log   p1 -7.866662e-08   -0.000220   0.000241           parametric_boots\n",
       "38    log   p2 -1.507231e+01  -74.060051  48.619996           parametric_boots\n",
       "39    log   p3  1.236503e+01  -34.976531  17.689025           parametric_boots"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nr of parameters\n",
    "d = thetas.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\"theta CI:scale:{scales},method:Wald/delta\")\n",
    "lst_theta_CI = [df_theta_CI_wald.assign(method= \"Wald/delta\")]\n",
    "\n",
    "for scale, theta in zip(scales, thetas):\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",
    "        btheta = boots_theta_results[(scale, method)]\n",
    "    \n",
    "        # quantiles\n",
    "        quant = np.quantile(btheta, 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\" : theta, \n",
    "            \"LCL\" : quant[0], \n",
    "            \"UCL\": quant[1]\n",
    "        })\n",
    "        \n",
    "        lst_theta_CI.append(df_tmp)\n",
    "\n",
    "df_theta_CI = pd.concat(lst_theta_CI, ignore_index=True)\n",
    "df_theta_CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a93d455317b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cubic_monotonic_results = {\n",
    "    \"model\": logit,\n",
    "    \"fit_results\": ress,\n",
    "    \"parameters\": thetas,\n",
    "    \"covariances\": cov_thetas,\n",
    "    \"parameter_ci_wald\": df_theta_CI_wald,\n",
    "    \"parameter_ci_comparison\": df_theta_CI,\n",
    "    \"bootstrap_parameters\": boots_theta_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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