{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "078900c6-d71d-44f0-9e76-cc5ce66f0ca9",
   "metadata": {},
   "source": [
    "# Logistic regression: using general fit\n",
    "\n",
    "Exploring different fitting techniques and using MLE asymptotic approx of parameter distribution to estimate model CI intervals.\n",
    "\n",
    "Using Logistic regression class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18cec5e",
   "metadata": {},
   "source": [
    "## Common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3b351af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "# our libs\n",
    "import data_utils\n",
    "import logit_utils_gen\n",
    "\n",
    "np.set_printoptions(precision=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0de7f02-942b-42ff-874c-3c0e2ac1e0dc",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3373f87f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "results_path = \"./results\"\n",
    "if not os.path.exists(results_path): os.makedirs(results_path)\n",
    "\n",
    "data_path = \"../../data/\"\n",
    "suv_filename = os.path.join(data_path, \"suv_percentilesSLOthenUWM.mat\")\n",
    "flags_filename = os.path.join(data_path,\"flags_combined.mat\")\n",
    "normal_range_filename = os.path.join(data_path, \"normal_range.mat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "811d990d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Loading all data\n",
    "\"\"\"\n",
    "suv_dict = scipy.io.loadmat(suv_filename)\n",
    "flags_dict = scipy.io.loadmat(flags_filename)\n",
    "normal_range_dict = scipy.io.loadmat(normal_range_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a292c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get data\n",
    "perc = 95\n",
    "organ = \"lung\"\n",
    "\n",
    "x0, y = data_utils.get_data(organ, perc, suv_dict, flags_dict)\n",
    "\n",
    "scales = [\"plain\", \"log\"]\n",
    "xs = [x0, np.log(x0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e9bb7b-5a50-4cae-9af4-261c11453dd4",
   "metadata": {},
   "source": [
    "## Fit\n",
    "\n",
    "Ref: logistic regression documentation\n",
    "* https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
    "* https://stats.stackexchange.com/questions/186830/what-is-scikit-learns-logisticregression-minimizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "239ae789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'logit_utils_gen' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# fits\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m lg \u001b[38;5;241m=\u001b[39m \u001b[43mlogit_utils_gen\u001b[49m\u001b[38;5;241m.\u001b[39mLogisticPolyRegression(\u001b[38;5;241m3\u001b[39m, mono \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m, lam \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1e-3\u001b[39m))\n\u001b[1;32m      3\u001b[0m ress \u001b[38;5;241m=\u001b[39m [lg\u001b[38;5;241m.\u001b[39mfit(x, y, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdiff_evol\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m xs]\n\u001b[1;32m      5\u001b[0m pd\u001b[38;5;241m.\u001b[39mDataFrame(ress, index \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mIndex(scales, name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscale\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'logit_utils_gen' is not defined"
     ]
    }
   ],
   "source": [
    "# fits\n",
    "lg = logit_utils_gen.LogisticPolyRegression(3, mono = True, lam = (0, 1e-3))\n",
    "ress = [lg.fit(x, y, method=\"diff_evol\") for x in xs]\n",
    "\n",
    "pd.DataFrame(ress, index = pd.Index(scales, name = 'scale'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d9044f0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pars:\n"
     ]
    },
    {
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>scale</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>log</th>\n",
       "      <td>-22.050226</td>\n",
       "      <td>0.000002</td>\n",
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       "               0         1         2         3\n",
       "scale                                         \n",
       "plain -24.952836  0.719704  1.375612 -4.742672\n",
       "log   -22.050226  0.000002  9.015180 -8.548204"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "id": "9a99322a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "------------------------\n",
      "pars=[np.float64(-24.952835540044568), np.float64(0.7197040288775033), np.float64(1.3756115410561167), np.float64(-4.742671756961299)]\n",
      "beta=[np.float64(-24.952835540044568), np.float64(23.010909283460883), np.float64(-6.524074004316852), np.float64(0.6307690372955947)]\n",
      "nllf = 6.310796258553427, jac = [np.float64(0.04990566392214985), np.float64(-0.001439408016133653), np.float64(-0.0027512214980024985), np.float64(0.009485341724485175)]\n",
      "------------------------\n",
      "pars=[np.float64(-22.050225858748224), np.float64(1.644483183013966e-06), np.float64(9.015179540496625), np.float64(-8.548204226439667)]\n",
      "beta=[np.float64(-22.050225858748224), np.float64(73.07179549692368), np.float64(-77.06359585018566), np.float64(27.091154049129646)]\n",
      "nllf = 4.9676223757332, jac = [np.float64(0.044100490125984805), np.float64(1.5062468017637075e-07), np.float64(-0.01803014829444692), np.float64(0.017096548916171614)]\n"
     ]
    }
   ],
   "source": [
    "for x, pars in zip(xs, parss): \n",
    "    r = lg.nllf(x, y, pars, jac = True)\n",
    "    beta = lg.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": 8,
   "id": "9cf6fec1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cov_pars = array([[88.66981718045956  , -1.2461162929855938, -7.2032252157241405,\n",
      "        12.643615608184488 ],\n",
      "       [-1.2461162929796519,  5.875091109892306 ,  1.086039589044909 ,\n",
      "         0.2688880961870854],\n",
      "       [-7.203225215732767 ,  1.08603958904545  ,  0.9368560858745143,\n",
      "        -1.105349253065405 ],\n",
      "       [12.643615608183422 ,  0.2688880961869379, -1.1053492530639295,\n",
      "         1.9662718284625142]])\n",
      "cov_pars = array([[ 4.0189309259208073e+01,  1.5283870335397518e-05,\n",
      "        -1.0539487960613590e+01,  8.4965481264772382e+00],\n",
      "       [ 1.5283870335398561e-05,  1.0456138304267554e+01,\n",
      "        -1.5255470929999245e-06,  5.7878951772164524e-06],\n",
      "       [-1.0539487960613663e+01, -1.5255470929995728e-06,\n",
      "         5.8583150404942561e+00, -3.0423947835076719e+00],\n",
      "       [ 8.4965481264773377e+00,  5.7878951772162592e-06,\n",
      "        -3.0423947835076688e+00,  2.0221168062093726e+00]])\n"
     ]
    }
   ],
   "source": [
    "# asymptotic covariance matrix of parameters\n",
    "cov_parss = np.array([lg.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": 9,
   "id": "cbc4c71e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.1084353421439957e+01 6.0574470013571613e+00 3.0447099119140469e-01\n",
      " 1.7647907003263518e-03]\n",
      "[4.5051054919554510e+01 3.0080446689362188e+00 1.0641517426858928e-02\n",
      " 1.0456138304261696e+01]\n"
     ]
    }
   ],
   "source": [
    "# check eigenvalues\n",
    "for cov_pars in cov_parss: print(np.linalg.eigvals(cov_pars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2359d3c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining two-sided confidence intervals\n",
    "alpha = 0.05   # significance level\n",
    "probs = [alpha/2, 1 - alpha/2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ba4d5d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pars_CI = array([[-43.40876822677736  ,  -4.030971772150962 ,  -0.5214635957804892,\n",
      "         -7.491008028113301 ],\n",
      "       [ -6.496902853311777 ,   5.4703798299059665,   3.272686677892722 ,\n",
      "         -1.9943354858092968]])\n",
      "pars_CI = array([[-34.47542511475169 ,  -6.337728605324418,   4.271291263280167,\n",
      "        -11.33529562100979 ],\n",
      "       [ -9.625026602744763,   6.337731894290783,  13.759067817713081,\n",
      "         -5.761112831869545]])\n"
     ]
    }
   ],
   "source": [
    "# CI of params (assuming asymptotic distr of parameters) : Wald approximation\n",
    "pars_CIs = np.array([lg.get_pars_quantiles_normal(probs, pars, cov_pars) for pars, cov_pars in zip(parss, cov_parss)])\n",
    "\n",
    "for pars_CI in pars_CIs: print(f\"{pars_CI = }\")"
   ]
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      "text/plain": [
       "   scale coef      value        LCL        UCL\n",
       "0  plain   p0 -24.952836 -43.408768  -6.496903\n",
       "1  plain   p1   0.719704  -4.030972   5.470380\n",
       "2  plain   p2   1.375612  -0.521464   3.272687\n",
       "3  plain   p3  -4.742672  -7.491008  -1.994335\n",
       "4    log   p0 -22.050226 -34.475425  -9.625027\n",
       "5    log   p1   0.000002  -6.337729   6.337732\n",
       "6    log   p2   9.015180   4.271291  13.759068\n",
       "7    log   p3  -8.548204 -11.335296  -5.761113"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 13,
   "id": "6ef6f46b",
   "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": "b01a6f6b-bc09-4d1d-a601-170cf17e85d7",
       "rows": [
        [
         "plain",
         "-6.310796258553427",
         "20.621592517106855",
         "28.86336455929253",
         "0.9655172413793104",
         "13.812457417951638",
         "0.9999999944452796",
         "58",
         "4",
         "54"
        ],
        [
         "log",
         "-4.9676223757332",
         "17.9352447514664",
         "26.177016793652076",
         "0.9655172413793104",
         "8.60216708864235",
         "0.9999999999998123",
         "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>-6.310796</td>\n",
       "      <td>20.621593</td>\n",
       "      <td>28.863365</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>13.812457</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>-4.967622</td>\n",
       "      <td>17.935245</td>\n",
       "      <td>26.177017</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>8.602167</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 -6.310796  20.621593  28.863365  0.965517  13.812457            1.0  58   \n",
       "log   -4.967622  17.935245  26.177017  0.965517   8.602167            1.0  58   \n",
       "\n",
       "       k  dof  \n",
       "scale          \n",
       "plain  4   54  \n",
       "log    4   54  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# goodness of fit measures\n",
    "pd.DataFrame(\n",
    "    [lg.goodness_of_fit(x, y, pars) for x, pars in zip(xs, parss)], \n",
    "    index = pd.Index(scales, name = 'scale')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "54f463d0",
   "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",
    "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, lab in enumerate([\"NC\", \"AE\"]): ax.scatter(x[y == i], y[y == i], label = lab)  \n",
    "\n",
    "    # plotting fitted model \n",
    "    #xp = np.linspace(min(x), max(x), 100)\n",
    "    xp = np.linspace(min(x), max(x)+1, 100)\n",
    "    yp = lg.model(xp, pars)\n",
    "\n",
    "    ax.plot(xp, yp, label = \"logistic reg\")\n",
    "\n",
    "    for lab, c in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        \n",
    "        # define quantile model\n",
    "        fname = f\"lg.get_model_quantiles_{lab}\"\n",
    "        \n",
    "        # get result of model quantiles at probs\n",
    "        # pars and cov_pars are obtained via MLE method\n",
    "        res = eval(fname)(xp, probs, pars, cov_pars)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *res, color = c, alpha = 0.5, label = f\"CI:{lab}\")\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",
    "plt.savefig(os.path.join(results_path, \"logit_fit.pdf\"))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67fa2c6c",
   "metadata": {},
   "source": [
    "##  Model CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "92080804",
   "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 = dict()\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, lab in enumerate([\"NC\", \"AE\"]): \n",
    "        ax.scatter(x[y == i], y[y == i], label = lab)  \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 = lg.model(xp, pars)\n",
    "\n",
    "    ax.plot(xp, yp, label = \"logistic reg\")\n",
    "\n",
    "    for method, c in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        \n",
    "        print(f\"model CI:scale:{scale},method:{method}\")\n",
    "\n",
    "        # define quantile model\n",
    "        fname = f\"lg.get_model_quantiles_{method}\"\n",
    "        \n",
    "        # get result of model quantiles at probs\n",
    "        # pars and cov_pars are obtained via MLE method\n",
    "        res = eval(fname)(xp, probs, pars, cov_pars)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *res, color = c, alpha = 0.5, label = f\"CI:{method}\")\n",
    "\n",
    "    # plot quantiles of models at bootstapped parameters\n",
    "    for method, c 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 boostrapped parameters\n",
    "        m = 10000\n",
    "        fname = f\"lg.get_{method}_pars\"\n",
    "        bpars = eval(fname)(x, y, m)\n",
    "    \n",
    "        boots_pars_results[(scale, method)] = bpars\n",
    "\n",
    "        # quantiles\n",
    "        quant = np.quantile([lg.model(xp, p) for p in bpars], probs, axis = 0)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(xp, *quant, color = c, 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",
    "plt.savefig(os.path.join(results_path, \"logit_fit.pdf\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce16edf",
   "metadata": {},
   "source": [
    "## Parameter CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "18dce684",
   "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"
     ]
    },
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "coef",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "value",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "LCL",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "UCL",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "method",
         "rawType": "object",
         "type": "string"
        }
       ],
       "ref": "e35addd3-a843-4c4c-a68d-f6465588cc4f",
       "rows": [
        [
         "0",
         "plain",
         "p0",
         "-24.952835540044568",
         "-43.40876822677736",
         "-6.496902853311777",
         "Wald/delta"
        ],
        [
         "1",
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         "24",
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         "25",
         "log",
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         "1.644483183013966e-06",
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         "6.22757306046279",
         "normal"
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         "26",
         "log",
         "p2",
         "9.015179540496625",
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         "27",
         "log",
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         "5.70538852637681",
         "11.299155092795226",
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        [
         "28",
         "log",
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         "-22.050225858748224",
         "-24.082833022347238",
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         "nonparam_boots"
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        [
         "29",
         "log",
         "p1",
         "1.644483183013966e-06",
         "-4.9925743259985795",
         "1.4303351569803362e-06",
         "nonparam_boots"
        ],
        [
         "30",
         "log",
         "p2",
         "9.015179540496625",
         "-15.348282729137194",
         "9.24177180837389",
         "nonparam_boots"
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        [
         "31",
         "log",
         "p3",
         "-8.548204226439667",
         "-8.481206473458332",
         "9.113680691923287",
         "nonparam_boots"
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        [
         "32",
         "log",
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         "-22.050225858748224",
         "-23.82393319041512",
         "-5.855994168706951",
         "nonparam_stratified_boots"
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        [
         "33",
         "log",
         "p1",
         "1.644483183013966e-06",
         "-5.034842752244022",
         "1.7085973650867422e-06",
         "nonparam_stratified_boots"
        ],
        [
         "34",
         "log",
         "p2",
         "9.015179540496625",
         "-15.433012964766467",
         "9.330228591631178",
         "nonparam_stratified_boots"
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        [
         "35",
         "log",
         "p3",
         "-8.548204226439667",
         "-8.644478543074978",
         "8.90445606865636",
         "nonparam_stratified_boots"
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        [
         "36",
         "log",
         "p0",
         "-22.050225858748224",
         "-22.05022728438542",
         "-5.810448297562306",
         "parametric_boots"
        ],
        [
         "37",
         "log",
         "p1",
         "1.644483183013966e-06",
         "-3.9927353271249117",
         "1.2155450728656106e-06",
         "parametric_boots"
        ],
        [
         "38",
         "log",
         "p2",
         "9.015179540496625",
         "-14.548704995919401",
         "9.015179575979962",
         "parametric_boots"
        ],
        [
         "39",
         "log",
         "p3",
         "-8.548204226439667",
         "-8.54820443838639",
         "7.856916612752412",
         "parametric_boots"
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       "shape": {
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       "<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>-24.952836</td>\n",
       "      <td>-43.408768</td>\n",
       "      <td>-6.496903</td>\n",
       "      <td>Wald/delta</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>0.719704</td>\n",
       "      <td>-4.030972</td>\n",
       "      <td>5.470380</td>\n",
       "      <td>Wald/delta</td>\n",
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       "      <th>2</th>\n",
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       "      <td>p2</td>\n",
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       "      <td>-0.521464</td>\n",
       "      <td>3.272687</td>\n",
       "      <td>Wald/delta</td>\n",
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       "      <th>3</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-4.742672</td>\n",
       "      <td>-7.491008</td>\n",
       "      <td>-1.994335</td>\n",
       "      <td>Wald/delta</td>\n",
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       "      <th>4</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-22.050226</td>\n",
       "      <td>-34.475425</td>\n",
       "      <td>-9.625027</td>\n",
       "      <td>Wald/delta</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>-6.337729</td>\n",
       "      <td>6.337732</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>9.015180</td>\n",
       "      <td>4.271291</td>\n",
       "      <td>13.759068</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-8.548204</td>\n",
       "      <td>-11.335296</td>\n",
       "      <td>-5.761113</td>\n",
       "      <td>Wald/delta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-24.952836</td>\n",
       "      <td>-43.349898</td>\n",
       "      <td>-5.856114</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>0.719704</td>\n",
       "      <td>-5.550920</td>\n",
       "      <td>3.961629</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.375612</td>\n",
       "      <td>-0.551786</td>\n",
       "      <td>3.238880</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-4.742672</td>\n",
       "      <td>-7.446438</td>\n",
       "      <td>-1.926512</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-24.952836</td>\n",
       "      <td>-33.248280</td>\n",
       "      <td>-8.585695</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>0.719704</td>\n",
       "      <td>-2.361955</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.375612</td>\n",
       "      <td>-6.192096</td>\n",
       "      <td>3.687263</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-4.742672</td>\n",
       "      <td>-5.985331</td>\n",
       "      <td>5.720057</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>plain</td>\n",
       "      <td>p0</td>\n",
       "      <td>-24.952836</td>\n",
       "      <td>-31.063968</td>\n",
       "      <td>-9.145434</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>0.719704</td>\n",
       "      <td>-2.427237</td>\n",
       "      <td>0.000002</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>1.375612</td>\n",
       "      <td>-6.232222</td>\n",
       "      <td>3.647423</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>-4.742672</td>\n",
       "      <td>-5.791922</td>\n",
       "      <td>5.313525</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>-24.952836</td>\n",
       "      <td>-24.952833</td>\n",
       "      <td>-5.272372</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>plain</td>\n",
       "      <td>p1</td>\n",
       "      <td>0.719704</td>\n",
       "      <td>-2.439194</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>plain</td>\n",
       "      <td>p2</td>\n",
       "      <td>1.375612</td>\n",
       "      <td>-4.555191</td>\n",
       "      <td>3.844330</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>plain</td>\n",
       "      <td>p3</td>\n",
       "      <td>-4.742672</td>\n",
       "      <td>-4.931461</td>\n",
       "      <td>4.356929</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-22.050226</td>\n",
       "      <td>-34.478942</td>\n",
       "      <td>-9.238552</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>-6.415694</td>\n",
       "      <td>6.227573</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>9.015180</td>\n",
       "      <td>-13.762226</td>\n",
       "      <td>-4.160883</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-8.548204</td>\n",
       "      <td>5.705389</td>\n",
       "      <td>11.299155</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-22.050226</td>\n",
       "      <td>-24.082833</td>\n",
       "      <td>-5.873931</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>-4.992574</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>9.015180</td>\n",
       "      <td>-15.348283</td>\n",
       "      <td>9.241772</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-8.548204</td>\n",
       "      <td>-8.481206</td>\n",
       "      <td>9.113681</td>\n",
       "      <td>nonparam_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>log</td>\n",
       "      <td>p0</td>\n",
       "      <td>-22.050226</td>\n",
       "      <td>-23.823933</td>\n",
       "      <td>-5.855994</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>0.000002</td>\n",
       "      <td>-5.034843</td>\n",
       "      <td>0.000002</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>9.015180</td>\n",
       "      <td>-15.433013</td>\n",
       "      <td>9.330229</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>-8.548204</td>\n",
       "      <td>-8.644479</td>\n",
       "      <td>8.904456</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>-22.050226</td>\n",
       "      <td>-22.050227</td>\n",
       "      <td>-5.810448</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>log</td>\n",
       "      <td>p1</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>-3.992735</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>log</td>\n",
       "      <td>p2</td>\n",
       "      <td>9.015180</td>\n",
       "      <td>-14.548705</td>\n",
       "      <td>9.015180</td>\n",
       "      <td>parametric_boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>log</td>\n",
       "      <td>p3</td>\n",
       "      <td>-8.548204</td>\n",
       "      <td>-8.548204</td>\n",
       "      <td>7.856917</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 -24.952836 -43.408768  -6.496903                 Wald/delta\n",
       "1   plain   p1   0.719704  -4.030972   5.470380                 Wald/delta\n",
       "2   plain   p2   1.375612  -0.521464   3.272687                 Wald/delta\n",
       "3   plain   p3  -4.742672  -7.491008  -1.994335                 Wald/delta\n",
       "4     log   p0 -22.050226 -34.475425  -9.625027                 Wald/delta\n",
       "5     log   p1   0.000002  -6.337729   6.337732                 Wald/delta\n",
       "6     log   p2   9.015180   4.271291  13.759068                 Wald/delta\n",
       "7     log   p3  -8.548204 -11.335296  -5.761113                 Wald/delta\n",
       "8   plain   p0 -24.952836 -43.349898  -5.856114                     normal\n",
       "9   plain   p1   0.719704  -5.550920   3.961629                     normal\n",
       "10  plain   p2   1.375612  -0.551786   3.238880                     normal\n",
       "11  plain   p3  -4.742672  -7.446438  -1.926512                     normal\n",
       "12  plain   p0 -24.952836 -33.248280  -8.585695             nonparam_boots\n",
       "13  plain   p1   0.719704  -2.361955   0.000002             nonparam_boots\n",
       "14  plain   p2   1.375612  -6.192096   3.687263             nonparam_boots\n",
       "15  plain   p3  -4.742672  -5.985331   5.720057             nonparam_boots\n",
       "16  plain   p0 -24.952836 -31.063968  -9.145434  nonparam_stratified_boots\n",
       "17  plain   p1   0.719704  -2.427237   0.000002  nonparam_stratified_boots\n",
       "18  plain   p2   1.375612  -6.232222   3.647423  nonparam_stratified_boots\n",
       "19  plain   p3  -4.742672  -5.791922   5.313525  nonparam_stratified_boots\n",
       "20  plain   p0 -24.952836 -24.952833  -5.272372           parametric_boots\n",
       "21  plain   p1   0.719704  -2.439194   0.000007           parametric_boots\n",
       "22  plain   p2   1.375612  -4.555191   3.844330           parametric_boots\n",
       "23  plain   p3  -4.742672  -4.931461   4.356929           parametric_boots\n",
       "24    log   p0 -22.050226 -34.478942  -9.238552                     normal\n",
       "25    log   p1   0.000002  -6.415694   6.227573                     normal\n",
       "26    log   p2   9.015180 -13.762226  -4.160883                     normal\n",
       "27    log   p3  -8.548204   5.705389  11.299155                     normal\n",
       "28    log   p0 -22.050226 -24.082833  -5.873931             nonparam_boots\n",
       "29    log   p1   0.000002  -4.992574   0.000001             nonparam_boots\n",
       "30    log   p2   9.015180 -15.348283   9.241772             nonparam_boots\n",
       "31    log   p3  -8.548204  -8.481206   9.113681             nonparam_boots\n",
       "32    log   p0 -22.050226 -23.823933  -5.855994  nonparam_stratified_boots\n",
       "33    log   p1   0.000002  -5.034843   0.000002  nonparam_stratified_boots\n",
       "34    log   p2   9.015180 -15.433013   9.330229  nonparam_stratified_boots\n",
       "35    log   p3  -8.548204  -8.644479   8.904456  nonparam_stratified_boots\n",
       "36    log   p0 -22.050226 -22.050227  -5.810448           parametric_boots\n",
       "37    log   p1   0.000002  -3.992735   0.000001           parametric_boots\n",
       "38    log   p2   9.015180 -14.548705   9.015180           parametric_boots\n",
       "39    log   p3  -8.548204  -8.548204   7.856917           parametric_boots"
      ]
     },
     "execution_count": 24,
     "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, x, pars, cov_pars in zip(scales, xs, parss, cov_parss):\n",
    "\n",
    "    # plot quantiles of models at bootstapped parameters\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 boostrapped 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"
   ]
  }
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