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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Logistic regression: using general fit (for paper)\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 and considering full data and trimmed."
   ]
  },
  {
   "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",
    "import numdifftools as nd\n",
    "\n",
    "import pandas as pd\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": []
   },
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   "source": [
    "verbose = False\n",
    "\n",
    "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 from mat files\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)"
   ]
  },
  {
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   "source": [
    "\"\"\"\n",
    "Data preparation\n",
    "\"\"\"\n",
    "# get data\n",
    "perc = 95\n",
    "organ = \"lung\"\n",
    "\n",
    "x0, y0 = data_utils.get_data(organ, perc, suv_dict, flags_dict)\n",
    "\n",
    "# values for trimmed dataset\n",
    "value_to_drop = 2.48    # target value\n",
    "tol = 1e-2              # tolerance: matches 2.481... too\n",
    "drop_mask = (np.abs(x0 - value_to_drop) <= tol)\n",
    "\n",
    "df_data = data_utils.prepare_data(x0, y0, drop_mask)\n",
    "df_data"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f27d9a03",
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    {
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     "output_type": "stream",
     "text": [
      "Index(['X', 'Y', 'scale', 'dataset'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e9bb7b-5a50-4cae-9af4-261c11453dd4",
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   "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": 6,
   "id": "239ae789",
   "metadata": {
    "tags": []
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scale</th>\n",
       "      <th>dataset</th>\n",
       "      <th>beta</th>\n",
       "      <th>cost</th>\n",
       "      <th>cost_jac</th>\n",
       "      <th>|cost_jac|</th>\n",
       "      <th>nllf</th>\n",
       "      <th>nllf_jac</th>\n",
       "      <th>|nllf_jac|</th>\n",
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       "      <th>...</th>\n",
       "      <th>AIC</th>\n",
       "      <th>BIC</th>\n",
       "      <th>A</th>\n",
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       "      <th>p-value(chi2)</th>\n",
       "      <th>n</th>\n",
       "      <th>k</th>\n",
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       "      <th>0</th>\n",
       "      <td>log</td>\n",
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       "      <td>4.605007</td>\n",
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       "      <td>[-123.99215902865652, 163.7935965036444, -71.4...</td>\n",
       "      <td>4.782070</td>\n",
       "      <td>[1.0025115480013884e-07, -2.024296178486144e-0...</td>\n",
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       "      <td>TRIM</td>\n",
       "      <td>[-450.07747042343345, 727.829141442988, -395.5...</td>\n",
       "      <td>2.480642</td>\n",
       "      <td>[1.685638325077442e-07, 1.54062484724302e-05, ...</td>\n",
       "      <td>1.570442e-05</td>\n",
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       "      <td>[[404697.6454986483, -1102.448602898858, 7422....</td>\n",
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       "   scale dataset                                               beta      cost  \\\n",
       "0    log    FULL  [-40.708888462298674, 152.89617793139317, -186...  4.605007   \n",
       "1    log    TRIM  [-318.6321323732778, 1693.4889157771447, -3000...  1.700810   \n",
       "2  plain    FULL  [-123.99215902865652, 163.7935965036444, -71.4...  4.782070   \n",
       "3  plain    TRIM  [-450.07747042343345, 727.829141442988, -395.5...  2.480642   \n",
       "\n",
       "                                            cost_jac    |cost_jac|      nllf  \\\n",
       "0  [-1.1837073861670092e-07, -2.376873228961505e-...  1.307366e-07  4.602970   \n",
       "1  [2.2793551304441453e-08, 1.3834692958258074e-0...  1.450111e-06  1.592272   \n",
       "2  [1.0025115480013884e-07, -2.024296178486144e-0...  1.023512e-07  4.766501   \n",
       "3  [1.685638325077442e-07, 1.54062484724302e-05, ...  1.570442e-05  2.277127   \n",
       "\n",
       "                                            nllf_jac  |nllf_jac|  \\\n",
       "0  [8.129940618598064e-05, -2.3768539263645607e-0...    0.000090   \n",
       "1  [0.00063728705829786, 1.3834127755049543e-06, ...    0.000659   \n",
       "2  [0.0002480845692121132, -2.0242891189590214e-0...    0.000250   \n",
       "3  [0.0009003235046793746, 2.052334580013651e-05,...    0.000902   \n",
       "\n",
       "                                                 cov  ...        AIC  \\\n",
       "0  [[83.89851693316346, -3.5879891676637407e-07, ...  ...  17.205940   \n",
       "1  [[4027.3052602929115, 0.0010266690336562028, 1...  ...  11.184544   \n",
       "2  [[356.444278198669, -2.879083260976005e-08, 4....  ...  17.533001   \n",
       "3  [[404697.6454986483, -1102.448602898858, 7422....  ...  12.554255   \n",
       "\n",
       "         BIC         A      chi2  p-value(chi2)   n  k  dof  \\\n",
       "0  25.447712  0.948276  8.316720            1.0  58  4   54   \n",
       "1  19.356749  0.982456  2.348783            1.0  57  4   53   \n",
       "2  25.774773  0.965517  9.164976            1.0  58  4   54   \n",
       "3  20.726460  0.964912  3.848680            1.0  57  4   53   \n",
       "\n",
       "                                                pars  success  \n",
       "0  [-40.708888462298674, -9.651298472161144e-08, ...     True  \n",
       "1  [-318.6321323732778, 2.8260160426564025e-05, -...     True  \n",
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       "\n",
       "[4 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defining two-sided confidence intervals\n",
    "alpha = 0.05   # significance level\n",
    "probs = [alpha/2, 1 - alpha/2]\n",
    "\n",
    "# fits\n",
    "lg = logit_utils_gen.LogisticPolyRegression(3, mono = True, lam = (0, 1e-6))\n",
    "\n",
    "lst = []\n",
    "for (scale, dataset), g in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    if verbose: print(f\"Fitting: {scale}, {dataset}\")\n",
    "\n",
    "    # data associated to scale and dataset\n",
    "    X, Y = g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "    # fit\n",
    "    res_fit = lg.fit(X, Y, method=\"diff_evol\")\n",
    "    \n",
    "    # calc nllf and beta for fitted pars\n",
    "    res_nllf = lg.get_nllf(X, Y, res_fit['pars'], jac = True)\n",
    "\n",
    "    # cost and jacobian norm\n",
    "    cost = lg.get_cost(X, Y, res_fit['pars'], jac = True)\n",
    "    \n",
    "    # beta of fitted pars\n",
    "    beta = lg.get_beta(res_fit['pars'])\n",
    "    \n",
    "    # covariance matrix of pars\n",
    "    cov = lg.get_cov(X, Y, res_fit['pars'])\n",
    "    \n",
    "    # standard error of pars\n",
    "    pars_SE = lg.get_SE_pars_normal(cov)\n",
    "\n",
    "    # CI of params (assuming asymptotic distr of parameters) : Wald approximation\n",
    "    pars_CI = lg.get_pars_quantiles_normal(probs, res_fit['pars'], cov)\n",
    "    \n",
    "    # goodness of fit\n",
    "    res_gof = lg.goodness_of_fit(X, Y, res_fit['pars'])\n",
    "\n",
    "    lst.append({\"scale\": scale, \"dataset\": dataset, \"beta\": beta} | \n",
    "               {\"cost\": cost[0], \"cost_jac\": cost[1], \"|cost_jac|\": np.linalg.norm(cost[1])} |\n",
    "               {\"nllf\": res_nllf[0], \"nllf_jac\": res_nllf[1], \"|nllf_jac|\": np.linalg.norm(res_nllf[1])} | \n",
    "               {\"cov\": cov, \"cov_evals\": np.linalg.eigvals(cov)} |\n",
    "               {\"SE\": pars_SE, \"LCL\": pars_CI[0], \"UCL\": pars_CI[1]} |\n",
    "               res_gof |res_fit)\n",
    " \n",
    "df_fit = pd.DataFrame(lst)\n",
    "df_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa65982",
   "metadata": {},
   "source": [
    "## Checking stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2851e6d3",
   "metadata": {},
   "source": [
    "Problem: The entries in covariance matrices are quite large.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9de8d697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking: log, FULL\n",
      "Cost jacobian:\n"
     ]
    },
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       "3 -2.730043e-04  2.657631e-03 -5.475740e-02 -1.203661"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scales = df_data[\"scale\"].unique()\n",
    "datasets = df_data[\"dataset\"].unique()\n",
    "\n",
    "for (scale, dataset), g in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    print(f\"Checking: {scale}, {dataset}\")\n",
    "\n",
    "    # data associated to scale and dataset\n",
    "    X, Y = g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "    # fit\n",
    "    res_fit = df_fit.loc[(df_fit['scale'] == scale) & (df_fit['dataset'] == dataset)].iloc[0]\n",
    "    \n",
    "    pars = res_fit['pars']\n",
    "    \n",
    "    print(\"Cost jacobian:\")\n",
    "    cost_fun = lambda p: lg.get_cost(X, Y, p, jac = False)\n",
    "    display(pd.DataFrame({'cost_jac': res_fit['cost_jac'], 'gradient': nd.Gradient(cost_fun)(pars)}).T)\n",
    "\n",
    "    print(\"nllf jacobian:\")\n",
    "    nllf_fun = lambda p: lg.get_nllf(X, Y, p, jac = False)\n",
    "    display(pd.DataFrame({'nllf_jac': res_fit['nllf_jac'], 'gradient': nd.Gradient(nllf_fun)(pars)}).T)\n",
    "\n",
    "    numH = nd.Hessian(cost_fun)(pars)\n",
    "    directH = lg.get_cost_hessian(X, Y, pars)\n",
    "    print(\"num hess:\")\n",
    "    display(pd.DataFrame(numH))\n",
    "    print(\"direct hess:\")\n",
    "    display(pd.DataFrame(directH))\n",
    "\n",
    "    print('cov via directH:') \n",
    "    display(pd.DataFrame(res_fit['cov']))\n",
    "    print('inv_hess cost:')\n",
    "    display(pd.DataFrame(np.linalg.pinv(numH)))\n",
    "\n",
    "    print('eigenvalues directH:') \n",
    "    display(pd.DataFrame(np.linalg.eigvals(directH)))\n",
    "    print('eigenvalues numH:')\n",
    "    display(pd.DataFrame(np.linalg.eigvals(numH)))\n",
    "\n",
    "    print(\"cond directH:\", np.linalg.cond(directH))\n",
    "    print(\"cond numH:\", np.linalg.cond(numH))\n",
    "\n",
    "    print(\"difference between hessians:\")\n",
    "    display(pd.DataFrame(directH - numH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee83ebac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking: FULL\n",
      "Checking: TRIM\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_fit_res(scale, dataset):\n",
    "    return df_fit.loc[(df_fit['scale'] == scale) & (df_fit['dataset'] == dataset)].iloc[0]\n",
    "\n",
    "def get_data(scale, dataset):\n",
    "    g = df_data.loc[(df_data['scale'] == scale) & (df_data['dataset'] == dataset)]\n",
    "    return g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "scales = df_data[\"scale\"].unique()\n",
    "datasets = df_data[\"dataset\"].unique()\n",
    "\n",
    "fig, axs = plt.subplots(\n",
    "    ncols = len(datasets),\n",
    "    nrows = 1,\n",
    "    figsize = (7*len(datasets), 5))\n",
    "\n",
    "for ax, dataset in zip(axs, datasets):\n",
    "\n",
    "    print(f\"Checking: {dataset}\")\n",
    "    res_fit = get_fit_res(\"log\", dataset)\n",
    "    X, Y = get_data(\"log\", dataset)\n",
    "\n",
    "    pars = res_fit['pars']\n",
    "    ax.set_title(f\"log, {dataset}\")\n",
    "\n",
    "    for j in range(len(pars)):\n",
    "\n",
    "        x_tmp = np.linspace(-200, 200, 400)\n",
    "        y_tmp = np.zeros_like(x_tmp)\n",
    "        \n",
    "        for i, dp in enumerate(x_tmp):\n",
    "            pars_tmp = pars.copy()\n",
    "            pars_tmp[j] = pars_tmp[j] + dp\n",
    "            y_tmp[i] = lg.get_cost(X, Y, pars_tmp, jac = False)\n",
    "\n",
    "        ax.plot(x_tmp, y_tmp, label = f\"par {j}\")\n",
    "        ax.set_yscale(\"log\")\n",
    "        ax.set_xlabel(f\"pars perturbation\")\n",
    "        ax.set_ylabel(\"cost\")\n",
    "        ax.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4070360",
   "metadata": {},
   "source": [
    "## Show fit results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9e51e07c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['scale', 'dataset', 'beta', 'cost', 'cost_jac', '|cost_jac|', 'nllf',\n",
       "       'nllf_jac', '|nllf_jac|', 'cov', 'cov_evals', 'SE', 'LCL', 'UCL', 'LLF',\n",
       "       'AIC', 'BIC', 'A', 'chi2', 'p-value(chi2)', 'n', 'k', 'dof', 'pars',\n",
       "       'success'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fit.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1a0a518b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "scale",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "dataset",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "AIC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "BIC",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "A",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "n",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "k",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "|cost_jac|",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "dof",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "cost",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "chi2",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "p-value(chi2)",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "success",
         "rawType": "bool",
         "type": "boolean"
        }
       ],
       "ref": "610735ae-c8b3-41ba-9143-615ad8b30628",
       "rows": [
        [
         "0",
         "log",
         "FULL",
         "17.205940417091277",
         "25.447712459276957",
         "0.9482758620689655",
         "58",
         "4",
         "1.3073662600046852e-07",
         "54",
         "4.605007497178395",
         "8.31671997480477",
         "0.9999999999999134",
         "True"
        ],
        [
         "1",
         "log",
         "TRIM",
         "11.184543516358747",
         "19.35674858769695",
         "0.9824561403508771",
         "57",
         "4",
         "1.4501113401230194e-06",
         "53",
         "1.7008095127425606",
         "2.3487825965112825",
         "1.0",
         "True"
        ],
        [
         "2",
         "plain",
         "FULL",
         "17.533001100560185",
         "25.774773142745858",
         "0.9655172413793104",
         "58",
         "4",
         "1.0235118271719374e-07",
         "54",
         "4.782069565523081",
         "9.164975730545596",
         "0.9999999999992063",
         "True"
        ],
        [
         "3",
         "plain",
         "TRIM",
         "12.55425457321817",
         "20.72645964455637",
         "0.9649122807017544",
         "57",
         "4",
         "1.570442053828639e-05",
         "53",
         "2.4806417336663875",
         "3.8486801702780706",
         "1.0",
         "True"
        ]
       ],
       "shape": {
        "columns": 13,
        "rows": 4
       }
      },
      "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>scale</th>\n",
       "      <th>dataset</th>\n",
       "      <th>AIC</th>\n",
       "      <th>BIC</th>\n",
       "      <th>A</th>\n",
       "      <th>n</th>\n",
       "      <th>k</th>\n",
       "      <th>|cost_jac|</th>\n",
       "      <th>dof</th>\n",
       "      <th>cost</th>\n",
       "      <th>chi2</th>\n",
       "      <th>p-value(chi2)</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>log</td>\n",
       "      <td>FULL</td>\n",
       "      <td>17.205940</td>\n",
       "      <td>25.447712</td>\n",
       "      <td>0.948276</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>1.307366e-07</td>\n",
       "      <td>54</td>\n",
       "      <td>4.605007</td>\n",
       "      <td>8.316720</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>11.184544</td>\n",
       "      <td>19.356749</td>\n",
       "      <td>0.982456</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>1.450111e-06</td>\n",
       "      <td>53</td>\n",
       "      <td>1.700810</td>\n",
       "      <td>2.348783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>plain</td>\n",
       "      <td>FULL</td>\n",
       "      <td>17.533001</td>\n",
       "      <td>25.774773</td>\n",
       "      <td>0.965517</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>1.023512e-07</td>\n",
       "      <td>54</td>\n",
       "      <td>4.782070</td>\n",
       "      <td>9.164976</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>12.554255</td>\n",
       "      <td>20.726460</td>\n",
       "      <td>0.964912</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>1.570442e-05</td>\n",
       "      <td>53</td>\n",
       "      <td>2.480642</td>\n",
       "      <td>3.848680</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   scale dataset        AIC        BIC         A   n  k    |cost_jac|  dof  \\\n",
       "0    log    FULL  17.205940  25.447712  0.948276  58  4  1.307366e-07   54   \n",
       "1    log    TRIM  11.184544  19.356749  0.982456  57  4  1.450111e-06   53   \n",
       "2  plain    FULL  17.533001  25.774773  0.965517  58  4  1.023512e-07   54   \n",
       "3  plain    TRIM  12.554255  20.726460  0.964912  57  4  1.570442e-05   53   \n",
       "\n",
       "       cost      chi2  p-value(chi2)  success  \n",
       "0  4.605007  8.316720            1.0     True  \n",
       "1  1.700810  2.348783            1.0     True  \n",
       "2  4.782070  9.164976            1.0     True  \n",
       "3  2.480642  3.848680            1.0     True  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# goodness of fit measures\n",
    "df_fit[['scale', 'dataset', 'AIC', 'BIC', 'A', 'n', 'k', '|cost_jac|', 'dof', 'cost','chi2', \"p-value(chi2)\", 'success']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96acc3ab",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scale</th>\n",
       "      <th>dataset</th>\n",
       "      <th>idx</th>\n",
       "      <th>beta</th>\n",
       "      <th>pars</th>\n",
       "      <th>SE</th>\n",
       "      <th>LCL</th>\n",
       "      <th>UCL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>75.726285</td>\n",
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       "      <td>9.851628</td>\n",
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       "      <td>TRIM</td>\n",
       "      <td>0</td>\n",
       "      <td>-318.632132</td>\n",
       "      <td>-318.632132</td>\n",
       "      <td>63.461053</td>\n",
       "      <td>-443.013511</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>163.793597</td>\n",
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       "      <td>636.158507</td>\n",
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       "      <td>TRIM</td>\n",
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       "      <td>-2.558549</td>\n",
       "      <td>2.357388</td>\n",
       "      <td>-7.178944</td>\n",
       "      <td>2.061846</td>\n",
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       "      <th>14</th>\n",
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       "      <td>TRIM</td>\n",
       "      <td>2</td>\n",
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       "      <td>11.819092</td>\n",
       "      <td>-37.892131</td>\n",
       "      <td>8.437859</td>\n",
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       "      <td>TRIM</td>\n",
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       "      <td>26.856712</td>\n",
       "      <td>20.426174</td>\n",
       "      <td>-13.177854</td>\n",
       "      <td>66.891278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    scale dataset idx         beta        pars          SE          LCL  \\\n",
       "0     log    FULL   0   -40.708888  -40.708888    9.159613   -58.661401   \n",
       "1     log    FULL   1   152.896178        -0.0    2.015069    -3.949462   \n",
       "2     log    FULL   2  -186.372687  -15.072454    2.088265   -19.165378   \n",
       "3     log    FULL   3    75.726285   12.365119    1.282417     9.851628   \n",
       "4     log    TRIM   0  -318.632132 -318.632132   63.461053  -443.013511   \n",
       "5     log    TRIM   1  1693.488916    0.000028    4.519627    -8.858277   \n",
       "6     log    TRIM   2 -3000.947506  -72.923452    6.604797   -85.868616   \n",
       "7     log    TRIM   3  1772.609956   41.152022    3.342186    34.601458   \n",
       "8   plain    FULL   0  -123.992159 -123.992159   18.879732  -160.995754   \n",
       "9   plain    FULL   1   163.793597        -0.0    1.320492    -2.588117   \n",
       "10  plain    FULL   2   -71.447989   -5.582665    0.473537    -6.510779   \n",
       "11  plain    FULL   3    10.388715   12.798187    0.888696    11.056375   \n",
       "12  plain    TRIM   0   -450.07747  -450.07747  636.158507 -1696.925232   \n",
       "13  plain    TRIM   1   727.829141   -2.558549    2.357388    -7.178944   \n",
       "14  plain    TRIM   2  -395.522446  -14.727136   11.819092   -37.892131   \n",
       "15  plain    TRIM   3    72.296178   26.856712   20.426174   -13.177854   \n",
       "\n",
       "           UCL  \n",
       "0   -22.756376  \n",
       "1     3.949462  \n",
       "2   -10.979529  \n",
       "3    14.878611  \n",
       "4  -194.250754  \n",
       "5     8.858333  \n",
       "6   -59.978288  \n",
       "7    47.702586  \n",
       "8   -86.988564  \n",
       "9     2.588117  \n",
       "10    -4.65455  \n",
       "11   14.539999  \n",
       "12  796.770291  \n",
       "13    2.061846  \n",
       "14    8.437859  \n",
       "15   66.891278  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table of parameters and their CI (Wald approximation)\n",
    "df_pars = df_fit[[\"scale\", \"dataset\", \"beta\", \"pars\", \"SE\", \"LCL\", \"UCL\"]].copy()\n",
    "\n",
    "# add index column for exploding\n",
    "indices = df_pars[\"pars\"].apply(lambda x: list(range(len(x))))\n",
    "df_pars.insert(2, \"idx\", indices)\n",
    "\n",
    "# convert to long format by exploding the pars, SE, LCL, UCL, beta columns\n",
    "cols = [\"idx\", \"beta\", \"pars\", \"SE\", \"LCL\", \"UCL\"]\n",
    "df_pars_long = df_pars.explode(cols, ignore_index=True)\n",
    "df_pars_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7438f05d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x axis labels for plots\n",
    "xlabs = {\"plain\": r\"$X$\", \"log\": r\"$\\log(X)$\"}\n",
    "\n",
    "# set index for easier access to fit parameters\n",
    "df_fit_index = df_fit.set_index([\"scale\", \"dataset\"])\n",
    "scales = df_data[\"scale\"].unique()\n",
    "plot_labs = dict(zip(scales[::-1], ['A', 'B']))\n",
    "\n",
    "#\n",
    "# plotting data and fits\n",
    "#\n",
    "\n",
    "fig, axs = plt.subplots(ncols = len(scales), figsize = (6*len(scales), 5))\n",
    "\n",
    "for (scale, dataset), g in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    if verbose: print(f\"Plotting: {scale}, {dataset}\")\n",
    "\n",
    "    # data associated to scale and dataset\n",
    "    X, Y = g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "    # choose axis and set labels\n",
    "    ax = axs[0 if scale == \"log\" else 1]\n",
    "    ax.set_xlabel(xlabs[scale])\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # label on plots\n",
    "    ax.text(0.05, 0.9, f\"{plot_labs[scale]}\", transform=ax.transAxes)\n",
    "\n",
    "    # data\n",
    "    for i, lab in enumerate([\"NC\", \"AE\"]): \n",
    "        ax.scatter(X[Y == i], Y[Y == i], label = f'data: {dataset} {lab}', alpha = 0.5)\n",
    "\n",
    "    # fitted curve\n",
    "    fit_pars = df_fit_index.loc[(scale, dataset), \"pars\"]\n",
    "    x_fit = np.linspace(X.min(), X.max(), 100)\n",
    "    y_fit = lg.model(x_fit, fit_pars)\n",
    "\n",
    "    ax.plot(x_fit, y_fit, label = f\"fit {dataset}\")\n",
    " \n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(1.05, 0.5), loc='center right')\n",
    "\n",
    "plt.savefig(os.path.join(results_path, \"logit_fit_paper.pdf\"))\n",
    "plt.show()\n"
   ]
  },
  {
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     "text": [
      "/home/horvat/Documents/work/medfiz/git/uncertainty_study/python/logistic/logit_utils_gen.py:676: RuntimeWarning: overflow encountered in square\n",
      "  scales = np.sqrt(np.diag(S@cov_pars@S.T))/(4*np.cosh(F/2)**2)\n",
      "/home/horvat/Documents/work/medfiz/git/uncertainty_study/python/logistic/logit_utils_gen.py:676: RuntimeWarning: overflow encountered in multiply\n",
      "  scales = np.sqrt(np.diag(S@cov_pars@S.T))/(4*np.cosh(F/2)**2)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# plotting data and fits + simple confidence intervals\n",
    "#\n",
    "scales = df_data[\"scale\"].unique()\n",
    "datasets = df_data[\"dataset\"].unique()\n",
    "\n",
    "fig, axs = plt.subplots(ncols = len(scales), \n",
    "                        nrows = len(datasets), \n",
    "                        figsize = (6*len(scales), 5*len(datasets)))\n",
    "\n",
    "plot_labs = dict(zip([(scale, dataset) for dataset in datasets for scale in scales[::-1]], ['A','B','C','D']))\n",
    "if verbose: print(plot_labs)\n",
    "\n",
    "for (scale, dataset), g in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    if verbose: print(f\"Plotting: {scale}, {dataset}\")\n",
    "\n",
    "    # data associated to scale and dataset\n",
    "    X, Y = g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "    # choose axis and set labels\n",
    "    ax = axs[0 if dataset == \"FULL\" else 1, 0 if scale == \"log\" else 1]\n",
    "    ax.set_xlabel(xlabs[scale])\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "\n",
    "    # label on plots    \n",
    "    ax.text(0.05, 0.9, f\"{plot_labs[(scale, dataset)]}:{scale},{dataset}\", transform=ax.transAxes)\n",
    "\n",
    "    # data\n",
    "    for i, lab in enumerate([\"NC\", \"AE\"]): \n",
    "        ax.scatter(X[Y == i], Y[Y == i], label = f'data: {lab}', alpha = 0.5)\n",
    "    \n",
    "    # get fit parameters and covariance matrix for scale and dataset\n",
    "    fit_pars = df_fit_index.loc[(scale, dataset), \"pars\"]\n",
    "    fit_cov = df_fit_index.loc[(scale, dataset), \"cov\"]\n",
    "    \n",
    "    # plotting fitted curve\n",
    "    x_fit = np.linspace(X.min(), X.max(), 100)\n",
    "    y_fit = lg.model(x_fit, fit_pars)\n",
    "    ax.plot(x_fit, y_fit, label = f\"fit\")\n",
    "\n",
    "    # plotting simple confidence intervals of quantiles of the model at probs\n",
    "    for method, c in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        \n",
    "        # define quantile model\n",
    "        fname = f\"lg.get_model_quantiles_{method}\"\n",
    "        \n",
    "        # get result of model quantiles at probs\n",
    "        fit_cov1 = fit_cov\n",
    "        res = eval(fname)(x_fit, probs, fit_pars, fit_cov1)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(x_fit, *res, alpha = 0.5, color = c, label = f\"CI: {method}\")\n",
    " \n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, bbox_to_anchor=(1.05, 0.5), loc='center right')\n",
    "\n",
    "plt.savefig(os.path.join(results_path, \"logit_fit_CI_simple_paper.pdf\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67fa2c6c",
   "metadata": {},
   "source": [
    "##  Model CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00b2819a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/horvat/Documents/work/medfiz/git/uncertainty_study/python/logistic/logit_utils_gen.py:676: RuntimeWarning: overflow encountered in square\n",
      "  scales = np.sqrt(np.diag(S@cov_pars@S.T))/(4*np.cosh(F/2)**2)\n",
      "/home/horvat/Documents/work/medfiz/git/uncertainty_study/python/logistic/logit_utils_gen.py:676: RuntimeWarning: overflow encountered in multiply\n",
      "  scales = np.sqrt(np.diag(S@cov_pars@S.T))/(4*np.cosh(F/2)**2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# plotting data and fits + full set of confidence intervals\n",
    "#\n",
    "\n",
    "fig, axs = plt.subplots(ncols = len(scales), \n",
    "                        nrows = len(datasets), \n",
    "                        figsize = (6*len(scales), 5*len(datasets)))\n",
    "\n",
    "plot_labs = dict(zip([(scale, dataset) for dataset in datasets for scale in scales[::-1]], ['A','B','C','D']))\n",
    "if verbose: print(plot_labs)\n",
    "\n",
    "n_boots = 1000\n",
    "boots_pars_results = dict()\n",
    "\n",
    "for (scale, dataset), g in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    if verbose: print(f\"Plotting: {scale}, {dataset}\")\n",
    "\n",
    "    # data associated to scale and dataset\n",
    "    X, Y = g['X'].to_numpy(), g['Y'].to_numpy()\n",
    "\n",
    "    # choose axis and set labels\n",
    "    ax = axs[0 if dataset == \"FULL\" else 1, 0 if scale == \"log\" else 1]\n",
    "    ax.set_xlabel(xlabs[scale])\n",
    "    ax.set_ylabel(\"P(AE|X = x)\")\n",
    "    \n",
    "    #ax.text(0.05, 0.9, f\"{plot_labs[(scale, dataset)]}:{scale}, {dataset}\", transform=ax.transAxes)\n",
    "    ax.text(0.05, 0.9, f\"{plot_labs[(scale, dataset)]}\", transform=ax.transAxes)\n",
    "\n",
    "    # plotting data\n",
    "    for i, lab in enumerate([\"NC\", \"AE\"]): \n",
    "        ax.scatter(X[Y == i], Y[Y == i], label = f'data: {lab}', alpha = 0.5)\n",
    "    \n",
    "    # get fit parameters and covariance matrix for scale and dataset\n",
    "    fit_pars = df_fit_index.loc[(scale, dataset), \"pars\"]\n",
    "    fit_cov = df_fit_index.loc[(scale, dataset), \"cov\"]\n",
    "    \n",
    "    # plotting fitted curve\n",
    "    x_fit = np.linspace(X.min(), X.max(), 100)\n",
    "    y_fit = lg.model(x_fit, fit_pars)\n",
    "    ax.plot(x_fit, y_fit, label = f\"fit\")\n",
    "\n",
    "    # plotting simple confidence intervals of quantiles of the model at probs\n",
    "    for method, c in zip([\"normal\", \"delta\"], [\"red\", \"green\"]):\n",
    "        \n",
    "        # define quantile model\n",
    "        fname = f\"lg.get_model_quantiles_{method}\"\n",
    "        \n",
    "        # get result of model quantiles at probs\n",
    "        res = eval(fname)(x_fit, probs, fit_pars, fit_cov)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(x_fit, *res, color = c, alpha = 0.5, label = f\"CI:{lab}\")\n",
    "\n",
    "    # plot quantiles of models at bootstapped parameters using different bootstrapping methods\n",
    "    for method, c in zip(\n",
    "        [\"normal\", \"nonparam_boots\", \"nonparam_stratified_boots\", \"parametric_boots\"],\n",
    "        [\"orange\", \"pink\", \"purple\", \"cyan\"]):\n",
    "    \n",
    "        if verbose: print(f\"\\tmodel CI: scale:{scale}, dataset:{dataset}, method:{method}\")\n",
    "\n",
    "        # generate boostrapped parameters\n",
    "        fname = f\"lg.get_{method}_pars\"\n",
    "        bpars = eval(fname)(X, Y, m = n_boots)\n",
    "        boots_pars_results[(scale, dataset, method)] = bpars\n",
    "\n",
    "        # quantiles\n",
    "        quant = np.quantile([lg.model(x_fit, p) for p in bpars], probs, axis = 0)\n",
    "\n",
    "        # make plot of quantiles\n",
    "        ax.fill_between(x_fit, *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=(1.2, 0.5), loc='center right')\n",
    "\n",
    "plt.savefig(os.path.join(results_path, \"logit_fit_CI_paper.pdf\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce16edf",
   "metadata": {},
   "source": [
    "## Parameter CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91a199e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['scale', 'dataset', 'idx', 'beta', 'pars', 'SE', 'LCL', 'UCL'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pars.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "18dce684",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scale</th>\n",
       "      <th>dataset</th>\n",
       "      <th>idx</th>\n",
       "      <th>beta</th>\n",
       "      <th>pars</th>\n",
       "      <th>SE</th>\n",
       "      <th>LCL[Wald]</th>\n",
       "      <th>UCL[Wald]</th>\n",
       "      <th>LCL[normal]</th>\n",
       "      <th>UCL[normal]</th>\n",
       "      <th>LCL[nonparam_boots]</th>\n",
       "      <th>UCL[nonparam_boots]</th>\n",
       "      <th>LCL[nonparam_stratified_boots]</th>\n",
       "      <th>UCL[nonparam_stratified_boots]</th>\n",
       "      <th>LCL[parametric_boots]</th>\n",
       "      <th>UCL[parametric_boots]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>log</td>\n",
       "      <td>FULL</td>\n",
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       "      <td>[-6.469163562365495, 4.576050238215909e-06, 36...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[-318.6321323732778, 1693.4889157771447, -3000...</td>\n",
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       "      <td>[-14.066268776396683, -5.066610622088065e-10, ...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>[-13.854624218032699, 3.338353170707315e-05, 1...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "   scale dataset           idx  \\\n",
       "0    log    FULL  [0, 1, 2, 3]   \n",
       "1    log    TRIM  [0, 1, 2, 3]   \n",
       "2  plain    FULL  [0, 1, 2, 3]   \n",
       "3  plain    TRIM  [0, 1, 2, 3]   \n",
       "\n",
       "                                                beta  \\\n",
       "0  [-40.708888462298674, 152.89617793139317, -186...   \n",
       "1  [-318.6321323732778, 1693.4889157771447, -3000...   \n",
       "2  [-123.99215902865652, 163.7935965036444, -71.4...   \n",
       "3  [-450.07747042343345, 727.829141442988, -395.5...   \n",
       "\n",
       "                                                pars  \\\n",
       "0  [-40.708888462298674, -9.651298472161144e-08, ...   \n",
       "1  [-318.6321323732778, 2.8260160426564025e-05, -...   \n",
       "2  [-123.99215902865652, -3.5297635613473355e-09,...   \n",
       "3  [-450.07747042343345, -2.558548663853156, -14....   \n",
       "\n",
       "                                                  SE  \\\n",
       "0  [9.159613361554268, 2.0150687304186157, 2.0882...   \n",
       "1  [63.461053097887614, 4.5196265265592475, 6.604...   \n",
       "2  [18.87973194191774, 1.3204920172318018, 0.4735...   \n",
       "3  [636.1585065835781, 2.3573876653405152, 11.819...   \n",
       "\n",
       "                                           LCL[Wald]  \\\n",
       "0  [-58.6614007632569, -3.9494622345063237, -19.1...   \n",
       "1  [-443.0135108661216, -8.858276955467563, -85.8...   \n",
       "2  [-160.99575367258575, -2.58811679917674, -6.51...   \n",
       "3  [-1696.9252317860335, -7.178943585519528, -37....   \n",
       "\n",
       "                                           UCL[Wald]  \\\n",
       "0  [-22.75637616134045, 3.9494620414803534, -10.9...   \n",
       "1  [-194.25075388043405, 8.858333475788413, -59.9...   \n",
       "2  [-86.98856438472728, 2.588116792117212, -4.654...   \n",
       "3  [796.7702909391664, 2.0618462578132157, 8.4378...   \n",
       "\n",
       "                                         LCL[normal]  \\\n",
       "0  [-58.3336096526533, -4.013851717009624, -19.02...   \n",
       "1  [-439.8681211645074, -9.015950407244834, 59.49...   \n",
       "2  [-159.9912109220596, -2.6303142690898307, 4.64...   \n",
       "3  [-1658.649293479893, -7.0168456758069295, -9.8...   \n",
       "\n",
       "                                         UCL[normal]  \\\n",
       "0  [-21.553022589890016, 3.9521512398449894, -10....   \n",
       "1  [-187.6712954862221, 9.205264115492842, 86.282...   \n",
       "2  [-84.92631123202949, 2.589881511890865, 6.4854...   \n",
       "3  [861.1553843609629, 1.8570320383745857, 36.581...   \n",
       "\n",
       "                                 LCL[nonparam_boots]  \\\n",
       "0  [-323.85725851406823, -5.2513076921704, -69.55...   \n",
       "1  [-369.5465708187077, -13.543478132517475, -77....   \n",
       "2  [-659.637965045793, -2.765210239850389, -17.39...   \n",
       "3  [-732.5571367389293, -3.6330043809451076, -18....   \n",
       "\n",
       "                                 UCL[nonparam_boots]  \\\n",
       "0  [-6.089729002830446, 7.251345651387095e-06, 28...   \n",
       "1  [-11.37716340687003, 2.3109193375853133e-05, 6...   \n",
       "2  [-11.529580405692991, 7.922775438834798e-06, 1...   \n",
       "3  [-20.578776938003692, 1.8006999654167648e-05, ...   \n",
       "\n",
       "                      LCL[nonparam_stratified_boots]  \\\n",
       "0  [-320.1532250705583, -5.494921485279474, -70.3...   \n",
       "1  [-360.6063320469017, -8.054462744483482, -76.3...   \n",
       "2  [-658.3749083473575, -2.7303448042615655, -16....   \n",
       "3  [-708.850609572693, -3.644227321945749, -17.94...   \n",
       "\n",
       "                      UCL[nonparam_stratified_boots]  \\\n",
       "0  [-6.0334861841873515, 1.0749033611937977e-05, ...   \n",
       "1  [-11.289999897751402, 3.93829776908622e-05, 71...   \n",
       "2  [-11.660297718253965, 4.097971242734854e-06, 1...   \n",
       "3  [-21.084448712208758, 1.3742838556913198e-05, ...   \n",
       "\n",
       "                               LCL[parametric_boots]  \\\n",
       "0  [-318.6267145553689, -6.84252684038771, -74.06...   \n",
       "1  [-126.94885082911394, -5.200967198078771, -52....   \n",
       "2  [-519.9181692980177, -3.828433856483589, -17.7...   \n",
       "3  [-543.4651247601868, -3.923719934889914, -17.7...   \n",
       "\n",
       "                               UCL[parametric_boots]  \n",
       "0  [-6.469163562365495, 4.576050238215909e-06, 36...  \n",
       "1  [-14.066268776396683, -5.066610622088065e-10, ...  \n",
       "2  [-13.854624218032699, 3.338353170707315e-05, 1...  \n",
       "3  [-30.306069506729404, 2.4348035458824826e-06, ...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_res = dict()\n",
    "for (scale, dataset), _ in df_data.groupby(['scale', 'dataset']):\n",
    "    \n",
    "    if verbose: print(f\"Data: {scale}, {dataset}\")\n",
    "\n",
    "    # plot quantiles of models at bootstapped parameters\n",
    "    for method in [\"normal\", \"nonparam_boots\", \"nonparam_stratified_boots\", \"parametric_boots\"]:\n",
    "    \n",
    "        if verbose: print(f\"\\tmodel CI: scale:{scale}, dataset:{dataset}, method:{method}\")\n",
    "\n",
    "        # get boostrapped parameters\n",
    "        bpars = boots_pars_results[(scale, dataset, method)]\n",
    "    \n",
    "        # quantiles\n",
    "        quant = np.quantile(bpars, probs, axis = 0)\n",
    "        \n",
    "        if (scale, dataset) not in dt_res: dt_res[(scale, dataset)] = dict()\n",
    "        for lab, q in zip([\"LCL\", \"UCL\"], quant):\n",
    "            dt_res[(scale, dataset)][f\"{lab}[{method}]\"] = q\n",
    "\n",
    "\n",
    "# Joining results of bootstrapped pars quantiles with Wald CI of pars\n",
    "if verbose: print(f\"pars CI: scale:{scales}, dateset:{datasets}, method:Wald/delta\")\n",
    "\n",
    "method = \"Wald\"\n",
    "df_pars_CI_wald = df_pars.rename(columns={\"LCL\": f\"LCL[{method}]\", \"UCL\": f\"UCL[{method}]\"})\n",
    "df_pars_CI_wald = df_pars_CI_wald.set_index([\"scale\", \"dataset\"])\n",
    "\n",
    "df_res_CI_other = pd.DataFrame(dt_res).T\n",
    "df_res_CI_other.index.names = ['scale', 'dataset']\n",
    "\n",
    "df_pars_CI = df_pars_CI_wald.join(df_res_CI_other)\n",
    "df_pars_CI.reset_index(inplace=True)\n",
    "df_pars_CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e76c0167",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>scale</th>\n",
       "      <th>dataset</th>\n",
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       "      <td>0.000028</td>\n",
       "      <td>4.519627</td>\n",
       "      <td>-8.858277</td>\n",
       "      <td>8.858333</td>\n",
       "      <td>-9.01595</td>\n",
       "      <td>9.205264</td>\n",
       "      <td>-13.543478</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>-8.054463</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>-5.200967</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>log</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>2</td>\n",
       "      <td>-3000.947506</td>\n",
       "      <td>-72.923452</td>\n",
       "      <td>6.604797</td>\n",
       "      <td>-85.868616</td>\n",
       "      <td>-59.978288</td>\n",
       "      <td>59.493324</td>\n",
       "      <td>86.28298</td>\n",
       "      <td>-77.34694</td>\n",
       "      <td>62.584201</td>\n",
       "      <td>-76.338134</td>\n",
       "      <td>71.10271</td>\n",
       "      <td>-52.861454</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>log</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>3</td>\n",
       "      <td>1772.609956</td>\n",
       "      <td>41.152022</td>\n",
       "      <td>3.342186</td>\n",
       "      <td>34.601458</td>\n",
       "      <td>47.702586</td>\n",
       "      <td>-47.568018</td>\n",
       "      <td>-34.219937</td>\n",
       "      <td>-34.815949</td>\n",
       "      <td>43.442075</td>\n",
       "      <td>-39.709528</td>\n",
       "      <td>43.205955</td>\n",
       "      <td>-0.000174</td>\n",
       "      <td>10.677939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>plain</td>\n",
       "      <td>FULL</td>\n",
       "      <td>0</td>\n",
       "      <td>-123.992159</td>\n",
       "      <td>-123.992159</td>\n",
       "      <td>18.879732</td>\n",
       "      <td>-160.995754</td>\n",
       "      <td>-86.988564</td>\n",
       "      <td>-159.991211</td>\n",
       "      <td>-84.926311</td>\n",
       "      <td>-659.637965</td>\n",
       "      <td>-11.52958</td>\n",
       "      <td>-658.374908</td>\n",
       "      <td>-11.660298</td>\n",
       "      <td>-519.918169</td>\n",
       "      <td>-13.854624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>plain</td>\n",
       "      <td>FULL</td>\n",
       "      <td>1</td>\n",
       "      <td>163.793597</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.320492</td>\n",
       "      <td>-2.588117</td>\n",
       "      <td>2.588117</td>\n",
       "      <td>-2.630314</td>\n",
       "      <td>2.589882</td>\n",
       "      <td>-2.76521</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>-2.730345</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>-3.828434</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>plain</td>\n",
       "      <td>FULL</td>\n",
       "      <td>2</td>\n",
       "      <td>-71.447989</td>\n",
       "      <td>-5.582665</td>\n",
       "      <td>0.473537</td>\n",
       "      <td>-6.510779</td>\n",
       "      <td>-4.65455</td>\n",
       "      <td>4.648753</td>\n",
       "      <td>6.485462</td>\n",
       "      <td>-17.391757</td>\n",
       "      <td>16.386312</td>\n",
       "      <td>-16.788493</td>\n",
       "      <td>16.968439</td>\n",
       "      <td>-17.778928</td>\n",
       "      <td>10.099425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>plain</td>\n",
       "      <td>FULL</td>\n",
       "      <td>3</td>\n",
       "      <td>10.388715</td>\n",
       "      <td>12.798187</td>\n",
       "      <td>0.888696</td>\n",
       "      <td>11.056375</td>\n",
       "      <td>14.539999</td>\n",
       "      <td>-14.390005</td>\n",
       "      <td>-10.981529</td>\n",
       "      <td>-29.656556</td>\n",
       "      <td>31.55686</td>\n",
       "      <td>-31.101724</td>\n",
       "      <td>30.891778</td>\n",
       "      <td>-21.125974</td>\n",
       "      <td>29.140197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>0</td>\n",
       "      <td>-450.07747</td>\n",
       "      <td>-450.07747</td>\n",
       "      <td>636.158507</td>\n",
       "      <td>-1696.925232</td>\n",
       "      <td>796.770291</td>\n",
       "      <td>-1658.649293</td>\n",
       "      <td>861.155384</td>\n",
       "      <td>-732.557137</td>\n",
       "      <td>-20.578777</td>\n",
       "      <td>-708.85061</td>\n",
       "      <td>-21.084449</td>\n",
       "      <td>-543.465125</td>\n",
       "      <td>-30.30607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>1</td>\n",
       "      <td>727.829141</td>\n",
       "      <td>-2.558549</td>\n",
       "      <td>2.357388</td>\n",
       "      <td>-7.178944</td>\n",
       "      <td>2.061846</td>\n",
       "      <td>-7.016846</td>\n",
       "      <td>1.857032</td>\n",
       "      <td>-3.633004</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>-3.644227</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>-3.92372</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>2</td>\n",
       "      <td>-395.522446</td>\n",
       "      <td>-14.727136</td>\n",
       "      <td>11.819092</td>\n",
       "      <td>-37.892131</td>\n",
       "      <td>8.437859</td>\n",
       "      <td>-9.82464</td>\n",
       "      <td>36.581755</td>\n",
       "      <td>-18.862685</td>\n",
       "      <td>18.097706</td>\n",
       "      <td>-17.944366</td>\n",
       "      <td>18.786631</td>\n",
       "      <td>-17.79779</td>\n",
       "      <td>15.952449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>plain</td>\n",
       "      <td>TRIM</td>\n",
       "      <td>3</td>\n",
       "      <td>72.296178</td>\n",
       "      <td>26.856712</td>\n",
       "      <td>20.426174</td>\n",
       "      <td>-13.177854</td>\n",
       "      <td>66.891278</td>\n",
       "      <td>-65.269374</td>\n",
       "      <td>15.32401</td>\n",
       "      <td>-32.898522</td>\n",
       "      <td>33.985187</td>\n",
       "      <td>-33.840362</td>\n",
       "      <td>32.081962</td>\n",
       "      <td>-23.93846</td>\n",
       "      <td>30.691029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    scale dataset idx         beta        pars          SE    LCL[Wald]  \\\n",
       "0     log    FULL   0   -40.708888  -40.708888    9.159613   -58.661401   \n",
       "1     log    FULL   1   152.896178        -0.0    2.015069    -3.949462   \n",
       "2     log    FULL   2  -186.372687  -15.072454    2.088265   -19.165378   \n",
       "3     log    FULL   3    75.726285   12.365119    1.282417     9.851628   \n",
       "4     log    TRIM   0  -318.632132 -318.632132   63.461053  -443.013511   \n",
       "5     log    TRIM   1  1693.488916    0.000028    4.519627    -8.858277   \n",
       "6     log    TRIM   2 -3000.947506  -72.923452    6.604797   -85.868616   \n",
       "7     log    TRIM   3  1772.609956   41.152022    3.342186    34.601458   \n",
       "8   plain    FULL   0  -123.992159 -123.992159   18.879732  -160.995754   \n",
       "9   plain    FULL   1   163.793597        -0.0    1.320492    -2.588117   \n",
       "10  plain    FULL   2   -71.447989   -5.582665    0.473537    -6.510779   \n",
       "11  plain    FULL   3    10.388715   12.798187    0.888696    11.056375   \n",
       "12  plain    TRIM   0   -450.07747  -450.07747  636.158507 -1696.925232   \n",
       "13  plain    TRIM   1   727.829141   -2.558549    2.357388    -7.178944   \n",
       "14  plain    TRIM   2  -395.522446  -14.727136   11.819092   -37.892131   \n",
       "15  plain    TRIM   3    72.296178   26.856712   20.426174   -13.177854   \n",
       "\n",
       "     UCL[Wald]  LCL[normal] UCL[normal] LCL[nonparam_boots]  \\\n",
       "0   -22.756376    -58.33361  -21.553023         -323.857259   \n",
       "1     3.949462    -4.013852    3.952151           -5.251308   \n",
       "2   -10.979529   -19.026165  -10.924053            -69.5515   \n",
       "3    14.878611     9.746387   14.698292          -22.205673   \n",
       "4  -194.250754  -439.868121 -187.671295         -369.546571   \n",
       "5     8.858333     -9.01595    9.205264          -13.543478   \n",
       "6   -59.978288    59.493324    86.28298           -77.34694   \n",
       "7    47.702586   -47.568018  -34.219937          -34.815949   \n",
       "8   -86.988564  -159.991211  -84.926311         -659.637965   \n",
       "9     2.588117    -2.630314    2.589882            -2.76521   \n",
       "10    -4.65455     4.648753    6.485462          -17.391757   \n",
       "11   14.539999   -14.390005  -10.981529          -29.656556   \n",
       "12  796.770291 -1658.649293  861.155384         -732.557137   \n",
       "13    2.061846    -7.016846    1.857032           -3.633004   \n",
       "14    8.437859     -9.82464   36.581755          -18.862685   \n",
       "15   66.891278   -65.269374    15.32401          -32.898522   \n",
       "\n",
       "   UCL[nonparam_boots] LCL[nonparam_stratified_boots]  \\\n",
       "0            -6.089729                    -320.153225   \n",
       "1             0.000007                      -5.494921   \n",
       "2            28.629523                     -70.343116   \n",
       "3            38.981097                     -22.503161   \n",
       "4           -11.377163                    -360.606332   \n",
       "5             0.000023                      -8.054463   \n",
       "6            62.584201                     -76.338134   \n",
       "7            43.442075                     -39.709528   \n",
       "8            -11.52958                    -658.374908   \n",
       "9             0.000008                      -2.730345   \n",
       "10           16.386312                     -16.788493   \n",
       "11            31.55686                     -31.101724   \n",
       "12          -20.578777                     -708.85061   \n",
       "13            0.000018                      -3.644227   \n",
       "14           18.097706                     -17.944366   \n",
       "15           33.985187                     -33.840362   \n",
       "\n",
       "   UCL[nonparam_stratified_boots] LCL[parametric_boots] UCL[parametric_boots]  \n",
       "0                       -6.033486           -318.626715             -6.469164  \n",
       "1                        0.000011             -6.842527              0.000005  \n",
       "2                       28.169346            -74.060063             36.966623  \n",
       "3                       39.418558            -31.273302             41.151648  \n",
       "4                          -11.29           -126.948851            -14.066269  \n",
       "5                        0.000039             -5.200967                  -0.0  \n",
       "6                        71.10271            -52.861454                0.0001  \n",
       "7                       43.205955             -0.000174             10.677939  \n",
       "8                      -11.660298           -519.918169            -13.854624  \n",
       "9                        0.000004             -3.828434              0.000033  \n",
       "10                      16.968439            -17.778928             10.099425  \n",
       "11                      30.891778            -21.125974             29.140197  \n",
       "12                     -21.084449           -543.465125             -30.30607  \n",
       "13                       0.000014              -3.92372              0.000002  \n",
       "14                      18.786631             -17.79779             15.952449  \n",
       "15                      32.081962             -23.93846             30.691029  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert to long format by exploding the pars, SE, LCL, UCL, beta columns\n",
    "cols_CI = df_pars_CI.columns[df_pars_CI.columns.str.startswith((\"LCL\", \"UCL\"))].tolist()\n",
    "cols = [\"idx\", \"beta\", \"pars\", \"SE\"] + cols_CI\n",
    "\n",
    "df_pars_CI_long = df_pars_CI.explode(cols, ignore_index=True)\n",
    "df_pars_CI_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4084084d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllllllllllllll}\n",
      "\\toprule\n",
      " & scale & dataset & idx & beta & pars & SE & LCL[Wald] & UCL[Wald] & LCL[normal] & UCL[normal] & LCL[nonparam_boots] & UCL[nonparam_boots] & LCL[nonparam_stratified_boots] & UCL[nonparam_stratified_boots] & LCL[parametric_boots] & UCL[parametric_boots] \\\\\n",
      "\\midrule\n",
      "0 & log & FULL & 0 & -40.708888 & -40.708888 & 9.159613 & -58.661401 & -22.756376 & -58.333610 & -21.553023 & -323.857259 & -6.089729 & -320.153225 & -6.033486 & -318.626715 & -6.469164 \\\\\n",
      "1 & log & FULL & 1 & 152.896178 & -0.000000 & 2.015069 & -3.949462 & 3.949462 & -4.013852 & 3.952151 & -5.251308 & 0.000007 & -5.494921 & 0.000011 & -6.842527 & 0.000005 \\\\\n",
      "2 & log & FULL & 2 & -186.372687 & -15.072454 & 2.088265 & -19.165378 & -10.979529 & -19.026165 & -10.924053 & -69.551500 & 28.629523 & -70.343116 & 28.169346 & -74.060063 & 36.966623 \\\\\n",
      "3 & log & FULL & 3 & 75.726285 & 12.365119 & 1.282417 & 9.851628 & 14.878611 & 9.746387 & 14.698292 & -22.205673 & 38.981097 & -22.503161 & 39.418558 & -31.273302 & 41.151648 \\\\\n",
      "4 & log & TRIM & 0 & -318.632132 & -318.632132 & 63.461053 & -443.013511 & -194.250754 & -439.868121 & -187.671295 & -369.546571 & -11.377163 & -360.606332 & -11.290000 & -126.948851 & -14.066269 \\\\\n",
      "5 & log & TRIM & 1 & 1693.488916 & 0.000028 & 4.519627 & -8.858277 & 8.858333 & -9.015950 & 9.205264 & -13.543478 & 0.000023 & -8.054463 & 0.000039 & -5.200967 & -0.000000 \\\\\n",
      "6 & log & TRIM & 2 & -3000.947506 & -72.923452 & 6.604797 & -85.868616 & -59.978288 & 59.493324 & 86.282980 & -77.346940 & 62.584201 & -76.338134 & 71.102710 & -52.861454 & 0.000100 \\\\\n",
      "7 & log & TRIM & 3 & 1772.609956 & 41.152022 & 3.342186 & 34.601458 & 47.702586 & -47.568018 & -34.219937 & -34.815949 & 43.442075 & -39.709528 & 43.205955 & -0.000174 & 10.677939 \\\\\n",
      "8 & plain & FULL & 0 & -123.992159 & -123.992159 & 18.879732 & -160.995754 & -86.988564 & -159.991211 & -84.926311 & -659.637965 & -11.529580 & -658.374908 & -11.660298 & -519.918169 & -13.854624 \\\\\n",
      "9 & plain & FULL & 1 & 163.793597 & -0.000000 & 1.320492 & -2.588117 & 2.588117 & -2.630314 & 2.589882 & -2.765210 & 0.000008 & -2.730345 & 0.000004 & -3.828434 & 0.000033 \\\\\n",
      "10 & plain & FULL & 2 & -71.447989 & -5.582665 & 0.473537 & -6.510779 & -4.654550 & 4.648753 & 6.485462 & -17.391757 & 16.386312 & -16.788493 & 16.968439 & -17.778928 & 10.099425 \\\\\n",
      "11 & plain & FULL & 3 & 10.388715 & 12.798187 & 0.888696 & 11.056375 & 14.539999 & -14.390005 & -10.981529 & -29.656556 & 31.556860 & -31.101724 & 30.891778 & -21.125974 & 29.140197 \\\\\n",
      "12 & plain & TRIM & 0 & -450.077470 & -450.077470 & 636.158507 & -1696.925232 & 796.770291 & -1658.649293 & 861.155384 & -732.557137 & -20.578777 & -708.850610 & -21.084449 & -543.465125 & -30.306070 \\\\\n",
      "13 & plain & TRIM & 1 & 727.829141 & -2.558549 & 2.357388 & -7.178944 & 2.061846 & -7.016846 & 1.857032 & -3.633004 & 0.000018 & -3.644227 & 0.000014 & -3.923720 & 0.000002 \\\\\n",
      "14 & plain & TRIM & 2 & -395.522446 & -14.727136 & 11.819092 & -37.892131 & 8.437859 & -9.824640 & 36.581755 & -18.862685 & 18.097706 & -17.944366 & 18.786631 & -17.797790 & 15.952449 \\\\\n",
      "15 & plain & TRIM & 3 & 72.296178 & 26.856712 & 20.426174 & -13.177854 & 66.891278 & -65.269374 & 15.324010 & -32.898522 & 33.985187 & -33.840362 & 32.081962 & -23.938460 & 30.691029 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# to copy and past in latex\n",
    "print(df_pars_CI_long.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d60267f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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