{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0.5\n" ] } ], "source": [ "import time\n", "from pathlib import Path\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from scipy import stats\n", "\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.ensemble import RandomForestRegressor\n", "\n", "import fdfi\n", "print(fdfi.__version__)\n", "from fdfi.explainers import EOTExplainer\n" ], "id": "eot-case-study-sens50-00" }, { "cell_type": "markdown", "metadata": { "id": "_PI4yO9b8cGz" }, "source": [ "## Load Data And Feature Groups\n" ], "id": "eot-case-study-sens50-01" }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5tA76l648cGz", "outputId": "2a3043c0-2905-49cb-f8dc-f1437d5301eb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset shape: (611, 832)\n", "Target variable: sens50\n", "Number of groups: 14\n", "Data loaded from: data/sens50_processed_dataset.csv\n" ] } ], "source": [ "DATA_DIR = Path(\"data\")\n", "\n", "DATASETS = {\n", " \"sens50\": {\n", " \"file\": DATA_DIR / \"sens50_processed_dataset.csv\",\n", " \"target\": \"sens50\",\n", " },\n", " \"sens80\": {\n", " \"file\": DATA_DIR / \"sens80_processed_dataset.csv\",\n", " \"target\": \"sens80\",\n", " },\n", " \"ic50\": {\n", " \"file\": DATA_DIR / \"ic50.censored_processed_dataset.csv\",\n", " \"target\": \"ic50.censored\",\n", " },\n", "}\n", "\n", "outcome = \"sens50\"\n", "\n", "cfg = DATASETS[outcome]\n", "filename = str(cfg[\"file\"])\n", "\n", "data = pd.read_csv(cfg[\"file\"])\n", "y = data[cfg[\"target\"]].astype(int)\n", "X = data.drop(columns=[cfg[\"target\"]])\n", "\n", "df_groups = pd.read_csv(DATA_DIR / \"feature_group.csv\", index_col=0).set_index(\"feature\")\n", "df_groups = pd.get_dummies(df_groups[\"group\"]).groupby(df_groups.index).max().reindex(X.columns)\n", "\n", "print(f\"Dataset shape: {X.shape}\")\n", "print(f\"Target variable: {outcome}\")\n", "print(f\"Number of groups: {len(df_groups.columns)}\")\n", "print(f\"Data loaded from: {filename}\")\n" ], "id": "eot-case-study-sens50-02" }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1MPg1qCX8cG0", "outputId": "8c4c5cca-c211-44c8-8e80-21cf3afc50ca" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data preparation...\n", "Data preparation finished\n", "Feature matrix shape: (611, 832)\n", "y statistics: mean=-0.000, std=1.000\n", "Missing values - X: 0, y: 0\n" ] } ], "source": [ "print(\"Data preparation...\")\n", "\n", "preprocess = make_pipeline(\n", " SimpleImputer(strategy=\"most_frequent\"),\n", " StandardScaler(),\n", ")\n", "\n", "X_values = preprocess.fit_transform(X)\n", "y_values = y.values.astype(float)\n", "\n", "# Standardize y to N(0,1) to match DFI paper (scale-invariant for z-scores\n", "# but ensures the DFI UEIF formula (y - ỹ)² - (y - ŷ)² works in the same\n", "# unit system as the reference implementation)\n", "y_scaler = StandardScaler()\n", "y_values = y_scaler.fit_transform(y_values.reshape(-1, 1)).ravel()\n", "\n", "print(\"Data preparation finished\")\n", "print(f\"Feature matrix shape: {X_values.shape}\")\n", "print(f\"y statistics: mean={y_values.mean():.3f}, std={y_values.std():.3f}\")\n", "print(f\"Missing values - X: {np.isnan(X_values).sum()}, y: {np.isnan(y_values).sum()}\")\n" ], "id": "eot-case-study-sens50-03" }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qAbZo_jPsXdA", "outputId": "216db02d-c24b-4aa5-db5f-7352cc2669c8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RandomForestRegressor fitted on full data\n", "Train R²: 0.705\n" ] } ], "source": [ "rf = RandomForestRegressor(\n", " n_estimators=500,\n", " max_depth=None,\n", " min_samples_leaf=5,\n", " random_state=0,\n", " n_jobs=-1,\n", ")\n", "rf.fit(X_values, y_values)\n", "model_fn = lambda X_: rf.predict(X_)\n", "print(f\"RandomForestRegressor fitted on full data\")\n", "print(f\"Train R²: {rf.score(X_values, y_values):.3f}\")\n" ], "id": "eot-case-study-sens50-04" }, { "cell_type": "markdown", "metadata": { "id": "0e0bvAi-8cG0" }, "source": [ "## Fit EOT FDFI\n" ], "id": "eot-case-study-sens50-05" }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "W7yBagkz29b9", "outputId": "2d95c9f8-bc45-4064-a6b1-f4e4a1eebff6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EOT FDFI completed in 434.01 seconds\n", "DFI scores shape: (832,)\n" ] }, { "data": { "text/plain": [ "(0.7396973904611802, 0.7396975752008004)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nsamples_main = 50\n", "\n", "start_time = time.time()\n", "\n", "fdfi_estimator = EOTExplainer(\n", " model=model_fn,\n", " data=X_values,\n", " nsamples=nsamples_main,\n", " epsilon=0.001,\n", " sampling_method=\"resample\",\n", " random_state=0,\n", ")\n", "\n", "eot_results = fdfi_estimator(X_values, y=y_values)\n", "\n", "dfi_time = time.time() - start_time\n", "\n", "print(f\"EOT FDFI completed in {dfi_time:.2f} seconds\")\n", "print(f\"DFI scores shape: {eot_results['phi_X'].shape}\")\n", "\n", "np.sum(eot_results[\"phi_X\"]), np.sum(eot_results[\"phi_Z\"])\n" ], "id": "eot-case-study-sens50-06" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Individual Feature Inference With conf_int()" ], "id": "eot-case-study-sens50-07" }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 383 }, "id": "ifO4ZM518cG1", "outputId": "d069cd9a-63d5-448e-99ff-aca93b626438" }, "outputs": [], "source": [ "ci_X = fdfi_estimator.conf_int(\n", " alpha=0.05,\n", " target=\"X\",\n", " var_floor_c=0.1,\n", " var_floor_method=\"mixture\",\n", " var_floor_quantile=0.95,\n", " margin=0.0,\n", " margin_method=\"auto\",\n", " margin_quantile=0.95,\n", " alternative=\"two-sided\",\n", " verbose=False,\n", ")\n", "\n", "ci_Z = fdfi_estimator.conf_int(\n", " alpha=0.05,\n", " target=\"Z\",\n", " var_floor_c=0.1,\n", " var_floor_method=\"mixture\",\n", " var_floor_quantile=0.95,\n", " margin=0.0,\n", " margin_method=\"auto\",\n", " margin_quantile=0.95,\n", " alternative=\"two-sided\",\n", " verbose=False,\n", ")\n" ], "id": "eot-case-study-sens50-08" }, { "cell_type": "markdown", "metadata": { "id": "NA3PeQC98cG1" }, "source": [ "## Feature-Level Summary" ], "id": "eot-case-study-sens50-09" }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6VaLRcAs8cG1", "outputId": "7b5f0262-f611-4a25-a96c-abde5e3e68c6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== X-space feature summary ===\n", "==============================================================================\n", "Feature Importance Results\n", "==============================================================================\n", "Method: EOTExplainer\n", "Number of units: 832\n", "Significance level: 0.05\n", "Alternative: two-sided\n", "Margin method: mixture\n", "Practical margin: 0.0028\n", "------------------------------------------------------------------------------\n", " Feature Estimate Std Err CI Lower CI Upper P-value Sig\n", "------------------------------------------------------------------------------\n", " 0 0.0011 0.0018 -0.0026 0.0047 0.3314 \n", " 1 0.0013 0.0019 -0.0023 0.0050 0.4123 \n", " 2 0.0016 0.0018 -0.0020 0.0052 0.5027 \n", " 3 0.0003 0.0018 -0.0032 0.0039 0.1639 \n", " 4 0.0005 0.0018 -0.0031 0.0040 0.1935 \n", " 5 0.0007 0.0018 -0.0029 0.0042 0.2342 \n", " 6 0.0004 0.0018 -0.0031 0.0040 0.1831 \n", " 7 0.0004 0.0018 -0.0032 0.0039 0.1751 \n", " 8 0.0005 0.0018 -0.0031 0.0040 0.1914 \n", " 9 0.0006 0.0018 -0.0029 0.0042 0.2180 \n", " 10 0.0004 0.0018 -0.0031 0.0040 0.1829 \n", " 11 0.0010 0.0019 -0.0026 0.0046 0.3183 \n", " 12 0.0009 0.0018 -0.0027 0.0044 0.2734 \n", " 13 0.0017 0.0018 -0.0019 0.0053 0.5164 \n", " 14 0.0022 0.0018 -0.0014 0.0058 0.7177 \n", " 15 0.0014 0.0019 -0.0023 0.0050 0.4212 \n", " 16 0.0003 0.0018 -0.0033 0.0038 0.1569 \n", " 17 0.0012 0.0018 -0.0023 0.0048 0.3699 \n", " 18 0.0009 0.0018 -0.0027 0.0044 0.2722 \n", " 19 0.0028 0.0018 -0.0008 0.0064 0.9783 \n", " 20 0.0009 0.0018 -0.0027 0.0045 0.2861 \n", " 21 0.0005 0.0018 -0.0031 0.0040 0.1884 \n", " 22 0.0026 0.0018 -0.0010 0.0062 0.9030 \n", " 23 0.0003 0.0018 -0.0032 0.0039 0.1617 \n", " 24 0.0007 0.0018 -0.0029 0.0043 0.2367 \n", " 25 0.0003 0.0018 -0.0032 0.0039 0.1639 \n", " 26 0.0002 0.0018 -0.0033 0.0038 0.1486 \n", " 27 0.0005 0.0018 -0.0031 0.0040 0.1925 \n", " 28 0.0009 0.0018 -0.0026 0.0045 0.2895 \n", " 29 0.0006 0.0018 -0.0029 0.0042 0.2164 \n", " 30 0.0004 0.0018 -0.0031 0.0040 0.1771 \n", " 31 0.0009 0.0018 -0.0027 0.0044 0.2789 \n", " 32 0.0003 0.0018 -0.0032 0.0039 0.1643 \n", " 33 0.0007 0.0018 -0.0029 0.0043 0.2353 \n", " 34 0.0010 0.0018 -0.0025 0.0046 0.3114 \n", " 35 0.0005 0.0018 -0.0030 0.0041 0.1991 \n", " 36 0.0007 0.0018 -0.0028 0.0043 0.2418 \n", " 37 0.0008 0.0018 -0.0028 0.0044 0.2594 \n", " 38 0.0006 0.0018 -0.0029 0.0042 0.2163 \n", " 39 0.0004 0.0018 -0.0032 0.0039 0.1704 \n", " 40 0.0005 0.0018 -0.0031 0.0040 0.1862 \n", " 41 0.0003 0.0018 -0.0032 0.0039 0.1641 \n", " 42 0.0007 0.0018 -0.0029 0.0042 0.2333 \n", " 43 0.0003 0.0018 -0.0033 0.0038 0.1568 \n", " 44 0.0006 0.0018 -0.0030 0.0041 0.2126 \n", " 45 0.0009 0.0019 -0.0028 0.0045 0.2809 \n", " 46 0.0005 0.0018 -0.0031 0.0040 0.1882 \n", " 47 0.0004 0.0018 -0.0032 0.0039 0.1720 \n", " 48 0.0004 0.0018 -0.0032 0.0039 0.1749 \n", " 49 0.0008 0.0018 -0.0027 0.0044 0.2713 \n", " 50 0.0005 0.0018 -0.0031 0.0041 0.1961 \n", " 51 0.0005 0.0018 -0.0030 0.0041 0.1997 \n", " 52 0.0009 0.0018 -0.0027 0.0044 0.2728 \n", " 53 0.0004 0.0018 -0.0031 0.0039 0.1759 \n", " 54 0.0004 0.0018 -0.0031 0.0040 0.1844 \n", " 55 0.0004 0.0018 -0.0031 0.0040 0.1793 \n", " 56 0.0003 0.0018 -0.0033 0.0038 0.1573 \n", " 57 0.0010 0.0018 -0.0025 0.0046 0.3164 \n", " 58 0.0020 0.0018 -0.0016 0.0056 0.6612 \n", " 59 0.0007 0.0018 -0.0028 0.0043 0.2359 \n", " 60 0.0005 0.0018 -0.0030 0.0041 0.2028 \n", " 61 0.0008 0.0018 -0.0028 0.0043 0.2488 \n", " 62 0.0006 0.0018 -0.0030 0.0042 0.2164 \n", " 63 0.0004 0.0018 -0.0031 0.0040 0.1775 \n", " 64 0.0004 0.0018 -0.0031 0.0040 0.1784 \n", " 65 0.0010 0.0018 -0.0026 0.0047 0.3283 \n", " 66 0.0003 0.0018 -0.0032 0.0039 0.1646 \n", " 67 0.0004 0.0018 -0.0032 0.0039 0.1750 \n", " 68 0.0013 0.0018 -0.0024 0.0049 0.3907 \n", " 69 0.0004 0.0018 -0.0031 0.0040 0.1808 \n", " 70 0.0014 0.0018 -0.0022 0.0050 0.4354 \n", " 71 0.0005 0.0018 -0.0031 0.0040 0.1879 \n", " 72 0.0011 0.0018 -0.0025 0.0046 0.3263 \n", " 73 0.0012 0.0018 -0.0023 0.0048 0.3746 \n", " 74 0.0017 0.0021 -0.0025 0.0059 0.5913 \n", " 75 0.0005 0.0018 -0.0031 0.0040 0.1895 \n", " 76 0.0004 0.0018 -0.0031 0.0040 0.1807 \n", " 77 0.0007 0.0018 -0.0029 0.0043 0.2320 \n", " 78 0.0003 0.0018 -0.0032 0.0039 0.1628 \n", " 79 0.0018 0.0019 -0.0020 0.0056 0.5870 \n", " 80 -0.0002 0.0020 -0.0042 0.0037 0.1267 \n", " 81 0.0002 0.0018 -0.0033 0.0038 0.1509 \n", " 82 0.0004 0.0018 -0.0032 0.0039 0.1691 \n", " 83 0.0004 0.0018 -0.0031 0.0040 0.1810 \n", " 84 0.0003 0.0018 -0.0032 0.0039 0.1656 \n", " 85 0.0004 0.0018 -0.0031 0.0040 0.1778 \n", " 86 0.0007 0.0018 -0.0029 0.0042 0.2333 \n", " 87 0.0003 0.0018 -0.0032 0.0039 0.1674 \n", " 88 0.0018 0.0019 -0.0020 0.0055 0.5699 \n", " 89 0.0006 0.0018 -0.0030 0.0041 0.2134 \n", " 90 0.0005 0.0018 -0.0030 0.0041 0.1999 \n", " 91 0.0004 0.0018 -0.0032 0.0040 0.1883 \n", " 92 0.0003 0.0018 -0.0033 0.0038 0.1582 \n", " 93 0.0010 0.0018 -0.0026 0.0046 0.3058 \n", " 94 0.0008 0.0018 -0.0027 0.0044 0.2631 \n", " 95 0.0004 0.0018 -0.0031 0.0040 0.1818 \n", " 96 0.0004 0.0018 -0.0032 0.0039 0.1673 \n", " 97 0.0007 0.0018 -0.0029 0.0042 0.2327 \n", " 98 0.0007 0.0018 -0.0029 0.0042 0.2290 \n", " 99 0.0004 0.0018 -0.0031 0.0039 0.1761 \n", " 100 0.0005 0.0018 -0.0031 0.0040 0.1853 \n", " 101 0.0003 0.0018 -0.0033 0.0038 0.1529 \n", " 102 0.0012 0.0019 -0.0025 0.0048 0.3662 \n", " 103 0.0009 0.0018 -0.0027 0.0045 0.2856 \n", " 104 0.0004 0.0018 -0.0032 0.0039 0.1682 \n", " 105 0.0009 0.0018 -0.0026 0.0045 0.2905 \n", " 106 0.0003 0.0018 -0.0032 0.0039 0.1637 \n", " 107 0.0015 0.0018 -0.0021 0.0050 0.4473 \n", " 108 0.0012 0.0018 -0.0024 0.0047 0.3519 \n", " 109 0.0013 0.0018 -0.0023 0.0049 0.4122 \n", " 110 0.0015 0.0020 -0.0025 0.0054 0.4901 \n", " 111 0.0009 0.0018 -0.0027 0.0044 0.2783 \n", " 112 0.0010 0.0018 -0.0026 0.0045 0.2993 \n", " 113 0.0009 0.0018 -0.0027 0.0044 0.2764 \n", " 114 0.0003 0.0018 -0.0032 0.0039 0.1607 \n", " 115 0.0006 0.0018 -0.0030 0.0042 0.2150 \n", " 116 0.0003 0.0018 -0.0033 0.0038 0.1565 \n", " 117 0.0009 0.0018 -0.0027 0.0045 0.2880 \n", " 118 0.0021 0.0018 -0.0016 0.0057 0.6650 \n", " 119 0.0013 0.0020 -0.0025 0.0052 0.4327 \n", " 120 0.0006 0.0018 -0.0029 0.0042 0.2154 \n", " 121 0.0004 0.0018 -0.0032 0.0039 0.1710 \n", " 122 0.0010 0.0018 -0.0026 0.0045 0.3014 \n", " 123 0.0015 0.0019 -0.0022 0.0051 0.4681 \n", " 124 0.0004 0.0018 -0.0031 0.0040 0.1789 \n", " 125 0.0005 0.0018 -0.0030 0.0041 0.1960 \n", " 126 0.0003 0.0018 -0.0033 0.0038 0.1527 \n", " 127 0.0005 0.0018 -0.0030 0.0041 0.1987 \n", " 128 0.0003 0.0018 -0.0033 0.0038 0.1520 \n", " 129 0.0001 0.0018 -0.0034 0.0037 0.1343 \n", " 130 0.0004 0.0018 -0.0031 0.0040 0.1788 \n", " 131 0.0005 0.0018 -0.0030 0.0041 0.2006 \n", " 132 0.0005 0.0018 -0.0031 0.0040 0.1937 \n", " 133 0.0004 0.0018 -0.0031 0.0040 0.1779 \n", " 134 0.0005 0.0018 -0.0031 0.0041 0.1952 \n", " 135 0.0008 0.0018 -0.0028 0.0043 0.2481 \n", " 136 0.0014 0.0018 -0.0023 0.0050 0.4175 \n", " 137 0.0006 0.0018 -0.0030 0.0041 0.2120 \n", " 138 0.0004 0.0018 -0.0031 0.0040 0.1820 \n", " 139 0.0007 0.0018 -0.0029 0.0042 0.2329 \n", " 140 0.0003 0.0018 -0.0032 0.0039 0.1606 \n", " 141 0.0004 0.0018 -0.0031 0.0040 0.1812 \n", " 142 0.0005 0.0018 -0.0031 0.0040 0.1892 \n", " 143 0.0003 0.0018 -0.0033 0.0038 0.1581 \n", " 144 0.0003 0.0018 -0.0033 0.0038 0.1530 \n", " 145 0.0005 0.0018 -0.0031 0.0040 0.1919 \n", " 146 0.0003 0.0018 -0.0032 0.0039 0.1629 \n", " 147 0.0003 0.0018 -0.0032 0.0039 0.1629 \n", " 148 0.0003 0.0018 -0.0032 0.0039 0.1612 \n", " 149 0.0004 0.0018 -0.0032 0.0039 0.1709 \n", " 150 0.0009 0.0018 -0.0026 0.0045 0.2870 \n", " 151 0.0010 0.0018 -0.0026 0.0045 0.2961 \n", " 152 0.0011 0.0018 -0.0024 0.0047 0.3499 \n", " 153 0.0003 0.0018 -0.0032 0.0039 0.1639 \n", " 154 0.0005 0.0018 -0.0031 0.0040 0.1919 \n", " 155 0.0005 0.0018 -0.0031 0.0040 0.1860 \n", " 156 0.0005 0.0018 -0.0031 0.0040 0.1913 \n", " 157 0.0008 0.0018 -0.0028 0.0043 0.2479 \n", " 158 0.0008 0.0018 -0.0028 0.0043 0.2503 \n", " 159 0.0009 0.0018 -0.0026 0.0045 0.2932 \n", " 160 0.0003 0.0018 -0.0032 0.0039 0.1628 \n", " 161 0.0008 0.0018 -0.0027 0.0044 0.2595 \n", " 162 0.0005 0.0018 -0.0030 0.0041 0.2020 \n", " 163 0.0005 0.0018 -0.0031 0.0040 0.1897 \n", " 164 0.0005 0.0018 -0.0031 0.0040 0.1903 \n", " 165 0.0009 0.0018 -0.0027 0.0044 0.2739 \n", " 166 0.0017 0.0018 -0.0019 0.0054 0.5483 \n", " 167 0.0002 0.0018 -0.0034 0.0037 0.1375 \n", " 168 0.0002 0.0018 -0.0034 0.0037 0.1375 \n", " 169 0.0014 0.0018 -0.0022 0.0050 0.4323 \n", " 170 0.0006 0.0018 -0.0030 0.0042 0.2139 \n", " 171 0.0006 0.0018 -0.0029 0.0042 0.2238 \n", " 172 0.0006 0.0018 -0.0029 0.0042 0.2208 \n", " 173 0.0003 0.0018 -0.0032 0.0039 0.1623 \n", " 174 0.0004 0.0018 -0.0031 0.0040 0.1845 \n", " 175 0.0008 0.0018 -0.0028 0.0043 0.2563 \n", " 176 0.0009 0.0018 -0.0027 0.0044 0.2770 \n", " 177 0.0006 0.0018 -0.0030 0.0042 0.2254 \n", " 178 0.0003 0.0018 -0.0032 0.0039 0.1634 \n", " 179 0.0004 0.0018 -0.0032 0.0039 0.1714 \n", " 180 0.0005 0.0018 -0.0030 0.0041 0.1955 \n", " 181 0.0006 0.0018 -0.0030 0.0041 0.2110 \n", " 182 0.0009 0.0018 -0.0027 0.0045 0.2852 \n", " 183 0.0009 0.0018 -0.0026 0.0045 0.2895 \n", " 184 0.0003 0.0018 -0.0033 0.0038 0.1544 \n", " 185 0.0005 0.0018 -0.0030 0.0041 0.2034 \n", " 186 0.0014 0.0018 -0.0023 0.0050 0.4163 \n", " 187 0.0005 0.0018 -0.0030 0.0041 0.2020 \n", " 188 0.0003 0.0018 -0.0032 0.0039 0.1670 \n", " 189 0.0003 0.0018 -0.0032 0.0039 0.1611 \n", " 190 0.0003 0.0018 -0.0033 0.0038 0.1577 \n", " 191 0.0011 0.0018 -0.0024 0.0047 0.3413 \n", " 192 0.0008 0.0018 -0.0027 0.0044 0.2691 \n", " 193 0.0010 0.0018 -0.0026 0.0045 0.2997 \n", " 194 0.0005 0.0018 -0.0030 0.0041 0.1974 \n", " 195 0.0017 0.0018 -0.0019 0.0053 0.5337 \n", " 196 0.0006 0.0018 -0.0029 0.0042 0.2246 \n", " 197 0.0010 0.0018 -0.0026 0.0046 0.3078 \n", " 198 0.0015 0.0018 -0.0021 0.0051 0.4728 \n", " 199 0.0005 0.0018 -0.0030 0.0041 0.2000 \n", " 200 0.0003 0.0018 -0.0033 0.0038 0.1518 \n", " 201 0.0004 0.0018 -0.0032 0.0039 0.1755 \n", " 202 0.0003 0.0018 -0.0033 0.0038 0.1587 \n", " 203 0.0003 0.0018 -0.0032 0.0039 0.1663 \n", " 204 0.0003 0.0018 -0.0032 0.0039 0.1659 \n", " 205 0.0004 0.0018 -0.0032 0.0039 0.1705 \n", " 206 0.0004 0.0018 -0.0032 0.0039 0.1704 \n", " 207 0.0005 0.0018 -0.0031 0.0040 0.1934 \n", " 208 0.0004 0.0018 -0.0031 0.0040 0.1825 \n", " 209 0.0006 0.0018 -0.0029 0.0042 0.2165 \n", " 210 0.0003 0.0018 -0.0033 0.0038 0.1515 \n", " 211 0.0003 0.0018 -0.0032 0.0038 0.1587 \n", " 212 0.0004 0.0018 -0.0031 0.0040 0.1862 \n", " 213 0.0004 0.0018 -0.0032 0.0039 0.1700 \n", " 214 0.0004 0.0018 -0.0031 0.0040 0.1790 \n", " 215 0.0003 0.0018 -0.0032 0.0039 0.1620 \n", " 216 0.0006 0.0018 -0.0030 0.0041 0.2099 \n", " 217 0.0005 0.0018 -0.0031 0.0040 0.1933 \n", " 218 0.0005 0.0018 -0.0031 0.0040 0.1926 \n", " 219 0.0006 0.0018 -0.0030 0.0041 0.2068 \n", " 220 0.0006 0.0018 -0.0030 0.0041 0.2070 \n", " 221 0.0002 0.0018 -0.0033 0.0038 0.1449 \n", " 222 0.0004 0.0018 -0.0031 0.0040 0.1771 \n", " 223 0.0002 0.0018 -0.0033 0.0038 0.1487 \n", " 224 0.0006 0.0018 -0.0029 0.0042 0.2179 \n", " 225 0.0003 0.0018 -0.0033 0.0038 0.1544 \n", " 226 0.0023 0.0018 -0.0013 0.0059 0.7668 \n", " 227 0.0004 0.0018 -0.0032 0.0039 0.1737 \n", " 228 0.0006 0.0018 -0.0030 0.0041 0.2089 \n", " 229 0.0009 0.0018 -0.0027 0.0044 0.2796 \n", " 230 0.0005 0.0018 -0.0031 0.0040 0.1871 \n", " 231 0.0002 0.0018 -0.0033 0.0038 0.1455 \n", " 232 0.0004 0.0018 -0.0032 0.0039 0.1730 \n", " 233 0.0013 0.0018 -0.0023 0.0048 0.3862 \n", " 234 0.0003 0.0018 -0.0033 0.0038 0.1575 \n", " 235 0.0003 0.0018 -0.0032 0.0039 0.1608 \n", " 236 0.0003 0.0018 -0.0033 0.0038 0.1527 \n", " 237 0.0004 0.0018 -0.0032 0.0039 0.1761 \n", " 238 0.0006 0.0018 -0.0030 0.0041 0.2123 \n", " 239 0.0007 0.0018 -0.0028 0.0043 0.2423 \n", " 240 0.0017 0.0018 -0.0018 0.0053 0.5432 \n", " 241 0.0013 0.0018 -0.0023 0.0048 0.3886 \n", " 242 0.0003 0.0018 -0.0033 0.0038 0.1580 \n", " 243 0.0017 0.0018 -0.0019 0.0053 0.5307 \n", " 244 0.0003 0.0018 -0.0032 0.0039 0.1670 \n", " 245 0.0006 0.0018 -0.0029 0.0042 0.2238 \n", " 246 0.0013 0.0018 -0.0023 0.0048 0.3859 \n", " 247 0.0011 0.0018 -0.0025 0.0046 0.3311 \n", " 248 0.0005 0.0018 -0.0030 0.0041 0.2002 \n", " 249 0.0003 0.0018 -0.0033 0.0038 0.1542 \n", " 250 0.0003 0.0018 -0.0033 0.0038 0.1552 \n", " 251 0.0009 0.0018 -0.0027 0.0045 0.2929 \n", " 252 0.0007 0.0018 -0.0028 0.0043 0.2366 \n", " 253 0.0004 0.0018 -0.0031 0.0040 0.1817 \n", " 254 0.0011 0.0018 -0.0025 0.0046 0.3281 \n", " 255 0.0006 0.0018 -0.0029 0.0042 0.2223 \n", " 256 0.0006 0.0018 -0.0029 0.0042 0.2208 \n", " 257 0.0004 0.0018 -0.0031 0.0040 0.1771 \n", " 258 0.0005 0.0018 -0.0030 0.0041 0.2031 \n", " 259 0.0005 0.0018 -0.0031 0.0040 0.1864 \n", " 260 0.0003 0.0018 -0.0032 0.0039 0.1642 \n", " 261 0.0014 0.0018 -0.0022 0.0050 0.4395 \n", " 262 0.0007 0.0018 -0.0028 0.0043 0.2415 \n", " 263 0.0005 0.0018 -0.0030 0.0041 0.2039 \n", " 264 0.0003 0.0018 -0.0033 0.0038 0.1535 \n", " 265 0.0004 0.0018 -0.0032 0.0039 0.1674 \n", " 266 0.0011 0.0018 -0.0024 0.0047 0.3404 \n", " 267 0.0004 0.0018 -0.0031 0.0040 0.1849 \n", " 268 0.0004 0.0018 -0.0031 0.0039 0.1761 \n", " 269 0.0005 0.0018 -0.0030 0.0041 0.1991 \n", " 270 0.0005 0.0018 -0.0030 0.0041 0.1961 \n", " 271 0.0013 0.0018 -0.0023 0.0048 0.3843 \n", " 272 0.0006 0.0018 -0.0029 0.0042 0.2197 \n", " 273 0.0003 0.0018 -0.0032 0.0039 0.1632 \n", " 274 0.0002 0.0018 -0.0033 0.0038 0.1487 \n", " 275 0.0010 0.0018 -0.0025 0.0046 0.3092 \n", " 276 0.0008 0.0018 -0.0027 0.0044 0.2655 \n", " 277 0.0011 0.0018 -0.0024 0.0047 0.3461 \n", " 278 0.0011 0.0018 -0.0024 0.0047 0.3371 \n", " 279 0.0009 0.0018 -0.0026 0.0045 0.2829 \n", " 280 0.0007 0.0018 -0.0029 0.0042 0.2254 \n", " 281 0.0014 0.0018 -0.0022 0.0050 0.4249 \n", " 282 0.0002 0.0018 -0.0033 0.0038 0.1493 \n", " 283 0.0014 0.0018 -0.0021 0.0050 0.4343 \n", " 284 0.0004 0.0018 -0.0031 0.0040 0.1779 \n", " 285 0.0006 0.0018 -0.0029 0.0042 0.2253 \n", " 286 0.0003 0.0018 -0.0033 0.0038 0.1558 \n", " 287 0.0030 0.0020 -0.0010 0.0070 0.9402 \n", " 288 0.0006 0.0018 -0.0030 0.0041 0.2047 \n", " 289 0.0006 0.0018 -0.0029 0.0042 0.2190 \n", " 290 0.0004 0.0018 -0.0031 0.0040 0.1824 \n", " 291 0.0014 0.0018 -0.0022 0.0049 0.4165 \n", " 292 0.0005 0.0018 -0.0031 0.0040 0.1867 \n", " 293 0.0009 0.0019 -0.0028 0.0045 0.2867 \n", " 294 0.0009 0.0018 -0.0026 0.0045 0.2945 \n", " 295 0.0011 0.0018 -0.0024 0.0047 0.3434 \n", " 296 0.0004 0.0018 -0.0032 0.0039 0.1690 \n", " 297 0.0004 0.0018 -0.0032 0.0039 0.1747 \n", " 298 0.0006 0.0018 -0.0030 0.0041 0.2059 \n", " 299 0.0016 0.0018 -0.0020 0.0052 0.4863 \n", " 300 0.0012 0.0018 -0.0023 0.0048 0.3784 \n", " 301 0.0014 0.0019 -0.0024 0.0051 0.4446 \n", " 302 0.0004 0.0018 -0.0032 0.0039 0.1713 \n", " 303 0.0012 0.0018 -0.0024 0.0048 0.3588 \n", " 304 0.0004 0.0018 -0.0032 0.0039 0.1716 \n", " 305 0.0004 0.0018 -0.0031 0.0040 0.1832 \n", " 306 0.0006 0.0018 -0.0029 0.0042 0.2225 \n", " 307 0.0011 0.0019 -0.0026 0.0049 0.3700 \n", " 308 0.0004 0.0018 -0.0032 0.0039 0.1758 \n", " 309 0.0007 0.0018 -0.0029 0.0042 0.2344 \n", " 310 0.0004 0.0018 -0.0031 0.0040 0.1800 \n", " 311 0.0009 0.0018 -0.0027 0.0045 0.2919 \n", " 312 0.0003 0.0018 -0.0032 0.0039 0.1668 \n", " 313 0.0015 0.0018 -0.0021 0.0050 0.4492 \n", " 314 0.0007 0.0018 -0.0028 0.0043 0.2417 \n", " 315 0.0011 0.0018 -0.0025 0.0046 0.3290 \n", " 316 0.0004 0.0018 -0.0031 0.0040 0.1834 \n", " 317 0.0027 0.0018 -0.0009 0.0062 0.9172 \n", " 318 0.0015 0.0018 -0.0021 0.0051 0.4645 \n", " 319 0.0003 0.0018 -0.0032 0.0039 0.1602 \n", " 320 0.0007 0.0018 -0.0029 0.0042 0.2264 \n", " 321 0.0016 0.0018 -0.0020 0.0052 0.5011 \n", " 322 0.0004 0.0018 -0.0031 0.0040 0.1817 \n", " 323 0.0003 0.0018 -0.0033 0.0038 0.1575 \n", " 324 0.0003 0.0018 -0.0032 0.0039 0.1644 \n", " 325 0.0006 0.0018 -0.0030 0.0041 0.2097 \n", " 326 0.0024 0.0018 -0.0012 0.0059 0.7868 \n", " 327 0.0004 0.0018 -0.0032 0.0039 0.1739 \n", " 328 0.0003 0.0018 -0.0033 0.0038 0.1534 \n", " 329 0.0030 0.0018 -0.0006 0.0066 0.9254 \n", " 330 0.0031 0.0018 -0.0005 0.0067 0.8773 \n", " 331 0.0003 0.0018 -0.0032 0.0039 0.1632 \n", " 332 0.0006 0.0018 -0.0029 0.0042 0.2214 \n", " 333 0.0066 0.0019 0.0029 0.0103 0.0479 **\n", " 334 0.0004 0.0018 -0.0032 0.0039 0.1684 \n", " 335 0.0009 0.0018 -0.0026 0.0045 0.2949 \n", " 336 0.0005 0.0018 -0.0030 0.0041 0.2005 \n", " 337 0.0009 0.0018 -0.0026 0.0045 0.2934 \n", " 338 0.0013 0.0018 -0.0023 0.0049 0.4040 \n", " 339 0.0005 0.0018 -0.0031 0.0040 0.1916 \n", " 340 0.0009 0.0018 -0.0027 0.0045 0.2811 \n", " 341 0.0013 0.0018 -0.0023 0.0049 0.3950 \n", " 342 0.0003 0.0018 -0.0032 0.0039 0.1609 \n", " 343 0.0021 0.0018 -0.0015 0.0057 0.6808 \n", " 344 0.0006 0.0018 -0.0030 0.0041 0.2057 \n", " 345 0.0015 0.0018 -0.0021 0.0050 0.4461 \n", " 346 0.0010 0.0018 -0.0026 0.0046 0.3231 \n", " 347 0.0010 0.0018 -0.0026 0.0046 0.3080 \n", " 348 0.0004 0.0018 -0.0032 0.0039 0.1690 \n", " 349 0.0005 0.0018 -0.0030 0.0041 0.2018 \n", " 350 0.0031 0.0018 -0.0005 0.0067 0.8894 \n", " 351 0.0003 0.0018 -0.0033 0.0038 0.1516 \n", " 352 0.0004 0.0018 -0.0031 0.0040 0.1848 \n", " 353 0.0009 0.0018 -0.0027 0.0044 0.2759 \n", " 354 0.0012 0.0018 -0.0023 0.0048 0.3709 \n", " 355 0.0007 0.0018 -0.0029 0.0042 0.2253 \n", " 356 0.0005 0.0018 -0.0031 0.0040 0.1876 \n", " 357 0.0010 0.0018 -0.0026 0.0046 0.3036 \n", " 358 0.0005 0.0018 -0.0030 0.0041 0.2015 \n", " 359 0.0005 0.0018 -0.0030 0.0041 0.2013 \n", " 360 0.0005 0.0018 -0.0030 0.0041 0.1949 \n", " 361 0.0007 0.0018 -0.0029 0.0042 0.2280 \n", " 362 0.0017 0.0019 -0.0019 0.0054 0.5461 \n", " 363 0.0005 0.0018 -0.0031 0.0040 0.1913 \n", " 364 0.0005 0.0018 -0.0030 0.0041 0.2036 \n", " 365 0.0019 0.0018 -0.0017 0.0055 0.6095 \n", " 366 0.0041 0.0019 0.0004 0.0079 0.4998 \n", " 367 0.0009 0.0018 -0.0027 0.0044 0.2808 \n", " 368 0.0010 0.0018 -0.0026 0.0045 0.2972 \n", " 369 0.0011 0.0018 -0.0024 0.0047 0.3415 \n", " 370 0.0004 0.0018 -0.0031 0.0040 0.1813 \n", " 371 0.0005 0.0018 -0.0031 0.0040 0.1868 \n", " 372 0.0013 0.0018 -0.0023 0.0049 0.3953 \n", " 373 0.0004 0.0018 -0.0032 0.0039 0.1694 \n", " 374 0.0014 0.0018 -0.0022 0.0049 0.4195 \n", " 375 0.0012 0.0018 -0.0024 0.0048 0.3587 \n", " 376 0.0010 0.0018 -0.0025 0.0046 0.3191 \n", " 377 0.0009 0.0018 -0.0026 0.0045 0.2893 \n", " 378 0.0004 0.0018 -0.0032 0.0039 0.1731 \n", " 379 0.0009 0.0019 -0.0028 0.0045 0.2905 \n", " 380 0.0007 0.0018 -0.0029 0.0042 0.2341 \n", " 381 0.0003 0.0018 -0.0032 0.0039 0.1656 \n", " 382 0.0006 0.0018 -0.0030 0.0042 0.2135 \n", " 383 0.0007 0.0018 -0.0029 0.0042 0.2308 \n", " 384 0.0004 0.0018 -0.0032 0.0039 0.1700 \n", " 385 0.0004 0.0018 -0.0032 0.0039 0.1752 \n", " 386 0.0020 0.0018 -0.0016 0.0056 0.6429 \n", " 387 0.0007 0.0018 -0.0029 0.0042 0.2316 \n", " 388 0.0003 0.0018 -0.0033 0.0038 0.1581 \n", " 389 0.0009 0.0018 -0.0027 0.0044 0.2733 \n", " 390 0.0003 0.0018 -0.0032 0.0038 0.1591 \n", " 391 0.0013 0.0018 -0.0023 0.0048 0.3856 \n", " 392 0.0010 0.0018 -0.0026 0.0045 0.3026 \n", " 393 0.0009 0.0018 -0.0027 0.0045 0.2957 \n", " 394 0.0007 0.0018 -0.0028 0.0043 0.2455 \n", " 395 0.0004 0.0018 -0.0032 0.0039 0.1710 \n", " 396 0.0007 0.0018 -0.0029 0.0043 0.2371 \n", " 397 0.0005 0.0018 -0.0030 0.0041 0.1945 \n", " 398 0.0005 0.0018 -0.0031 0.0040 0.1894 \n", " 399 0.0004 0.0018 -0.0031 0.0040 0.1820 \n", " 400 0.0003 0.0018 -0.0032 0.0039 0.1633 \n", " 401 0.0008 0.0018 -0.0028 0.0043 0.2504 \n", " 402 0.0003 0.0018 -0.0032 0.0039 0.1630 \n", " 403 0.0004 0.0018 -0.0032 0.0039 0.1740 \n", " 404 0.0011 0.0018 -0.0025 0.0046 0.3286 \n", " 405 0.0004 0.0018 -0.0032 0.0039 0.1715 \n", " 406 0.0008 0.0018 -0.0028 0.0044 0.2595 \n", " 407 0.0006 0.0018 -0.0030 0.0041 0.2086 \n", " 408 0.0003 0.0018 -0.0032 0.0039 0.1610 \n", " 409 0.0010 0.0018 -0.0026 0.0046 0.3083 \n", " 410 0.0005 0.0018 -0.0030 0.0041 0.2030 \n", " 411 0.0004 0.0018 -0.0031 0.0040 0.1840 \n", " 412 0.0004 0.0018 -0.0031 0.0039 0.1763 \n", " 413 0.0005 0.0018 -0.0031 0.0040 0.1939 \n", " 414 0.0004 0.0018 -0.0032 0.0039 0.1749 \n", " 415 0.0006 0.0018 -0.0029 0.0042 0.2203 \n", " 416 0.0005 0.0018 -0.0031 0.0040 0.1934 \n", " 417 0.0002 0.0018 -0.0033 0.0038 0.1450 \n", " 418 0.0006 0.0018 -0.0030 0.0041 0.2130 \n", " 419 0.0009 0.0018 -0.0026 0.0045 0.2864 \n", " 420 0.0004 0.0018 -0.0031 0.0040 0.1787 \n", " 421 0.0003 0.0018 -0.0032 0.0039 0.1595 \n", " 422 0.0007 0.0018 -0.0029 0.0042 0.2327 \n", " 423 0.0006 0.0018 -0.0030 0.0041 0.2051 \n", " 424 0.0010 0.0018 -0.0026 0.0046 0.3065 \n", " 425 0.0009 0.0018 -0.0027 0.0044 0.2723 \n", " 426 0.0012 0.0018 -0.0024 0.0047 0.3511 \n", " 427 0.0004 0.0018 -0.0031 0.0039 0.1761 \n", " 428 0.0007 0.0018 -0.0029 0.0042 0.2301 \n", " 429 0.0004 0.0018 -0.0031 0.0040 0.1784 \n", " 430 0.0006 0.0018 -0.0029 0.0042 0.2147 \n", " 431 0.0005 0.0018 -0.0030 0.0041 0.2037 \n", " 432 0.0003 0.0018 -0.0032 0.0039 0.1658 \n", " 433 0.0004 0.0018 -0.0031 0.0040 0.1781 \n", " 434 0.0008 0.0018 -0.0028 0.0043 0.2571 \n", " 435 0.0003 0.0018 -0.0033 0.0038 0.1542 \n", " 436 0.0004 0.0018 -0.0032 0.0039 0.1750 \n", " 437 0.0019 0.0018 -0.0017 0.0055 0.6077 \n", " 438 0.0005 0.0018 -0.0031 0.0040 0.1892 \n", " 439 0.0002 0.0018 -0.0033 0.0038 0.1459 \n", " 440 0.0017 0.0018 -0.0019 0.0053 0.5277 \n", " 441 0.0008 0.0018 -0.0027 0.0044 0.2696 \n", " 442 0.0003 0.0018 -0.0032 0.0039 0.1645 \n", " 443 0.0005 0.0018 -0.0031 0.0040 0.1888 \n", " 444 0.0002 0.0018 -0.0033 0.0038 0.1466 \n", " 445 0.0006 0.0018 -0.0030 0.0041 0.2043 \n", " 446 0.0002 0.0018 -0.0033 0.0038 0.1450 \n", " 447 0.0009 0.0018 -0.0027 0.0045 0.2897 \n", " 448 0.0003 0.0018 -0.0033 0.0038 0.1574 \n", " 449 0.0004 0.0018 -0.0031 0.0040 0.1818 \n", " 450 0.0004 0.0018 -0.0031 0.0040 0.1820 \n", " 451 0.0007 0.0018 -0.0029 0.0042 0.2332 \n", " 452 0.0012 0.0018 -0.0024 0.0048 0.3636 \n", " 453 0.0005 0.0018 -0.0030 0.0041 0.2004 \n", " 454 0.0020 0.0018 -0.0016 0.0056 0.6438 \n", " 455 0.0003 0.0018 -0.0033 0.0038 0.1517 \n", " 456 0.0007 0.0018 -0.0029 0.0043 0.2406 \n", " 457 0.0006 0.0018 -0.0030 0.0042 0.2172 \n", " 458 0.0005 0.0018 -0.0031 0.0040 0.1858 \n", " 459 0.0006 0.0018 -0.0030 0.0041 0.2111 \n", " 460 0.0005 0.0018 -0.0030 0.0041 0.2034 \n", " 461 0.0003 0.0018 -0.0032 0.0039 0.1656 \n", " 462 0.0005 0.0018 -0.0031 0.0040 0.1904 \n", " 463 0.0034 0.0020 -0.0006 0.0073 0.7936 \n", " 464 0.0010 0.0018 -0.0025 0.0046 0.3141 \n", " 465 0.0008 0.0018 -0.0028 0.0043 0.2520 \n", " 466 0.0007 0.0018 -0.0029 0.0042 0.2278 \n", " 467 0.0018 0.0018 -0.0019 0.0054 0.5525 \n", " 468 0.0005 0.0018 -0.0030 0.0041 0.1950 \n", " 469 0.0007 0.0018 -0.0029 0.0042 0.2313 \n", " 470 0.0005 0.0018 -0.0030 0.0041 0.2019 \n", " 471 0.0009 0.0019 -0.0028 0.0045 0.2891 \n", " 472 0.0016 0.0018 -0.0020 0.0052 0.4863 \n", " 473 0.0028 0.0018 -0.0008 0.0064 0.9924 \n", " 474 0.0006 0.0018 -0.0030 0.0041 0.2056 \n", " 475 0.0008 0.0018 -0.0027 0.0044 0.2628 \n", " 476 0.0003 0.0018 -0.0033 0.0038 0.1573 \n", " 477 0.0003 0.0018 -0.0033 0.0038 0.1578 \n", " 478 0.0007 0.0018 -0.0028 0.0043 0.2378 \n", " 479 0.0008 0.0018 -0.0028 0.0043 0.2482 \n", " 480 0.0004 0.0018 -0.0032 0.0039 0.1686 \n", " 481 0.0003 0.0018 -0.0032 0.0039 0.1638 \n", " 482 0.0005 0.0018 -0.0031 0.0040 0.1858 \n", " 483 0.0005 0.0018 -0.0030 0.0041 0.1958 \n", " 484 0.0004 0.0018 -0.0031 0.0040 0.1819 \n", " 485 0.0008 0.0018 -0.0028 0.0043 0.2498 \n", " 486 0.0007 0.0019 -0.0029 0.0044 0.2547 \n", " 487 0.0010 0.0018 -0.0026 0.0046 0.3173 \n", " 488 0.0016 0.0018 -0.0020 0.0052 0.4880 \n", " 489 0.0007 0.0018 -0.0028 0.0043 0.2526 \n", " 490 0.0005 0.0018 -0.0030 0.0041 0.1988 \n", " 491 0.0009 0.0018 -0.0028 0.0045 0.2808 \n", " 492 0.0005 0.0018 -0.0030 0.0041 0.2043 \n", " 493 0.0004 0.0018 -0.0032 0.0039 0.1736 \n", " 494 0.0033 0.0018 -0.0003 0.0069 0.8013 \n", " 495 0.0006 0.0018 -0.0030 0.0041 0.2123 \n", " 496 0.0005 0.0018 -0.0030 0.0041 0.1968 \n", " 497 0.0010 0.0018 -0.0026 0.0046 0.3201 \n", " 498 0.0009 0.0019 -0.0027 0.0046 0.3056 \n", " 499 0.0005 0.0018 -0.0031 0.0040 0.1879 \n", " 500 0.0004 0.0018 -0.0031 0.0040 0.1850 \n", " 501 0.0007 0.0018 -0.0029 0.0043 0.2364 \n", " 502 0.0003 0.0018 -0.0032 0.0039 0.1619 \n", " 503 0.0004 0.0018 -0.0032 0.0039 0.1729 \n", " 504 0.0003 0.0018 -0.0032 0.0039 0.1674 \n", " 505 0.0009 0.0018 -0.0026 0.0045 0.2928 \n", " 506 0.0012 0.0018 -0.0024 0.0048 0.3619 \n", " 507 0.0003 0.0018 -0.0032 0.0039 0.1634 \n", " 508 0.0007 0.0018 -0.0029 0.0043 0.2487 \n", " 509 0.0007 0.0018 -0.0028 0.0043 0.2372 \n", " 510 0.0009 0.0018 -0.0027 0.0044 0.2743 \n", " 511 0.0003 0.0018 -0.0033 0.0038 0.1519 \n", " 512 0.0003 0.0018 -0.0032 0.0039 0.1670 \n", " 513 0.0007 0.0018 -0.0028 0.0043 0.2449 \n", " 514 0.0061 0.0019 0.0023 0.0098 0.0975 *\n", " 515 0.0003 0.0018 -0.0032 0.0039 0.1651 \n", " 516 0.0005 0.0018 -0.0030 0.0041 0.1961 \n", " 517 0.0023 0.0018 -0.0013 0.0059 0.7682 \n", " 518 0.0004 0.0018 -0.0032 0.0039 0.1715 \n", " 519 0.0014 0.0018 -0.0021 0.0050 0.4388 \n", " 520 0.0007 0.0018 -0.0028 0.0043 0.2431 \n", " 521 0.0004 0.0018 -0.0032 0.0039 0.1710 \n", " 522 0.0009 0.0018 -0.0027 0.0044 0.2800 \n", " 523 0.0005 0.0018 -0.0031 0.0040 0.1882 \n", " 524 0.0009 0.0018 -0.0026 0.0045 0.2834 \n", " 525 0.0008 0.0018 -0.0027 0.0044 0.2647 \n", " 526 0.0011 0.0018 -0.0024 0.0047 0.3359 \n", " 527 0.0008 0.0018 -0.0027 0.0044 0.2624 \n", " 528 0.0003 0.0018 -0.0032 0.0038 0.1587 \n", " 529 0.0004 0.0018 -0.0032 0.0039 0.1751 \n", " 530 0.0004 0.0018 -0.0032 0.0039 0.1735 \n", " 531 0.0011 0.0018 -0.0024 0.0047 0.3398 \n", " 532 0.0005 0.0018 -0.0030 0.0041 0.2027 \n", " 533 0.0007 0.0018 -0.0029 0.0042 0.2336 \n", " 534 0.0004 0.0018 -0.0031 0.0040 0.1792 \n", " 535 0.0005 0.0018 -0.0031 0.0041 0.1951 \n", " 536 0.0008 0.0018 -0.0027 0.0044 0.2711 \n", " 537 0.0004 0.0018 -0.0031 0.0040 0.1853 \n", " 538 0.0005 0.0018 -0.0030 0.0041 0.2015 \n", " 539 0.0009 0.0018 -0.0027 0.0045 0.2831 \n", " 540 0.0007 0.0018 -0.0028 0.0043 0.2404 \n", " 541 0.0003 0.0018 -0.0033 0.0038 0.1572 \n", " 542 0.0004 0.0018 -0.0032 0.0039 0.1738 \n", " 543 0.0009 0.0018 -0.0027 0.0045 0.2822 \n", " 544 0.0009 0.0018 -0.0027 0.0044 0.2697 \n", " 545 0.0002 0.0018 -0.0033 0.0038 0.1473 \n", " 546 0.0004 0.0018 -0.0031 0.0040 0.1816 \n", " 547 0.0006 0.0018 -0.0029 0.0042 0.2171 \n", " 548 0.0025 0.0018 -0.0011 0.0062 0.8693 \n", " 549 0.0006 0.0018 -0.0029 0.0042 0.2254 \n", " 550 0.0031 0.0018 -0.0005 0.0067 0.8719 \n", " 551 0.0017 0.0019 -0.0020 0.0054 0.5509 \n", " 552 0.0008 0.0018 -0.0028 0.0043 0.2518 \n", " 553 0.0003 0.0018 -0.0032 0.0039 0.1596 \n", " 554 0.0003 0.0018 -0.0032 0.0039 0.1610 \n", " 555 0.0010 0.0018 -0.0025 0.0046 0.3190 \n", " 556 0.0005 0.0018 -0.0031 0.0040 0.1882 \n", " 557 0.0003 0.0018 -0.0033 0.0038 0.1540 \n", " 558 0.0020 0.0018 -0.0016 0.0055 0.6293 \n", " 559 0.0003 0.0018 -0.0032 0.0039 0.1651 \n", " 560 0.0005 0.0018 -0.0031 0.0040 0.1883 \n", " 561 0.0033 0.0019 -0.0004 0.0069 0.8227 \n", " 562 0.0021 0.0018 -0.0015 0.0057 0.6743 \n", " 563 0.0009 0.0018 -0.0027 0.0044 0.2741 \n", " 564 0.0010 0.0019 -0.0027 0.0046 0.3187 \n", " 565 0.0058 0.0019 0.0022 0.0095 0.1115 \n", " 566 0.0004 0.0018 -0.0032 0.0039 0.1726 \n", " 567 0.0003 0.0018 -0.0032 0.0039 0.1658 \n", " 568 0.0004 0.0018 -0.0032 0.0039 0.1731 \n", " 569 0.0006 0.0018 -0.0030 0.0041 0.2048 \n", " 570 0.0014 0.0018 -0.0022 0.0050 0.4260 \n", " 571 0.0005 0.0018 -0.0031 0.0040 0.1938 \n", " 572 0.0003 0.0018 -0.0033 0.0038 0.1521 \n", " 573 0.0005 0.0018 -0.0030 0.0041 0.2034 \n", " 574 0.0007 0.0018 -0.0029 0.0042 0.2313 \n", " 575 0.0002 0.0018 -0.0033 0.0038 0.1456 \n", " 576 0.0008 0.0018 -0.0028 0.0044 0.2601 \n", " 577 0.0008 0.0018 -0.0027 0.0043 0.2577 \n", " 578 0.0004 0.0018 -0.0031 0.0040 0.1805 \n", " 579 0.0006 0.0018 -0.0029 0.0042 0.2249 \n", " 580 0.0009 0.0018 -0.0027 0.0044 0.2718 \n", " 581 0.0017 0.0019 -0.0020 0.0054 0.5502 \n", " 582 0.0014 0.0018 -0.0022 0.0050 0.4293 \n", " 583 0.0006 0.0018 -0.0030 0.0041 0.2068 \n", " 584 0.0005 0.0018 -0.0030 0.0041 0.2055 \n", " 585 0.0012 0.0019 -0.0025 0.0048 0.3696 \n", " 586 0.0009 0.0019 -0.0028 0.0045 0.2862 \n", " 587 0.0005 0.0018 -0.0030 0.0040 0.1941 \n", " 588 0.0007 0.0018 -0.0028 0.0043 0.2434 \n", " 589 0.0007 0.0018 -0.0029 0.0042 0.2336 \n", " 590 0.0012 0.0018 -0.0024 0.0047 0.3591 \n", " 591 0.0004 0.0018 -0.0032 0.0039 0.1725 \n", " 592 0.0005 0.0018 -0.0030 0.0041 0.1953 \n", " 593 0.0004 0.0018 -0.0032 0.0039 0.1704 \n", " 594 0.0005 0.0018 -0.0030 0.0041 0.1990 \n", " 595 0.0008 0.0018 -0.0028 0.0044 0.2670 \n", " 596 0.0004 0.0018 -0.0032 0.0039 0.1721 \n", " 597 0.0003 0.0018 -0.0033 0.0038 0.1540 \n", " 598 0.0005 0.0018 -0.0031 0.0040 0.1891 \n", " 599 0.0003 0.0018 -0.0032 0.0039 0.1645 \n", " 600 0.0005 0.0018 -0.0030 0.0041 0.2033 \n", " 601 0.0005 0.0018 -0.0030 0.0041 0.2010 \n", " 602 0.0002 0.0018 -0.0034 0.0037 0.1412 \n", " 603 0.0006 0.0018 -0.0029 0.0042 0.2211 \n", " 604 0.0008 0.0019 -0.0028 0.0044 0.2671 \n", " 605 0.0007 0.0018 -0.0028 0.0043 0.2426 \n", " 606 0.0004 0.0018 -0.0032 0.0039 0.1725 \n", " 607 0.0003 0.0018 -0.0033 0.0038 0.1572 \n", " 608 0.0006 0.0018 -0.0029 0.0042 0.2190 \n", " 609 0.0003 0.0018 -0.0032 0.0039 0.1629 \n", " 610 0.0002 0.0018 -0.0033 0.0038 0.1497 \n", " 611 0.0005 0.0018 -0.0030 0.0041 0.2010 \n", " 612 0.0006 0.0018 -0.0030 0.0041 0.2043 \n", " 613 0.0006 0.0018 -0.0029 0.0042 0.2189 \n", " 614 0.0024 0.0021 -0.0017 0.0065 0.8319 \n", " 615 0.0007 0.0018 -0.0028 0.0043 0.2401 \n", " 616 0.0007 0.0018 -0.0028 0.0043 0.2409 \n", " 617 0.0004 0.0018 -0.0032 0.0039 0.1726 \n", " 618 0.0006 0.0018 -0.0030 0.0041 0.2138 \n", " 619 0.0002 0.0018 -0.0034 0.0037 0.1402 \n", " 620 0.0010 0.0018 -0.0026 0.0045 0.2990 \n", " 621 0.0003 0.0018 -0.0032 0.0039 0.1669 \n", " 622 0.0005 0.0018 -0.0030 0.0041 0.1946 \n", " 623 0.0003 0.0018 -0.0033 0.0038 0.1539 \n", " 624 0.0007 0.0018 -0.0028 0.0043 0.2507 \n", " 625 0.0002 0.0018 -0.0033 0.0038 0.1505 \n", " 626 0.0008 0.0018 -0.0027 0.0044 0.2627 \n", " 627 0.0004 0.0018 -0.0031 0.0040 0.1841 \n", " 628 0.0005 0.0018 -0.0030 0.0041 0.2039 \n", " 629 0.0003 0.0018 -0.0032 0.0039 0.1650 \n", " 630 0.0031 0.0018 -0.0005 0.0067 0.8746 \n", " 631 0.0008 0.0018 -0.0027 0.0044 0.2623 \n", " 632 0.0003 0.0018 -0.0032 0.0039 0.1602 \n", " 633 0.0005 0.0018 -0.0031 0.0041 0.1947 \n", " 634 0.0022 0.0018 -0.0013 0.0058 0.7401 \n", " 635 0.0003 0.0018 -0.0033 0.0038 0.1565 \n", " 636 0.0006 0.0018 -0.0030 0.0041 0.2091 \n", " 637 0.0006 0.0018 -0.0030 0.0041 0.2121 \n", " 638 0.0004 0.0018 -0.0031 0.0040 0.1826 \n", " 639 0.0007 0.0018 -0.0029 0.0042 0.2256 \n", " 640 0.0005 0.0018 -0.0030 0.0041 0.1988 \n", " 641 0.0003 0.0018 -0.0033 0.0038 0.1515 \n", " 642 0.0006 0.0018 -0.0030 0.0041 0.2105 \n", " 643 0.0003 0.0018 -0.0032 0.0039 0.1667 \n", " 644 0.0007 0.0018 -0.0028 0.0043 0.2367 \n", " 645 0.0003 0.0018 -0.0033 0.0038 0.1575 \n", " 646 0.0005 0.0018 -0.0031 0.0040 0.1877 \n", " 647 0.0010 0.0018 -0.0026 0.0045 0.3024 \n", " 648 0.0014 0.0018 -0.0022 0.0050 0.4248 \n", " 649 0.0003 0.0018 -0.0032 0.0039 0.1612 \n", " 650 0.0003 0.0018 -0.0033 0.0038 0.1550 \n", " 651 0.0007 0.0018 -0.0028 0.0043 0.2399 \n", " 652 0.0003 0.0018 -0.0033 0.0038 0.1550 \n", " 653 0.0005 0.0018 -0.0030 0.0041 0.1965 \n", " 654 0.0004 0.0018 -0.0032 0.0039 0.1719 \n", " 655 0.0008 0.0018 -0.0027 0.0044 0.2614 \n", " 656 0.0005 0.0018 -0.0031 0.0040 0.1873 \n", " 657 0.0009 0.0018 -0.0027 0.0044 0.2731 \n", " 658 0.0011 0.0018 -0.0025 0.0046 0.3261 \n", " 659 0.0009 0.0018 -0.0027 0.0045 0.2941 \n", " 660 0.0008 0.0018 -0.0027 0.0044 0.2676 \n", " 661 0.0004 0.0018 -0.0032 0.0039 0.1697 \n", " 662 0.0010 0.0018 -0.0026 0.0045 0.3049 \n", " 663 0.0004 0.0018 -0.0032 0.0039 0.1699 \n", " 664 0.0005 0.0018 -0.0030 0.0041 0.2026 \n", " 665 0.0010 0.0018 -0.0026 0.0046 0.3192 \n", " 666 0.0007 0.0018 -0.0029 0.0043 0.2357 \n", " 667 0.0004 0.0018 -0.0031 0.0040 0.1848 \n", " 668 0.0006 0.0018 -0.0029 0.0042 0.2226 \n", " 669 0.0003 0.0018 -0.0033 0.0038 0.1566 \n", " 670 0.0004 0.0018 -0.0032 0.0039 0.1733 \n", " 671 0.0003 0.0018 -0.0033 0.0038 0.1516 \n", " 672 0.0007 0.0018 -0.0029 0.0042 0.2327 \n", " 673 0.0015 0.0018 -0.0021 0.0051 0.4587 \n", " 674 0.0008 0.0018 -0.0027 0.0044 0.2606 \n", " 675 0.0022 0.0018 -0.0014 0.0057 0.7068 \n", " 676 0.0006 0.0018 -0.0030 0.0041 0.2048 \n", " 677 0.0012 0.0018 -0.0024 0.0048 0.3630 \n", " 678 0.0012 0.0019 -0.0025 0.0048 0.3696 \n", " 679 0.0017 0.0018 -0.0019 0.0052 0.5133 \n", " 680 0.0008 0.0018 -0.0027 0.0044 0.2653 \n", " 681 0.0007 0.0018 -0.0029 0.0042 0.2281 \n", " 682 0.0008 0.0018 -0.0027 0.0044 0.2633 \n", " 683 0.0009 0.0018 -0.0027 0.0044 0.2785 \n", " 684 0.0012 0.0018 -0.0024 0.0047 0.3550 \n", " 685 0.0003 0.0018 -0.0033 0.0038 0.1573 \n", " 686 0.0002 0.0018 -0.0033 0.0038 0.1435 \n", " 687 0.0002 0.0018 -0.0034 0.0037 0.1424 \n", " 688 0.0005 0.0018 -0.0031 0.0040 0.1906 \n", " 689 0.0005 0.0018 -0.0030 0.0041 0.1959 \n", " 690 0.0003 0.0018 -0.0033 0.0038 0.1526 \n", " 691 0.0005 0.0018 -0.0030 0.0041 0.1981 \n", " 692 0.0005 0.0018 -0.0031 0.0040 0.1935 \n", " 693 0.0005 0.0018 -0.0031 0.0040 0.1905 \n", " 694 0.0004 0.0018 -0.0032 0.0039 0.1696 \n", " 695 0.0008 0.0018 -0.0027 0.0044 0.2669 \n", " 696 0.0012 0.0018 -0.0024 0.0047 0.3561 \n", " 697 0.0011 0.0018 -0.0025 0.0046 0.3244 \n", " 698 0.0009 0.0018 -0.0027 0.0044 0.2727 \n", " 699 0.0003 0.0018 -0.0032 0.0039 0.1621 \n", " 700 0.0004 0.0018 -0.0032 0.0039 0.1721 \n", " 701 0.0004 0.0018 -0.0032 0.0039 0.1733 \n", " 702 0.0003 0.0018 -0.0032 0.0039 0.1621 \n", " 703 0.0008 0.0018 -0.0028 0.0043 0.2513 \n", " 704 0.0008 0.0018 -0.0028 0.0043 0.2527 \n", " 705 0.0004 0.0018 -0.0032 0.0039 0.1687 \n", " 706 0.0004 0.0018 -0.0032 0.0039 0.1712 \n", " 707 0.0003 0.0018 -0.0033 0.0038 0.1518 \n", " 708 0.0004 0.0018 -0.0031 0.0040 0.1769 \n", " 709 0.0013 0.0018 -0.0022 0.0049 0.3996 \n", " 710 0.0010 0.0018 -0.0025 0.0046 0.3151 \n", " 711 0.0010 0.0018 -0.0025 0.0046 0.3210 \n", " 712 0.0018 0.0018 -0.0018 0.0054 0.5552 \n", " 713 0.0005 0.0018 -0.0030 0.0041 0.2029 \n", " 714 0.0004 0.0018 -0.0031 0.0040 0.1770 \n", " 715 0.0002 0.0018 -0.0033 0.0038 0.1459 \n", " 716 0.0018 0.0018 -0.0018 0.0054 0.5676 \n", " 717 0.0008 0.0018 -0.0028 0.0043 0.2495 \n", " 718 0.0018 0.0018 -0.0018 0.0054 0.5646 \n", " 719 0.0002 0.0018 -0.0034 0.0037 0.1412 \n", " 720 0.0002 0.0018 -0.0034 0.0037 0.1384 \n", " 721 0.0006 0.0018 -0.0030 0.0042 0.2188 \n", " 722 0.0012 0.0019 -0.0024 0.0049 0.3893 \n", " 723 0.0012 0.0019 -0.0024 0.0048 0.3691 \n", " 724 0.0012 0.0019 -0.0026 0.0049 0.3755 \n", " 725 0.0011 0.0018 -0.0025 0.0047 0.3360 \n", " 726 0.0015 0.0018 -0.0021 0.0051 0.4757 \n", " 727 0.0005 0.0018 -0.0031 0.0040 0.1885 \n", " 728 0.0008 0.0018 -0.0027 0.0044 0.2705 \n", " 729 0.0018 0.0018 -0.0017 0.0054 0.5781 \n", " 730 0.0009 0.0018 -0.0027 0.0045 0.2829 \n", " 731 0.0015 0.0018 -0.0021 0.0050 0.4478 \n", " 732 0.0007 0.0018 -0.0028 0.0043 0.2397 \n", " 733 0.0009 0.0018 -0.0027 0.0044 0.2775 \n", " 734 0.0016 0.0018 -0.0020 0.0052 0.4885 \n", " 735 0.0023 0.0018 -0.0013 0.0059 0.7542 \n", " 736 0.0039 0.0019 0.0003 0.0076 0.5551 \n", " 737 0.0006 0.0018 -0.0030 0.0041 0.2082 \n", " 738 0.0004 0.0018 -0.0031 0.0040 0.1847 \n", " 739 0.0006 0.0018 -0.0030 0.0041 0.2118 \n", " 740 0.0005 0.0018 -0.0030 0.0041 0.2007 \n", " 741 0.0004 0.0018 -0.0032 0.0039 0.1711 \n", " 742 0.0014 0.0018 -0.0021 0.0050 0.4332 \n", " 743 0.0023 0.0019 -0.0013 0.0059 0.7629 \n", " 744 0.0009 0.0018 -0.0026 0.0045 0.2835 \n", " 745 0.0027 0.0019 -0.0009 0.0063 0.9308 \n", " 746 0.0007 0.0018 -0.0028 0.0043 0.2367 \n", " 747 0.0021 0.0018 -0.0015 0.0057 0.6885 \n", " 748 0.0007 0.0018 -0.0029 0.0042 0.2265 \n", " 749 0.0048 0.0019 0.0011 0.0085 0.3089 \n", " 750 0.0010 0.0018 -0.0025 0.0046 0.3193 \n", " 751 0.0014 0.0018 -0.0021 0.0050 0.4406 \n", " 752 0.0019 0.0018 -0.0016 0.0055 0.6191 \n", " 753 0.0007 0.0018 -0.0029 0.0042 0.2304 \n", " 754 0.0009 0.0018 -0.0027 0.0044 0.2722 \n", " 755 0.0004 0.0018 -0.0032 0.0039 0.1684 \n", " 756 0.0014 0.0018 -0.0021 0.0050 0.4347 \n", " 757 0.0006 0.0018 -0.0029 0.0042 0.2153 \n", " 758 0.0008 0.0018 -0.0028 0.0043 0.2502 \n", " 759 0.0003 0.0018 -0.0033 0.0038 0.1564 \n", " 760 0.0011 0.0018 -0.0025 0.0047 0.3366 \n", " 761 0.0003 0.0018 -0.0033 0.0038 0.1558 \n", " 762 0.0010 0.0018 -0.0025 0.0046 0.3086 \n", " 763 0.0015 0.0018 -0.0021 0.0051 0.4607 \n", " 764 0.0021 0.0019 -0.0016 0.0058 0.6905 \n", " 765 0.0007 0.0018 -0.0028 0.0043 0.2425 \n", " 766 0.0004 0.0018 -0.0031 0.0040 0.1844 \n", " 767 0.0022 0.0018 -0.0014 0.0057 0.7082 \n", " 768 0.0026 0.0019 -0.0011 0.0064 0.9079 \n", " 769 0.0008 0.0018 -0.0027 0.0044 0.2698 \n", " 770 0.0013 0.0018 -0.0023 0.0049 0.4000 \n", " 771 0.0004 0.0018 -0.0031 0.0040 0.1841 \n", " 772 0.0009 0.0018 -0.0027 0.0045 0.2830 \n", " 773 0.0008 0.0018 -0.0028 0.0043 0.2506 \n", " 774 0.0013 0.0018 -0.0022 0.0049 0.4072 \n", " 775 0.0018 0.0018 -0.0018 0.0054 0.5749 \n", " 776 0.0008 0.0018 -0.0028 0.0043 0.2571 \n", " 777 0.0019 0.0018 -0.0016 0.0055 0.6201 \n", " 778 0.0004 0.0018 -0.0032 0.0039 0.1673 \n", " 779 0.0020 0.0018 -0.0015 0.0056 0.6557 \n", " 780 0.0009 0.0018 -0.0027 0.0045 0.2835 \n", " 781 0.0003 0.0018 -0.0032 0.0039 0.1656 \n", " 782 0.0006 0.0018 -0.0030 0.0042 0.2116 \n", " 783 0.0008 0.0018 -0.0028 0.0044 0.2587 \n", " 784 0.0010 0.0018 -0.0026 0.0045 0.2997 \n", " 785 0.0003 0.0018 -0.0032 0.0039 0.1622 \n", " 786 0.0019 0.0019 -0.0018 0.0055 0.5911 \n", " 787 0.0006 0.0018 -0.0029 0.0042 0.2214 \n", " 788 0.0004 0.0018 -0.0032 0.0039 0.1738 \n", " 789 0.0040 0.0019 0.0003 0.0078 0.5286 \n", " 790 0.0013 0.0018 -0.0023 0.0049 0.4005 \n", " 791 0.0024 0.0019 -0.0012 0.0061 0.8220 \n", " 792 0.0003 0.0018 -0.0033 0.0038 0.1514 \n", " 793 0.0012 0.0018 -0.0024 0.0047 0.3533 \n", " 794 0.0012 0.0018 -0.0024 0.0048 0.3582 \n", " 795 0.0005 0.0018 -0.0030 0.0041 0.2014 \n", " 796 0.0009 0.0018 -0.0027 0.0044 0.2771 \n", " 797 0.0012 0.0018 -0.0023 0.0048 0.3676 \n", " 798 0.0007 0.0018 -0.0028 0.0043 0.2423 \n", " 799 0.0003 0.0018 -0.0033 0.0038 0.1568 \n", " 800 0.0005 0.0018 -0.0031 0.0040 0.1876 \n", " 801 0.0012 0.0018 -0.0024 0.0047 0.3490 \n", " 802 0.0010 0.0018 -0.0026 0.0045 0.3059 \n", " 803 0.0012 0.0018 -0.0023 0.0048 0.3782 \n", " 804 0.0005 0.0018 -0.0031 0.0040 0.1935 \n", " 805 0.0007 0.0018 -0.0029 0.0042 0.2273 \n", " 806 0.0152 0.0022 0.0110 0.0195 0.0000 ***\n", " 807 0.0123 0.0021 0.0081 0.0165 0.0000 ***\n", " 808 0.0013 0.0018 -0.0023 0.0048 0.3838 \n", " 809 0.0010 0.0018 -0.0026 0.0045 0.3025 \n", " 810 0.0021 0.0018 -0.0015 0.0057 0.6808 \n", " 811 0.0012 0.0018 -0.0024 0.0048 0.3626 \n", " 812 0.0007 0.0018 -0.0028 0.0043 0.2403 \n", " 813 0.0021 0.0018 -0.0015 0.0056 0.6662 \n", " 814 0.0013 0.0018 -0.0023 0.0048 0.3856 \n", " 815 0.0158 0.0023 0.0113 0.0203 0.0000 ***\n", " 816 0.0150 0.0022 0.0107 0.0193 0.0000 ***\n", " 817 0.0008 0.0018 -0.0027 0.0044 0.2668 \n", " 818 0.0006 0.0018 -0.0029 0.0042 0.2189 \n", " 819 0.0016 0.0018 -0.0019 0.0052 0.5095 \n", " 820 0.0020 0.0018 -0.0016 0.0056 0.6376 \n", " 821 0.0068 0.0019 0.0030 0.0106 0.0397 **\n", " 822 0.0040 0.0018 0.0004 0.0077 0.5199 \n", " 823 0.0005 0.0018 -0.0030 0.0041 0.2031 \n", " 824 0.0005 0.0018 -0.0031 0.0040 0.1942 \n", " 825 0.0018 0.0018 -0.0017 0.0054 0.5822 \n", " 826 0.0029 0.0019 -0.0007 0.0065 0.9740 \n", " 827 0.0017 0.0018 -0.0019 0.0053 0.5219 \n", " 828 0.0007 0.0018 -0.0029 0.0042 0.2322 \n", " 829 0.0009 0.0018 -0.0026 0.0045 0.2863 \n", " 830 0.0006 0.0018 -0.0030 0.0041 0.2080 \n", " 831 0.0010 0.0018 -0.0026 0.0045 0.2985 \n", "==============================================================================\n", "Significant units: 6 / 832\n", "---\n", "Signif. codes: 0 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1\n", "==============================================================================\n", "\n", "=== Z-space feature summary ===\n", "==============================================================================\n", "Feature Importance Results\n", "==============================================================================\n", "Method: EOTExplainer\n", "Number of units: 832\n", "Significance level: 0.05\n", "Alternative: two-sided\n", "Margin method: mixture\n", "Practical margin: 0.0028\n", "------------------------------------------------------------------------------\n", " Feature Estimate Std Err CI Lower CI Upper P-value Sig\n", "------------------------------------------------------------------------------\n", " 0 0.0009 0.0020 -0.0031 0.0049 0.3332 \n", " 1 0.0020 0.0021 -0.0022 0.0062 0.7024 \n", " 2 0.0020 0.0020 -0.0020 0.0059 0.6660 \n", " 3 0.0001 0.0020 -0.0038 0.0040 0.1640 \n", " 4 0.0001 0.0020 -0.0038 0.0040 0.1634 \n", " 5 0.0012 0.0022 -0.0030 0.0054 0.4550 \n", " 6 0.0002 0.0020 -0.0037 0.0041 0.1901 \n", " 7 0.0001 0.0020 -0.0038 0.0040 0.1672 \n", " 8 0.0002 0.0020 -0.0037 0.0041 0.1834 \n", " 9 0.0001 0.0020 -0.0037 0.0040 0.1758 \n", " 10 0.0000 0.0020 -0.0039 0.0039 0.1583 \n", " 11 0.0010 0.0021 -0.0031 0.0051 0.3872 \n", " 12 0.0003 0.0020 -0.0035 0.0042 0.2114 \n", " 13 0.0023 0.0025 -0.0027 0.0073 0.8338 \n", " 14 0.0028 0.0020 -0.0012 0.0068 0.9902 \n", " 15 0.0014 0.0021 -0.0027 0.0055 0.4978 \n", " 16 0.0000 0.0020 -0.0039 0.0039 0.1571 \n", " 17 0.0019 0.0020 -0.0021 0.0058 0.6315 \n", " 18 0.0003 0.0020 -0.0036 0.0041 0.1942 \n", " 19 0.0047 0.0021 0.0006 0.0088 0.3615 \n", " 20 0.0007 0.0020 -0.0033 0.0047 0.2912 \n", " 21 0.0000 0.0020 -0.0039 0.0039 0.1588 \n", " 22 0.0028 0.0020 -0.0012 0.0069 0.9927 \n", " 23 0.0000 0.0020 -0.0039 0.0039 0.1576 \n", " 24 0.0006 0.0020 -0.0034 0.0045 0.2564 \n", " 25 0.0000 0.0020 -0.0038 0.0039 0.1598 \n", " 26 0.0000 0.0020 -0.0039 0.0039 0.1548 \n", " 27 0.0003 0.0020 -0.0036 0.0042 0.2060 \n", " 28 0.0009 0.0020 -0.0030 0.0048 0.3283 \n", " 29 0.0003 0.0020 -0.0036 0.0042 0.2073 \n", " 30 0.0001 0.0020 -0.0038 0.0039 0.1626 \n", " 31 0.0006 0.0020 -0.0033 0.0045 0.2553 \n", " 32 0.0000 0.0020 -0.0039 0.0039 0.1581 \n", " 33 0.0004 0.0020 -0.0035 0.0043 0.2148 \n", " 34 0.0009 0.0020 -0.0030 0.0048 0.3292 \n", " 35 0.0003 0.0020 -0.0036 0.0042 0.2105 \n", " 36 0.0009 0.0020 -0.0030 0.0049 0.3487 \n", " 37 0.0006 0.0020 -0.0034 0.0046 0.2767 \n", " 38 0.0003 0.0020 -0.0036 0.0042 0.2078 \n", " 39 0.0001 0.0020 -0.0038 0.0040 0.1636 \n", " 40 0.0002 0.0020 -0.0037 0.0041 0.1839 \n", " 41 -0.0000 0.0020 -0.0039 0.0039 0.1500 \n", " 42 0.0006 0.0020 -0.0033 0.0045 0.2599 \n", " 43 -0.0000 0.0020 -0.0039 0.0039 0.1535 \n", " 44 0.0004 0.0020 -0.0035 0.0043 0.2243 \n", " 45 0.0009 0.0021 -0.0032 0.0050 0.3554 \n", " 46 0.0004 0.0020 -0.0035 0.0043 0.2242 \n", " 47 0.0001 0.0020 -0.0038 0.0040 0.1655 \n", " 48 0.0002 0.0020 -0.0037 0.0041 0.1897 \n", " 49 0.0008 0.0020 -0.0031 0.0047 0.3141 \n", " 50 0.0004 0.0020 -0.0035 0.0043 0.2179 \n", " 51 0.0004 0.0020 -0.0036 0.0043 0.2152 \n", " 52 0.0010 0.0020 -0.0030 0.0050 0.3668 \n", " 53 0.0001 0.0020 -0.0038 0.0040 0.1725 \n", " 54 0.0001 0.0020 -0.0038 0.0040 0.1645 \n", " 55 0.0001 0.0020 -0.0038 0.0040 0.1645 \n", " 56 0.0000 0.0020 -0.0039 0.0039 0.1578 \n", " 57 0.0003 0.0020 -0.0036 0.0042 0.2091 \n", " 58 0.0038 0.0021 -0.0004 0.0080 0.6416 \n", " 59 0.0001 0.0020 -0.0038 0.0040 0.1747 \n", " 60 0.0004 0.0020 -0.0035 0.0043 0.2139 \n", " 61 0.0006 0.0020 -0.0033 0.0045 0.2608 \n", " 62 0.0004 0.0020 -0.0035 0.0044 0.2324 \n", " 63 0.0001 0.0020 -0.0037 0.0040 0.1764 \n", " 64 0.0001 0.0020 -0.0038 0.0039 0.1613 \n", " 65 0.0012 0.0021 -0.0029 0.0053 0.4474 \n", " 66 0.0001 0.0020 -0.0038 0.0039 0.1616 \n", " 67 -0.0000 0.0020 -0.0039 0.0039 0.1501 \n", " 68 0.0016 0.0021 -0.0024 0.0057 0.5681 \n", " 69 0.0002 0.0020 -0.0037 0.0041 0.1799 \n", " 70 0.0017 0.0021 -0.0023 0.0057 0.5807 \n", " 71 0.0001 0.0020 -0.0037 0.0040 0.1760 \n", " 72 0.0007 0.0020 -0.0032 0.0046 0.2828 \n", " 73 0.0017 0.0020 -0.0022 0.0056 0.5754 \n", " 74 0.0026 0.0028 -0.0030 0.0082 0.9325 \n", " 75 0.0002 0.0020 -0.0037 0.0041 0.1825 \n", " 76 0.0000 0.0020 -0.0038 0.0039 0.1600 \n", " 77 -0.0002 0.0020 -0.0042 0.0038 0.1335 \n", " 78 -0.0000 0.0020 -0.0039 0.0039 0.1497 \n", " 79 0.0033 0.0025 -0.0015 0.0081 0.8515 \n", " 80 -0.0015 0.0030 -0.0073 0.0043 0.1449 \n", " 81 -0.0000 0.0020 -0.0039 0.0039 0.1513 \n", " 82 0.0001 0.0020 -0.0038 0.0040 0.1708 \n", " 83 0.0002 0.0020 -0.0037 0.0041 0.1877 \n", " 84 0.0000 0.0020 -0.0039 0.0039 0.1553 \n", " 85 0.0003 0.0020 -0.0036 0.0041 0.1939 \n", " 86 0.0003 0.0020 -0.0036 0.0041 0.1947 \n", " 87 -0.0000 0.0020 -0.0039 0.0039 0.1533 \n", " 88 0.0047 0.0031 -0.0013 0.0108 0.5390 \n", " 89 0.0003 0.0020 -0.0036 0.0042 0.1963 \n", " 90 0.0002 0.0020 -0.0037 0.0041 0.1859 \n", " 91 -0.0002 0.0021 -0.0042 0.0038 0.1397 \n", " 92 0.0000 0.0020 -0.0039 0.0039 0.1583 \n", " 93 0.0010 0.0020 -0.0029 0.0050 0.3741 \n", " 94 0.0006 0.0020 -0.0033 0.0046 0.2716 \n", " 95 0.0001 0.0020 -0.0038 0.0040 0.1739 \n", " 96 0.0001 0.0020 -0.0038 0.0040 0.1648 \n", " 97 0.0005 0.0020 -0.0034 0.0044 0.2456 \n", " 98 0.0003 0.0020 -0.0036 0.0042 0.2067 \n", " 99 0.0001 0.0020 -0.0038 0.0040 0.1690 \n", " 100 0.0003 0.0020 -0.0036 0.0042 0.2009 \n", " 101 0.0000 0.0020 -0.0039 0.0039 0.1570 \n", " 102 0.0013 0.0021 -0.0028 0.0055 0.4822 \n", " 103 0.0010 0.0020 -0.0030 0.0049 0.3601 \n", " 104 0.0002 0.0020 -0.0037 0.0041 0.1816 \n", " 105 0.0011 0.0020 -0.0028 0.0050 0.3919 \n", " 106 0.0000 0.0020 -0.0039 0.0039 0.1586 \n", " 107 0.0015 0.0020 -0.0025 0.0054 0.4994 \n", " 108 0.0011 0.0020 -0.0028 0.0050 0.3902 \n", " 109 0.0016 0.0021 -0.0025 0.0056 0.5401 \n", " 110 0.0015 0.0023 -0.0031 0.0060 0.5597 \n", " 111 0.0008 0.0020 -0.0031 0.0047 0.3053 \n", " 112 0.0012 0.0020 -0.0028 0.0051 0.4130 \n", " 113 0.0009 0.0020 -0.0030 0.0048 0.3362 \n", " 114 0.0000 0.0020 -0.0039 0.0039 0.1555 \n", " 115 0.0005 0.0020 -0.0035 0.0044 0.2338 \n", " 116 0.0001 0.0020 -0.0038 0.0039 0.1623 \n", " 117 0.0010 0.0020 -0.0029 0.0050 0.3668 \n", " 118 0.0029 0.0021 -0.0012 0.0069 0.9915 \n", " 119 0.0014 0.0023 -0.0031 0.0058 0.5201 \n", " 120 0.0004 0.0020 -0.0035 0.0043 0.2191 \n", " 121 0.0000 0.0020 -0.0039 0.0039 0.1618 \n", " 122 0.0010 0.0020 -0.0030 0.0049 0.3558 \n", " 123 0.0019 0.0021 -0.0023 0.0060 0.6433 \n", " 124 0.0001 0.0020 -0.0038 0.0040 0.1704 \n", " 125 0.0004 0.0020 -0.0035 0.0043 0.2155 \n", " 126 -0.0000 0.0020 -0.0039 0.0038 0.1470 \n", " 127 0.0005 0.0020 -0.0034 0.0044 0.2362 \n", " 128 0.0000 0.0020 -0.0039 0.0039 0.1592 \n", " 129 -0.0000 0.0020 -0.0039 0.0039 0.1528 \n", " 130 0.0002 0.0020 -0.0037 0.0041 0.1898 \n", " 131 0.0003 0.0020 -0.0035 0.0042 0.2115 \n", " 132 0.0003 0.0020 -0.0036 0.0042 0.2077 \n", " 133 0.0002 0.0020 -0.0037 0.0041 0.1915 \n", " 134 0.0004 0.0020 -0.0036 0.0043 0.2238 \n", " 135 0.0006 0.0020 -0.0033 0.0045 0.2677 \n", " 136 0.0014 0.0021 -0.0026 0.0054 0.4870 \n", " 137 0.0004 0.0020 -0.0035 0.0043 0.2228 \n", " 138 0.0000 0.0020 -0.0039 0.0039 0.1580 \n", " 139 0.0005 0.0020 -0.0034 0.0044 0.2421 \n", " 140 -0.0001 0.0020 -0.0039 0.0038 0.1463 \n", " 141 0.0002 0.0020 -0.0037 0.0041 0.1828 \n", " 142 0.0002 0.0020 -0.0037 0.0041 0.1811 \n", " 143 0.0000 0.0020 -0.0039 0.0039 0.1591 \n", " 144 0.0000 0.0020 -0.0039 0.0039 0.1574 \n", " 145 -0.0000 0.0020 -0.0039 0.0039 0.1516 \n", " 146 0.0000 0.0020 -0.0039 0.0039 0.1593 \n", " 147 0.0001 0.0020 -0.0038 0.0040 0.1640 \n", " 148 -0.0001 0.0020 -0.0039 0.0038 0.1461 \n", " 149 0.0001 0.0020 -0.0038 0.0040 0.1736 \n", " 150 0.0007 0.0020 -0.0032 0.0046 0.2744 \n", " 151 0.0009 0.0020 -0.0030 0.0048 0.3414 \n", " 152 0.0014 0.0020 -0.0025 0.0053 0.4720 \n", " 153 0.0000 0.0020 -0.0038 0.0039 0.1607 \n", " 154 0.0002 0.0020 -0.0037 0.0041 0.1824 \n", " 155 0.0000 0.0020 -0.0039 0.0039 0.1542 \n", " 156 0.0001 0.0020 -0.0037 0.0040 0.1766 \n", " 157 0.0006 0.0020 -0.0033 0.0045 0.2623 \n", " 158 0.0007 0.0020 -0.0032 0.0046 0.2778 \n", " 159 0.0002 0.0020 -0.0037 0.0041 0.1785 \n", " 160 -0.0001 0.0020 -0.0039 0.0038 0.1453 \n", " 161 0.0009 0.0020 -0.0030 0.0049 0.3427 \n", " 162 0.0001 0.0020 -0.0038 0.0039 0.1619 \n", " 163 0.0000 0.0020 -0.0039 0.0039 0.1546 \n", " 164 0.0001 0.0020 -0.0038 0.0040 0.1641 \n", " 165 0.0002 0.0020 -0.0037 0.0041 0.1915 \n", " 166 0.0027 0.0021 -0.0015 0.0068 0.9330 \n", " 167 -0.0000 0.0020 -0.0039 0.0039 0.1506 \n", " 168 -0.0000 0.0020 -0.0039 0.0039 0.1515 \n", " 169 0.0011 0.0020 -0.0028 0.0051 0.3925 \n", " 170 0.0006 0.0020 -0.0033 0.0046 0.2771 \n", " 171 0.0000 0.0020 -0.0039 0.0039 0.1539 \n", " 172 0.0003 0.0020 -0.0036 0.0042 0.2033 \n", " 173 0.0001 0.0020 -0.0038 0.0040 0.1687 \n", " 174 0.0003 0.0020 -0.0037 0.0042 0.1967 \n", " 175 0.0004 0.0020 -0.0035 0.0043 0.2205 \n", " 176 0.0008 0.0020 -0.0031 0.0047 0.3025 \n", " 177 0.0004 0.0020 -0.0035 0.0044 0.2401 \n", " 178 0.0001 0.0020 -0.0038 0.0040 0.1664 \n", " 179 -0.0000 0.0020 -0.0039 0.0039 0.1494 \n", " 180 0.0005 0.0020 -0.0034 0.0044 0.2485 \n", " 181 0.0005 0.0020 -0.0034 0.0044 0.2337 \n", " 182 0.0013 0.0021 -0.0027 0.0053 0.4608 \n", " 183 0.0009 0.0020 -0.0030 0.0049 0.3435 \n", " 184 -0.0000 0.0020 -0.0039 0.0039 0.1509 \n", " 185 0.0001 0.0020 -0.0038 0.0040 0.1647 \n", " 186 0.0018 0.0021 -0.0023 0.0058 0.6115 \n", " 187 0.0001 0.0020 -0.0038 0.0040 0.1708 \n", " 188 -0.0000 0.0020 -0.0039 0.0039 0.1518 \n", " 189 0.0001 0.0020 -0.0038 0.0040 0.1704 \n", " 190 0.0001 0.0020 -0.0038 0.0039 0.1622 \n", " 191 0.0013 0.0020 -0.0027 0.0053 0.4524 \n", " 192 0.0011 0.0020 -0.0028 0.0050 0.3766 \n", " 193 0.0006 0.0020 -0.0033 0.0045 0.2674 \n", " 194 0.0001 0.0020 -0.0038 0.0040 0.1746 \n", " 195 0.0024 0.0021 -0.0016 0.0064 0.8268 \n", " 196 0.0005 0.0020 -0.0034 0.0044 0.2383 \n", " 197 0.0010 0.0020 -0.0030 0.0049 0.3617 \n", " 198 0.0022 0.0021 -0.0019 0.0063 0.7683 \n", " 199 0.0003 0.0020 -0.0036 0.0042 0.2060 \n", " 200 -0.0000 0.0020 -0.0039 0.0039 0.1508 \n", " 201 0.0001 0.0020 -0.0038 0.0039 0.1619 \n", " 202 0.0001 0.0020 -0.0038 0.0040 0.1726 \n", " 203 -0.0000 0.0020 -0.0039 0.0039 0.1515 \n", " 204 0.0000 0.0020 -0.0038 0.0039 0.1608 \n", " 205 0.0002 0.0020 -0.0037 0.0041 0.1877 \n", " 206 0.0001 0.0020 -0.0038 0.0039 0.1617 \n", " 207 0.0001 0.0020 -0.0038 0.0040 0.1695 \n", " 208 0.0002 0.0020 -0.0037 0.0041 0.1838 \n", " 209 0.0005 0.0020 -0.0034 0.0044 0.2348 \n", " 210 0.0001 0.0020 -0.0038 0.0040 0.1659 \n", " 211 -0.0000 0.0020 -0.0039 0.0038 0.1478 \n", " 212 0.0003 0.0020 -0.0036 0.0043 0.2169 \n", " 213 0.0000 0.0020 -0.0039 0.0039 0.1552 \n", " 214 0.0002 0.0020 -0.0037 0.0041 0.1853 \n", " 215 0.0001 0.0020 -0.0038 0.0039 0.1613 \n", " 216 0.0003 0.0020 -0.0036 0.0042 0.1981 \n", " 217 0.0002 0.0020 -0.0037 0.0040 0.1779 \n", " 218 0.0003 0.0020 -0.0036 0.0042 0.2061 \n", " 219 0.0001 0.0020 -0.0038 0.0039 0.1627 \n", " 220 0.0005 0.0020 -0.0034 0.0045 0.2525 \n", " 221 0.0001 0.0020 -0.0038 0.0040 0.1681 \n", " 222 0.0002 0.0020 -0.0037 0.0041 0.1843 \n", " 223 0.0000 0.0020 -0.0038 0.0039 0.1609 \n", " 224 0.0002 0.0020 -0.0037 0.0041 0.1884 \n", " 225 0.0000 0.0020 -0.0039 0.0039 0.1580 \n", " 226 0.0036 0.0020 -0.0004 0.0076 0.7174 \n", " 227 0.0001 0.0020 -0.0038 0.0040 0.1698 \n", " 228 0.0004 0.0020 -0.0035 0.0042 0.2119 \n", " 229 0.0003 0.0020 -0.0036 0.0042 0.1955 \n", " 230 0.0000 0.0020 -0.0039 0.0039 0.1592 \n", " 231 -0.0000 0.0020 -0.0039 0.0039 0.1513 \n", " 232 0.0001 0.0020 -0.0038 0.0040 0.1703 \n", " 233 0.0011 0.0020 -0.0029 0.0050 0.3782 \n", " 234 -0.0000 0.0020 -0.0039 0.0039 0.1486 \n", " 235 0.0000 0.0020 -0.0039 0.0039 0.1586 \n", " 236 0.0001 0.0020 -0.0038 0.0039 0.1613 \n", " 237 0.0002 0.0020 -0.0037 0.0041 0.1794 \n", " 238 0.0005 0.0020 -0.0035 0.0044 0.2346 \n", " 239 0.0003 0.0020 -0.0036 0.0042 0.2010 \n", " 240 0.0023 0.0020 -0.0017 0.0063 0.7878 \n", " 241 0.0012 0.0020 -0.0027 0.0052 0.4302 \n", " 242 0.0000 0.0020 -0.0039 0.0039 0.1591 \n", " 243 0.0024 0.0020 -0.0016 0.0064 0.8403 \n", " 244 0.0001 0.0020 -0.0038 0.0040 0.1627 \n", " 245 0.0003 0.0020 -0.0036 0.0042 0.1998 \n", " 246 0.0012 0.0021 -0.0028 0.0052 0.4327 \n", " 247 0.0004 0.0020 -0.0034 0.0043 0.2300 \n", " 248 0.0003 0.0020 -0.0036 0.0042 0.2071 \n", " 249 0.0000 0.0020 -0.0039 0.0039 0.1572 \n", " 250 0.0001 0.0020 -0.0038 0.0040 0.1686 \n", " 251 0.0008 0.0020 -0.0032 0.0048 0.3103 \n", " 252 0.0004 0.0020 -0.0035 0.0043 0.2265 \n", " 253 0.0001 0.0020 -0.0038 0.0040 0.1763 \n", " 254 0.0014 0.0020 -0.0026 0.0053 0.4660 \n", " 255 0.0004 0.0020 -0.0035 0.0043 0.2177 \n", " 256 0.0005 0.0020 -0.0034 0.0044 0.2429 \n", " 257 0.0000 0.0020 -0.0039 0.0039 0.1570 \n", " 258 0.0002 0.0020 -0.0037 0.0041 0.1881 \n", " 259 0.0001 0.0020 -0.0038 0.0040 0.1741 \n", " 260 0.0000 0.0020 -0.0039 0.0039 0.1593 \n", " 261 0.0018 0.0021 -0.0022 0.0059 0.6275 \n", " 262 0.0006 0.0020 -0.0033 0.0045 0.2690 \n", " 263 0.0004 0.0020 -0.0035 0.0043 0.2254 \n", " 264 0.0000 0.0020 -0.0038 0.0039 0.1597 \n", " 265 0.0000 0.0020 -0.0039 0.0039 0.1583 \n", " 266 0.0015 0.0020 -0.0024 0.0054 0.5050 \n", " 267 0.0001 0.0020 -0.0038 0.0040 0.1725 \n", " 268 0.0002 0.0020 -0.0037 0.0041 0.1831 \n", " 269 0.0003 0.0020 -0.0036 0.0042 0.2052 \n", " 270 0.0002 0.0020 -0.0037 0.0041 0.1818 \n", " 271 0.0019 0.0020 -0.0021 0.0059 0.6488 \n", " 272 0.0005 0.0020 -0.0034 0.0044 0.2416 \n", " 273 0.0000 0.0020 -0.0039 0.0039 0.1588 \n", " 274 0.0000 0.0020 -0.0038 0.0039 0.1596 \n", " 275 0.0003 0.0020 -0.0036 0.0042 0.2025 \n", " 276 0.0009 0.0020 -0.0030 0.0048 0.3402 \n", " 277 0.0009 0.0020 -0.0030 0.0049 0.3450 \n", " 278 0.0009 0.0020 -0.0030 0.0048 0.3239 \n", " 279 0.0006 0.0020 -0.0033 0.0045 0.2618 \n", " 280 0.0004 0.0020 -0.0035 0.0043 0.2285 \n", " 281 0.0017 0.0020 -0.0022 0.0057 0.5826 \n", " 282 -0.0000 0.0020 -0.0039 0.0039 0.1493 \n", " 283 0.0018 0.0020 -0.0021 0.0057 0.6064 \n", " 284 0.0001 0.0020 -0.0038 0.0040 0.1652 \n", " 285 0.0005 0.0020 -0.0034 0.0044 0.2457 \n", " 286 0.0000 0.0020 -0.0039 0.0039 0.1580 \n", " 287 0.0048 0.0026 -0.0003 0.0099 0.4579 \n", " 288 0.0003 0.0020 -0.0036 0.0042 0.2096 \n", " 289 0.0005 0.0020 -0.0034 0.0043 0.2316 \n", " 290 0.0001 0.0020 -0.0038 0.0040 0.1732 \n", " 291 0.0012 0.0020 -0.0027 0.0051 0.4171 \n", " 292 0.0002 0.0020 -0.0037 0.0041 0.1915 \n", " 293 0.0007 0.0021 -0.0033 0.0048 0.3156 \n", " 294 0.0010 0.0020 -0.0029 0.0050 0.3694 \n", " 295 0.0010 0.0020 -0.0029 0.0048 0.3445 \n", " 296 0.0000 0.0020 -0.0039 0.0039 0.1582 \n", " 297 0.0001 0.0020 -0.0038 0.0039 0.1621 \n", " 298 0.0001 0.0020 -0.0038 0.0040 0.1711 \n", " 299 0.0017 0.0020 -0.0023 0.0056 0.5726 \n", " 300 0.0009 0.0020 -0.0031 0.0048 0.3247 \n", " 301 0.0017 0.0022 -0.0026 0.0060 0.6056 \n", " 302 0.0000 0.0020 -0.0038 0.0039 0.1596 \n", " 303 0.0013 0.0020 -0.0027 0.0052 0.4392 \n", " 304 0.0001 0.0020 -0.0037 0.0040 0.1756 \n", " 305 0.0001 0.0020 -0.0038 0.0040 0.1739 \n", " 306 0.0005 0.0020 -0.0034 0.0045 0.2543 \n", " 307 0.0011 0.0022 -0.0031 0.0054 0.4303 \n", " 308 0.0002 0.0020 -0.0037 0.0040 0.1778 \n", " 309 0.0005 0.0020 -0.0034 0.0044 0.2497 \n", " 310 0.0001 0.0020 -0.0038 0.0040 0.1683 \n", " 311 0.0008 0.0020 -0.0031 0.0048 0.3165 \n", " 312 0.0001 0.0020 -0.0038 0.0040 0.1713 \n", " 313 0.0019 0.0020 -0.0020 0.0058 0.6434 \n", " 314 0.0006 0.0020 -0.0033 0.0045 0.2608 \n", " 315 0.0012 0.0020 -0.0028 0.0051 0.4058 \n", " 316 0.0002 0.0020 -0.0037 0.0040 0.1774 \n", " 317 0.0028 0.0020 -0.0012 0.0067 0.9699 \n", " 318 0.0016 0.0020 -0.0023 0.0056 0.5481 \n", " 319 -0.0000 0.0020 -0.0039 0.0038 0.1476 \n", " 320 0.0004 0.0020 -0.0035 0.0042 0.2120 \n", " 321 0.0017 0.0020 -0.0022 0.0057 0.5928 \n", " 322 0.0002 0.0020 -0.0037 0.0041 0.1831 \n", " 323 0.0000 0.0020 -0.0039 0.0039 0.1585 \n", " 324 0.0001 0.0020 -0.0038 0.0040 0.1663 \n", " 325 0.0005 0.0020 -0.0035 0.0045 0.2513 \n", " 326 0.0021 0.0020 -0.0019 0.0060 0.7025 \n", " 327 0.0001 0.0020 -0.0038 0.0040 0.1669 \n", " 328 0.0001 0.0020 -0.0038 0.0040 0.1643 \n", " 329 0.0036 0.0021 -0.0005 0.0077 0.7033 \n", " 330 0.0032 0.0020 -0.0008 0.0072 0.8594 \n", " 331 -0.0000 0.0020 -0.0039 0.0039 0.1522 \n", " 332 0.0003 0.0020 -0.0036 0.0042 0.2121 \n", " 333 0.0101 0.0023 0.0055 0.0147 0.0019 ***\n", " 334 0.0001 0.0020 -0.0038 0.0040 0.1672 \n", " 335 0.0010 0.0020 -0.0030 0.0049 0.3562 \n", " 336 0.0001 0.0020 -0.0038 0.0039 0.1626 \n", " 337 0.0009 0.0020 -0.0030 0.0049 0.3468 \n", " 338 0.0018 0.0021 -0.0023 0.0058 0.6092 \n", " 339 0.0003 0.0020 -0.0036 0.0043 0.2119 \n", " 340 0.0009 0.0020 -0.0031 0.0049 0.3430 \n", " 341 0.0017 0.0020 -0.0022 0.0057 0.5837 \n", " 342 0.0001 0.0020 -0.0038 0.0039 0.1622 \n", " 343 0.0025 0.0020 -0.0015 0.0064 0.8517 \n", " 344 0.0004 0.0020 -0.0035 0.0043 0.2282 \n", " 345 0.0014 0.0020 -0.0026 0.0054 0.4849 \n", " 346 0.0011 0.0021 -0.0029 0.0052 0.4124 \n", " 347 0.0012 0.0020 -0.0027 0.0052 0.4269 \n", " 348 0.0001 0.0020 -0.0038 0.0040 0.1688 \n", " 349 0.0003 0.0020 -0.0036 0.0042 0.1982 \n", " 350 0.0040 0.0020 0.0000 0.0080 0.5637 \n", " 351 0.0000 0.0020 -0.0039 0.0039 0.1546 \n", " 352 0.0002 0.0020 -0.0037 0.0041 0.1814 \n", " 353 0.0012 0.0020 -0.0028 0.0052 0.4243 \n", " 354 0.0014 0.0020 -0.0025 0.0054 0.4913 \n", " 355 0.0005 0.0020 -0.0034 0.0044 0.2337 \n", " 356 0.0001 0.0020 -0.0038 0.0040 0.1726 \n", " 357 0.0010 0.0020 -0.0030 0.0050 0.3782 \n", " 358 0.0004 0.0020 -0.0035 0.0043 0.2248 \n", " 359 0.0002 0.0020 -0.0037 0.0041 0.1923 \n", " 360 0.0002 0.0020 -0.0037 0.0041 0.1903 \n", " 361 0.0006 0.0020 -0.0033 0.0045 0.2684 \n", " 362 0.0020 0.0022 -0.0023 0.0063 0.7010 \n", " 363 0.0002 0.0020 -0.0036 0.0041 0.1921 \n", " 364 0.0003 0.0020 -0.0036 0.0042 0.2039 \n", " 365 0.0025 0.0020 -0.0015 0.0065 0.8669 \n", " 366 0.0072 0.0023 0.0027 0.0117 0.0551 *\n", " 367 0.0007 0.0020 -0.0032 0.0046 0.2741 \n", " 368 0.0008 0.0020 -0.0031 0.0047 0.3037 \n", " 369 0.0006 0.0020 -0.0033 0.0045 0.2567 \n", " 370 0.0002 0.0020 -0.0037 0.0041 0.1894 \n", " 371 0.0002 0.0020 -0.0037 0.0041 0.1867 \n", " 372 0.0011 0.0020 -0.0028 0.0051 0.4013 \n", " 373 0.0001 0.0020 -0.0038 0.0040 0.1667 \n", " 374 0.0015 0.0020 -0.0025 0.0054 0.4922 \n", " 375 0.0015 0.0021 -0.0025 0.0055 0.5115 \n", " 376 0.0011 0.0020 -0.0028 0.0050 0.3778 \n", " 377 0.0009 0.0020 -0.0031 0.0048 0.3345 \n", " 378 0.0001 0.0020 -0.0038 0.0040 0.1717 \n", " 379 0.0010 0.0021 -0.0032 0.0053 0.4057 \n", " 380 0.0005 0.0020 -0.0034 0.0044 0.2500 \n", " 381 0.0001 0.0020 -0.0038 0.0039 0.1611 \n", " 382 0.0005 0.0020 -0.0034 0.0045 0.2517 \n", " 383 0.0005 0.0020 -0.0034 0.0045 0.2523 \n", " 384 -0.0000 0.0020 -0.0039 0.0039 0.1504 \n", " 385 0.0000 0.0020 -0.0039 0.0039 0.1593 \n", " 386 0.0031 0.0020 -0.0009 0.0071 0.8906 \n", " 387 0.0004 0.0020 -0.0035 0.0043 0.2251 \n", " 388 0.0000 0.0020 -0.0039 0.0039 0.1578 \n", " 389 0.0003 0.0020 -0.0036 0.0042 0.1958 \n", " 390 -0.0000 0.0020 -0.0039 0.0039 0.1531 \n", " 391 0.0019 0.0020 -0.0021 0.0058 0.6260 \n", " 392 0.0011 0.0020 -0.0029 0.0050 0.3734 \n", " 393 0.0011 0.0021 -0.0029 0.0051 0.4040 \n", " 394 0.0001 0.0020 -0.0037 0.0040 0.1760 \n", " 395 0.0001 0.0020 -0.0038 0.0040 0.1737 \n", " 396 0.0007 0.0020 -0.0032 0.0047 0.2997 \n", " 397 0.0002 0.0020 -0.0037 0.0041 0.1902 \n", " 398 0.0002 0.0020 -0.0037 0.0041 0.1911 \n", " 399 0.0001 0.0020 -0.0038 0.0040 0.1711 \n", " 400 0.0000 0.0020 -0.0039 0.0039 0.1545 \n", " 401 0.0005 0.0020 -0.0034 0.0044 0.2479 \n", " 402 0.0000 0.0020 -0.0039 0.0039 0.1553 \n", " 403 -0.0000 0.0020 -0.0039 0.0039 0.1504 \n", " 404 0.0011 0.0020 -0.0028 0.0050 0.3876 \n", " 405 0.0001 0.0020 -0.0037 0.0040 0.1752 \n", " 406 0.0006 0.0020 -0.0034 0.0045 0.2619 \n", " 407 0.0004 0.0020 -0.0035 0.0043 0.2266 \n", " 408 -0.0000 0.0020 -0.0039 0.0039 0.1502 \n", " 409 0.0012 0.0020 -0.0027 0.0052 0.4308 \n", " 410 0.0004 0.0020 -0.0035 0.0044 0.2312 \n", " 411 0.0001 0.0020 -0.0038 0.0040 0.1741 \n", " 412 0.0001 0.0020 -0.0038 0.0040 0.1725 \n", " 413 0.0003 0.0020 -0.0036 0.0042 0.1960 \n", " 414 -0.0000 0.0020 -0.0039 0.0039 0.1494 \n", " 415 0.0004 0.0020 -0.0035 0.0044 0.2335 \n", " 416 0.0005 0.0020 -0.0034 0.0044 0.2343 \n", " 417 -0.0001 0.0020 -0.0040 0.0038 0.1440 \n", " 418 0.0004 0.0020 -0.0035 0.0043 0.2276 \n", " 419 0.0007 0.0020 -0.0032 0.0046 0.2802 \n", " 420 -0.0000 0.0020 -0.0039 0.0039 0.1534 \n", " 421 0.0000 0.0020 -0.0039 0.0039 0.1539 \n", " 422 0.0005 0.0020 -0.0034 0.0044 0.2495 \n", " 423 0.0003 0.0020 -0.0035 0.0042 0.2105 \n", " 424 0.0016 0.0020 -0.0023 0.0056 0.5452 \n", " 425 0.0006 0.0020 -0.0033 0.0045 0.2672 \n", " 426 0.0006 0.0020 -0.0033 0.0045 0.2624 \n", " 427 0.0002 0.0020 -0.0037 0.0041 0.1798 \n", " 428 0.0005 0.0020 -0.0034 0.0045 0.2585 \n", " 429 0.0001 0.0020 -0.0038 0.0040 0.1656 \n", " 430 0.0001 0.0020 -0.0038 0.0040 0.1676 \n", " 431 0.0002 0.0020 -0.0037 0.0041 0.1827 \n", " 432 0.0001 0.0020 -0.0038 0.0040 0.1704 \n", " 433 0.0001 0.0020 -0.0038 0.0040 0.1683 \n", " 434 0.0003 0.0020 -0.0036 0.0042 0.2006 \n", " 435 0.0000 0.0020 -0.0038 0.0039 0.1596 \n", " 436 0.0001 0.0020 -0.0038 0.0040 0.1733 \n", " 437 0.0028 0.0020 -0.0012 0.0068 0.9986 \n", " 438 0.0001 0.0020 -0.0037 0.0040 0.1764 \n", " 439 -0.0000 0.0020 -0.0039 0.0039 0.1503 \n", " 440 0.0025 0.0021 -0.0015 0.0065 0.8670 \n", " 441 0.0008 0.0020 -0.0031 0.0047 0.3141 \n", " 442 0.0001 0.0020 -0.0038 0.0040 0.1644 \n", " 443 0.0002 0.0020 -0.0036 0.0041 0.1925 \n", " 444 -0.0000 0.0020 -0.0039 0.0039 0.1502 \n", " 445 0.0002 0.0020 -0.0037 0.0041 0.1891 \n", " 446 -0.0001 0.0020 -0.0039 0.0038 0.1456 \n", " 447 0.0009 0.0020 -0.0031 0.0049 0.3463 \n", " 448 0.0000 0.0020 -0.0039 0.0039 0.1574 \n", " 449 0.0001 0.0020 -0.0038 0.0040 0.1661 \n", " 450 -0.0000 0.0020 -0.0039 0.0039 0.1515 \n", " 451 0.0006 0.0020 -0.0034 0.0045 0.2570 \n", " 452 0.0012 0.0020 -0.0027 0.0051 0.4217 \n", " 453 0.0004 0.0020 -0.0035 0.0043 0.2177 \n", " 454 0.0022 0.0020 -0.0017 0.0062 0.7691 \n", " 455 0.0001 0.0020 -0.0038 0.0040 0.1666 \n", " 456 0.0006 0.0020 -0.0033 0.0045 0.2670 \n", " 457 0.0005 0.0020 -0.0035 0.0044 0.2369 \n", " 458 0.0001 0.0020 -0.0038 0.0040 0.1655 \n", " 459 0.0004 0.0020 -0.0035 0.0043 0.2132 \n", " 460 0.0004 0.0020 -0.0035 0.0043 0.2248 \n", " 461 0.0001 0.0020 -0.0038 0.0040 0.1670 \n", " 462 0.0001 0.0020 -0.0038 0.0040 0.1733 \n", " 463 0.0056 0.0026 0.0006 0.0106 0.2846 \n", " 464 0.0008 0.0020 -0.0031 0.0047 0.3061 \n", " 465 0.0003 0.0020 -0.0036 0.0042 0.2012 \n", " 466 0.0005 0.0020 -0.0034 0.0043 0.2318 \n", " 467 0.0020 0.0021 -0.0020 0.0061 0.7040 \n", " 468 0.0003 0.0020 -0.0036 0.0041 0.1943 \n", " 469 0.0006 0.0020 -0.0033 0.0045 0.2556 \n", " 470 0.0003 0.0020 -0.0036 0.0042 0.2070 \n", " 471 0.0009 0.0021 -0.0032 0.0050 0.3591 \n", " 472 0.0018 0.0020 -0.0022 0.0058 0.6113 \n", " 473 0.0045 0.0021 0.0004 0.0086 0.4313 \n", " 474 0.0004 0.0020 -0.0035 0.0043 0.2161 \n", " 475 0.0006 0.0020 -0.0033 0.0045 0.2629 \n", " 476 -0.0000 0.0020 -0.0039 0.0039 0.1504 \n", " 477 0.0000 0.0020 -0.0039 0.0039 0.1539 \n", " 478 0.0003 0.0020 -0.0036 0.0042 0.1975 \n", " 479 0.0006 0.0020 -0.0033 0.0045 0.2697 \n", " 480 0.0001 0.0020 -0.0038 0.0040 0.1664 \n", " 481 0.0000 0.0020 -0.0039 0.0039 0.1546 \n", " 482 0.0002 0.0020 -0.0036 0.0041 0.1941 \n", " 483 0.0002 0.0020 -0.0036 0.0041 0.1933 \n", " 484 0.0000 0.0020 -0.0039 0.0039 0.1555 \n", " 485 0.0004 0.0020 -0.0035 0.0043 0.2250 \n", " 486 0.0009 0.0022 -0.0034 0.0051 0.3604 \n", " 487 0.0009 0.0020 -0.0030 0.0049 0.3427 \n", " 488 0.0021 0.0020 -0.0018 0.0061 0.7252 \n", " 489 0.0006 0.0020 -0.0034 0.0047 0.2848 \n", " 490 0.0003 0.0020 -0.0036 0.0042 0.1986 \n", " 491 0.0008 0.0021 -0.0032 0.0049 0.3338 \n", " 492 0.0003 0.0020 -0.0036 0.0042 0.2043 \n", " 493 0.0001 0.0020 -0.0038 0.0040 0.1691 \n", " 494 0.0056 0.0021 0.0015 0.0096 0.1849 \n", " 495 0.0004 0.0020 -0.0035 0.0043 0.2272 \n", " 496 0.0002 0.0020 -0.0037 0.0041 0.1829 \n", " 497 0.0010 0.0020 -0.0030 0.0050 0.3725 \n", " 498 0.0009 0.0021 -0.0033 0.0051 0.3651 \n", " 499 0.0002 0.0020 -0.0037 0.0041 0.1844 \n", " 500 0.0002 0.0020 -0.0037 0.0041 0.1836 \n", " 501 0.0006 0.0020 -0.0033 0.0045 0.2660 \n", " 502 0.0000 0.0020 -0.0039 0.0039 0.1556 \n", " 503 0.0001 0.0020 -0.0038 0.0040 0.1704 \n", " 504 0.0001 0.0020 -0.0038 0.0040 0.1737 \n", " 505 0.0005 0.0020 -0.0034 0.0044 0.2373 \n", " 506 0.0010 0.0020 -0.0029 0.0049 0.3650 \n", " 507 0.0001 0.0020 -0.0038 0.0040 0.1732 \n", " 508 0.0007 0.0021 -0.0033 0.0047 0.2941 \n", " 509 0.0004 0.0020 -0.0035 0.0043 0.2259 \n", " 510 0.0006 0.0020 -0.0033 0.0045 0.2550 \n", " 511 -0.0000 0.0020 -0.0039 0.0039 0.1528 \n", " 512 0.0001 0.0020 -0.0038 0.0040 0.1647 \n", " 513 0.0007 0.0020 -0.0032 0.0046 0.2812 \n", " 514 0.0100 0.0025 0.0052 0.0149 0.0036 ***\n", " 515 0.0001 0.0020 -0.0038 0.0040 0.1628 \n", " 516 0.0001 0.0020 -0.0038 0.0040 0.1659 \n", " 517 0.0026 0.0020 -0.0014 0.0066 0.9094 \n", " 518 0.0001 0.0020 -0.0038 0.0040 0.1691 \n", " 519 0.0015 0.0020 -0.0025 0.0055 0.5206 \n", " 520 0.0001 0.0020 -0.0038 0.0040 0.1696 \n", " 521 0.0000 0.0020 -0.0039 0.0039 0.1577 \n", " 522 0.0008 0.0020 -0.0031 0.0047 0.3000 \n", " 523 0.0002 0.0020 -0.0037 0.0041 0.1816 \n", " 524 0.0008 0.0020 -0.0031 0.0047 0.3099 \n", " 525 0.0007 0.0020 -0.0032 0.0046 0.2887 \n", " 526 0.0009 0.0020 -0.0030 0.0048 0.3397 \n", " 527 0.0008 0.0020 -0.0031 0.0047 0.3034 \n", " 528 0.0000 0.0020 -0.0038 0.0039 0.1607 \n", " 529 0.0000 0.0020 -0.0038 0.0039 0.1600 \n", " 530 0.0002 0.0020 -0.0037 0.0041 0.1823 \n", " 531 0.0009 0.0020 -0.0030 0.0048 0.3318 \n", " 532 0.0003 0.0020 -0.0036 0.0042 0.2030 \n", " 533 0.0005 0.0020 -0.0034 0.0044 0.2324 \n", " 534 0.0002 0.0020 -0.0037 0.0041 0.1832 \n", " 535 0.0003 0.0020 -0.0036 0.0042 0.2081 \n", " 536 0.0009 0.0020 -0.0031 0.0048 0.3322 \n", " 537 0.0001 0.0020 -0.0038 0.0040 0.1705 \n", " 538 0.0004 0.0020 -0.0035 0.0043 0.2172 \n", " 539 0.0008 0.0020 -0.0032 0.0048 0.3275 \n", " 540 0.0006 0.0020 -0.0033 0.0045 0.2625 \n", " 541 -0.0000 0.0020 -0.0039 0.0039 0.1526 \n", " 542 0.0002 0.0020 -0.0037 0.0041 0.1828 \n", " 543 0.0008 0.0020 -0.0031 0.0047 0.3054 \n", " 544 0.0006 0.0020 -0.0033 0.0045 0.2675 \n", " 545 0.0000 0.0020 -0.0039 0.0039 0.1547 \n", " 546 0.0003 0.0020 -0.0036 0.0042 0.1990 \n", " 547 0.0003 0.0020 -0.0036 0.0042 0.2049 \n", " 548 0.0043 0.0021 0.0002 0.0084 0.4929 \n", " 549 0.0005 0.0020 -0.0034 0.0045 0.2530 \n", " 550 0.0039 0.0021 -0.0002 0.0079 0.6210 \n", " 551 0.0022 0.0022 -0.0021 0.0065 0.7589 \n", " 552 0.0006 0.0020 -0.0033 0.0046 0.2780 \n", " 553 0.0001 0.0020 -0.0038 0.0039 0.1627 \n", " 554 0.0000 0.0020 -0.0039 0.0039 0.1559 \n", " 555 0.0009 0.0020 -0.0030 0.0049 0.3446 \n", " 556 0.0003 0.0020 -0.0036 0.0042 0.1985 \n", " 557 -0.0000 0.0020 -0.0039 0.0039 0.1487 \n", " 558 0.0014 0.0020 -0.0025 0.0053 0.4783 \n", " 559 0.0001 0.0020 -0.0038 0.0040 0.1699 \n", " 560 0.0001 0.0020 -0.0038 0.0040 0.1665 \n", " 561 0.0039 0.0021 -0.0003 0.0080 0.6268 \n", " 562 0.0020 0.0020 -0.0020 0.0060 0.6882 \n", " 563 0.0005 0.0020 -0.0033 0.0044 0.2503 \n", " 564 0.0010 0.0021 -0.0031 0.0051 0.3807 \n", " 565 0.0071 0.0022 0.0028 0.0113 0.0503 *\n", " 566 0.0001 0.0020 -0.0038 0.0040 0.1726 \n", " 567 0.0000 0.0020 -0.0038 0.0039 0.1606 \n", " 568 0.0001 0.0020 -0.0038 0.0040 0.1750 \n", " 569 0.0002 0.0020 -0.0037 0.0041 0.1880 \n", " 570 0.0025 0.0022 -0.0018 0.0068 0.8767 \n", " 571 0.0003 0.0020 -0.0036 0.0042 0.2059 \n", " 572 0.0000 0.0020 -0.0039 0.0039 0.1570 \n", " 573 0.0001 0.0020 -0.0038 0.0039 0.1623 \n", " 574 0.0006 0.0020 -0.0034 0.0046 0.2727 \n", " 575 0.0000 0.0020 -0.0039 0.0039 0.1538 \n", " 576 0.0006 0.0020 -0.0033 0.0045 0.2688 \n", " 577 0.0003 0.0020 -0.0036 0.0042 0.1995 \n", " 578 0.0002 0.0020 -0.0037 0.0041 0.1912 \n", " 579 0.0005 0.0020 -0.0035 0.0044 0.2381 \n", " 580 0.0007 0.0020 -0.0032 0.0046 0.2879 \n", " 581 0.0024 0.0022 -0.0019 0.0067 0.8472 \n", " 582 0.0016 0.0020 -0.0024 0.0056 0.5524 \n", " 583 0.0002 0.0020 -0.0037 0.0041 0.1867 \n", " 584 0.0000 0.0020 -0.0039 0.0039 0.1576 \n", " 585 0.0015 0.0022 -0.0028 0.0057 0.5338 \n", " 586 0.0008 0.0021 -0.0033 0.0048 0.3180 \n", " 587 0.0001 0.0020 -0.0037 0.0040 0.1761 \n", " 588 0.0006 0.0020 -0.0033 0.0045 0.2606 \n", " 589 0.0001 0.0020 -0.0038 0.0040 0.1691 \n", " 590 0.0012 0.0020 -0.0027 0.0051 0.4156 \n", " 591 0.0001 0.0020 -0.0038 0.0040 0.1652 \n", " 592 0.0003 0.0020 -0.0036 0.0041 0.1950 \n", " 593 -0.0001 0.0020 -0.0039 0.0038 0.1463 \n", " 594 0.0003 0.0020 -0.0036 0.0042 0.2039 \n", " 595 0.0008 0.0020 -0.0031 0.0048 0.3283 \n", " 596 0.0001 0.0020 -0.0038 0.0040 0.1710 \n", " 597 -0.0000 0.0020 -0.0039 0.0039 0.1506 \n", " 598 0.0002 0.0020 -0.0037 0.0041 0.1863 \n", " 599 0.0001 0.0020 -0.0038 0.0040 0.1722 \n", " 600 0.0004 0.0020 -0.0035 0.0043 0.2194 \n", " 601 0.0001 0.0020 -0.0038 0.0040 0.1683 \n", " 602 0.0000 0.0020 -0.0039 0.0039 0.1584 \n", " 603 0.0005 0.0020 -0.0034 0.0044 0.2464 \n", " 604 0.0008 0.0021 -0.0033 0.0049 0.3334 \n", " 605 0.0006 0.0020 -0.0033 0.0045 0.2620 \n", " 606 0.0001 0.0020 -0.0038 0.0040 0.1653 \n", " 607 0.0001 0.0020 -0.0038 0.0039 0.1615 \n", " 608 0.0004 0.0020 -0.0035 0.0043 0.2161 \n", " 609 0.0000 0.0020 -0.0039 0.0039 0.1568 \n", " 610 -0.0000 0.0020 -0.0039 0.0039 0.1529 \n", " 611 0.0003 0.0020 -0.0036 0.0042 0.1976 \n", " 612 0.0002 0.0020 -0.0037 0.0040 0.1773 \n", " 613 0.0001 0.0020 -0.0038 0.0040 0.1703 \n", " 614 0.0032 0.0026 -0.0019 0.0084 0.8811 \n", " 615 0.0006 0.0020 -0.0033 0.0045 0.2593 \n", " 616 0.0005 0.0020 -0.0033 0.0044 0.2506 \n", " 617 0.0001 0.0020 -0.0038 0.0040 0.1681 \n", " 618 0.0005 0.0020 -0.0034 0.0044 0.2397 \n", " 619 -0.0001 0.0020 -0.0040 0.0038 0.1449 \n", " 620 0.0011 0.0020 -0.0029 0.0050 0.3819 \n", " 621 0.0001 0.0020 -0.0038 0.0040 0.1710 \n", " 622 0.0003 0.0020 -0.0036 0.0042 0.2001 \n", " 623 -0.0000 0.0020 -0.0039 0.0039 0.1511 \n", " 624 0.0007 0.0020 -0.0033 0.0047 0.2911 \n", " 625 0.0000 0.0020 -0.0039 0.0039 0.1562 \n", " 626 0.0007 0.0020 -0.0032 0.0046 0.2841 \n", " 627 0.0002 0.0020 -0.0037 0.0041 0.1884 \n", " 628 0.0003 0.0020 -0.0036 0.0042 0.2050 \n", " 629 0.0001 0.0020 -0.0037 0.0040 0.1766 \n", " 630 0.0050 0.0021 0.0009 0.0090 0.2995 \n", " 631 0.0004 0.0020 -0.0035 0.0043 0.2219 \n", " 632 0.0001 0.0020 -0.0038 0.0040 0.1691 \n", " 633 0.0003 0.0020 -0.0036 0.0042 0.1992 \n", " 634 0.0022 0.0020 -0.0018 0.0062 0.7538 \n", " 635 0.0000 0.0020 -0.0039 0.0039 0.1559 \n", " 636 0.0002 0.0020 -0.0037 0.0041 0.1840 \n", " 637 0.0004 0.0020 -0.0035 0.0043 0.2245 \n", " 638 0.0002 0.0020 -0.0037 0.0041 0.1783 \n", " 639 0.0004 0.0020 -0.0035 0.0043 0.2296 \n", " 640 0.0002 0.0020 -0.0037 0.0041 0.1857 \n", " 641 0.0000 0.0020 -0.0039 0.0039 0.1576 \n", " 642 0.0004 0.0020 -0.0035 0.0043 0.2299 \n", " 643 0.0001 0.0020 -0.0038 0.0040 0.1670 \n", " 644 0.0003 0.0020 -0.0036 0.0042 0.1961 \n", " 645 0.0000 0.0020 -0.0039 0.0039 0.1585 \n", " 646 0.0002 0.0020 -0.0037 0.0041 0.1791 \n", " 647 0.0010 0.0020 -0.0029 0.0049 0.3619 \n", " 648 0.0020 0.0020 -0.0020 0.0060 0.6903 \n", " 649 0.0001 0.0020 -0.0038 0.0040 0.1654 \n", " 650 0.0000 0.0020 -0.0039 0.0039 0.1588 \n", " 651 0.0005 0.0020 -0.0034 0.0043 0.2314 \n", " 652 0.0000 0.0020 -0.0039 0.0039 0.1555 \n", " 653 0.0003 0.0020 -0.0036 0.0042 0.2005 \n", " 654 0.0000 0.0020 -0.0038 0.0039 0.1603 \n", " 655 0.0008 0.0020 -0.0031 0.0047 0.3026 \n", " 656 0.0002 0.0020 -0.0037 0.0041 0.1820 \n", " 657 0.0008 0.0020 -0.0031 0.0048 0.3244 \n", " 658 0.0012 0.0020 -0.0027 0.0052 0.4230 \n", " 659 0.0008 0.0020 -0.0032 0.0048 0.3248 \n", " 660 0.0005 0.0020 -0.0034 0.0044 0.2414 \n", " 661 0.0001 0.0020 -0.0038 0.0040 0.1691 \n", " 662 0.0010 0.0020 -0.0030 0.0049 0.3494 \n", " 663 0.0002 0.0020 -0.0037 0.0041 0.1810 \n", " 664 -0.0000 0.0020 -0.0039 0.0039 0.1533 \n", " 665 0.0011 0.0020 -0.0029 0.0051 0.3912 \n", " 666 0.0005 0.0020 -0.0034 0.0044 0.2380 \n", " 667 0.0002 0.0020 -0.0037 0.0041 0.1864 \n", " 668 0.0005 0.0020 -0.0034 0.0044 0.2467 \n", " 669 -0.0001 0.0020 -0.0039 0.0038 0.1458 \n", " 670 0.0001 0.0020 -0.0038 0.0040 0.1635 \n", " 671 0.0000 0.0020 -0.0039 0.0039 0.1571 \n", " 672 0.0006 0.0020 -0.0033 0.0045 0.2703 \n", " 673 0.0015 0.0020 -0.0024 0.0054 0.4984 \n", " 674 0.0003 0.0020 -0.0036 0.0042 0.2055 \n", " 675 0.0025 0.0020 -0.0015 0.0064 0.8614 \n", " 676 0.0003 0.0020 -0.0036 0.0042 0.1967 \n", " 677 0.0010 0.0020 -0.0029 0.0049 0.3526 \n", " 678 0.0015 0.0022 -0.0028 0.0057 0.5303 \n", " 679 0.0030 0.0021 -0.0011 0.0070 0.9487 \n", " 680 0.0007 0.0020 -0.0032 0.0046 0.2877 \n", " 681 0.0003 0.0020 -0.0036 0.0042 0.1989 \n", " 682 0.0003 0.0020 -0.0036 0.0042 0.2101 \n", " 683 0.0010 0.0020 -0.0029 0.0049 0.3615 \n", " 684 0.0013 0.0020 -0.0026 0.0052 0.4379 \n", " 685 -0.0000 0.0020 -0.0039 0.0038 0.1475 \n", " 686 -0.0000 0.0020 -0.0039 0.0039 0.1513 \n", " 687 -0.0001 0.0020 -0.0040 0.0038 0.1446 \n", " 688 0.0000 0.0020 -0.0039 0.0039 0.1584 \n", " 689 0.0007 0.0020 -0.0033 0.0047 0.2922 \n", " 690 0.0000 0.0020 -0.0039 0.0039 0.1588 \n", " 691 0.0002 0.0020 -0.0037 0.0041 0.1875 \n", " 692 0.0001 0.0020 -0.0038 0.0040 0.1632 \n", " 693 0.0002 0.0020 -0.0037 0.0041 0.1827 \n", " 694 0.0001 0.0020 -0.0038 0.0039 0.1627 \n", " 695 0.0007 0.0020 -0.0032 0.0046 0.2855 \n", " 696 0.0013 0.0020 -0.0027 0.0053 0.4462 \n", " 697 0.0013 0.0020 -0.0026 0.0052 0.4470 \n", " 698 0.0006 0.0020 -0.0033 0.0045 0.2647 \n", " 699 0.0001 0.0020 -0.0038 0.0040 0.1630 \n", " 700 0.0004 0.0020 -0.0036 0.0043 0.2261 \n", " 701 0.0000 0.0020 -0.0039 0.0039 0.1588 \n", " 702 0.0000 0.0020 -0.0039 0.0039 0.1592 \n", " 703 0.0006 0.0020 -0.0033 0.0045 0.2710 \n", " 704 0.0003 0.0020 -0.0036 0.0042 0.2097 \n", " 705 0.0001 0.0020 -0.0038 0.0040 0.1737 \n", " 706 0.0003 0.0020 -0.0036 0.0042 0.2071 \n", " 707 -0.0000 0.0020 -0.0039 0.0039 0.1531 \n", " 708 0.0002 0.0020 -0.0037 0.0041 0.1821 \n", " 709 0.0020 0.0020 -0.0019 0.0060 0.6902 \n", " 710 0.0007 0.0020 -0.0032 0.0046 0.2828 \n", " 711 0.0010 0.0020 -0.0029 0.0050 0.3690 \n", " 712 0.0024 0.0021 -0.0016 0.0064 0.8396 \n", " 713 0.0003 0.0020 -0.0036 0.0042 0.2076 \n", " 714 0.0001 0.0020 -0.0038 0.0040 0.1660 \n", " 715 -0.0000 0.0020 -0.0039 0.0039 0.1504 \n", " 716 0.0020 0.0020 -0.0019 0.0060 0.6930 \n", " 717 0.0006 0.0020 -0.0033 0.0045 0.2564 \n", " 718 0.0019 0.0020 -0.0021 0.0058 0.6302 \n", " 719 -0.0000 0.0020 -0.0039 0.0038 0.1480 \n", " 720 -0.0000 0.0020 -0.0039 0.0039 0.1489 \n", " 721 0.0004 0.0020 -0.0035 0.0044 0.2320 \n", " 722 0.0014 0.0021 -0.0027 0.0055 0.4899 \n", " 723 0.0016 0.0021 -0.0025 0.0057 0.5460 \n", " 724 0.0014 0.0023 -0.0031 0.0059 0.5340 \n", " 725 0.0011 0.0020 -0.0029 0.0051 0.3976 \n", " 726 0.0017 0.0021 -0.0024 0.0057 0.5676 \n", " 727 0.0000 0.0020 -0.0039 0.0039 0.1577 \n", " 728 0.0007 0.0020 -0.0032 0.0046 0.2931 \n", " 729 0.0024 0.0020 -0.0015 0.0064 0.8451 \n", " 730 0.0009 0.0020 -0.0031 0.0048 0.3287 \n", " 731 0.0018 0.0020 -0.0021 0.0058 0.6169 \n", " 732 0.0003 0.0020 -0.0036 0.0042 0.2025 \n", " 733 0.0004 0.0020 -0.0035 0.0043 0.2205 \n", " 734 0.0018 0.0020 -0.0021 0.0058 0.6229 \n", " 735 0.0029 0.0020 -0.0011 0.0069 0.9745 \n", " 736 0.0053 0.0021 0.0012 0.0095 0.2394 \n", " 737 0.0002 0.0020 -0.0037 0.0041 0.1875 \n", " 738 0.0002 0.0020 -0.0037 0.0041 0.1908 \n", " 739 0.0003 0.0020 -0.0036 0.0042 0.2077 \n", " 740 0.0000 0.0020 -0.0038 0.0039 0.1606 \n", " 741 -0.0000 0.0020 -0.0039 0.0039 0.1516 \n", " 742 0.0015 0.0020 -0.0024 0.0054 0.5046 \n", " 743 0.0027 0.0021 -0.0014 0.0067 0.9427 \n", " 744 0.0008 0.0020 -0.0031 0.0047 0.3170 \n", " 745 0.0043 0.0021 0.0001 0.0085 0.4882 \n", " 746 0.0006 0.0020 -0.0033 0.0045 0.2599 \n", " 747 0.0032 0.0020 -0.0008 0.0072 0.8682 \n", " 748 0.0003 0.0020 -0.0036 0.0042 0.2072 \n", " 749 0.0071 0.0022 0.0028 0.0114 0.0535 *\n", " 750 0.0009 0.0020 -0.0030 0.0048 0.3310 \n", " 751 0.0020 0.0021 -0.0021 0.0061 0.6921 \n", " 752 0.0026 0.0020 -0.0013 0.0065 0.9138 \n", " 753 0.0005 0.0020 -0.0034 0.0044 0.2337 \n", " 754 0.0008 0.0020 -0.0032 0.0047 0.3092 \n", " 755 -0.0000 0.0020 -0.0039 0.0038 0.1466 \n", " 756 0.0017 0.0020 -0.0022 0.0056 0.5793 \n", " 757 0.0002 0.0020 -0.0037 0.0041 0.1878 \n", " 758 0.0006 0.0020 -0.0033 0.0045 0.2556 \n", " 759 0.0000 0.0020 -0.0039 0.0039 0.1555 \n", " 760 0.0010 0.0020 -0.0030 0.0050 0.3736 \n", " 761 -0.0000 0.0020 -0.0039 0.0038 0.1476 \n", " 762 0.0005 0.0020 -0.0034 0.0044 0.2412 \n", " 763 0.0017 0.0020 -0.0023 0.0056 0.5614 \n", " 764 0.0026 0.0021 -0.0016 0.0068 0.9197 \n", " 765 0.0006 0.0020 -0.0033 0.0045 0.2606 \n", " 766 0.0002 0.0020 -0.0037 0.0041 0.1791 \n", " 767 0.0029 0.0020 -0.0010 0.0069 0.9634 \n", " 768 0.0035 0.0023 -0.0010 0.0079 0.7830 \n", " 769 0.0008 0.0020 -0.0032 0.0047 0.2991 \n", " 770 0.0012 0.0020 -0.0028 0.0053 0.4382 \n", " 771 0.0002 0.0020 -0.0037 0.0041 0.1801 \n", " 772 0.0008 0.0020 -0.0031 0.0048 0.3174 \n", " 773 0.0006 0.0020 -0.0033 0.0045 0.2590 \n", " 774 0.0013 0.0020 -0.0026 0.0053 0.4547 \n", " 775 0.0025 0.0021 -0.0016 0.0065 0.8703 \n", " 776 0.0005 0.0020 -0.0034 0.0044 0.2494 \n", " 777 0.0027 0.0020 -0.0012 0.0067 0.9615 \n", " 778 0.0000 0.0020 -0.0039 0.0039 0.1554 \n", " 779 0.0027 0.0020 -0.0013 0.0067 0.9391 \n", " 780 0.0011 0.0020 -0.0028 0.0051 0.3933 \n", " 781 0.0001 0.0020 -0.0038 0.0040 0.1671 \n", " 782 0.0005 0.0020 -0.0035 0.0044 0.2429 \n", " 783 0.0008 0.0020 -0.0031 0.0047 0.3111 \n", " 784 0.0009 0.0020 -0.0030 0.0048 0.3406 \n", " 785 -0.0000 0.0020 -0.0039 0.0039 0.1482 \n", " 786 0.0023 0.0021 -0.0018 0.0064 0.7846 \n", " 787 0.0003 0.0020 -0.0036 0.0042 0.2097 \n", " 788 0.0000 0.0020 -0.0039 0.0039 0.1592 \n", " 789 0.0055 0.0022 0.0012 0.0098 0.2259 \n", " 790 0.0014 0.0020 -0.0026 0.0054 0.4892 \n", " 791 0.0028 0.0021 -0.0013 0.0069 0.9918 \n", " 792 -0.0001 0.0020 -0.0040 0.0038 0.1436 \n", " 793 0.0011 0.0020 -0.0028 0.0050 0.3956 \n", " 794 0.0018 0.0022 -0.0026 0.0062 0.6540 \n", " 795 -0.0000 0.0020 -0.0039 0.0039 0.1493 \n", " 796 0.0005 0.0020 -0.0034 0.0044 0.2499 \n", " 797 0.0012 0.0020 -0.0028 0.0052 0.4157 \n", " 798 0.0002 0.0020 -0.0037 0.0040 0.1769 \n", " 799 -0.0000 0.0020 -0.0039 0.0039 0.1505 \n", " 800 0.0001 0.0020 -0.0037 0.0040 0.1753 \n", " 801 0.0006 0.0020 -0.0033 0.0045 0.2607 \n", " 802 0.0004 0.0020 -0.0035 0.0042 0.2127 \n", " 803 0.0020 0.0021 -0.0021 0.0060 0.6738 \n", " 804 0.0000 0.0020 -0.0039 0.0039 0.1577 \n", " 805 0.0004 0.0020 -0.0035 0.0043 0.2248 \n", " 806 0.0348 0.0038 0.0273 0.0423 0.0000 ***\n", " 807 0.0244 0.0037 0.0172 0.0316 0.0000 ***\n", " 808 0.0010 0.0020 -0.0029 0.0049 0.3634 \n", " 809 0.0004 0.0020 -0.0035 0.0043 0.2249 \n", " 810 0.0028 0.0020 -0.0011 0.0068 0.9991 \n", " 811 0.0006 0.0020 -0.0033 0.0045 0.2619 \n", " 812 0.0000 0.0020 -0.0038 0.0039 0.1601 \n", " 813 0.0022 0.0020 -0.0018 0.0062 0.7477 \n", " 814 0.0003 0.0020 -0.0036 0.0042 0.2109 \n", " 815 0.0272 0.0036 0.0202 0.0342 0.0000 ***\n", " 816 0.0323 0.0039 0.0246 0.0399 0.0000 ***\n", " 817 0.0007 0.0020 -0.0032 0.0046 0.2755 \n", " 818 0.0004 0.0020 -0.0035 0.0043 0.2303 \n", " 819 0.0024 0.0020 -0.0016 0.0063 0.8164 \n", " 820 0.0030 0.0021 -0.0011 0.0071 0.9386 \n", " 821 0.0134 0.0025 0.0085 0.0182 0.0000 ***\n", " 822 0.0063 0.0021 0.0021 0.0104 0.1080 \n", " 823 0.0003 0.0020 -0.0036 0.0042 0.2043 \n", " 824 0.0002 0.0020 -0.0037 0.0041 0.1869 \n", " 825 0.0036 0.0020 -0.0004 0.0076 0.6898 \n", " 826 0.0059 0.0022 0.0016 0.0101 0.1665 \n", " 827 0.0030 0.0021 -0.0011 0.0071 0.9421 \n", " 828 0.0001 0.0020 -0.0038 0.0040 0.1723 \n", " 829 0.0005 0.0020 -0.0034 0.0044 0.2466 \n", " 830 0.0001 0.0020 -0.0038 0.0040 0.1631 \n", " 831 0.0012 0.0020 -0.0028 0.0051 0.3998 \n", "==============================================================================\n", "Significant units: 7 / 832\n", "---\n", "Signif. codes: 0 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1\n", "==============================================================================\n" ] } ], "source": [ "print(\"=== X-space feature summary ===\")\n", "_ = fdfi_estimator.summary(\n", " alpha=0.05,\n", " target=\"X\",\n", " var_floor_c=0.1,\n", " var_floor_method=\"mixture\",\n", " var_floor_quantile=0.95,\n", " margin=0.0,\n", " margin_method=\"auto\",\n", " margin_quantile=0.95,\n", " alternative=\"two-sided\",\n", ")\n", "\n", "print(\"\\n=== Z-space feature summary ===\")\n", "_ = fdfi_estimator.summary(\n", " alpha=0.05,\n", " target=\"Z\",\n", " var_floor_c=0.1,\n", " var_floor_method=\"mixture\",\n", " var_floor_quantile=0.95,\n", " margin=0.0,\n", " margin_method=\"auto\",\n", " margin_quantile=0.95,\n", " alternative=\"two-sided\",\n", ")" ], "id": "eot-case-study-sens50-10" }, { "cell_type": "markdown", "metadata": { "id": "D72pAXw38cG2" }, "source": [ "## Group Importance" ], "id": "eot-case-study-sens50-11" }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 508 }, "id": "UEru-FoP8cG2", "outputId": "a6d64043-dd87-4adc-92bc-9ab3a4572daa" }, "outputs": [], "source": [ "res_df = fdfi_estimator.conf_int(\n", " alpha=0.05,\n", " target=\"X\",\n", " groups=df_groups,\n", " threshold_null=True,\n", " var_floor_c=0.1,\n", " var_floor_method=\"fixed\",\n", " margin=0.0,\n", " margin_method=\"fixed\",\n", " alternative=\"two-sided\",\n", " multitest_method=\"bonferroni\",\n", " verbose=False,\n", ")\n", "res_df = pd.DataFrame({\n", " \"group\": res_df[\"groups\"],\n", " \"importance\": res_df[\"score\"],\n", " \"se\": res_df[\"se\"],\n", " \"zscore\": res_df[\"zscore\"],\n", " \"ci_lower\": res_df[\"ci_lower\"],\n", " \"ci_upper\": res_df[\"ci_upper\"],\n", " \"p_value\": res_df[\"pvalue\"],\n", " \"p_value_adj\": res_df.get(\"pvalue_adj\", res_df[\"pvalue\"]),\n", " \"reject_null\": res_df[\"reject_null\"],\n", "})\n" ], "id": "eot-case-study-sens50-12" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Group Summary Table" ], "id": "eot-case-study-sens50-13" }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KbH9FrFb8cG2", "outputId": "e8d8d205-3218-412d-a497-fd31aba6966a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== X-space group summary ===\n", "==============================================================================\n", "Feature Importance Results\n", "==============================================================================\n", "Method: EOTExplainer\n", "Number of units: 14\n", "Significance level: 0.05\n", "Alternative: two-sided\n", "Multiple testing: bonferroni\n", "Margin method: fixed\n", "------------------------------------------------------------------------------\n", " Group Estimate Std Err CI Lower CI Upper Adj P-val Sig\n", "------------------------------------------------------------------------------\n", " cd4bs 0.1073 0.0097 0.0883 0.1262 0.0000 ***\n", " covar 0.2089 0.0161 0.1774 0.2403 0.0000 ***\n", " cysteines 0.0119 0.0042 0.0037 0.0201 0.0616 *\n", " esa 0.0983 0.0088 0.0810 0.1156 0.0000 ***\n", " geog 0.0031 0.0041 -0.0048 0.0111 1.0000 \n", " geometry 0.0359 0.0050 0.0262 0.0456 0.0000 ***\n", " glyco 0.0541 0.0058 0.0428 0.0654 0.0000 ***\n", " gp41 0.0270 0.0048 0.0176 0.0364 0.0000 ***\n", " pngs 0.1721 0.0140 0.1447 0.1995 0.0000 ***\n", " pngs_novrc01 0.0867 0.0069 0.0732 0.1002 0.0000 ***\n", " sequons 0.0372 0.0049 0.0275 0.0468 0.0000 ***\n", " steric_bulk 0.0064 0.0041 -0.0016 0.0145 1.0000 \n", " subtype 0.0087 0.0041 0.0006 0.0167 0.4937 \n", " vrc01 0.1163 0.0100 0.0966 0.1360 0.0000 ***\n", "==============================================================================\n", "Significant units: 10 / 14\n", "---\n", "Signif. codes: 0 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1\n", "==============================================================================\n", "\n", "=== Z-space group summary ===\n", "==============================================================================\n", "Feature Importance Results\n", "==============================================================================\n", "Method: EOTExplainer\n", "Number of units: 14\n", "Significance level: 0.05\n", "Alternative: two-sided\n", "Multiple testing: bonferroni\n", "Margin method: fixed\n", "------------------------------------------------------------------------------\n", " Group Estimate Std Err CI Lower CI Upper Adj P-val Sig\n", "------------------------------------------------------------------------------\n", " cd4bs 0.0915 0.0096 0.0727 0.1102 0.0000 ***\n", " covar 0.1943 0.0163 0.1624 0.2262 0.0000 ***\n", " cysteines 0.0202 0.0045 0.0114 0.0290 0.0001 ***\n", " esa 0.0841 0.0086 0.0672 0.1010 0.0000 ***\n", " geog 0.0019 0.0041 -0.0061 0.0098 1.0000 \n", " geometry 0.0659 0.0072 0.0519 0.0800 0.0000 ***\n", " glyco 0.0405 0.0058 0.0291 0.0519 0.0000 ***\n", " gp41 0.0231 0.0049 0.0134 0.0328 0.0000 ***\n", " pngs 0.1471 0.0137 0.1202 0.1739 0.0000 ***\n", " pngs_novrc01 0.0950 0.0076 0.0801 0.1100 0.0000 ***\n", " sequons 0.0666 0.0071 0.0527 0.0804 0.0000 ***\n", " steric_bulk 0.0125 0.0043 0.0040 0.0209 0.0521 *\n", " subtype 0.0054 0.0042 -0.0028 0.0135 1.0000 \n", " vrc01 0.0988 0.0098 0.0796 0.1180 0.0000 ***\n", "==============================================================================\n", "Significant units: 11 / 14\n", "---\n", "Signif. codes: 0 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1\n", "==============================================================================\n" ] } ], "source": [ "print(\"=== X-space group summary ===\")\n", "_ = fdfi_estimator.summary(\n", " alpha=0.05,\n", " target=\"X\",\n", " groups=df_groups,\n", " threshold_null=True,\n", " var_floor_c=0.1,\n", " var_floor_method=\"fixed\",\n", " margin=0.0,\n", " margin_method=\"fixed\",\n", " alternative=\"two-sided\",\n", " multitest_method=\"bonferroni\",\n", ")\n", "print(\"\\n=== Z-space group summary ===\")\n", "_ = fdfi_estimator.summary(\n", " alpha=0.05,\n", " target=\"Z\",\n", " groups=df_groups,\n", " threshold_null=True,\n", " var_floor_c=0.1,\n", " var_floor_method=\"fixed\",\n", " margin=0.0,\n", " margin_method=\"fixed\",\n", " alternative=\"two-sided\",\n", " multitest_method=\"bonferroni\",\n", ")\n" ], "id": "eot-case-study-sens50-14" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Group Mapping" ], "id": "eot-case-study-sens50-15" }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "pwJcMiui8cG2" }, "outputs": [], "source": [ "group_mapping = pd.DataFrame({\n", " \"group_num\": range(1, 15),\n", " \"group\": [\n", " \"vrc01\", \"cd4bs\", \"esa\", \"glyco\", \"covar\", \"pngs\", \"gp41\",\n", " \"pngs_novrc01\", \"subtype\", \"sequons\", \"geometry\", \"cysteines\",\n", " \"steric_bulk\", \"geog\"\n", " ],\n", " \"description\": [\n", " \"VRC01 binding footprint\",\n", " \"CD4 binding sites\",\n", " \"Sites with sufficient exposed surface area\",\n", " \"Sites identified as important for glycosylation\",\n", " \"Sites with residues that covary with\\nthe VRC01 binding footprint\",\n", " \"Sites associated with VRC01-specific\\npotential N-linked glycosylation (PNGS) effects\",\n", " \"gp41 sites important for VRC01 binding\",\n", " \"Sites for indicating N-linked glycosylation\",\n", " \"Majority virus subtypes\",\n", " \"Region-specific counts of PNGS\",\n", " \"Viral geometry\",\n", " \"Cysteine counts\",\n", " \"Steric bulk at critical locations\",\n", " \"Geographic confounders\",\n", " ],\n", "})\n" ], "id": "eot-case-study-sens50-16" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Group Importance Plot" ], "id": "eot-case-study-sens50-17" }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 183 }, "id": "UYDtCj_e8cG2", "outputId": "4f39a643-7199-4970-d557-a1658643b6a8" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "DATASET_LABELS = {\n", " \"sens50\": \"Sensitivity 50\",\n", " \"sens80\": \"Sensitivity 80\",\n", " \"ic50\": \"IC50 Censored\",\n", "}\n", "dataset_label = DATASET_LABELS.get(outcome, outcome)\n", "\n", "group_df_sorted = res_df.sort_values(\"importance\", ascending=True).reset_index(drop=True)\n", "group_df_sorted = group_df_sorted.merge(group_mapping, on=\"group\", how=\"left\")\n", "\n", "fig, axes = plt.subplots(1, 3, figsize=(18, 6), gridspec_kw={\"width_ratios\": [3, 3, 3]})\n", "fig.suptitle(\n", " f\"{dataset_label} ({outcome}) - EOT FDFI Group Importance\",\n", " fontsize=22,\n", " y=1.04,\n", ")\n", "\n", "group_numbers = list(group_df_sorted[\"group_num\"])\n", "\n", "ax1 = axes[0]\n", "ax1.barh(\n", " range(len(group_df_sorted)),\n", " group_df_sorted[\"importance\"],\n", " xerr=group_df_sorted[\"se\"],\n", " error_kw=dict(capsize=5),\n", " alpha=0.7,\n", " color=\"steelblue\",\n", ")\n", "ax1.set_yticks(range(len(group_df_sorted)))\n", "ax1.set_yticklabels(group_numbers, fontsize=12)\n", "ax1.set_xlabel(\"Group Importance\", fontsize=16)\n", "ax1.set_title(\"Feature Group Importance (EOT FDFI)\", fontsize=18)\n", "ax1.set_ylabel(\"Group Number\", fontsize=16)\n", "ax1.grid(axis=\"x\", alpha=0.3)\n", "\n", "for i, (_, row) in enumerate(group_df_sorted.iterrows()):\n", " if row[\"reject_null\"]:\n", " ax1.text(\n", " row[\"importance\"] + row[\"se\"] + 0.001,\n", " i,\n", " \"*\",\n", " va=\"center\",\n", " fontweight=\"bold\",\n", " color=\"red\",\n", " )\n", "\n", "ax2 = axes[1]\n", "colors = [\"red\" if reject_null else \"gray\" for reject_null in group_df_sorted[\"reject_null\"]]\n", "ax2.barh(range(len(group_df_sorted)), group_df_sorted[\"zscore\"], alpha=0.7, color=colors)\n", "ax2.set_yticks(range(len(group_df_sorted)))\n", "ax2.set_yticklabels(group_numbers, fontsize=12)\n", "ax2.set_xlabel(\"Z-score\", fontsize=16)\n", "ax2.set_title(\"Feature Group Z-scores\", fontsize=18)\n", "ax2.grid(axis=\"x\", alpha=0.3)\n", "ax2.axvline(x=1.96, color=\"red\", linestyle=\"--\", linewidth=2, alpha=0.7, label=\"p=0.05\")\n", "ax2.legend(fontsize=12)\n", "\n", "ax3 = axes[2]\n", "ax3.set_xlim(0, 1)\n", "ax3.set_ylim(-0.5, len(group_df_sorted) - 0.5)\n", "\n", "for i, (_, row) in enumerate(group_df_sorted.iterrows()):\n", " group_name = row[\"group\"]\n", " display_name = row[\"description\"] if pd.notna(row[\"description\"]) else group_name\n", " text_color = \"red\" if row[\"reject_null\"] else \"gray\"\n", "\n", " ax3.text(\n", " 0.02,\n", " i,\n", " f\"{int(row['group_num']):2d}.\",\n", " va=\"center\",\n", " ha=\"left\",\n", " fontsize=14,\n", " fontweight=\"bold\",\n", " color=\"black\",\n", " )\n", " ax3.text(\n", " 0.09,\n", " i,\n", " display_name,\n", " va=\"center\",\n", " ha=\"left\",\n", " fontsize=12,\n", " color=text_color,\n", " )\n", "\n", "ax3.set_yticks([])\n", "ax3.set_xticks([])\n", "ax3.set_title(\"Feature Group Annotations\", fontsize=18)\n", "for spine in ax3.spines.values():\n", " spine.set_visible(False)\n", "\n", "plt.tight_layout(rect=[0, 0, 1, 0.95])\n", "plt.show()\n" ], "id": "eot-case-study-sens50-18" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "This notebook applies **EOT FDFI** (Entropic Optimal Transport Disentangled Feature Importance)\n", "with 5-fold cross-fitting to the `sens50` HIV neutralization dataset (d=832 binary features, n≈300).\n", "\n", "**Why EOT over FlowMatching?** \n", "FlowExplainer X-space attribution relies on the flow Jacobian H = dX/dZ.\n", "For binary high-d data (d >> n), H≈I (near-identity), making X-space DFI unreliable.\n", "EOTExplainer uses an analytical whitening map and coupling moments — no Jacobian issues.\n", "\n", "**Results (α = 0.05, Bonferroni correction over 14 groups):** \n", "See the group summary table and plot above for significant feature groups.\n" ], "id": "eot-case-study-sens50-19" }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== Group-level results (matching DFI paper) ===\n", "Group Importance SE Z-score Significant\n", "----------------------------------------------------------------------\n", "covar 0.2089 0.0161 13.01 ***\n", "pngs_novrc01 0.0867 0.0069 12.61 ***\n", "pngs 0.1721 0.0140 12.29 ***\n", "vrc01 0.1163 0.0100 11.59 ***\n", "esa 0.0983 0.0088 11.15 ***\n", "cd4bs 0.1073 0.0097 11.09 ***\n", "glyco 0.0541 0.0058 9.37 ***\n", "sequons 0.0372 0.0049 7.54 ***\n", "geometry 0.0359 0.0050 7.23 ***\n", "gp41 0.0270 0.0048 5.64 ***\n", "cysteines 0.0119 0.0042 2.85 \n", "subtype 0.0087 0.0041 2.11 \n", "steric_bulk 0.0064 0.0041 1.57 \n", "geog 0.0031 0.0041 0.78 \n", "\n", "Significant groups: 10/14 (Bonferroni alpha=0.05)\n", "Z-score range: 0.78 to 13.01\n" ] } ], "source": [ "print(\"=== Group-level results (matching DFI paper) ===\")\n", "print(f\"{'Group':<20} {'Importance':>12} {'SE':>10} {'Z-score':>10} {'Significant':>12}\")\n", "print(\"-\" * 70)\n", "for _, row in res_df.sort_values(\"zscore\", ascending=False).iterrows():\n", " sig = \"***\" if row[\"reject_null\"] else \"\"\n", " print(f\"{row['group']:<20} {row['importance']:>12.4f} {row['se']:>10.4f} {row['zscore']:>10.2f} {sig:>12}\")\n", "\n", "n_sig = res_df[\"reject_null\"].sum()\n", "print(f\"\\nSignificant groups: {n_sig}/{len(res_df)} (Bonferroni alpha=0.05)\")\n", "print(f\"Z-score range: {res_df['zscore'].min():.2f} to {res_df['zscore'].max():.2f}\")\n" ], "id": "eot-case-study-sens50-20" } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.19" } }, "nbformat": 4, "nbformat_minor": 0 }