multivariate_BWAS_replicability_analysis_FC_extensive_motion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing replicability of functional connectivity-based multivariate BWAS on the Human Connectome Project dataset\n",
    "\n",
    "Comprehensive analysis with 52 HCP phenotypes\n",
    "\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T20:04:15.431840Z",
     "start_time": "2021-08-03T20:04:14.753565Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import KFold, train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from joblib import Parallel, delayed\n",
    "from mlxtend.evaluate import permutation_test\n",
    "from statsmodels.formula.api import ols as ols_f\n",
    "from mlconfound.stats import partial_confound_test\n",
    "sns.set(rc={\"figure.figsize\":(4, 2)})\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load HCP data\n",
    "\n",
    "We load functional network matrices (netmats) from the HCP1200-release, as published on connectomeDB: https://db.humanconnectome.org/\n",
    "Due to licensing issues, data is not supplied with the repository, but can be downloaded from the ConnectomeDB or via `get_data.ipynb` (requires credentials).\n",
    "See [readme.md](readme.md) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[<AxesSubplot: title={'center': 'motion'}>]], dtype=object)"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 400x200 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "motion = pd.read_csv('hcp_data/motion.csv', index_col=0).set_index('ID', drop=True, inplace=False)\n",
    "motion.hist()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "          T1_Count     T2_Count  3T_RS-fMRI_Count  3T_RS-fMRI_PctCompl  \\\ncount  1094.000000  1094.000000       1094.000000          1094.000000   \nmean      1.603291     1.520110          3.861974            95.959049   \nstd       0.489438     0.499824          0.513459            14.799877   \nmin       1.000000     1.000000          1.000000             0.000000   \n25%       1.000000     1.000000          4.000000           100.000000   \n50%       2.000000     2.000000          4.000000           100.000000   \n75%       2.000000     2.000000          4.000000           100.000000   \nmax       2.000000     2.000000          4.000000           100.000000   \n\n       3T_tMRI_PctCompl  fMRI_WM_PctCompl  fMRI_Gamb_PctCompl  \\\ncount       1094.000000       1094.000000         1094.000000   \nmean          96.945978         98.537477           98.685923   \nstd           11.835880         12.010211           11.262753   \nmin            0.000000          0.000000            0.000000   \n25%          100.000000        100.000000          100.000000   \n50%          100.000000        100.000000          100.000000   \n75%          100.000000        100.000000          100.000000   \nmax          100.000000        100.000000          100.000000   \n\n       fMRI_Mot_PctCompl  fMRI_Lang_PctCompl  fMRI_Soc_PctCompl  ...  \\\ncount        1094.000000         1094.000000        1094.000000  ...   \nmean           98.533638           95.662431          95.676417  ...   \nstd            12.010414           20.291338          20.293050  ...   \nmin             0.000000            0.000000           0.000000  ...   \n25%           100.000000          100.000000         100.000000  ...   \n50%           100.000000          100.000000         100.000000  ...   \n75%           100.000000          100.000000         100.000000  ...   \nmax           100.000000          100.000000         100.000000  ...   \n\n       Odor_AgeAdj  PainIntens_RawScore  PainInterf_Tscore  Taste_Unadj  \\\ncount  1092.000000          1089.000000        1093.000000  1088.000000   \nmean     97.942894             1.410468          45.704117    95.119991   \nstd      11.115135             1.734575           7.581136    14.358339   \nmin      59.860000             0.000000          38.600000    56.350000   \n25%      87.110000             0.000000          38.600000    84.070000   \n50%      98.040000             1.000000          45.900000    95.850000   \n75%     110.450000             2.000000          52.200000   104.660000   \nmax     111.410000            10.000000          75.300000   134.650000   \n\n       Taste_AgeAdj  Mars_Log_Score    Mars_Errs   Mars_Final       motion  \\\ncount   1088.000000     1087.000000  1084.000000  1084.000000  1094.000000   \nmean      93.948309        1.846578     0.582103     1.823349     0.088170   \nstd       14.614322        0.568080     0.983564     0.569718     0.033718   \nmin       59.500000        1.560000     0.000000     1.080000     0.039544   \n25%       83.220000        1.800000     0.000000     1.760000     0.064558   \n50%       94.990000        1.800000     0.000000     1.800000     0.079880   \n75%      102.920000        1.880000     1.000000     1.840000     0.101661   \nmax      131.380000       15.000000    17.000000    15.000000     0.302853   \n\n               age  \ncount  1094.000000  \nmean     28.871572  \nstd       3.583816  \nmin      23.500000  \n25%      28.000000  \n50%      28.000000  \n75%      33.000000  \nmax      36.000000  \n\n[8 rows x 457 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>T1_Count</th>\n      <th>T2_Count</th>\n      <th>3T_RS-fMRI_Count</th>\n      <th>3T_RS-fMRI_PctCompl</th>\n      <th>3T_tMRI_PctCompl</th>\n      <th>fMRI_WM_PctCompl</th>\n      <th>fMRI_Gamb_PctCompl</th>\n      <th>fMRI_Mot_PctCompl</th>\n      <th>fMRI_Lang_PctCompl</th>\n      <th>fMRI_Soc_PctCompl</th>\n      <th>...</th>\n      <th>Odor_AgeAdj</th>\n      <th>PainIntens_RawScore</th>\n      <th>PainInterf_Tscore</th>\n      <th>Taste_Unadj</th>\n      <th>Taste_AgeAdj</th>\n      <th>Mars_Log_Score</th>\n      <th>Mars_Errs</th>\n      <th>Mars_Final</th>\n      <th>motion</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1094.000000</td>\n      <td>1094.000000</td>\n      <td>1094.000000</td>\n      <td>1094.000000</td>\n      <td>1094.000000</td>\n      <td>1094.000000</td>\n      <td>1094.000000</td>\n      <td>1094.000000</td>\n      <td>1094.000000</td>\n      <td>1094.000000</td>\n      <td>...</td>\n      <td>1092.000000</td>\n      <td>1089.000000</td>\n      <td>1093.000000</td>\n      <td>1088.000000</td>\n      <td>1088.000000</td>\n      <td>1087.000000</td>\n      <td>1084.000000</td>\n      <td>1084.000000</td>\n      <td>1094.000000</td>\n      <td>1094.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.603291</td>\n      <td>1.520110</td>\n      <td>3.861974</td>\n      <td>95.959049</td>\n      <td>96.945978</td>\n      <td>98.537477</td>\n      <td>98.685923</td>\n      <td>98.533638</td>\n      <td>95.662431</td>\n      <td>95.676417</td>\n      <td>...</td>\n      <td>97.942894</td>\n      <td>1.410468</td>\n      <td>45.704117</td>\n      <td>95.119991</td>\n      <td>93.948309</td>\n      <td>1.846578</td>\n      <td>0.582103</td>\n      <td>1.823349</td>\n      <td>0.088170</td>\n      <td>28.871572</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.489438</td>\n      <td>0.499824</td>\n      <td>0.513459</td>\n      <td>14.799877</td>\n      <td>11.835880</td>\n      <td>12.010211</td>\n      <td>11.262753</td>\n      <td>12.010414</td>\n      <td>20.291338</td>\n      <td>20.293050</td>\n      <td>...</td>\n      <td>11.115135</td>\n      <td>1.734575</td>\n      <td>7.581136</td>\n      <td>14.358339</td>\n      <td>14.614322</td>\n      <td>0.568080</td>\n      <td>0.983564</td>\n      <td>0.569718</td>\n      <td>0.033718</td>\n      <td>3.583816</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>59.860000</td>\n      <td>0.000000</td>\n      <td>38.600000</td>\n      <td>56.350000</td>\n      <td>59.500000</td>\n      <td>1.560000</td>\n      <td>0.000000</td>\n      <td>1.080000</td>\n      <td>0.039544</td>\n      <td>23.500000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>...</td>\n      <td>87.110000</td>\n      <td>0.000000</td>\n      <td>38.600000</td>\n      <td>84.070000</td>\n      <td>83.220000</td>\n      <td>1.800000</td>\n      <td>0.000000</td>\n      <td>1.760000</td>\n      <td>0.064558</td>\n      <td>28.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>4.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>...</td>\n      <td>98.040000</td>\n      <td>1.000000</td>\n      <td>45.900000</td>\n      <td>95.850000</td>\n      <td>94.990000</td>\n      <td>1.800000</td>\n      <td>0.000000</td>\n      <td>1.800000</td>\n      <td>0.079880</td>\n      <td>28.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>4.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>...</td>\n      <td>110.450000</td>\n      <td>2.000000</td>\n      <td>52.200000</td>\n      <td>104.660000</td>\n      <td>102.920000</td>\n      <td>1.880000</td>\n      <td>1.000000</td>\n      <td>1.840000</td>\n      <td>0.101661</td>\n      <td>33.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>4.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>...</td>\n      <td>111.410000</td>\n      <td>10.000000</td>\n      <td>75.300000</td>\n      <td>134.650000</td>\n      <td>131.380000</td>\n      <td>15.000000</td>\n      <td>17.000000</td>\n      <td>15.000000</td>\n      <td>0.302853</td>\n      <td>36.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 457 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HCP data can be obtained from the connectomeDB\n",
    "# data is not part of this repository\n",
    "subjectIDs = pd.read_csv('hcp_data/subjectIDs.txt', header=None)\n",
    "\n",
    "netmats_pearson = pd.read_csv('hcp_data/netmats1_correlationZ.txt',\n",
    "                             sep=' ',\n",
    "                             header=None)\n",
    "netmats_pearson['ID'] = subjectIDs[0]\n",
    "netmats_pearson.set_index('ID', drop=True, inplace=True)\n",
    "\n",
    "\n",
    "netmats_parcor = pd.read_csv('hcp_data/netmats2_partial-correlation.txt',\n",
    "                             sep=' ',\n",
    "                             header=None)\n",
    "netmats_parcor['ID'] = subjectIDs[0]\n",
    "netmats_parcor.set_index('ID', drop=True, inplace=True)\n",
    "\n",
    "behavior = pd.read_csv('hcp_data/hcp1200_behavioral_data.csv')\n",
    "behavior = behavior.set_index('Subject', drop=True)\n",
    "\n",
    "# add motion\n",
    "behavior = behavior.merge(motion, left_index=True, right_index=True)\n",
    "\n",
    "# convert age to numeric\n",
    "age = []\n",
    "for s in behavior['Age']:\n",
    "    if s == '36+':\n",
    "        age.append(36)\n",
    "    else:\n",
    "        split = s.split(sep='-')\n",
    "        age.append(np.mean((float(split[0]), float(split[1]))))\n",
    "\n",
    "behavior['age'] = age\n",
    "behavior.describe()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper function to prepare target variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_data(target='CogTotalComp_AgeAdj', feature_data=netmats_parcor):\n",
    "    # it's a good practice to use pandas for merging, messing up subject order can be painful\n",
    "    features = feature_data.columns\n",
    "    df = behavior\n",
    "    df = df.merge(feature_data, left_index=True, right_index=True, how='left')\n",
    "\n",
    "    df = df.dropna(subset = [target] + features.values.tolist())\n",
    "    y = df[target].values\n",
    "    X = df[features].values\n",
    "    return X, y, df.motion.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper function implementing a single bootstrap iteration\n",
    "\n",
    "We define a workhorse function which:\n",
    "- randomly samples the discovery and the replication datasets,\n",
    "- creates cross-validated estimates of predictive performance within the discovery sample\n",
    "- finalizes the model by fitting it to the whole discovery sample (overfits the discovery but not the replication sample)\n",
    "- use it to predict the replication sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def corr(X, Y):\n",
    "    # just a small wrapper function (pandas correlation is silent in \"unlucky\" bootstraps with constant values)\n",
    "    return pd.Series(X).corr( pd.Series(Y))\n",
    "\n",
    "def bootstrap_workhorse(X, y, fd, sample_size, model, random_state, shuffle_y=False):\n",
    "\n",
    "    #create discovery and replication samples by random sampling from the whole dataset (without replacement)\n",
    "\n",
    "    # if shuffle_y is true, a null model is created bz permuting y\n",
    "    if shuffle_y:\n",
    "        rng = np.random.default_rng(random_state)\n",
    "        y = rng.permutation(y)\n",
    "\n",
    "    # sample the discovery and replication sets *without replacement* (with replacement introduces spurious dependencies)\n",
    "    std = 0\n",
    "    rs = random_state\n",
    "    while std == 0 or np.isnan(std):\n",
    "        X_discovery, X_replication, y_discovery, y_replication, fd_discovery, fd_replication = train_test_split(X, y, fd, train_size=sample_size, test_size=sample_size, shuffle=True, random_state=rs)\n",
    "        std = np.std(y_discovery) * np.std(y_replication)\n",
    "        rs = rs + 1\n",
    "\n",
    "    # standard 10-fold cross-validation\n",
    "    cv = KFold(10)\n",
    "\n",
    "    # below we obtain cross-validated predictions in the discovery sample\n",
    "    predicted_discovery_cv = np.zeros_like(y_discovery)  # here we collect the predictions for each fold\n",
    "    cor_per_fold = np.zeros(cv.n_splits)  # here we collect the predictive performance in each fold\n",
    "    i = 0  # just a counter\n",
    "    for train, test in cv.split(X=X_discovery, y=y_discovery):  # loop to leave one fold out\n",
    "\n",
    "        # regress out fd from the target and predict fd-orthogonal target in the test phase\n",
    "        tmp = pd.DataFrame({\n",
    "                'y': y_discovery,\n",
    "                'fd': fd_discovery\n",
    "            })\n",
    "        OLS_model = ols_f(\"y ~ fd\", tmp.iloc[train]).fit()\n",
    "        y_discovery[train] = OLS_model.resid\n",
    "        y_discovery[test] = y_discovery[test] - OLS_model.predict(tmp.iloc[test])\n",
    "\n",
    "        model.fit(X=X_discovery[train], y=y_discovery[train]) # fit model to the training set\n",
    "        predicted_discovery_cv[test] = model.predict(X=X_discovery[test]) # use fitted model to predict teh test set\n",
    "        cor_per_fold[i] = corr(y_discovery[test], predicted_discovery_cv[test]) # calculate performance on tne test set\n",
    "        i += 1\n",
    "    # calculate mean test performance across all folds\n",
    "    r_disc_cv = np.mean(cor_per_fold)\n",
    "    # 'finalize' model by training it on the full discovery sample (without cross-validation)\n",
    "    final_model = model.fit(X=X_discovery, y=y_discovery)\n",
    "    # obtain predictions with the final model on the discovery sample, note that this model actually overfits this sample.\n",
    "    # we do this only to demonstrate biased estimates\n",
    "    predicted_discovery_overfit = final_model.predict(X=X_discovery)\n",
    "    # here we obtain the biased effect size (r) estimates for demonstrational purposes\n",
    "    r_disc_overfit = corr(predicted_discovery_overfit, y_discovery)\n",
    "\n",
    "    # We use the final model to predict the replication sample\n",
    "    # This is correct (no overfitting here), the final model did not see this data during training\n",
    "    predicted_replication = final_model.predict(X=X_replication)\n",
    "    # we obtain the out-of-sample prediction performance estimates\n",
    "    tmp = pd.DataFrame({\n",
    "                'y': y_replication,\n",
    "                'fd': fd_replication\n",
    "            })\n",
    "    OLS_model = ols_f(\"y ~ fd\", tmp).fit()\n",
    "    y_replication_orig = y_replication\n",
    "    y_replication = OLS_model.resid\n",
    "    r_rep = corr(predicted_replication, y_replication)\n",
    "\n",
    "    # below we calculate permutation-based p-values for all three effect size estimates (in-sample unbiased, in-sample biased, out-of-sample)\n",
    "    # (one sided tests, testing for positive correlation)\n",
    "    p_disc_cv = permutation_test(predicted_discovery_cv, y_discovery, method='approximate', num_rounds=1000, func=lambda x, y: corr(x, y),seed=random_state)\n",
    "    p_disc_overfit = permutation_test(predicted_discovery_overfit, y_discovery, method='approximate', num_rounds=1000, func=lambda x, y: corr(x, y),seed=random_state)\n",
    "    p_rep = permutation_test(predicted_replication, y_replication, method='approximate', num_rounds=1000, func=lambda x, y: corr(x, y),seed=random_state)\n",
    "\n",
    "    # here we calculate motion bias p-value in the replication sample\n",
    "    p_bias = partial_confound_test(y=y_replication_orig, yhat=predicted_replication, c=fd_replication, random_state=random_state, progress=False).p\n",
    "\n",
    "    return r_disc_cv, r_disc_overfit, r_rep, p_disc_cv, p_disc_overfit, p_rep, p_bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All set, now we start the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replicability with sample sizes n=50, 100, 200, 300 and max\n",
    "Here we train a few different models on 100 bootstrap samples.\n",
    "\n",
    "We aggregate the results of our workhorse function in `n_bootstrap`=100 bootstrap cases (run in parallel).\n",
    "\n",
    "The whole process is repeated for all sample sizes, fetaure_sets and target variables.\n",
    "\n",
    "## Here we test all 33 variables, proposed by Marek et al. in their response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = [\n",
    "#demographics\n",
    "    'age',\n",
    "# cognition\n",
    "    'MMSE_Score',\n",
    "    'PSQI_Score',\n",
    "    'PicSeq_AgeAdj',\n",
    "    'CardSort_AgeAdj',\n",
    "    'Flanker_AgeAdj',\n",
    "    'PMAT24_A_CR',\n",
    "    'CogFluidComp_AgeAdj',\n",
    "    'CogCrystalComp_AgeAdj',\n",
    "    'CogEarlyComp_AgeAdj',\n",
    "    'CogTotalComp_AgeAdj',\n",
    "    'ReadEng_AgeAdj',\n",
    "    'PicVocab_AgeAdj',\n",
    "    'ProcSpeed_AgeAdj',\n",
    "    'DDisc_AUC_200',\n",
    "    'VSPLOT_TC',\n",
    "    'SCPT_SEN',\n",
    "    'SCPT_SPEC',\n",
    "# memory\n",
    "    'IWRD_TOT',\n",
    "    'ListSort_AgeAdj',\n",
    "# emotion\n",
    "    'ER40ANG',\n",
    "    'ER40FEAR',\n",
    "    'ER40HAP',\n",
    "    'ER40NOE',\n",
    "    'ER40SAD',\n",
    "    'AngAffect_Unadj',\n",
    "    'AngHostil_Unadj',\n",
    "    'AngAggr_Unadj',\n",
    "    'FearAffect_Unadj',\n",
    "    'FearSomat_Unadj',\n",
    "    'FearSomat_Unadj',\n",
    "    'Sadness_Unadj',\n",
    "    'LifeSatisf_Unadj',\n",
    "    'MeanPurp_Unadj',\n",
    "    'PosAffect_Unadj',\n",
    "    'Friendship_Unadj',\n",
    "    'Loneliness_Unadj',\n",
    "    'PercHostil_Unadj',\n",
    "    'PercReject_Unadj',\n",
    "    'EmotSupp_Unadj',\n",
    "    'InstruSupp_Unadj',\n",
    "    'PercStress_Unadj',\n",
    "    'SelfEff_Unadj',\n",
    "    'Dexterity_AgeAdj',\n",
    " # personality\n",
    "    'NEOFAC_A',\n",
    "    'NEOFAC_O',\n",
    "    'NEOFAC_C',\n",
    "    'NEOFAC_N',\n",
    "    'NEOFAC_E',\n",
    "# sensory\n",
    "    'Noise_Comp',\n",
    "    'Odor_AgeAdj',\n",
    "    'PainInterf_Tscore',\n",
    "    'Taste_AgeAdj',\n",
    "    'Mars_Final'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now we fit a simple Ridge regression\n",
    "(no feature selection, no hyperparameter optimization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2853: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n",
      "/home/tspisak/src/BWAS_comment/venv/lib/python3.10/site-packages/numpy/lib/function_base.py:2854: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "%%time\n",
    "random_state = 42\n",
    "n_bootstrap = 100\n",
    "\n",
    "features = {\n",
    "    'netmats_parcor': netmats_parcor#,\n",
    "    #'netmats_pearson': netmats_pearson\n",
    "}\n",
    "\n",
    "models = {\n",
    "    'ridge': Ridge()\n",
    "}\n",
    "\n",
    "# We aggregate all results here:\n",
    "df = pd.DataFrame(columns=['connectivity','model','target','n','r_discovery_cv','r_discovery_overfit','r_replication','p_discovery_cv','p_discovery_overfit','p_replication', 'p_bias'])\n",
    "\n",
    "for feature_set in features:\n",
    "    for model in models:\n",
    "        for target_var in targets:\n",
    "            for sample_size in ['max']:\n",
    "\n",
    "                print('*****************************************************************')\n",
    "                print(feature_set, model, target_var, sample_size)\n",
    "\n",
    "                X, y, fd = create_data(target=target_var, feature_data=features[feature_set])\n",
    "\n",
    "                if sample_size=='max':\n",
    "                    sample_size = int(len(y)/2)\n",
    "\n",
    "                # create random seeds for each bootstrap iteration for reproducibility\n",
    "                rng = np.random.default_rng(random_state)\n",
    "                random_sates = rng.integers(np.iinfo(np.int32).max, size=n_bootstrap)\n",
    "\n",
    "                # run bootstrap iterations in parallel\n",
    "                r_discovery_cv, r_discovery_overfit, r_replication, p_discovery_cv, p_discovery_overfit, p_replication, p_bias = zip(\n",
    "                    *Parallel(n_jobs=1)(\n",
    "                    delayed(bootstrap_workhorse)(X.copy(), y.copy(), fd.copy(), sample_size, models[model], seed) for seed in random_sates))\n",
    "\n",
    "                tmp_data_frame = pd.DataFrame({\n",
    "                    'connectivity' : feature_set,\n",
    "                    'model' : model,\n",
    "                    'target' : target_var,\n",
    "                    'n' : sample_size,\n",
    "                    'r_discovery_cv': r_discovery_cv,\n",
    "                    'r_discovery_overfit': r_discovery_overfit,\n",
    "                    'r_replication': r_replication,\n",
    "                    'p_discovery_cv': p_discovery_cv,\n",
    "                    'p_discovery_overfit': p_discovery_overfit,\n",
    "                    'p_replication': p_replication,\n",
    "                    'p_bias': p_bias\n",
    "                })\n",
    "                #sns.scatterplot(x='r_replication', y='r_discovery_cv', data=tmp_data_frame)\n",
    "                #plt.ylabel('in-sample (r)')\n",
    "                #plt.xlabel('out-of-sample (r_pred)')\n",
    "                #plt.show()\n",
    "                print('r discovery (with cv) :', tmp_data_frame.r_discovery_cv.mean(), 'r replication:', tmp_data_frame.r_replication.mean())\n",
    "\n",
    "                for alpha in [0.05]:\n",
    "                    print('Replicability at alpha =', alpha, ':',\n",
    "                          (tmp_data_frame.loc[tmp_data_frame['p_discovery_cv']<alpha,'p_replication']<alpha).sum() / (tmp_data_frame['p_discovery_cv']<0.05).sum() * 100, '%')\n",
    "                print(\"Motion bias:\", tmp_data_frame.p_bias.mean(), tmp_data_frame.p_bias.std())\n",
    "\n",
    "                df = pd.concat((df, tmp_data_frame))\n",
    "                df.reset_index(drop=True, inplace=True)\n",
    "                df.to_csv('res/revised_results_Ridge_fd.csv')\n",
    "\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                                                r_discovery_cv  r_replication  \\\nconnectivity   model target                n                                    \nnetmats_parcor ridge AngAffect_Unadj       500        0.083911       0.097523   \n                     AngAggr_Unadj         500        0.174860       0.178205   \n                     AngHostil_Unadj       500        0.065354       0.066244   \n                     CardSort_AgeAdj       499        0.168875       0.174110   \n                     CogCrystalComp_AgeAdj 498        0.478393       0.488006   \n                     CogEarlyComp_AgeAdj   496        0.318284       0.314487   \n                     CogFluidComp_AgeAdj   495        0.286584       0.281001   \n                     CogTotalComp_AgeAdj   494        0.440612       0.444544   \n                     DDisc_AUC_200         499        0.181074       0.178266   \n                     Dexterity_AgeAdj      500        0.148792       0.150019   \n                     ER40ANG               499        0.065080       0.072857   \n                     ER40FEAR              499        0.036240       0.029427   \n                     ER40HAP               499       -0.040475      -0.040974   \n                     ER40NOE               499        0.046832       0.054712   \n                     ER40SAD               499        0.028999       0.033857   \n                     EmotSupp_Unadj        500        0.124379       0.113373   \n                     FearAffect_Unadj      500        0.142047       0.153561   \n                     FearSomat_Unadj       500        0.053006       0.042376   \n                     Flanker_AgeAdj        500        0.124328       0.133105   \n                     Friendship_Unadj      500        0.088484       0.083519   \n                     IWRD_TOT              499        0.062716       0.056784   \n                     InstruSupp_Unadj      500        0.036281       0.032797   \n                     LifeSatisf_Unadj      500        0.183808       0.184778   \n                     ListSort_AgeAdj       500        0.261719       0.276666   \n                     Loneliness_Unadj      500        0.098174       0.100536   \n                     MMSE_Score            500             NaN            NaN   \n                     Mars_Final            498        0.061327       0.026108   \n                     MeanPurp_Unadj        500        0.092541       0.095710   \n                     NEOFAC_A              499        0.170426       0.180072   \n                     NEOFAC_C              499        0.183556       0.195523   \n                     NEOFAC_E              499        0.157592       0.169936   \n                     NEOFAC_N              499        0.067182       0.079192   \n                     NEOFAC_O              499        0.252453       0.265905   \n                     Noise_Comp            497        0.134067       0.123952   \n                     Odor_AgeAdj           499        0.022999       0.024210   \n                     PMAT24_A_CR           498        0.273698       0.270505   \n                     PSQI_Score            500             NaN       0.080488   \n                     PainInterf_Tscore     500        0.021068       0.028892   \n                     PercHostil_Unadj      500        0.045781       0.036761   \n                     PercReject_Unadj      500        0.037067       0.037709   \n                     PercStress_Unadj      500        0.089891       0.097533   \n                     PicSeq_AgeAdj         500        0.166367       0.167290   \n                     PicVocab_AgeAdj       500        0.449331       0.446806   \n                     PosAffect_Unadj       500        0.084311       0.095997   \n                     ProcSpeed_AgeAdj      500        0.111175       0.120839   \n                     ReadEng_AgeAdj        500        0.421093       0.430223   \n                     SCPT_SEN              499        0.002666       0.002837   \n                     SCPT_SPEC             499        0.135545       0.141939   \n                     Sadness_Unadj         500        0.121922       0.128428   \n                     SelfEff_Unadj         500        0.107843       0.099681   \n                     Taste_AgeAdj          498        0.081493       0.073910   \n                     VSPLOT_TC             499        0.304904       0.307482   \n                     age                   500        0.468826       0.479300   \n\n                                                 p_bias  \nconnectivity   model target                n             \nnetmats_parcor ridge AngAffect_Unadj       500  0.48600  \n                     AngAggr_Unadj         500  0.19396  \n                     AngHostil_Unadj       500  0.40929  \n                     CardSort_AgeAdj       499  0.51301  \n                     CogCrystalComp_AgeAdj 498  0.61958  \n                     CogEarlyComp_AgeAdj   496  0.40190  \n                     CogFluidComp_AgeAdj   495  0.47973  \n                     CogTotalComp_AgeAdj   494  0.58473  \n                     DDisc_AUC_200         499  0.23137  \n                     Dexterity_AgeAdj      500  0.39907  \n                     ER40ANG               499  0.39046  \n                     ER40FEAR              499  0.34259  \n                     ER40HAP               499  0.43610  \n                     ER40NOE               499  0.41809  \n                     ER40SAD               499  0.45220  \n                     EmotSupp_Unadj        500  0.22181  \n                     FearAffect_Unadj      500  0.42446  \n                     FearSomat_Unadj       500  0.44691  \n                     Flanker_AgeAdj        500  0.36686  \n                     Friendship_Unadj      500  0.34509  \n                     IWRD_TOT              499  0.33328  \n                     InstruSupp_Unadj      500  0.35826  \n                     LifeSatisf_Unadj      500  0.27947  \n                     ListSort_AgeAdj       500  0.48862  \n                     Loneliness_Unadj      500  0.42129  \n                     MMSE_Score            500  0.00000  \n                     Mars_Final            498  0.44606  \n                     MeanPurp_Unadj        500  0.39125  \n                     NEOFAC_A              499  0.28425  \n                     NEOFAC_C              499  0.43345  \n                     NEOFAC_E              499  0.40248  \n                     NEOFAC_N              499  0.50425  \n                     NEOFAC_O              499  0.28290  \n                     Noise_Comp            497  0.34268  \n                     Odor_AgeAdj           499  0.42186  \n                     PMAT24_A_CR           498  0.45357  \n                     PSQI_Score            500  0.46591  \n                     PainInterf_Tscore     500  0.40455  \n                     PercHostil_Unadj      500  0.39934  \n                     PercReject_Unadj      500  0.46279  \n                     PercStress_Unadj      500  0.42411  \n                     PicSeq_AgeAdj         500  0.26611  \n                     PicVocab_AgeAdj       500  0.46671  \n                     PosAffect_Unadj       500  0.35642  \n                     ProcSpeed_AgeAdj      500  0.44762  \n                     ReadEng_AgeAdj        500  0.56276  \n                     SCPT_SEN              499  0.36600  \n                     SCPT_SPEC             499  0.35593  \n                     Sadness_Unadj         500  0.40169  \n                     SelfEff_Unadj         500  0.41418  \n                     Taste_AgeAdj          498  0.43259  \n                     VSPLOT_TC             499  0.40537  \n                     age                   500  0.44262  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>r_discovery_cv</th>\n      <th>r_replication</th>\n      <th>p_bias</th>\n    </tr>\n    <tr>\n      <th>connectivity</th>\n      <th>model</th>\n      <th>target</th>\n      <th>n</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"53\" valign=\"top\">netmats_parcor</th>\n      <th rowspan=\"53\" valign=\"top\">ridge</th>\n      <th>AngAffect_Unadj</th>\n      <th>500</th>\n      <td>0.083911</td>\n      <td>0.097523</td>\n      <td>0.48600</td>\n    </tr>\n    <tr>\n      <th>AngAggr_Unadj</th>\n      <th>500</th>\n      <td>0.174860</td>\n      <td>0.178205</td>\n      <td>0.19396</td>\n    </tr>\n    <tr>\n      <th>AngHostil_Unadj</th>\n      <th>500</th>\n      <td>0.065354</td>\n      <td>0.066244</td>\n      <td>0.40929</td>\n    </tr>\n    <tr>\n      <th>CardSort_AgeAdj</th>\n      <th>499</th>\n      <td>0.168875</td>\n      <td>0.174110</td>\n      <td>0.51301</td>\n    </tr>\n    <tr>\n      <th>CogCrystalComp_AgeAdj</th>\n      <th>498</th>\n      <td>0.478393</td>\n      <td>0.488006</td>\n      <td>0.61958</td>\n    </tr>\n    <tr>\n      <th>CogEarlyComp_AgeAdj</th>\n      <th>496</th>\n      <td>0.318284</td>\n      <td>0.314487</td>\n      <td>0.40190</td>\n    </tr>\n    <tr>\n      <th>CogFluidComp_AgeAdj</th>\n      <th>495</th>\n      <td>0.286584</td>\n      <td>0.281001</td>\n      <td>0.47973</td>\n    </tr>\n    <tr>\n      <th>CogTotalComp_AgeAdj</th>\n      <th>494</th>\n      <td>0.440612</td>\n      <td>0.444544</td>\n      <td>0.58473</td>\n    </tr>\n    <tr>\n      <th>DDisc_AUC_200</th>\n      <th>499</th>\n      <td>0.181074</td>\n      <td>0.178266</td>\n      <td>0.23137</td>\n    </tr>\n    <tr>\n      <th>Dexterity_AgeAdj</th>\n      <th>500</th>\n      <td>0.148792</td>\n      <td>0.150019</td>\n      <td>0.39907</td>\n    </tr>\n    <tr>\n      <th>ER40ANG</th>\n      <th>499</th>\n      <td>0.065080</td>\n      <td>0.072857</td>\n      <td>0.39046</td>\n    </tr>\n    <tr>\n      <th>ER40FEAR</th>\n      <th>499</th>\n      <td>0.036240</td>\n      <td>0.029427</td>\n      <td>0.34259</td>\n    </tr>\n    <tr>\n      <th>ER40HAP</th>\n      <th>499</th>\n      <td>-0.040475</td>\n      <td>-0.040974</td>\n      <td>0.43610</td>\n    </tr>\n    <tr>\n      <th>ER40NOE</th>\n      <th>499</th>\n      <td>0.046832</td>\n      <td>0.054712</td>\n      <td>0.41809</td>\n    </tr>\n    <tr>\n      <th>ER40SAD</th>\n      <th>499</th>\n      <td>0.028999</td>\n      <td>0.033857</td>\n      <td>0.45220</td>\n    </tr>\n    <tr>\n      <th>EmotSupp_Unadj</th>\n      <th>500</th>\n      <td>0.124379</td>\n      <td>0.113373</td>\n      <td>0.22181</td>\n    </tr>\n    <tr>\n      <th>FearAffect_Unadj</th>\n      <th>500</th>\n      <td>0.142047</td>\n      <td>0.153561</td>\n      <td>0.42446</td>\n    </tr>\n    <tr>\n      <th>FearSomat_Unadj</th>\n      <th>500</th>\n      <td>0.053006</td>\n      <td>0.042376</td>\n      <td>0.44691</td>\n    </tr>\n    <tr>\n      <th>Flanker_AgeAdj</th>\n      <th>500</th>\n      <td>0.124328</td>\n      <td>0.133105</td>\n      <td>0.36686</td>\n    </tr>\n    <tr>\n      <th>Friendship_Unadj</th>\n      <th>500</th>\n      <td>0.088484</td>\n      <td>0.083519</td>\n      <td>0.34509</td>\n    </tr>\n    <tr>\n      <th>IWRD_TOT</th>\n      <th>499</th>\n      <td>0.062716</td>\n      <td>0.056784</td>\n      <td>0.33328</td>\n    </tr>\n    <tr>\n      <th>InstruSupp_Unadj</th>\n      <th>500</th>\n      <td>0.036281</td>\n      <td>0.032797</td>\n      <td>0.35826</td>\n    </tr>\n    <tr>\n      <th>LifeSatisf_Unadj</th>\n      <th>500</th>\n      <td>0.183808</td>\n      <td>0.184778</td>\n      <td>0.27947</td>\n    </tr>\n    <tr>\n      <th>ListSort_AgeAdj</th>\n      <th>500</th>\n      <td>0.261719</td>\n      <td>0.276666</td>\n      <td>0.48862</td>\n    </tr>\n    <tr>\n      <th>Loneliness_Unadj</th>\n      <th>500</th>\n      <td>0.098174</td>\n      <td>0.100536</td>\n      <td>0.42129</td>\n    </tr>\n    <tr>\n      <th>MMSE_Score</th>\n      <th>500</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>Mars_Final</th>\n      <th>498</th>\n      <td>0.061327</td>\n      <td>0.026108</td>\n      <td>0.44606</td>\n    </tr>\n    <tr>\n      <th>MeanPurp_Unadj</th>\n      <th>500</th>\n      <td>0.092541</td>\n      <td>0.095710</td>\n      <td>0.39125</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_A</th>\n      <th>499</th>\n      <td>0.170426</td>\n      <td>0.180072</td>\n      <td>0.28425</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_C</th>\n      <th>499</th>\n      <td>0.183556</td>\n      <td>0.195523</td>\n      <td>0.43345</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_E</th>\n      <th>499</th>\n      <td>0.157592</td>\n      <td>0.169936</td>\n      <td>0.40248</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_N</th>\n      <th>499</th>\n      <td>0.067182</td>\n      <td>0.079192</td>\n      <td>0.50425</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_O</th>\n      <th>499</th>\n      <td>0.252453</td>\n      <td>0.265905</td>\n      <td>0.28290</td>\n    </tr>\n    <tr>\n      <th>Noise_Comp</th>\n      <th>497</th>\n      <td>0.134067</td>\n      <td>0.123952</td>\n      <td>0.34268</td>\n    </tr>\n    <tr>\n      <th>Odor_AgeAdj</th>\n      <th>499</th>\n      <td>0.022999</td>\n      <td>0.024210</td>\n      <td>0.42186</td>\n    </tr>\n    <tr>\n      <th>PMAT24_A_CR</th>\n      <th>498</th>\n      <td>0.273698</td>\n      <td>0.270505</td>\n      <td>0.45357</td>\n    </tr>\n    <tr>\n      <th>PSQI_Score</th>\n      <th>500</th>\n      <td>NaN</td>\n      <td>0.080488</td>\n      <td>0.46591</td>\n    </tr>\n    <tr>\n      <th>PainInterf_Tscore</th>\n      <th>500</th>\n      <td>0.021068</td>\n      <td>0.028892</td>\n      <td>0.40455</td>\n    </tr>\n    <tr>\n      <th>PercHostil_Unadj</th>\n      <th>500</th>\n      <td>0.045781</td>\n      <td>0.036761</td>\n      <td>0.39934</td>\n    </tr>\n    <tr>\n      <th>PercReject_Unadj</th>\n      <th>500</th>\n      <td>0.037067</td>\n      <td>0.037709</td>\n      <td>0.46279</td>\n    </tr>\n    <tr>\n      <th>PercStress_Unadj</th>\n      <th>500</th>\n      <td>0.089891</td>\n      <td>0.097533</td>\n      <td>0.42411</td>\n    </tr>\n    <tr>\n      <th>PicSeq_AgeAdj</th>\n      <th>500</th>\n      <td>0.166367</td>\n      <td>0.167290</td>\n      <td>0.26611</td>\n    </tr>\n    <tr>\n      <th>PicVocab_AgeAdj</th>\n      <th>500</th>\n      <td>0.449331</td>\n      <td>0.446806</td>\n      <td>0.46671</td>\n    </tr>\n    <tr>\n      <th>PosAffect_Unadj</th>\n      <th>500</th>\n      <td>0.084311</td>\n      <td>0.095997</td>\n      <td>0.35642</td>\n    </tr>\n    <tr>\n      <th>ProcSpeed_AgeAdj</th>\n      <th>500</th>\n      <td>0.111175</td>\n      <td>0.120839</td>\n      <td>0.44762</td>\n    </tr>\n    <tr>\n      <th>ReadEng_AgeAdj</th>\n      <th>500</th>\n      <td>0.421093</td>\n      <td>0.430223</td>\n      <td>0.56276</td>\n    </tr>\n    <tr>\n      <th>SCPT_SEN</th>\n      <th>499</th>\n      <td>0.002666</td>\n      <td>0.002837</td>\n      <td>0.36600</td>\n    </tr>\n    <tr>\n      <th>SCPT_SPEC</th>\n      <th>499</th>\n      <td>0.135545</td>\n      <td>0.141939</td>\n      <td>0.35593</td>\n    </tr>\n    <tr>\n      <th>Sadness_Unadj</th>\n      <th>500</th>\n      <td>0.121922</td>\n      <td>0.128428</td>\n      <td>0.40169</td>\n    </tr>\n    <tr>\n      <th>SelfEff_Unadj</th>\n      <th>500</th>\n      <td>0.107843</td>\n      <td>0.099681</td>\n      <td>0.41418</td>\n    </tr>\n    <tr>\n      <th>Taste_AgeAdj</th>\n      <th>498</th>\n      <td>0.081493</td>\n      <td>0.073910</td>\n      <td>0.43259</td>\n    </tr>\n    <tr>\n      <th>VSPLOT_TC</th>\n      <th>499</th>\n      <td>0.304904</td>\n      <td>0.307482</td>\n      <td>0.40537</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <th>500</th>\n      <td>0.468826</td>\n      <td>0.479300</td>\n      <td>0.44262</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['connectivity', 'model', 'target', 'n']).mean()[['r_discovery_cv', 'r_replication', 'p_bias']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_219850/3689450029.py:12: FutureWarning:\n",
      "\n",
      "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "\n",
      "/tmp/ipykernel_219850/3689450029.py:13: FutureWarning:\n",
      "\n",
      "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1400x200 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set(rc={\"figure.figsize\":(14, 2)})\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "phenotypes = pd.read_csv('hcp_data/phenotypes-of-interest.csv').iloc[:, 1:]\n",
    "phenotypes = phenotypes.drop([2,3]) # Age duplicates\n",
    "\n",
    "def prep(x, alpha=0.05):\n",
    "    return (x.loc[x['p_discovery_cv']<alpha,'p_replication']<alpha).sum() / (x['p_discovery_cv']<0.05).sum() * 100\n",
    "\n",
    "df_plot = pd.DataFrame(df[df.connectivity=='netmats_parcor'].groupby('target').apply(prep))\n",
    "\n",
    "df_plot['r_discovery'] = df[df.connectivity=='netmats_parcor'].groupby('target').mean()['r_discovery_cv']\n",
    "df_plot['p_bias'] = df[df.connectivity=='netmats_parcor'].groupby('target').mean()['p_bias']\n",
    "df_plot = df_plot[df_plot.index!='age']\n",
    "df_plot = df_plot[df_plot.index!='MMSE_Score'] # nan because of not enough levels for bootstrapping\n",
    "df_plot = df_plot[df_plot.index!='PSQI_Score'] # nan because of not enough levels for bootstrapping\n",
    "                       \n",
    "df_plot['category'] = ''\n",
    "df_plot['phenotype'] = df_plot.index\n",
    "\n",
    "for i in df_plot.index:\n",
    "    df_plot.loc[i, 'category'] = phenotypes.loc[phenotypes.columnHeader==i, 'category'].values[0]\n",
    "\n",
    "                       \n",
    "df_plot = df_plot.sort_values(by='r_discovery', ascending=False)                      \n",
    "                       \n",
    "pal = \"muted\"\n",
    "my_colormap = [\n",
    "              sns.color_palette(pal)[4],\n",
    "              sns.color_palette(pal)[0], # cognition\n",
    "              sns.color_palette(pal)[3], # emotion\n",
    "              sns.color_palette(pal)[2], # dexterity\n",
    "              sns.color_palette(pal)[1], # big5\n",
    "              sns.color_palette(pal)[5], # sensory\n",
    "              sns.color_palette(pal)[6], # ?\n",
    "              sns.color_palette(pal)[3]]\n",
    "colors = []\n",
    "for code in pd.Categorical(df_plot.category).codes:\n",
    "    colors.append(my_colormap[code])\n",
    "\n",
    "ax=sns.barplot(data=df_plot, x='phenotype', y=0, palette=colors)\n",
    "plt.xticks(rotation=90)\n",
    "ax.axhline(80, color='black', linestyle=':')\n",
    "plt.savefig('fig/replicability_all_phenotypes_fd.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x200 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.barplot(data=df_plot, x='phenotype', y='r_discovery', palette=colors)\n",
    "plt.xticks(rotation=90)\n",
    "ax.axhline(0.074, color='black', linestyle=':') # parametric one-sided significance threshold\n",
    "ax.axhline(0, color='black')\n",
    "plt.savefig('fig/r_discovery_all_phenotypes_fd.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "outputs": [
    {
     "data": {
      "text/plain": "                                0  r_discovery   p_bias     category  \\\ntarget                                                                 \nCogCrystalComp_AgeAdj  100.000000     0.478393  0.61958    Cognition   \nPicVocab_AgeAdj        100.000000     0.449331  0.46671    Cognition   \nCogTotalComp_AgeAdj    100.000000     0.440612  0.58473    Cognition   \nReadEng_AgeAdj         100.000000     0.421093  0.56276    Cognition   \nCogEarlyComp_AgeAdj    100.000000     0.318284  0.40190    Cognition   \nVSPLOT_TC              100.000000     0.304904  0.40537    Cognition   \nCogFluidComp_AgeAdj    100.000000     0.286584  0.47973    Cognition   \nPMAT24_A_CR            100.000000     0.273698  0.45357    Cognition   \nListSort_AgeAdj        100.000000     0.261719  0.48862    Cognition   \nNEOFAC_O               100.000000     0.252453  0.28290  Personality   \nLifeSatisf_Unadj       100.000000     0.183808  0.27947      Emotion   \nNEOFAC_C               100.000000     0.183556  0.43345  Personality   \nDDisc_AUC_200          100.000000     0.181074  0.23137    Cognition   \nAngAggr_Unadj          100.000000     0.174860  0.19396      Emotion   \nNEOFAC_A               100.000000     0.170426  0.28425  Personality   \nCardSort_AgeAdj        100.000000     0.168875  0.51301    Cognition   \nPicSeq_AgeAdj          100.000000     0.166367  0.26611    Cognition   \nNEOFAC_E               100.000000     0.157592  0.40248  Personality   \nDexterity_AgeAdj       100.000000     0.148792  0.39907        Motor   \nFearAffect_Unadj       100.000000     0.142047  0.42446      Emotion   \nSCPT_SPEC               97.727273     0.135545  0.35593    Cognition   \nNoise_Comp              94.382022     0.134067  0.34268      Sensory   \nEmotSupp_Unadj          85.185185     0.124379  0.22181      Emotion   \nFlanker_AgeAdj          95.238095     0.124328  0.36686    Cognition   \nSadness_Unadj           96.341463     0.121922  0.40169      Emotion   \nProcSpeed_AgeAdj        90.277778     0.111175  0.44762    Cognition   \nSelfEff_Unadj           73.239437     0.107843  0.41418      Emotion   \nLoneliness_Unadj        75.384615     0.098174  0.42129      Emotion   \nMeanPurp_Unadj          63.768116     0.092541  0.39125      Emotion   \nPercStress_Unadj        59.701493     0.089891  0.42411      Emotion   \nFriendship_Unadj        46.666667     0.088484  0.34509      Emotion   \nPosAffect_Unadj         60.714286     0.084311  0.35642      Emotion   \nAngAffect_Unadj         64.516129     0.083911  0.48600      Emotion   \nTaste_AgeAdj            23.636364     0.081493  0.43259      Sensory   \nNEOFAC_N                40.909091     0.067182  0.50425  Personality   \nAngHostil_Unadj         20.454545     0.065354  0.40929      Emotion   \nER40ANG                 34.090909     0.065080  0.39046      Emotion   \nIWRD_TOT                17.073171     0.062716  0.33328    Cognition   \nMars_Final              16.000000     0.061327  0.44606      Sensory   \nFearSomat_Unadj          5.263158     0.053006  0.44691      Emotion   \nER40NOE                 12.903226     0.046832  0.41809      Emotion   \nPercHostil_Unadj         9.677419     0.045781  0.39934      Emotion   \nPercReject_Unadj         7.407407     0.037067  0.46279      Emotion   \nInstruSupp_Unadj         0.000000     0.036281  0.35826      Emotion   \nER40FEAR                 0.000000     0.036240  0.34259      Emotion   \nER40SAD                  0.000000     0.028999  0.45220      Emotion   \nOdor_AgeAdj              9.090909     0.022999  0.42186      Sensory   \nPainInterf_Tscore        0.000000     0.021068  0.40455      Sensory   \nSCPT_SEN                 0.000000     0.002666  0.36600    Cognition   \nER40HAP                  0.000000    -0.040475  0.43610      Emotion   \n\n                                   phenotype  \\\ntarget                                         \nCogCrystalComp_AgeAdj  CogCrystalComp_AgeAdj   \nPicVocab_AgeAdj              PicVocab_AgeAdj   \nCogTotalComp_AgeAdj      CogTotalComp_AgeAdj   \nReadEng_AgeAdj                ReadEng_AgeAdj   \nCogEarlyComp_AgeAdj      CogEarlyComp_AgeAdj   \nVSPLOT_TC                          VSPLOT_TC   \nCogFluidComp_AgeAdj      CogFluidComp_AgeAdj   \nPMAT24_A_CR                      PMAT24_A_CR   \nListSort_AgeAdj              ListSort_AgeAdj   \nNEOFAC_O                            NEOFAC_O   \nLifeSatisf_Unadj            LifeSatisf_Unadj   \nNEOFAC_C                            NEOFAC_C   \nDDisc_AUC_200                  DDisc_AUC_200   \nAngAggr_Unadj                  AngAggr_Unadj   \nNEOFAC_A                            NEOFAC_A   \nCardSort_AgeAdj              CardSort_AgeAdj   \nPicSeq_AgeAdj                  PicSeq_AgeAdj   \nNEOFAC_E                            NEOFAC_E   \nDexterity_AgeAdj            Dexterity_AgeAdj   \nFearAffect_Unadj            FearAffect_Unadj   \nSCPT_SPEC                          SCPT_SPEC   \nNoise_Comp                        Noise_Comp   \nEmotSupp_Unadj                EmotSupp_Unadj   \nFlanker_AgeAdj                Flanker_AgeAdj   \nSadness_Unadj                  Sadness_Unadj   \nProcSpeed_AgeAdj            ProcSpeed_AgeAdj   \nSelfEff_Unadj                  SelfEff_Unadj   \nLoneliness_Unadj            Loneliness_Unadj   \nMeanPurp_Unadj                MeanPurp_Unadj   \nPercStress_Unadj            PercStress_Unadj   \nFriendship_Unadj            Friendship_Unadj   \nPosAffect_Unadj              PosAffect_Unadj   \nAngAffect_Unadj              AngAffect_Unadj   \nTaste_AgeAdj                    Taste_AgeAdj   \nNEOFAC_N                            NEOFAC_N   \nAngHostil_Unadj              AngHostil_Unadj   \nER40ANG                              ER40ANG   \nIWRD_TOT                            IWRD_TOT   \nMars_Final                        Mars_Final   \nFearSomat_Unadj              FearSomat_Unadj   \nER40NOE                              ER40NOE   \nPercHostil_Unadj            PercHostil_Unadj   \nPercReject_Unadj            PercReject_Unadj   \nInstruSupp_Unadj            InstruSupp_Unadj   \nER40FEAR                            ER40FEAR   \nER40SAD                              ER40SAD   \nOdor_AgeAdj                      Odor_AgeAdj   \nPainInterf_Tscore          PainInterf_Tscore   \nSCPT_SEN                            SCPT_SEN   \nER40HAP                              ER40HAP   \n\n                                                          phenotype_long  \ntarget                                                                    \nCogCrystalComp_AgeAdj  NIH Toolbox Cognition Crystallized Composite: ...  \nPicVocab_AgeAdj        NIH Toolbox Picture Vocabulary Test: Age-Adjus...  \nCogTotalComp_AgeAdj    NIH Toolbox Cognition Total Composite Score: A...  \nReadEng_AgeAdj         NIH Toolbox Oral Reading Recognition Test: Age...  \nCogEarlyComp_AgeAdj    NIH Toolbox Cognition Early Childhood Composit...  \nVSPLOT_TC              Variable Short Penn Line Orientation: Total Nu...  \nCogFluidComp_AgeAdj    NIH Toolbox Cognition Fluid Composite: Age Adj...  \nPMAT24_A_CR            Penn Progressive Matrices: Number of Correct R...  \nListSort_AgeAdj        NIH Toolbox List Sorting Working Memory Test: ...  \nNEOFAC_O                       NEO-FFI Openness to Experience (NEOFAC_O)  \nLifeSatisf_Unadj       NIH Toolbox General Life Satisfaction Survey: ...  \nNEOFAC_C                            NEO-FFI Conscientiousness (NEOFAC_C)  \nDDisc_AUC_200          Delay Discounting: Area Under the Curve for Di...  \nAngAggr_Unadj          NIH Toolbox Anger-Physical Aggression Survey: ...  \nNEOFAC_A                                NEO-FFI Agreeableness (NEOFAC_A)  \nCardSort_AgeAdj        NIH Toolbox Dimensional Change Card Sort Test:...  \nPicSeq_AgeAdj          NIH Toolbox Picture Sequence Memory Test: Age-...  \nNEOFAC_E                                 NEO-FFI Extraversion (NEOFAC_E)  \nDexterity_AgeAdj       NIH Toolbox 9-hole Pegboard Dexterity Test : A...  \nFearAffect_Unadj       NIH Toolbox Fear-Affect Survey: Unadjusted Sca...  \nSCPT_SPEC              Short Penn Continuous Performance Test: Specif...  \nNoise_Comp             NIH Toolbox Words-In-Noise Age 6+: Computed Score  \nEmotSupp_Unadj         NIH Toolbox Emotional Support Survey: Unadjust...  \nFlanker_AgeAdj         NIH Toolbox Flanker Inhibitory Control and Att...  \nSadness_Unadj          NIH Toolbox Sadness Survey: Unadjusted Scale S...  \nProcSpeed_AgeAdj       NIH Toolbox Pattern Comparison Processing Spee...  \nSelfEff_Unadj          NIH Toolbox Self-Efficacy Survey: Unadjusted S...  \nLoneliness_Unadj       NIH Toolbox Loneliness Survey: Unadjusted Scal...  \nMeanPurp_Unadj         NIH Toolbox Meaning and Purpose Survey: Unadju...  \nPercStress_Unadj       NIH Toolbox Perceived Stress Survey: Unadjuste...  \nFriendship_Unadj       NIH Toolbox Friendship Survey: Unadjusted Scal...  \nPosAffect_Unadj        NIH Toolbox Positive Affect Survey: Unadjusted...  \nAngAffect_Unadj        NIH Toolbox Anger-Affect Survey: Unadjusted Sc...  \nTaste_AgeAdj           NIH Toolbox Regional Taste Intensity Age 12+ A...  \nNEOFAC_N                                  NEO-FFI Neuroticism (NEOFAC_N)  \nAngHostil_Unadj        NIH Toolbox Anger-Hostility Survey: Unadjusted...  \nER40ANG                Penn Emotion Recognition Test: Number of Corre...  \nIWRD_TOT               Penn Word Memory Test: Total Number of Correct...  \nMars_Final                         Mars Final Contrast Sensitivity Score  \nFearSomat_Unadj        NIH Toolbox Fear-Somatic Arousal Survey: Unadj...  \nER40NOE                Penn Emotion Recognition Test: Number of Corre...  \nPercHostil_Unadj       NIH Toolbox Perceived Hostility Survey: Unadju...  \nPercReject_Unadj       NIH Toolbox Perceived Rejection Survey: Unadju...  \nInstruSupp_Unadj       NIH Toolbox Instrumental Support Survey: Unadj...  \nER40FEAR               Penn Emotion Recognition Test: Number of Corre...  \nER40SAD                Penn Emotion Recognition Test: Number of Corre...  \nOdor_AgeAdj            NIH Toolbox Odor Identification Age 3+ Age-Adj...  \nPainInterf_Tscore      NIH Toolbox Pain Interference Survey Age 18+: ...  \nSCPT_SEN               Short Penn Continuous Performance Test: Sensit...  \nER40HAP                Penn Emotion Recognition Test: Number of Corre...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>r_discovery</th>\n      <th>p_bias</th>\n      <th>category</th>\n      <th>phenotype</th>\n      <th>phenotype_long</th>\n    </tr>\n    <tr>\n      <th>target</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>CogCrystalComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.478393</td>\n      <td>0.61958</td>\n      <td>Cognition</td>\n      <td>CogCrystalComp_AgeAdj</td>\n      <td>NIH Toolbox Cognition Crystallized Composite: ...</td>\n    </tr>\n    <tr>\n      <th>PicVocab_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.449331</td>\n      <td>0.46671</td>\n      <td>Cognition</td>\n      <td>PicVocab_AgeAdj</td>\n      <td>NIH Toolbox Picture Vocabulary Test: Age-Adjus...</td>\n    </tr>\n    <tr>\n      <th>CogTotalComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.440612</td>\n      <td>0.58473</td>\n      <td>Cognition</td>\n      <td>CogTotalComp_AgeAdj</td>\n      <td>NIH Toolbox Cognition Total Composite Score: A...</td>\n    </tr>\n    <tr>\n      <th>ReadEng_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.421093</td>\n      <td>0.56276</td>\n      <td>Cognition</td>\n      <td>ReadEng_AgeAdj</td>\n      <td>NIH Toolbox Oral Reading Recognition Test: Age...</td>\n    </tr>\n    <tr>\n      <th>CogEarlyComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.318284</td>\n      <td>0.40190</td>\n      <td>Cognition</td>\n      <td>CogEarlyComp_AgeAdj</td>\n      <td>NIH Toolbox Cognition Early Childhood Composit...</td>\n    </tr>\n    <tr>\n      <th>VSPLOT_TC</th>\n      <td>100.000000</td>\n      <td>0.304904</td>\n      <td>0.40537</td>\n      <td>Cognition</td>\n      <td>VSPLOT_TC</td>\n      <td>Variable Short Penn Line Orientation: Total Nu...</td>\n    </tr>\n    <tr>\n      <th>CogFluidComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.286584</td>\n      <td>0.47973</td>\n      <td>Cognition</td>\n      <td>CogFluidComp_AgeAdj</td>\n      <td>NIH Toolbox Cognition Fluid Composite: Age Adj...</td>\n    </tr>\n    <tr>\n      <th>PMAT24_A_CR</th>\n      <td>100.000000</td>\n      <td>0.273698</td>\n      <td>0.45357</td>\n      <td>Cognition</td>\n      <td>PMAT24_A_CR</td>\n      <td>Penn Progressive Matrices: Number of Correct R...</td>\n    </tr>\n    <tr>\n      <th>ListSort_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.261719</td>\n      <td>0.48862</td>\n      <td>Cognition</td>\n      <td>ListSort_AgeAdj</td>\n      <td>NIH Toolbox List Sorting Working Memory Test: ...</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_O</th>\n      <td>100.000000</td>\n      <td>0.252453</td>\n      <td>0.28290</td>\n      <td>Personality</td>\n      <td>NEOFAC_O</td>\n      <td>NEO-FFI Openness to Experience (NEOFAC_O)</td>\n    </tr>\n    <tr>\n      <th>LifeSatisf_Unadj</th>\n      <td>100.000000</td>\n      <td>0.183808</td>\n      <td>0.27947</td>\n      <td>Emotion</td>\n      <td>LifeSatisf_Unadj</td>\n      <td>NIH Toolbox General Life Satisfaction Survey: ...</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_C</th>\n      <td>100.000000</td>\n      <td>0.183556</td>\n      <td>0.43345</td>\n      <td>Personality</td>\n      <td>NEOFAC_C</td>\n      <td>NEO-FFI Conscientiousness (NEOFAC_C)</td>\n    </tr>\n    <tr>\n      <th>DDisc_AUC_200</th>\n      <td>100.000000</td>\n      <td>0.181074</td>\n      <td>0.23137</td>\n      <td>Cognition</td>\n      <td>DDisc_AUC_200</td>\n      <td>Delay Discounting: Area Under the Curve for Di...</td>\n    </tr>\n    <tr>\n      <th>AngAggr_Unadj</th>\n      <td>100.000000</td>\n      <td>0.174860</td>\n      <td>0.19396</td>\n      <td>Emotion</td>\n      <td>AngAggr_Unadj</td>\n      <td>NIH Toolbox Anger-Physical Aggression Survey: ...</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_A</th>\n      <td>100.000000</td>\n      <td>0.170426</td>\n      <td>0.28425</td>\n      <td>Personality</td>\n      <td>NEOFAC_A</td>\n      <td>NEO-FFI Agreeableness (NEOFAC_A)</td>\n    </tr>\n    <tr>\n      <th>CardSort_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.168875</td>\n      <td>0.51301</td>\n      <td>Cognition</td>\n      <td>CardSort_AgeAdj</td>\n      <td>NIH Toolbox Dimensional Change Card Sort Test:...</td>\n    </tr>\n    <tr>\n      <th>PicSeq_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.166367</td>\n      <td>0.26611</td>\n      <td>Cognition</td>\n      <td>PicSeq_AgeAdj</td>\n      <td>NIH Toolbox Picture Sequence Memory Test: Age-...</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_E</th>\n      <td>100.000000</td>\n      <td>0.157592</td>\n      <td>0.40248</td>\n      <td>Personality</td>\n      <td>NEOFAC_E</td>\n      <td>NEO-FFI Extraversion (NEOFAC_E)</td>\n    </tr>\n    <tr>\n      <th>Dexterity_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.148792</td>\n      <td>0.39907</td>\n      <td>Motor</td>\n      <td>Dexterity_AgeAdj</td>\n      <td>NIH Toolbox 9-hole Pegboard Dexterity Test : A...</td>\n    </tr>\n    <tr>\n      <th>FearAffect_Unadj</th>\n      <td>100.000000</td>\n      <td>0.142047</td>\n      <td>0.42446</td>\n      <td>Emotion</td>\n      <td>FearAffect_Unadj</td>\n      <td>NIH Toolbox Fear-Affect Survey: Unadjusted Sca...</td>\n    </tr>\n    <tr>\n      <th>SCPT_SPEC</th>\n      <td>97.727273</td>\n      <td>0.135545</td>\n      <td>0.35593</td>\n      <td>Cognition</td>\n      <td>SCPT_SPEC</td>\n      <td>Short Penn Continuous Performance Test: Specif...</td>\n    </tr>\n    <tr>\n      <th>Noise_Comp</th>\n      <td>94.382022</td>\n      <td>0.134067</td>\n      <td>0.34268</td>\n      <td>Sensory</td>\n      <td>Noise_Comp</td>\n      <td>NIH Toolbox Words-In-Noise Age 6+: Computed Score</td>\n    </tr>\n    <tr>\n      <th>EmotSupp_Unadj</th>\n      <td>85.185185</td>\n      <td>0.124379</td>\n      <td>0.22181</td>\n      <td>Emotion</td>\n      <td>EmotSupp_Unadj</td>\n      <td>NIH Toolbox Emotional Support Survey: Unadjust...</td>\n    </tr>\n    <tr>\n      <th>Flanker_AgeAdj</th>\n      <td>95.238095</td>\n      <td>0.124328</td>\n      <td>0.36686</td>\n      <td>Cognition</td>\n      <td>Flanker_AgeAdj</td>\n      <td>NIH Toolbox Flanker Inhibitory Control and Att...</td>\n    </tr>\n    <tr>\n      <th>Sadness_Unadj</th>\n      <td>96.341463</td>\n      <td>0.121922</td>\n      <td>0.40169</td>\n      <td>Emotion</td>\n      <td>Sadness_Unadj</td>\n      <td>NIH Toolbox Sadness Survey: Unadjusted Scale S...</td>\n    </tr>\n    <tr>\n      <th>ProcSpeed_AgeAdj</th>\n      <td>90.277778</td>\n      <td>0.111175</td>\n      <td>0.44762</td>\n      <td>Cognition</td>\n      <td>ProcSpeed_AgeAdj</td>\n      <td>NIH Toolbox Pattern Comparison Processing Spee...</td>\n    </tr>\n    <tr>\n      <th>SelfEff_Unadj</th>\n      <td>73.239437</td>\n      <td>0.107843</td>\n      <td>0.41418</td>\n      <td>Emotion</td>\n      <td>SelfEff_Unadj</td>\n      <td>NIH Toolbox Self-Efficacy Survey: Unadjusted S...</td>\n    </tr>\n    <tr>\n      <th>Loneliness_Unadj</th>\n      <td>75.384615</td>\n      <td>0.098174</td>\n      <td>0.42129</td>\n      <td>Emotion</td>\n      <td>Loneliness_Unadj</td>\n      <td>NIH Toolbox Loneliness Survey: Unadjusted Scal...</td>\n    </tr>\n    <tr>\n      <th>MeanPurp_Unadj</th>\n      <td>63.768116</td>\n      <td>0.092541</td>\n      <td>0.39125</td>\n      <td>Emotion</td>\n      <td>MeanPurp_Unadj</td>\n      <td>NIH Toolbox Meaning and Purpose Survey: Unadju...</td>\n    </tr>\n    <tr>\n      <th>PercStress_Unadj</th>\n      <td>59.701493</td>\n      <td>0.089891</td>\n      <td>0.42411</td>\n      <td>Emotion</td>\n      <td>PercStress_Unadj</td>\n      <td>NIH Toolbox Perceived Stress Survey: Unadjuste...</td>\n    </tr>\n    <tr>\n      <th>Friendship_Unadj</th>\n      <td>46.666667</td>\n      <td>0.088484</td>\n      <td>0.34509</td>\n      <td>Emotion</td>\n      <td>Friendship_Unadj</td>\n      <td>NIH Toolbox Friendship Survey: Unadjusted Scal...</td>\n    </tr>\n    <tr>\n      <th>PosAffect_Unadj</th>\n      <td>60.714286</td>\n      <td>0.084311</td>\n      <td>0.35642</td>\n      <td>Emotion</td>\n      <td>PosAffect_Unadj</td>\n      <td>NIH Toolbox Positive Affect Survey: Unadjusted...</td>\n    </tr>\n    <tr>\n      <th>AngAffect_Unadj</th>\n      <td>64.516129</td>\n      <td>0.083911</td>\n      <td>0.48600</td>\n      <td>Emotion</td>\n      <td>AngAffect_Unadj</td>\n      <td>NIH Toolbox Anger-Affect Survey: Unadjusted Sc...</td>\n    </tr>\n    <tr>\n      <th>Taste_AgeAdj</th>\n      <td>23.636364</td>\n      <td>0.081493</td>\n      <td>0.43259</td>\n      <td>Sensory</td>\n      <td>Taste_AgeAdj</td>\n      <td>NIH Toolbox Regional Taste Intensity Age 12+ A...</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_N</th>\n      <td>40.909091</td>\n      <td>0.067182</td>\n      <td>0.50425</td>\n      <td>Personality</td>\n      <td>NEOFAC_N</td>\n      <td>NEO-FFI Neuroticism (NEOFAC_N)</td>\n    </tr>\n    <tr>\n      <th>AngHostil_Unadj</th>\n      <td>20.454545</td>\n      <td>0.065354</td>\n      <td>0.40929</td>\n      <td>Emotion</td>\n      <td>AngHostil_Unadj</td>\n      <td>NIH Toolbox Anger-Hostility Survey: Unadjusted...</td>\n    </tr>\n    <tr>\n      <th>ER40ANG</th>\n      <td>34.090909</td>\n      <td>0.065080</td>\n      <td>0.39046</td>\n      <td>Emotion</td>\n      <td>ER40ANG</td>\n      <td>Penn Emotion Recognition Test: Number of Corre...</td>\n    </tr>\n    <tr>\n      <th>IWRD_TOT</th>\n      <td>17.073171</td>\n      <td>0.062716</td>\n      <td>0.33328</td>\n      <td>Cognition</td>\n      <td>IWRD_TOT</td>\n      <td>Penn Word Memory Test: Total Number of Correct...</td>\n    </tr>\n    <tr>\n      <th>Mars_Final</th>\n      <td>16.000000</td>\n      <td>0.061327</td>\n      <td>0.44606</td>\n      <td>Sensory</td>\n      <td>Mars_Final</td>\n      <td>Mars Final Contrast Sensitivity Score</td>\n    </tr>\n    <tr>\n      <th>FearSomat_Unadj</th>\n      <td>5.263158</td>\n      <td>0.053006</td>\n      <td>0.44691</td>\n      <td>Emotion</td>\n      <td>FearSomat_Unadj</td>\n      <td>NIH Toolbox Fear-Somatic Arousal Survey: Unadj...</td>\n    </tr>\n    <tr>\n      <th>ER40NOE</th>\n      <td>12.903226</td>\n      <td>0.046832</td>\n      <td>0.41809</td>\n      <td>Emotion</td>\n      <td>ER40NOE</td>\n      <td>Penn Emotion Recognition Test: Number of Corre...</td>\n    </tr>\n    <tr>\n      <th>PercHostil_Unadj</th>\n      <td>9.677419</td>\n      <td>0.045781</td>\n      <td>0.39934</td>\n      <td>Emotion</td>\n      <td>PercHostil_Unadj</td>\n      <td>NIH Toolbox Perceived Hostility Survey: Unadju...</td>\n    </tr>\n    <tr>\n      <th>PercReject_Unadj</th>\n      <td>7.407407</td>\n      <td>0.037067</td>\n      <td>0.46279</td>\n      <td>Emotion</td>\n      <td>PercReject_Unadj</td>\n      <td>NIH Toolbox Perceived Rejection Survey: Unadju...</td>\n    </tr>\n    <tr>\n      <th>InstruSupp_Unadj</th>\n      <td>0.000000</td>\n      <td>0.036281</td>\n      <td>0.35826</td>\n      <td>Emotion</td>\n      <td>InstruSupp_Unadj</td>\n      <td>NIH Toolbox Instrumental Support Survey: Unadj...</td>\n    </tr>\n    <tr>\n      <th>ER40FEAR</th>\n      <td>0.000000</td>\n      <td>0.036240</td>\n      <td>0.34259</td>\n      <td>Emotion</td>\n      <td>ER40FEAR</td>\n      <td>Penn Emotion Recognition Test: Number of Corre...</td>\n    </tr>\n    <tr>\n      <th>ER40SAD</th>\n      <td>0.000000</td>\n      <td>0.028999</td>\n      <td>0.45220</td>\n      <td>Emotion</td>\n      <td>ER40SAD</td>\n      <td>Penn Emotion Recognition Test: Number of Corre...</td>\n    </tr>\n    <tr>\n      <th>Odor_AgeAdj</th>\n      <td>9.090909</td>\n      <td>0.022999</td>\n      <td>0.42186</td>\n      <td>Sensory</td>\n      <td>Odor_AgeAdj</td>\n      <td>NIH Toolbox Odor Identification Age 3+ Age-Adj...</td>\n    </tr>\n    <tr>\n      <th>PainInterf_Tscore</th>\n      <td>0.000000</td>\n      <td>0.021068</td>\n      <td>0.40455</td>\n      <td>Sensory</td>\n      <td>PainInterf_Tscore</td>\n      <td>NIH Toolbox Pain Interference Survey Age 18+: ...</td>\n    </tr>\n    <tr>\n      <th>SCPT_SEN</th>\n      <td>0.000000</td>\n      <td>0.002666</td>\n      <td>0.36600</td>\n      <td>Cognition</td>\n      <td>SCPT_SEN</td>\n      <td>Short Penn Continuous Performance Test: Sensit...</td>\n    </tr>\n    <tr>\n      <th>ER40HAP</th>\n      <td>0.000000</td>\n      <td>-0.040475</td>\n      <td>0.43610</td>\n      <td>Emotion</td>\n      <td>ER40HAP</td>\n      <td>Penn Emotion Recognition Test: Number of Corre...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in df_plot.index:\n",
    "    df_plot.loc[i, 'phenotype_long'] = phenotypes.loc[phenotypes.columnHeader==i, 'fullDisplayName'].values[0]\n",
    "df_plot"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "outputs": [
    {
     "data": {
      "text/plain": "                                0  r_discovery   p_bias     category  \\\ntarget                                                                 \nCogCrystalComp_AgeAdj  100.000000     0.478393  0.61958    Cognition   \nPicVocab_AgeAdj        100.000000     0.449331  0.46671    Cognition   \nCogTotalComp_AgeAdj    100.000000     0.440612  0.58473    Cognition   \nReadEng_AgeAdj         100.000000     0.421093  0.56276    Cognition   \nCogEarlyComp_AgeAdj    100.000000     0.318284  0.40190    Cognition   \nVSPLOT_TC              100.000000     0.304904  0.40537    Cognition   \nCogFluidComp_AgeAdj    100.000000     0.286584  0.47973    Cognition   \nPMAT24_A_CR            100.000000     0.273698  0.45357    Cognition   \nListSort_AgeAdj        100.000000     0.261719  0.48862    Cognition   \nNEOFAC_O               100.000000     0.252453  0.28290  Personality   \nLifeSatisf_Unadj       100.000000     0.183808  0.27947      Emotion   \nNEOFAC_C               100.000000     0.183556  0.43345  Personality   \nDDisc_AUC_200          100.000000     0.181074  0.23137    Cognition   \nAngAggr_Unadj          100.000000     0.174860  0.19396      Emotion   \nNEOFAC_A               100.000000     0.170426  0.28425  Personality   \nCardSort_AgeAdj        100.000000     0.168875  0.51301    Cognition   \nPicSeq_AgeAdj          100.000000     0.166367  0.26611    Cognition   \nNEOFAC_E               100.000000     0.157592  0.40248  Personality   \nDexterity_AgeAdj       100.000000     0.148792  0.39907        Motor   \nFearAffect_Unadj       100.000000     0.142047  0.42446      Emotion   \nSCPT_SPEC               97.727273     0.135545  0.35593    Cognition   \nNoise_Comp              94.382022     0.134067  0.34268      Sensory   \nEmotSupp_Unadj          85.185185     0.124379  0.22181      Emotion   \nFlanker_AgeAdj          95.238095     0.124328  0.36686    Cognition   \nSadness_Unadj           96.341463     0.121922  0.40169      Emotion   \nProcSpeed_AgeAdj        90.277778     0.111175  0.44762    Cognition   \nSelfEff_Unadj           73.239437     0.107843  0.41418      Emotion   \nLoneliness_Unadj        75.384615     0.098174  0.42129      Emotion   \nMeanPurp_Unadj          63.768116     0.092541  0.39125      Emotion   \nPercStress_Unadj        59.701493     0.089891  0.42411      Emotion   \nFriendship_Unadj        46.666667     0.088484  0.34509      Emotion   \nPosAffect_Unadj         60.714286     0.084311  0.35642      Emotion   \nAngAffect_Unadj         64.516129     0.083911  0.48600      Emotion   \nTaste_AgeAdj            23.636364     0.081493  0.43259      Sensory   \nNEOFAC_N                40.909091     0.067182  0.50425  Personality   \nAngHostil_Unadj         20.454545     0.065354  0.40929      Emotion   \nER40ANG                 34.090909     0.065080  0.39046      Emotion   \nIWRD_TOT                17.073171     0.062716  0.33328    Cognition   \nMars_Final              16.000000     0.061327  0.44606      Sensory   \nFearSomat_Unadj          5.263158     0.053006  0.44691      Emotion   \nER40NOE                 12.903226     0.046832  0.41809      Emotion   \nPercHostil_Unadj         9.677419     0.045781  0.39934      Emotion   \nPercReject_Unadj         7.407407     0.037067  0.46279      Emotion   \nInstruSupp_Unadj         0.000000     0.036281  0.35826      Emotion   \nER40FEAR                 0.000000     0.036240  0.34259      Emotion   \nER40SAD                  0.000000     0.028999  0.45220      Emotion   \nOdor_AgeAdj              9.090909     0.022999  0.42186      Sensory   \nPainInterf_Tscore        0.000000     0.021068  0.40455      Sensory   \nSCPT_SEN                 0.000000     0.002666  0.36600    Cognition   \nER40HAP                  0.000000    -0.040475  0.43610      Emotion   \n\n                                   phenotype  \\\ntarget                                         \nCogCrystalComp_AgeAdj  CogCrystalComp_AgeAdj   \nPicVocab_AgeAdj              PicVocab_AgeAdj   \nCogTotalComp_AgeAdj      CogTotalComp_AgeAdj   \nReadEng_AgeAdj                ReadEng_AgeAdj   \nCogEarlyComp_AgeAdj      CogEarlyComp_AgeAdj   \nVSPLOT_TC                          VSPLOT_TC   \nCogFluidComp_AgeAdj      CogFluidComp_AgeAdj   \nPMAT24_A_CR                      PMAT24_A_CR   \nListSort_AgeAdj              ListSort_AgeAdj   \nNEOFAC_O                            NEOFAC_O   \nLifeSatisf_Unadj            LifeSatisf_Unadj   \nNEOFAC_C                            NEOFAC_C   \nDDisc_AUC_200                  DDisc_AUC_200   \nAngAggr_Unadj                  AngAggr_Unadj   \nNEOFAC_A                            NEOFAC_A   \nCardSort_AgeAdj              CardSort_AgeAdj   \nPicSeq_AgeAdj                  PicSeq_AgeAdj   \nNEOFAC_E                            NEOFAC_E   \nDexterity_AgeAdj            Dexterity_AgeAdj   \nFearAffect_Unadj            FearAffect_Unadj   \nSCPT_SPEC                          SCPT_SPEC   \nNoise_Comp                        Noise_Comp   \nEmotSupp_Unadj                EmotSupp_Unadj   \nFlanker_AgeAdj                Flanker_AgeAdj   \nSadness_Unadj                  Sadness_Unadj   \nProcSpeed_AgeAdj            ProcSpeed_AgeAdj   \nSelfEff_Unadj                  SelfEff_Unadj   \nLoneliness_Unadj            Loneliness_Unadj   \nMeanPurp_Unadj                MeanPurp_Unadj   \nPercStress_Unadj            PercStress_Unadj   \nFriendship_Unadj            Friendship_Unadj   \nPosAffect_Unadj              PosAffect_Unadj   \nAngAffect_Unadj              AngAffect_Unadj   \nTaste_AgeAdj                    Taste_AgeAdj   \nNEOFAC_N                            NEOFAC_N   \nAngHostil_Unadj              AngHostil_Unadj   \nER40ANG                              ER40ANG   \nIWRD_TOT                            IWRD_TOT   \nMars_Final                        Mars_Final   \nFearSomat_Unadj              FearSomat_Unadj   \nER40NOE                              ER40NOE   \nPercHostil_Unadj            PercHostil_Unadj   \nPercReject_Unadj            PercReject_Unadj   \nInstruSupp_Unadj            InstruSupp_Unadj   \nER40FEAR                            ER40FEAR   \nER40SAD                              ER40SAD   \nOdor_AgeAdj                      Odor_AgeAdj   \nPainInterf_Tscore          PainInterf_Tscore   \nSCPT_SEN                            SCPT_SEN   \nER40HAP                              ER40HAP   \n\n                                                          phenotype_long  \ntarget                                                                    \nCogCrystalComp_AgeAdj  NIH Toolbox Cognition Crystallized Composite: ...  \nPicVocab_AgeAdj        NIH Toolbox Picture Vocabulary Test: Age-Adjus...  \nCogTotalComp_AgeAdj    NIH Toolbox Cognition Total Composite Score: A...  \nReadEng_AgeAdj         NIH Toolbox Oral Reading Recognition Test: Age...  \nCogEarlyComp_AgeAdj    NIH Toolbox Cognition Early Childhood Composit...  \nVSPLOT_TC              Variable Short Penn Line Orientation: Total Nu...  \nCogFluidComp_AgeAdj    NIH Toolbox Cognition Fluid Composite: Age Adj...  \nPMAT24_A_CR            Penn Progressive Matrices: Number of Correct R...  \nListSort_AgeAdj        NIH Toolbox List Sorting Working Memory Test: ...  \nNEOFAC_O                       NEO-FFI Openness to Experience (NEOFAC_O)  \nLifeSatisf_Unadj       NIH Toolbox General Life Satisfaction Survey: ...  \nNEOFAC_C                            NEO-FFI Conscientiousness (NEOFAC_C)  \nDDisc_AUC_200          Delay Discounting: Area Under the Curve for Di...  \nAngAggr_Unadj          NIH Toolbox Anger-Physical Aggression Survey: ...  \nNEOFAC_A                                NEO-FFI Agreeableness (NEOFAC_A)  \nCardSort_AgeAdj        NIH Toolbox Dimensional Change Card Sort Test:...  \nPicSeq_AgeAdj          NIH Toolbox Picture Sequence Memory Test: Age-...  \nNEOFAC_E                                 NEO-FFI Extraversion (NEOFAC_E)  \nDexterity_AgeAdj       NIH Toolbox 9-hole Pegboard Dexterity Test : A...  \nFearAffect_Unadj       NIH Toolbox Fear-Affect Survey: Unadjusted Sca...  \nSCPT_SPEC              Short Penn Continuous Performance Test: Specif...  \nNoise_Comp             NIH Toolbox Words-In-Noise Age 6+: Computed Score  \nEmotSupp_Unadj         NIH Toolbox Emotional Support Survey: Unadjust...  \nFlanker_AgeAdj         NIH Toolbox Flanker Inhibitory Control and Att...  \nSadness_Unadj          NIH Toolbox Sadness Survey: Unadjusted Scale S...  \nProcSpeed_AgeAdj       NIH Toolbox Pattern Comparison Processing Spee...  \nSelfEff_Unadj          NIH Toolbox Self-Efficacy Survey: Unadjusted S...  \nLoneliness_Unadj       NIH Toolbox Loneliness Survey: Unadjusted Scal...  \nMeanPurp_Unadj         NIH Toolbox Meaning and Purpose Survey: Unadju...  \nPercStress_Unadj       NIH Toolbox Perceived Stress Survey: Unadjuste...  \nFriendship_Unadj       NIH Toolbox Friendship Survey: Unadjusted Scal...  \nPosAffect_Unadj        NIH Toolbox Positive Affect Survey: Unadjusted...  \nAngAffect_Unadj        NIH Toolbox Anger-Affect Survey: Unadjusted Sc...  \nTaste_AgeAdj           NIH Toolbox Regional Taste Intensity Age 12+ A...  \nNEOFAC_N                                  NEO-FFI Neuroticism (NEOFAC_N)  \nAngHostil_Unadj        NIH Toolbox Anger-Hostility Survey: Unadjusted...  \nER40ANG                Penn Emotion Recognition Test: Number of Corre...  \nIWRD_TOT               Penn Word Memory Test: Total Number of Correct...  \nMars_Final                         Mars Final Contrast Sensitivity Score  \nFearSomat_Unadj        NIH Toolbox Fear-Somatic Arousal Survey: Unadj...  \nER40NOE                Penn Emotion Recognition Test: Number of Corre...  \nPercHostil_Unadj       NIH Toolbox Perceived Hostility Survey: Unadju...  \nPercReject_Unadj       NIH Toolbox Perceived Rejection Survey: Unadju...  \nInstruSupp_Unadj       NIH Toolbox Instrumental Support Survey: Unadj...  \nER40FEAR               Penn Emotion Recognition Test: Number of Corre...  \nER40SAD                Penn Emotion Recognition Test: Number of Corre...  \nOdor_AgeAdj            NIH Toolbox Odor Identification Age 3+ Age-Adj...  \nPainInterf_Tscore      NIH Toolbox Pain Interference Survey Age 18+: ...  \nSCPT_SEN               Short Penn Continuous Performance Test: Sensit...  \nER40HAP                Penn Emotion Recognition Test: Number of Corre...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>r_discovery</th>\n      <th>p_bias</th>\n      <th>category</th>\n      <th>phenotype</th>\n      <th>phenotype_long</th>\n    </tr>\n    <tr>\n      <th>target</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>CogCrystalComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.478393</td>\n      <td>0.61958</td>\n      <td>Cognition</td>\n      <td>CogCrystalComp_AgeAdj</td>\n      <td>NIH Toolbox Cognition Crystallized Composite: ...</td>\n    </tr>\n    <tr>\n      <th>PicVocab_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.449331</td>\n      <td>0.46671</td>\n      <td>Cognition</td>\n      <td>PicVocab_AgeAdj</td>\n      <td>NIH Toolbox Picture Vocabulary Test: Age-Adjus...</td>\n    </tr>\n    <tr>\n      <th>CogTotalComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.440612</td>\n      <td>0.58473</td>\n      <td>Cognition</td>\n      <td>CogTotalComp_AgeAdj</td>\n      <td>NIH Toolbox Cognition Total Composite Score: A...</td>\n    </tr>\n    <tr>\n      <th>ReadEng_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.421093</td>\n      <td>0.56276</td>\n      <td>Cognition</td>\n      <td>ReadEng_AgeAdj</td>\n      <td>NIH Toolbox Oral Reading Recognition Test: Age...</td>\n    </tr>\n    <tr>\n      <th>CogEarlyComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.318284</td>\n      <td>0.40190</td>\n      <td>Cognition</td>\n      <td>CogEarlyComp_AgeAdj</td>\n      <td>NIH Toolbox Cognition Early Childhood Composit...</td>\n    </tr>\n    <tr>\n      <th>VSPLOT_TC</th>\n      <td>100.000000</td>\n      <td>0.304904</td>\n      <td>0.40537</td>\n      <td>Cognition</td>\n      <td>VSPLOT_TC</td>\n      <td>Variable Short Penn Line Orientation: Total Nu...</td>\n    </tr>\n    <tr>\n      <th>CogFluidComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.286584</td>\n      <td>0.47973</td>\n      <td>Cognition</td>\n      <td>CogFluidComp_AgeAdj</td>\n      <td>NIH Toolbox Cognition Fluid Composite: Age Adj...</td>\n    </tr>\n    <tr>\n      <th>PMAT24_A_CR</th>\n      <td>100.000000</td>\n      <td>0.273698</td>\n      <td>0.45357</td>\n      <td>Cognition</td>\n      <td>PMAT24_A_CR</td>\n      <td>Penn Progressive Matrices: Number of Correct R...</td>\n    </tr>\n    <tr>\n      <th>ListSort_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.261719</td>\n      <td>0.48862</td>\n      <td>Cognition</td>\n      <td>ListSort_AgeAdj</td>\n      <td>NIH Toolbox List Sorting Working Memory Test: ...</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_O</th>\n      <td>100.000000</td>\n      <td>0.252453</td>\n      <td>0.28290</td>\n      <td>Personality</td>\n      <td>NEOFAC_O</td>\n      <td>NEO-FFI Openness to Experience (NEOFAC_O)</td>\n    </tr>\n    <tr>\n      <th>LifeSatisf_Unadj</th>\n      <td>100.000000</td>\n      <td>0.183808</td>\n      <td>0.27947</td>\n      <td>Emotion</td>\n      <td>LifeSatisf_Unadj</td>\n      <td>NIH Toolbox General Life Satisfaction Survey: ...</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_C</th>\n      <td>100.000000</td>\n      <td>0.183556</td>\n      <td>0.43345</td>\n      <td>Personality</td>\n      <td>NEOFAC_C</td>\n      <td>NEO-FFI Conscientiousness (NEOFAC_C)</td>\n    </tr>\n    <tr>\n      <th>DDisc_AUC_200</th>\n      <td>100.000000</td>\n      <td>0.181074</td>\n      <td>0.23137</td>\n      <td>Cognition</td>\n      <td>DDisc_AUC_200</td>\n      <td>Delay Discounting: Area Under the Curve for Di...</td>\n    </tr>\n    <tr>\n      <th>AngAggr_Unadj</th>\n      <td>100.000000</td>\n      <td>0.174860</td>\n      <td>0.19396</td>\n      <td>Emotion</td>\n      <td>AngAggr_Unadj</td>\n      <td>NIH Toolbox Anger-Physical Aggression Survey: ...</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_A</th>\n      <td>100.000000</td>\n      <td>0.170426</td>\n      <td>0.28425</td>\n      <td>Personality</td>\n      <td>NEOFAC_A</td>\n      <td>NEO-FFI Agreeableness (NEOFAC_A)</td>\n    </tr>\n    <tr>\n      <th>CardSort_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.168875</td>\n      <td>0.51301</td>\n      <td>Cognition</td>\n      <td>CardSort_AgeAdj</td>\n      <td>NIH Toolbox Dimensional Change Card Sort Test:...</td>\n    </tr>\n    <tr>\n      <th>PicSeq_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.166367</td>\n      <td>0.26611</td>\n      <td>Cognition</td>\n      <td>PicSeq_AgeAdj</td>\n      <td>NIH Toolbox Picture Sequence Memory Test: Age-...</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_E</th>\n      <td>100.000000</td>\n      <td>0.157592</td>\n      <td>0.40248</td>\n      <td>Personality</td>\n      <td>NEOFAC_E</td>\n      <td>NEO-FFI Extraversion (NEOFAC_E)</td>\n    </tr>\n    <tr>\n      <th>Dexterity_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.148792</td>\n      <td>0.39907</td>\n      <td>Motor</td>\n      <td>Dexterity_AgeAdj</td>\n      <td>NIH Toolbox 9-hole Pegboard Dexterity Test : A...</td>\n    </tr>\n    <tr>\n      <th>FearAffect_Unadj</th>\n      <td>100.000000</td>\n      <td>0.142047</td>\n      <td>0.42446</td>\n      <td>Emotion</td>\n      <td>FearAffect_Unadj</td>\n      <td>NIH Toolbox Fear-Affect Survey: Unadjusted Sca...</td>\n    </tr>\n    <tr>\n      <th>SCPT_SPEC</th>\n      <td>97.727273</td>\n      <td>0.135545</td>\n      <td>0.35593</td>\n      <td>Cognition</td>\n      <td>SCPT_SPEC</td>\n      <td>Short Penn Continuous Performance Test: Specif...</td>\n    </tr>\n    <tr>\n      <th>Noise_Comp</th>\n      <td>94.382022</td>\n      <td>0.134067</td>\n      <td>0.34268</td>\n      <td>Sensory</td>\n      <td>Noise_Comp</td>\n      <td>NIH Toolbox Words-In-Noise Age 6+: Computed Score</td>\n    </tr>\n    <tr>\n      <th>EmotSupp_Unadj</th>\n      <td>85.185185</td>\n      <td>0.124379</td>\n      <td>0.22181</td>\n      <td>Emotion</td>\n      <td>EmotSupp_Unadj</td>\n      <td>NIH Toolbox Emotional Support Survey: Unadjust...</td>\n    </tr>\n    <tr>\n      <th>Flanker_AgeAdj</th>\n      <td>95.238095</td>\n      <td>0.124328</td>\n      <td>0.36686</td>\n      <td>Cognition</td>\n      <td>Flanker_AgeAdj</td>\n      <td>NIH Toolbox Flanker Inhibitory Control and Att...</td>\n    </tr>\n    <tr>\n      <th>Sadness_Unadj</th>\n      <td>96.341463</td>\n      <td>0.121922</td>\n      <td>0.40169</td>\n      <td>Emotion</td>\n      <td>Sadness_Unadj</td>\n      <td>NIH Toolbox Sadness Survey: Unadjusted Scale S...</td>\n    </tr>\n    <tr>\n      <th>ProcSpeed_AgeAdj</th>\n      <td>90.277778</td>\n      <td>0.111175</td>\n      <td>0.44762</td>\n      <td>Cognition</td>\n      <td>ProcSpeed_AgeAdj</td>\n      <td>NIH Toolbox Pattern Comparison Processing Spee...</td>\n    </tr>\n    <tr>\n      <th>SelfEff_Unadj</th>\n      <td>73.239437</td>\n      <td>0.107843</td>\n      <td>0.41418</td>\n      <td>Emotion</td>\n      <td>SelfEff_Unadj</td>\n      <td>NIH Toolbox Self-Efficacy Survey: Unadjusted S...</td>\n    </tr>\n    <tr>\n      <th>Loneliness_Unadj</th>\n      <td>75.384615</td>\n      <td>0.098174</td>\n      <td>0.42129</td>\n      <td>Emotion</td>\n      <td>Loneliness_Unadj</td>\n      <td>NIH Toolbox Loneliness Survey: Unadjusted Scal...</td>\n    </tr>\n    <tr>\n      <th>MeanPurp_Unadj</th>\n      <td>63.768116</td>\n      <td>0.092541</td>\n      <td>0.39125</td>\n      <td>Emotion</td>\n      <td>MeanPurp_Unadj</td>\n      <td>NIH Toolbox Meaning and Purpose Survey: Unadju...</td>\n    </tr>\n    <tr>\n      <th>PercStress_Unadj</th>\n      <td>59.701493</td>\n      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<td>0.081493</td>\n      <td>0.43259</td>\n      <td>Sensory</td>\n      <td>Taste_AgeAdj</td>\n      <td>NIH Toolbox Regional Taste Intensity Age 12+ A...</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_N</th>\n      <td>40.909091</td>\n      <td>0.067182</td>\n      <td>0.50425</td>\n      <td>Personality</td>\n      <td>NEOFAC_N</td>\n      <td>NEO-FFI Neuroticism (NEOFAC_N)</td>\n    </tr>\n    <tr>\n      <th>AngHostil_Unadj</th>\n      <td>20.454545</td>\n      <td>0.065354</td>\n      <td>0.40929</td>\n      <td>Emotion</td>\n      <td>AngHostil_Unadj</td>\n      <td>NIH Toolbox Anger-Hostility Survey: Unadjusted...</td>\n    </tr>\n    <tr>\n      <th>ER40ANG</th>\n      <td>34.090909</td>\n      <td>0.065080</td>\n      <td>0.39046</td>\n      <td>Emotion</td>\n      <td>ER40ANG</td>\n      <td>Penn Emotion Recognition Test: Number of Corre...</td>\n    </tr>\n    <tr>\n      <th>IWRD_TOT</th>\n      <td>17.073171</td>\n      <td>0.062716</td>\n      <td>0.33328</td>\n      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<td>PercHostil_Unadj</td>\n      <td>NIH Toolbox Perceived Hostility Survey: Unadju...</td>\n    </tr>\n    <tr>\n      <th>PercReject_Unadj</th>\n      <td>7.407407</td>\n      <td>0.037067</td>\n      <td>0.46279</td>\n      <td>Emotion</td>\n      <td>PercReject_Unadj</td>\n      <td>NIH Toolbox Perceived Rejection Survey: Unadju...</td>\n    </tr>\n    <tr>\n      <th>InstruSupp_Unadj</th>\n      <td>0.000000</td>\n      <td>0.036281</td>\n      <td>0.35826</td>\n      <td>Emotion</td>\n      <td>InstruSupp_Unadj</td>\n      <td>NIH Toolbox Instrumental Support Survey: Unadj...</td>\n    </tr>\n    <tr>\n      <th>ER40FEAR</th>\n      <td>0.000000</td>\n      <td>0.036240</td>\n      <td>0.34259</td>\n      <td>Emotion</td>\n      <td>ER40FEAR</td>\n      <td>Penn Emotion Recognition Test: Number of Corre...</td>\n    </tr>\n    <tr>\n      <th>ER40SAD</th>\n      <td>0.000000</td>\n      <td>0.028999</td>\n      <td>0.45220</td>\n      <td>Emotion</td>\n      <td>ER40SAD</td>\n    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     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_plot"
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  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [],
   "source": [
    "df_plot.loc[:, 'phenotype'] = ['CogCrystal', 'PicVocab', 'CogTotal',\n",
    " 'ReadEng', 'CogEarly', 'Visuospat', 'CogFluid',\n",
    " 'FluidIQ', 'ListSort', 'DelayDisc', 'CardSort',\n",
    " 'PicSeqMem', 'ContPerfSpec', 'Flanker', 'ProcSpeed',\n",
    " 'WordMem', 'ContPerfSen', 'Open', 'Conscient', 'Agreeable', 'Extrovert',\n",
    " 'Neurotic', 'Dexterity', 'NoiseComp',\n",
    " 'Taste', 'ContrastSens', 'Odor', 'PainInterf',\n",
    " 'LifeSatisf', 'AngerAggr', 'FearAffect', 'EmotSupp',\n",
    " 'Sadness', 'SelfEff', 'Loneliness', 'MeaningPurp',\n",
    " 'PercStress', 'Friendship', 'PosAffect', 'AngerAffect',\n",
    " 'AngerHost', 'AngerRec', 'FearSomat', 'NeutralRec',\n",
    " 'PercHost', 'PercReject', 'InstruSupp', 'FearRec',\n",
    " 'SadRec', 'HappyRec']"
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  {
   "cell_type": "code",
   "execution_count": 108,
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probability with n=500\"},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('431784b1-b06c-4126-9534-885327dd7dbc');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>       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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig = px.bar(df_plot, y=0, x='phenotype', text_auto='.2s',\n",
    "            title=\"Replication probability with n=500\",\n",
    "            labels={'0':'$P_{rep}$'},\n",
    "            hover_data=['category', 'phenotype_long', 'r_discovery', 'p_bias'], color='category')\n",
    "fig.update_traces(textfont_size=12, textangle=0, textposition=\"outside\", cliponaxis=False)\n",
    "fig.show()\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_219850/776328709.py:9: FutureWarning:\n",
      "\n",
      "The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "colors = [\"#de324c\",\"#f4895f\",\"#95cf92\",\"#9656a2\",\"#369acc\"]\n",
    "\n",
    "cols = [ colors[c] for c in pd.Categorical(df_plot['category']).codes]\n",
    "\n",
    "df_plot['category'] = pd.Categorical(df_plot['category'])\n",
    "df_plot['category'].cat.reorder_categories(['Emotion', 'Sensory',  'Motor', 'Personality',  'Cognition'], inplace= True)\n",
    "df_plot = df_plot.sort_values(by=['category', 'r_discovery'], ascending=False)\n",
    "\n",
    "trace1l = go.Scatter( name='',\n",
    "            x=df_plot['phenotype'].values,\n",
    "            y=[80] * len(df_plot['phenotype'].values),\n",
    "            mode='lines',\n",
    "            line = dict(color='gray', width=1, dash='dash'),\n",
    "            showlegend=False,\n",
    "            hovertemplate=\"Replication threshold\"\n",
    "        )\n",
    "\n",
    "trace1 = go.Bar(x=df_plot['phenotype'].values,y=df_plot[0].values, name='',\n",
    "                marker_color=cols, showlegend=False,\n",
    "                customdata=df_plot.values,\n",
    "                #texttemplate=\"%{y:0.0f}%\",\n",
    "                textposition=\"outside\",\n",
    "                textangle=0,\n",
    "                textfont_color=\"gray\",\n",
    "                hovertemplate=\"<br>\".join([\n",
    "                    \"Domain: %{customdata[3]}\",\n",
    "                    \"Effect Size: r=%{customdata[1]:.2f}\",\n",
    "                    \"Replication Probability: %{customdata[0]:.0f}%\",\n",
    "                    \"Motion bias: p=%{customdata[2]:.2f}\",\n",
    "                    \"Description: %{customdata[5]}\"\n",
    "                ])\n",
    "                #hovertext=pd.Categorical(df_plot['category'])\n",
    "                )\n",
    "\n",
    "trace2l = go.Scatter( name='',\n",
    "            x=df_plot['phenotype'].values,\n",
    "            y=[0.074] * len(df_plot['phenotype'].values),\n",
    "            mode='lines',\n",
    "            line = dict(color='gray', width=1, dash='dash'),\n",
    "            showlegend=False,\n",
    "            hovertemplate=\"Significance threshold\",\n",
    "            visible=False\n",
    "        )\n",
    "\n",
    "\n",
    "trace2 = go.Bar(x=df_plot['phenotype'].values,y=df_plot['r_discovery'], visible=False, name='',\n",
    "                marker_color=cols, showlegend=False,\n",
    "                customdata=df_plot.values,\n",
    "                #texttemplate=\"%{y:0.2f}\",\n",
    "                textposition=\"outside\",\n",
    "                textangle=0,\n",
    "                textfont_color=\"gray\",\n",
    "                hovertemplate=\"<br>\".join([\n",
    "                    \"Domain: %{customdata[3]}\",\n",
    "                    \"Effect Size: r=%{customdata[1]:.2f}\",\n",
    "                    \"Replication Probability: %{customdata[0]:.0f}%\",\n",
    "                    \"Motion bias: p=%{customdata[2]:.2f}\",\n",
    "                    \"Description: %{customdata[5]}\"\n",
    "                ])\n",
    "                )\n",
    "data = [trace1l, trace1, trace2l, trace2]\n",
    "\n",
    "updatemenus = list([\n",
    "    dict(active=0,\n",
    "         showactive = True,\n",
    "         buttons=list([\n",
    "            dict(label = \"Replication Probability\",\n",
    "                 method = \"update\",\n",
    "                 args = [{\"visible\": [True, True, False, False]}]), # hide trace2\n",
    "            dict(label = \"Predictive Effect Size\",\n",
    "                 method = \"update\",\n",
    "                 args = [{\"visible\": [False, False, True, True]}]) # hide trace1\n",
    "            ]),\n",
    "         x = 0,\n",
    "         xanchor = 'left',\n",
    "         y = 1.5,\n",
    "         yanchor = 'top',\n",
    "         )])\n",
    "\n",
    "layout = dict(title=\"\",\n",
    "              showlegend=True,\n",
    "              xaxis=dict(title=\"Phenotype\",\n",
    "                        showgrid=False, # thin lines in the background\n",
    "                        #showticklabels=False,  # numbers below),\n",
    "                        zeroline=True # thick line at x=0\n",
    "                         ),\n",
    "              updatemenus=updatemenus,\n",
    "              template='simple_white',\n",
    "              font=dict(\n",
    "                size=10,\n",
    "                color=\"gray\"),\n",
    "              autosize=True,\n",
    "              height=220,\n",
    "              margin=dict(\n",
    "              l=5,\n",
    "              r=5,\n",
    "              b=10,\n",
    "              t=10,\n",
    "              pad=4\n",
    "    ),\n",
    "    )\n",
    "\n",
    "fig=dict(data=data, layout=layout)\n",
    "\n",
    "#plotly.offline.plot(fig)\n",
    "#go.Figure(fig).show()\n",
    "import plotly.io as pio\n",
    "pio.write_html(fig, file='docs/_includes/figure_fd.html', auto_open=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                                0  r_discovery   p_bias     category  \\\ntarget                                                                 \nCogCrystalComp_AgeAdj  100.000000     0.478393  0.61958    Cognition   \nPicVocab_AgeAdj        100.000000     0.449331  0.46671    Cognition   \nCogTotalComp_AgeAdj    100.000000     0.440612  0.58473    Cognition   \nReadEng_AgeAdj         100.000000     0.421093  0.56276    Cognition   \nCogEarlyComp_AgeAdj    100.000000     0.318284  0.40190    Cognition   \nVSPLOT_TC              100.000000     0.304904  0.40537    Cognition   \nCogFluidComp_AgeAdj    100.000000     0.286584  0.47973    Cognition   \nPMAT24_A_CR            100.000000     0.273698  0.45357    Cognition   \nListSort_AgeAdj        100.000000     0.261719  0.48862    Cognition   \nDDisc_AUC_200          100.000000     0.181074  0.23137    Cognition   \nCardSort_AgeAdj        100.000000     0.168875  0.51301    Cognition   \nPicSeq_AgeAdj          100.000000     0.166367  0.26611    Cognition   \nSCPT_SPEC               97.727273     0.135545  0.35593    Cognition   \nFlanker_AgeAdj          95.238095     0.124328  0.36686    Cognition   \nProcSpeed_AgeAdj        90.277778     0.111175  0.44762    Cognition   \nIWRD_TOT                17.073171     0.062716  0.33328    Cognition   \nSCPT_SEN                 0.000000     0.002666  0.36600    Cognition   \nNEOFAC_O               100.000000     0.252453  0.28290  Personality   \nNEOFAC_C               100.000000     0.183556  0.43345  Personality   \nNEOFAC_A               100.000000     0.170426  0.28425  Personality   \nNEOFAC_E               100.000000     0.157592  0.40248  Personality   \nNEOFAC_N                40.909091     0.067182  0.50425  Personality   \nDexterity_AgeAdj       100.000000     0.148792  0.39907        Motor   \nNoise_Comp              94.382022     0.134067  0.34268      Sensory   \nTaste_AgeAdj            23.636364     0.081493  0.43259      Sensory   \nMars_Final              16.000000     0.061327  0.44606      Sensory   \nOdor_AgeAdj              9.090909     0.022999  0.42186      Sensory   \nPainInterf_Tscore        0.000000     0.021068  0.40455      Sensory   \nLifeSatisf_Unadj       100.000000     0.183808  0.27947      Emotion   \nAngAggr_Unadj          100.000000     0.174860  0.19396      Emotion   \nFearAffect_Unadj       100.000000     0.142047  0.42446      Emotion   \nEmotSupp_Unadj          85.185185     0.124379  0.22181      Emotion   \nSadness_Unadj           96.341463     0.121922  0.40169      Emotion   \nSelfEff_Unadj           73.239437     0.107843  0.41418      Emotion   \nLoneliness_Unadj        75.384615     0.098174  0.42129      Emotion   \nMeanPurp_Unadj          63.768116     0.092541  0.39125      Emotion   \nPercStress_Unadj        59.701493     0.089891  0.42411      Emotion   \nFriendship_Unadj        46.666667     0.088484  0.34509      Emotion   \nPosAffect_Unadj         60.714286     0.084311  0.35642      Emotion   \nAngAffect_Unadj         64.516129     0.083911  0.48600      Emotion   \nAngHostil_Unadj         20.454545     0.065354  0.40929      Emotion   \nER40ANG                 34.090909     0.065080  0.39046      Emotion   \nFearSomat_Unadj          5.263158     0.053006  0.44691      Emotion   \nER40NOE                 12.903226     0.046832  0.41809      Emotion   \nPercHostil_Unadj         9.677419     0.045781  0.39934      Emotion   \nPercReject_Unadj         7.407407     0.037067  0.46279      Emotion   \nInstruSupp_Unadj         0.000000     0.036281  0.35826      Emotion   \nER40FEAR                 0.000000     0.036240  0.34259      Emotion   \nER40SAD                  0.000000     0.028999  0.45220      Emotion   \nER40HAP                  0.000000    -0.040475  0.43610      Emotion   \n\n                          phenotype  \\\ntarget                                \nCogCrystalComp_AgeAdj    CogCrystal   \nPicVocab_AgeAdj            PicVocab   \nCogTotalComp_AgeAdj        CogTotal   \nReadEng_AgeAdj              ReadEng   \nCogEarlyComp_AgeAdj        CogEarly   \nVSPLOT_TC                 Visuospat   \nCogFluidComp_AgeAdj        CogFluid   \nPMAT24_A_CR                 FluidIQ   \nListSort_AgeAdj            ListSort   \nDDisc_AUC_200             DelayDisc   \nCardSort_AgeAdj            CardSort   \nPicSeq_AgeAdj             PicSeqMem   \nSCPT_SPEC              ContPerfSpec   \nFlanker_AgeAdj              Flanker   \nProcSpeed_AgeAdj          ProcSpeed   \nIWRD_TOT                    WordMem   \nSCPT_SEN                ContPerfSen   \nNEOFAC_O                       Open   \nNEOFAC_C                  Conscient   \nNEOFAC_A                  Agreeable   \nNEOFAC_E                  Extrovert   \nNEOFAC_N                   Neurotic   \nDexterity_AgeAdj          Dexterity   \nNoise_Comp                NoiseComp   \nTaste_AgeAdj                  Taste   \nMars_Final             ContrastSens   \nOdor_AgeAdj                    Odor   \nPainInterf_Tscore        PainInterf   \nLifeSatisf_Unadj         LifeSatisf   \nAngAggr_Unadj             AngerAggr   \nFearAffect_Unadj         FearAffect   \nEmotSupp_Unadj             EmotSupp   \nSadness_Unadj               Sadness   \nSelfEff_Unadj               SelfEff   \nLoneliness_Unadj         Loneliness   \nMeanPurp_Unadj          MeaningPurp   \nPercStress_Unadj         PercStress   \nFriendship_Unadj         Friendship   \nPosAffect_Unadj           PosAffect   \nAngAffect_Unadj         AngerAffect   \nAngHostil_Unadj           AngerHost   \nER40ANG                    AngerRec   \nFearSomat_Unadj           FearSomat   \nER40NOE                  NeutralRec   \nPercHostil_Unadj           PercHost   \nPercReject_Unadj         PercReject   \nInstruSupp_Unadj         InstruSupp   \nER40FEAR                    FearRec   \nER40SAD                      SadRec   \nER40HAP                    HappyRec   \n\n                                                          phenotype_long  \ntarget                                                                    \nCogCrystalComp_AgeAdj  NIH Toolbox Cognition Crystallized Composite: ...  \nPicVocab_AgeAdj        NIH Toolbox Picture Vocabulary Test: Age-Adjus...  \nCogTotalComp_AgeAdj    NIH Toolbox Cognition Total Composite Score: A...  \nReadEng_AgeAdj         NIH Toolbox Oral Reading Recognition Test: Age...  \nCogEarlyComp_AgeAdj    NIH Toolbox Cognition Early Childhood Composit...  \nVSPLOT_TC              Variable Short Penn Line Orientation: Total Nu...  \nCogFluidComp_AgeAdj    NIH Toolbox Cognition Fluid Composite: Age Adj...  \nPMAT24_A_CR            Penn Progressive Matrices: Number of Correct R...  \nListSort_AgeAdj        NIH Toolbox List Sorting Working Memory Test: ...  \nDDisc_AUC_200          Delay Discounting: Area Under the Curve for Di...  \nCardSort_AgeAdj        NIH Toolbox Dimensional Change Card Sort Test:...  \nPicSeq_AgeAdj          NIH Toolbox Picture Sequence Memory Test: Age-...  \nSCPT_SPEC              Short Penn Continuous Performance Test: Specif...  \nFlanker_AgeAdj         NIH Toolbox Flanker Inhibitory Control and Att...  \nProcSpeed_AgeAdj       NIH Toolbox Pattern Comparison Processing Spee...  \nIWRD_TOT               Penn Word Memory Test: Total Number of Correct...  \nSCPT_SEN               Short Penn Continuous Performance Test: Sensit...  \nNEOFAC_O                       NEO-FFI Openness to Experience (NEOFAC_O)  \nNEOFAC_C                            NEO-FFI Conscientiousness (NEOFAC_C)  \nNEOFAC_A                                NEO-FFI Agreeableness (NEOFAC_A)  \nNEOFAC_E                                 NEO-FFI Extraversion (NEOFAC_E)  \nNEOFAC_N                                  NEO-FFI Neuroticism (NEOFAC_N)  \nDexterity_AgeAdj       NIH Toolbox 9-hole Pegboard Dexterity Test : A...  \nNoise_Comp             NIH Toolbox Words-In-Noise Age 6+: Computed Score  \nTaste_AgeAdj           NIH Toolbox Regional Taste Intensity Age 12+ A...  \nMars_Final                         Mars Final Contrast Sensitivity Score  \nOdor_AgeAdj            NIH Toolbox Odor Identification Age 3+ Age-Adj...  \nPainInterf_Tscore      NIH Toolbox Pain Interference Survey Age 18+: ...  \nLifeSatisf_Unadj       NIH Toolbox General Life Satisfaction Survey: ...  \nAngAggr_Unadj          NIH Toolbox Anger-Physical Aggression Survey: ...  \nFearAffect_Unadj       NIH Toolbox Fear-Affect Survey: Unadjusted Sca...  \nEmotSupp_Unadj         NIH Toolbox Emotional Support Survey: Unadjust...  \nSadness_Unadj          NIH Toolbox Sadness Survey: Unadjusted Scale S...  \nSelfEff_Unadj          NIH Toolbox Self-Efficacy Survey: Unadjusted S...  \nLoneliness_Unadj       NIH Toolbox Loneliness Survey: Unadjusted Scal...  \nMeanPurp_Unadj         NIH Toolbox Meaning and Purpose Survey: Unadju...  \nPercStress_Unadj       NIH Toolbox Perceived Stress Survey: Unadjuste...  \nFriendship_Unadj       NIH Toolbox Friendship Survey: Unadjusted Scal...  \nPosAffect_Unadj        NIH Toolbox Positive Affect Survey: Unadjusted...  \nAngAffect_Unadj        NIH Toolbox Anger-Affect Survey: Unadjusted Sc...  \nAngHostil_Unadj        NIH Toolbox Anger-Hostility Survey: Unadjusted...  \nER40ANG                Penn Emotion Recognition Test: Number of Corre...  \nFearSomat_Unadj        NIH Toolbox Fear-Somatic Arousal Survey: Unadj...  \nER40NOE                Penn Emotion Recognition Test: Number of Corre...  \nPercHostil_Unadj       NIH Toolbox Perceived Hostility Survey: Unadju...  \nPercReject_Unadj       NIH Toolbox Perceived Rejection Survey: Unadju...  \nInstruSupp_Unadj       NIH Toolbox Instrumental Support Survey: Unadj...  \nER40FEAR               Penn Emotion Recognition Test: Number of Corre...  \nER40SAD                Penn Emotion Recognition Test: Number of Corre...  \nER40HAP                Penn Emotion Recognition Test: Number of Corre...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>r_discovery</th>\n      <th>p_bias</th>\n      <th>category</th>\n      <th>phenotype</th>\n      <th>phenotype_long</th>\n    </tr>\n    <tr>\n      <th>target</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>CogCrystalComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.478393</td>\n      <td>0.61958</td>\n      <td>Cognition</td>\n      <td>CogCrystal</td>\n      <td>NIH Toolbox Cognition Crystallized Composite: ...</td>\n    </tr>\n    <tr>\n      <th>PicVocab_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.449331</td>\n      <td>0.46671</td>\n      <td>Cognition</td>\n      <td>PicVocab</td>\n      <td>NIH Toolbox Picture Vocabulary Test: Age-Adjus...</td>\n    </tr>\n    <tr>\n      <th>CogTotalComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.440612</td>\n      <td>0.58473</td>\n      <td>Cognition</td>\n      <td>CogTotal</td>\n      <td>NIH Toolbox Cognition Total Composite Score: A...</td>\n    </tr>\n    <tr>\n      <th>ReadEng_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.421093</td>\n      <td>0.56276</td>\n      <td>Cognition</td>\n      <td>ReadEng</td>\n      <td>NIH Toolbox Oral Reading Recognition Test: Age...</td>\n    </tr>\n    <tr>\n      <th>CogEarlyComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.318284</td>\n      <td>0.40190</td>\n      <td>Cognition</td>\n      <td>CogEarly</td>\n      <td>NIH Toolbox Cognition Early Childhood Composit...</td>\n    </tr>\n    <tr>\n      <th>VSPLOT_TC</th>\n      <td>100.000000</td>\n      <td>0.304904</td>\n      <td>0.40537</td>\n      <td>Cognition</td>\n      <td>Visuospat</td>\n      <td>Variable Short Penn Line Orientation: Total Nu...</td>\n    </tr>\n    <tr>\n      <th>CogFluidComp_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.286584</td>\n      <td>0.47973</td>\n      <td>Cognition</td>\n      <td>CogFluid</td>\n      <td>NIH Toolbox Cognition Fluid Composite: Age Adj...</td>\n    </tr>\n    <tr>\n      <th>PMAT24_A_CR</th>\n      <td>100.000000</td>\n      <td>0.273698</td>\n      <td>0.45357</td>\n      <td>Cognition</td>\n      <td>FluidIQ</td>\n      <td>Penn Progressive Matrices: Number of Correct R...</td>\n    </tr>\n    <tr>\n      <th>ListSort_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.261719</td>\n      <td>0.48862</td>\n      <td>Cognition</td>\n      <td>ListSort</td>\n      <td>NIH Toolbox List Sorting Working Memory Test: ...</td>\n    </tr>\n    <tr>\n      <th>DDisc_AUC_200</th>\n      <td>100.000000</td>\n      <td>0.181074</td>\n      <td>0.23137</td>\n      <td>Cognition</td>\n      <td>DelayDisc</td>\n      <td>Delay Discounting: Area Under the Curve for Di...</td>\n    </tr>\n    <tr>\n      <th>CardSort_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.168875</td>\n      <td>0.51301</td>\n      <td>Cognition</td>\n      <td>CardSort</td>\n      <td>NIH Toolbox Dimensional Change Card Sort Test:...</td>\n    </tr>\n    <tr>\n      <th>PicSeq_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.166367</td>\n      <td>0.26611</td>\n      <td>Cognition</td>\n      <td>PicSeqMem</td>\n      <td>NIH Toolbox Picture Sequence Memory Test: Age-...</td>\n    </tr>\n    <tr>\n      <th>SCPT_SPEC</th>\n      <td>97.727273</td>\n      <td>0.135545</td>\n      <td>0.35593</td>\n      <td>Cognition</td>\n      <td>ContPerfSpec</td>\n      <td>Short Penn Continuous Performance Test: Specif...</td>\n    </tr>\n    <tr>\n      <th>Flanker_AgeAdj</th>\n      <td>95.238095</td>\n      <td>0.124328</td>\n      <td>0.36686</td>\n      <td>Cognition</td>\n      <td>Flanker</td>\n      <td>NIH Toolbox Flanker Inhibitory Control and Att...</td>\n    </tr>\n    <tr>\n      <th>ProcSpeed_AgeAdj</th>\n      <td>90.277778</td>\n      <td>0.111175</td>\n      <td>0.44762</td>\n      <td>Cognition</td>\n      <td>ProcSpeed</td>\n      <td>NIH Toolbox Pattern Comparison Processing Spee...</td>\n    </tr>\n    <tr>\n      <th>IWRD_TOT</th>\n      <td>17.073171</td>\n      <td>0.062716</td>\n      <td>0.33328</td>\n      <td>Cognition</td>\n      <td>WordMem</td>\n      <td>Penn Word Memory Test: Total Number of Correct...</td>\n    </tr>\n    <tr>\n      <th>SCPT_SEN</th>\n      <td>0.000000</td>\n      <td>0.002666</td>\n      <td>0.36600</td>\n      <td>Cognition</td>\n      <td>ContPerfSen</td>\n      <td>Short Penn Continuous Performance Test: Sensit...</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_O</th>\n      <td>100.000000</td>\n      <td>0.252453</td>\n      <td>0.28290</td>\n      <td>Personality</td>\n      <td>Open</td>\n      <td>NEO-FFI Openness to Experience (NEOFAC_O)</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_C</th>\n      <td>100.000000</td>\n      <td>0.183556</td>\n      <td>0.43345</td>\n      <td>Personality</td>\n      <td>Conscient</td>\n      <td>NEO-FFI Conscientiousness (NEOFAC_C)</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_A</th>\n      <td>100.000000</td>\n      <td>0.170426</td>\n      <td>0.28425</td>\n      <td>Personality</td>\n      <td>Agreeable</td>\n      <td>NEO-FFI Agreeableness (NEOFAC_A)</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_E</th>\n      <td>100.000000</td>\n      <td>0.157592</td>\n      <td>0.40248</td>\n      <td>Personality</td>\n      <td>Extrovert</td>\n      <td>NEO-FFI Extraversion (NEOFAC_E)</td>\n    </tr>\n    <tr>\n      <th>NEOFAC_N</th>\n      <td>40.909091</td>\n      <td>0.067182</td>\n      <td>0.50425</td>\n      <td>Personality</td>\n      <td>Neurotic</td>\n      <td>NEO-FFI Neuroticism (NEOFAC_N)</td>\n    </tr>\n    <tr>\n      <th>Dexterity_AgeAdj</th>\n      <td>100.000000</td>\n      <td>0.148792</td>\n      <td>0.39907</td>\n      <td>Motor</td>\n      <td>Dexterity</td>\n      <td>NIH Toolbox 9-hole Pegboard Dexterity Test : A...</td>\n    </tr>\n    <tr>\n      <th>Noise_Comp</th>\n      <td>94.382022</td>\n      <td>0.134067</td>\n      <td>0.34268</td>\n      <td>Sensory</td>\n      <td>NoiseComp</td>\n      <td>NIH Toolbox Words-In-Noise Age 6+: Computed Score</td>\n    </tr>\n    <tr>\n      <th>Taste_AgeAdj</th>\n      <td>23.636364</td>\n      <td>0.081493</td>\n      <td>0.43259</td>\n      <td>Sensory</td>\n      <td>Taste</td>\n      <td>NIH Toolbox Regional Taste Intensity Age 12+ A...</td>\n    </tr>\n    <tr>\n      <th>Mars_Final</th>\n      <td>16.000000</td>\n      <td>0.061327</td>\n      <td>0.44606</td>\n      <td>Sensory</td>\n      <td>ContrastSens</td>\n      <td>Mars Final Contrast Sensitivity Score</td>\n    </tr>\n    <tr>\n      <th>Odor_AgeAdj</th>\n      <td>9.090909</td>\n      <td>0.022999</td>\n      <td>0.42186</td>\n      <td>Sensory</td>\n      <td>Odor</td>\n      <td>NIH Toolbox Odor Identification Age 3+ Age-Adj...</td>\n    </tr>\n    <tr>\n      <th>PainInterf_Tscore</th>\n      <td>0.000000</td>\n      <td>0.021068</td>\n      <td>0.40455</td>\n      <td>Sensory</td>\n      <td>PainInterf</td>\n      <td>NIH Toolbox Pain Interference Survey Age 18+: ...</td>\n    </tr>\n    <tr>\n      <th>LifeSatisf_Unadj</th>\n      <td>100.000000</td>\n      <td>0.183808</td>\n      <td>0.27947</td>\n      <td>Emotion</td>\n      <td>LifeSatisf</td>\n      <td>NIH Toolbox General Life Satisfaction Survey: ...</td>\n    </tr>\n    <tr>\n      <th>AngAggr_Unadj</th>\n      <td>100.000000</td>\n      <td>0.174860</td>\n      <td>0.19396</td>\n      <td>Emotion</td>\n      <td>AngerAggr</td>\n      <td>NIH Toolbox Anger-Physical Aggression 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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "labels = [\"Replicable\", \"Not Replicable\"]\n",
    "\n",
    "# Create subplots: use 'domain' type for Pie subplot\n",
    "fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])\n",
    "fig.add_trace(go.Pie(labels=labels, values=[15+4+1+1, 2+1+0+3], name=\"Trait-like\",  marker_colors=['green', 'red'], rotation=-90),\n",
    "              1, 1)\n",
    "fig.add_trace(go.Pie(labels=labels, values=[5, 18], name=\"State-like\", rotation=90),\n",
    "              1, 2)\n",
    "\n",
    "# Use `hole` to create a donut-like pie chart\n",
    "fig.update_traces(hole=.5, hoverinfo=\"label+percent+name\")\n",
    "\n",
    "fig.update_layout(\n",
    "    template='simple_white',\n",
    "    title_text=\"\",\n",
    "    font=dict(\n",
    "                size=10,\n",
    "                color=\"gray\"),\n",
    "    autosize=True,\n",
    "    height=70,\n",
    "    width=500,\n",
    "    margin=dict(\n",
    "              l=5,\n",
    "              r=5,\n",
    "              b=10,\n",
    "              t=10,\n",
    "              pad=4),\n",
    "    legend=dict(\n",
    "        yanchor=\"top\",\n",
    "        y=0.99,\n",
    "        xanchor=\"left\",\n",
    "        x=-0.4,\n",
    "        font_color='black'\n",
    "    ),\n",
    "    # Add annotations in the center of the donut pies.\n",
    "    annotations=[dict(text='Trait-like', x=-0.03, y=1.1, font_size=12, font_color='black', showarrow=False),\n",
    "                 dict(text='State-like', x=0.7, y=1.1, font_size=12, font_color='black', showarrow=False)])\n",
    "fig.show()\n",
    "import plotly.io as pio\n",
    "pio.write_html(fig, file='docs/_includes/pie_fd.html', auto_open=False)"
   ]
  },
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