hcp_behavioral_data_factor_analysis_overall.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "% matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directory paths\n",
    "datadir = '/projects/f_mc1689_1/LatentFC/data'\n",
    "behavdir = '%s/hcpbehavior' % (datadir)\n",
    "subj_group = 'overall'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "351"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load list of subjects included in the fMRI analysis\n",
    "fmri_subjs_f = '%s/hcpPostProcCiric/%s_subjects.npy' % (datadir, subj_group)\n",
    "fmri_subjs = np.load(fmri_subjs_f)\n",
    "fmri_subjs = list(np.array(fmri_subjs, dtype=int))\n",
    "fmri_subjs.remove(393247)#remove subject with missing data\n",
    "len(fmri_subjs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1206, 581)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load behavioral data\n",
    "df_f = '%s/hcp_unrestricted_behavior.csv' % (behavdir)\n",
    "df = pd.read_csv(df_f, index_col=0)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Release</th>\n",
       "      <th>Acquisition</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>3T_Full_MR_Compl</th>\n",
       "      <th>T1_Count</th>\n",
       "      <th>T2_Count</th>\n",
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       "      <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",
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       "      <td>S900</td>\n",
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       "      <td>26-30</td>\n",
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       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>108.79</td>\n",
       "      <td>97.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49.7</td>\n",
       "      <td>72.63</td>\n",
       "      <td>72.03</td>\n",
       "      <td>1.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.84</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>108.79</td>\n",
       "      <td>98.04</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.6</td>\n",
       "      <td>114.01</td>\n",
       "      <td>113.59</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.68</td>\n",
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       "      <td>31-35</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>122.25</td>\n",
       "      <td>111.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>123.80</td>\n",
       "      <td>123.31</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.80</td>\n",
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       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>122.25</td>\n",
       "      <td>110.45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52.2</td>\n",
       "      <td>98.31</td>\n",
       "      <td>99.06</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.80</td>\n",
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       "      <td>54.3</td>\n",
       "      <td>84.07</td>\n",
       "      <td>82.37</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.92</td>\n",
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       "<p>5 rows × 581 columns</p>\n",
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      "text/plain": [
       "        Release Acquisition Gender    Age  3T_Full_MR_Compl  T1_Count  \\\n",
       "Subject                                                                 \n",
       "100206     S900         Q11      M  26-30              True         1   \n",
       "100408       Q3         Q03      M  31-35              True         1   \n",
       "101006     S500         Q06      F  31-35              True         2   \n",
       "102311     S500         Q06      F  26-30              True         1   \n",
       "102513     S900         Q10      M  26-30              True         2   \n",
       "\n",
       "         T2_Count  3T_RS-fMRI_Count  3T_RS-fMRI_PctCompl  3T_Full_Task_fMRI  \\\n",
       "Subject                                                                       \n",
       "100206          1                 4                100.0               True   \n",
       "100408          1                 4                100.0               True   \n",
       "101006          2                 4                100.0               True   \n",
       "102311          1                 4                100.0               True   \n",
       "102513          2                 4                100.0               True   \n",
       "\n",
       "         ...  Noise_Comp  Odor_Unadj  Odor_AgeAdj  PainIntens_RawScore  \\\n",
       "Subject  ...                                                             \n",
       "100206   ...         6.0      108.79        97.19                  1.0   \n",
       "100408   ...         2.0      108.79        98.04                  2.0   \n",
       "101006   ...         6.0      122.25       111.41                  0.0   \n",
       "102311   ...         4.4      122.25       110.45                  1.0   \n",
       "102513   ...         5.2      108.79        98.04                  2.0   \n",
       "\n",
       "         PainInterf_Tscore  Taste_Unadj  Taste_AgeAdj  Mars_Log_Score  \\\n",
       "Subject                                                                 \n",
       "100206                49.7        72.63         72.03            1.84   \n",
       "100408                52.6       114.01        113.59            1.76   \n",
       "101006                38.6       123.80        123.31            1.80   \n",
       "102311                52.2        98.31         99.06            1.80   \n",
       "102513                54.3        84.07         82.37            1.92   \n",
       "\n",
       "         Mars_Errs  Mars_Final  \n",
       "Subject                         \n",
       "100206         0.0        1.84  \n",
       "100408         2.0        1.68  \n",
       "101006         0.0        1.80  \n",
       "102311         0.0        1.80  \n",
       "102513         0.0        1.92  \n",
       "\n",
       "[5 rows x 581 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract data only for subjects in the fMRI analysis\n",
    "df_fmri_subjs = df.loc[fmri_subjs]\n",
    "df_fmri_subjs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load spreadsheet with descriptions of each of the columns reflecting a cognitive task\n",
    "# in the HCP behavioral spreadsheet\n",
    "info_df_f = '%s/results/HPC_Data_Behavior_info.xlsx' % (behavdir)\n",
    "info_df = pd.read_excel(info_df_f, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a \"mask\", where the value of each row is True if FA_inclusion is \"Yes\" and False if \"No\"\n",
    "df_cols_mask = info_df['FA_inclusion'] == 'Yes'\n",
    "# Use the \"mask\" to select only the Column_header values where FA_inclusion is \"True\"\n",
    "df_cols = list(info_df['Column_header'][df_cols_mask].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a new spreadsheet with only the columns of interest\n",
    "fa_df = df_fmri_subjs[df_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/karnemann/anaconda3/envs/r-env/lib/python2.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "tmp = 1 / fa_df['IWRD_RTC'].values\n",
    "fa_df['IWRD_RTC'] = tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PicSeq_Unadj</th>\n",
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       "      <td>0.249911</td>\n",
       "      <td>0.118682</td>\n",
       "      <td>0.295753</td>\n",
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       "      <th>PicVocab_Unadj</th>\n",
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       "      <td>0.124656</td>\n",
       "      <td>0.147849</td>\n",
       "      <td>0.431842</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.344163</td>\n",
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       "      <td>0.071203</td>\n",
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       "      <td>0.160794</td>\n",
       "      <td>0.383378</td>\n",
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       "      <td>0.050961</td>\n",
       "      <td>0.291110</td>\n",
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       "    <tr>\n",
       "      <th>IWRD_TOT</th>\n",
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       "      <td>0.102116</td>\n",
       "      <td>0.049249</td>\n",
       "      <td>0.169786</td>\n",
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       "      <td>0.154888</td>\n",
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       "    <tr>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.093497</td>\n",
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       "    <tr>\n",
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       "      <td>0.106358</td>\n",
       "      <td>0.098549</td>\n",
       "      <td>0.331164</td>\n",
       "      <td>0.295753</td>\n",
       "      <td>0.344163</td>\n",
       "      <td>0.071203</td>\n",
       "      <td>0.291110</td>\n",
       "      <td>0.154888</td>\n",
       "      <td>0.093497</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 PicSeq_Unadj  CardSort_Unadj  Flanker_Unadj  PMAT24_A_CR  \\\n",
       "PicSeq_Unadj         1.000000        0.135318       0.062667     0.294872   \n",
       "CardSort_Unadj       0.135318        1.000000       0.525642     0.200100   \n",
       "Flanker_Unadj        0.062667        0.525642       1.000000     0.098268   \n",
       "PMAT24_A_CR          0.294872        0.200100       0.098268     1.000000   \n",
       "ReadEng_Unadj        0.166733        0.199863       0.110804     0.430549   \n",
       "PicVocab_Unadj       0.143083        0.124656       0.147849     0.431842   \n",
       "ProcSpeed_Unadj      0.210558        0.292065       0.303807     0.095481   \n",
       "VSPLOT_TC            0.180994        0.206310       0.160794     0.383378   \n",
       "IWRD_TOT             0.201249        0.102116       0.049249     0.169786   \n",
       "IWRD_RTC             0.171290        0.134807       0.135527     0.078945   \n",
       "ListSort_Unadj       0.363765        0.106358       0.098549     0.331164   \n",
       "\n",
       "                 ReadEng_Unadj  PicVocab_Unadj  ProcSpeed_Unadj  VSPLOT_TC  \\\n",
       "PicSeq_Unadj          0.166733        0.143083         0.210558   0.180994   \n",
       "CardSort_Unadj        0.199863        0.124656         0.292065   0.206310   \n",
       "Flanker_Unadj         0.110804        0.147849         0.303807   0.160794   \n",
       "PMAT24_A_CR           0.430549        0.431842         0.095481   0.383378   \n",
       "ReadEng_Unadj         1.000000        0.634472         0.087961   0.367025   \n",
       "PicVocab_Unadj        0.634472        1.000000         0.075910   0.302511   \n",
       "ProcSpeed_Unadj       0.087961        0.075910         1.000000   0.072992   \n",
       "VSPLOT_TC             0.367025        0.302511         0.072992   1.000000   \n",
       "IWRD_TOT              0.249911        0.299405         0.153496   0.038912   \n",
       "IWRD_RTC              0.118682        0.201665         0.275457   0.050961   \n",
       "ListSort_Unadj        0.295753        0.344163         0.071203   0.291110   \n",
       "\n",
       "                 IWRD_TOT  IWRD_RTC  ListSort_Unadj  \n",
       "PicSeq_Unadj     0.201249  0.171290        0.363765  \n",
       "CardSort_Unadj   0.102116  0.134807        0.106358  \n",
       "Flanker_Unadj    0.049249  0.135527        0.098549  \n",
       "PMAT24_A_CR      0.169786  0.078945        0.331164  \n",
       "ReadEng_Unadj    0.249911  0.118682        0.295753  \n",
       "PicVocab_Unadj   0.299405  0.201665        0.344163  \n",
       "ProcSpeed_Unadj  0.153496  0.275457        0.071203  \n",
       "VSPLOT_TC        0.038912  0.050961        0.291110  \n",
       "IWRD_TOT         1.000000  0.373554        0.154888  \n",
       "IWRD_RTC         0.373554  1.000000        0.093497  \n",
       "ListSort_Unadj   0.154888  0.093497        1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform pairwise correlations of the spreadsheet, resulting in correlations of\n",
    "#each task with each other task\n",
    "fa_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Task number')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the correlations between the tasks\n",
    "#This matrix is used for Factor Analysis\n",
    "plt.imshow(fa_df.corr().values, interpolation='nearest', cmap='bwr')\n",
    "# Sets the range of values when plotting colors to -1 to 1\n",
    "plt.clim(-1,1)\n",
    "# Show the color legend\n",
    "plt.colorbar()\n",
    "plt.xlabel('Task number')\n",
    "plt.ylabel('Task number')\n",
    "#plt.savefig('/home/csg397/Documents/figures/correlation.png', dpi=300, transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run factor analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "%load_ext rpy2.ipython\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R \n",
    "#install.packages(\"psych\", repos='http://cran.us.r-project.org', quiet=TRUE)\n",
    "library(psych)\n",
    "library(nFactors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fa_input_dat = fa_df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fa_input_mat = fa_df.corr().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "351\n"
     ]
    }
   ],
   "source": [
    "n_subjs = len(fmri_subjs)\n",
    "print n_subjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Factor Analysis using method =  minres\n",
       "Call: fa(r = fa_input_dat, nfactors = 1, n.obs = n_subjs, rotate = \"oblimin\", \n",
       "    scores = \"regression\", covar = FALSE, fm = \"minres\")\n",
       "Standardized loadings (pattern matrix) based upon correlation matrix\n",
       "    MR1    h2   u2 com\n",
       "1  0.40 0.157 0.84   1\n",
       "2  0.38 0.141 0.86   1\n",
       "3  0.31 0.095 0.90   1\n",
       "4  0.61 0.369 0.63   1\n",
       "5  0.67 0.449 0.55   1\n",
       "6  0.67 0.454 0.55   1\n",
       "7  0.28 0.079 0.92   1\n",
       "8  0.49 0.236 0.76   1\n",
       "9  0.37 0.137 0.86   1\n",
       "10 0.30 0.090 0.91   1\n",
       "11 0.50 0.245 0.75   1\n",
       "\n",
       "                MR1\n",
       "SS loadings    2.45\n",
       "Proportion Var 0.22\n",
       "\n",
       "Mean item complexity =  1\n",
       "Test of the hypothesis that 1 factor is sufficient.\n",
       "\n",
       "The degrees of freedom for the null model are  55  and the objective function was  2.35 FALSE with Chi Square of  810.21\n",
       "The degrees of freedom for the model are 44  and the objective function was  0.91 \n",
       " FALSE\n",
       "The root mean square of the residuals (RMSR) is  0.11 \n",
       "The df corrected root mean square of the residuals is  0.12 \n",
       " FALSE\n",
       "The harmonic number of observations is  351 with the empirical chi square  445.31  with prob <  8.6e-68 \n",
       " FALSEThe total number of observations was  351  with Likelihood Chi Square =  312.29  with prob <  4e-42 \n",
       " FALSE\n",
       "Tucker Lewis Index of factoring reliability =  0.555\n",
       "RMSEA index =  0.132  and the 90 % confidence intervals are  0.118 0.146 FALSE\n",
       "BIC =  54.42\n",
       "Fit based upon off diagonal values = 0.81\n",
       "Measures of factor score adequacy             \n",
       "                                                   MR1\n",
       "Correlation of (regression) scores with factors   0.89\n",
       "Multiple R square of scores with factors          0.78\n",
       "Minimum correlation of possible factor scores     0.57\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i fa_input_dat -i n_subjs -o fa_output -o scores -o loads\n",
    "fa_output <- fa(fa_input_dat, nfactors=1, covar=FALSE, fm=\"minres\", n.obs=n_subjs, rotate=\"oblimin\", scores='regression')\n",
    "loads <- fa_output$loadings\n",
    "scores <-fa_output$scores\n",
    "fa_output\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "~~~\n",
    "%%R -i fa_input_dat -i n_subjs -o fa_output -o scores -o loads -o test\n",
    "fa_output <- fa(fa_input_dat, nfactors=2, covar=FALSE, fm=\"minres\", n.obs=176, rotate=\"oblimin\", scores='regression')\n",
    "#loads <- fa_output$loadings\n",
    "#scores <-fa_output$scores\n",
    "fa_output\n",
    "test <- omega(fa_input_dat,nfactors=3,fm=\"minres\",n.iter=1,p=.05,poly=FALSE,key=NULL,\n",
    "    flip=TRUE,digits=2, title=\"Omega\",sl=TRUE,labels=NULL,\n",
    "    plot=TRUE,n.obs=NA,rotate=\"oblimin\",Phi=NULL,option=\"equal\",covar=FALSE)\n",
    "scores <- test$scores\n",
    "loads <- test$loadings\n",
    "test\n",
    "~~~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_df = pd.DataFrame(loads.T[0], index=df_cols, columns=['Load'])\n",
    "load_df.to_excel('%s/results/fa/%s/%s_factor_loadings_nfactors_1.xlsx' % (behavdir, subj_group, subj_group))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PicSeq_Unadj</th>\n",
       "      <td>0.396715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CardSort_Unadj</th>\n",
       "      <td>0.375706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flanker_Unadj</th>\n",
       "      <td>0.308403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMAT24_A_CR</th>\n",
       "      <td>0.607387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ReadEng_Unadj</th>\n",
       "      <td>0.669821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PicVocab_Unadj</th>\n",
       "      <td>0.673930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProcSpeed_Unadj</th>\n",
       "      <td>0.280330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VSPLOT_TC</th>\n",
       "      <td>0.485364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IWRD_TOT</th>\n",
       "      <td>0.370019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IWRD_RTC</th>\n",
       "      <td>0.300739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ListSort_Unadj</th>\n",
       "      <td>0.495047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Load\n",
       "PicSeq_Unadj     0.396715\n",
       "CardSort_Unadj   0.375706\n",
       "Flanker_Unadj    0.308403\n",
       "PMAT24_A_CR      0.607387\n",
       "ReadEng_Unadj    0.669821\n",
       "PicVocab_Unadj   0.673930\n",
       "ProcSpeed_Unadj  0.280330\n",
       "VSPLOT_TC        0.485364\n",
       "IWRD_TOT         0.370019\n",
       "IWRD_RTC         0.300739\n",
       "ListSort_Unadj   0.495047"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_df = pd.DataFrame(scores, index=fmri_subjs, columns=['g'])\n",
    "scores_df.to_excel('%s/results/fa/%s/%s_factor_scores_nfactors_1.xlsx' % (behavdir, subj_group, subj_group))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.277380563333654e-16\n",
      "0.8837408679728593\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'# Subjects')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(scores_df['g'].dropna(), bins=15);\n",
    "print np.nanmean(scores_df['g'])\n",
    "print np.nanstd(scores_df['g'])\n",
    "plt.xlabel('g (Intelligence Factor)')\n",
    "plt.ylabel('# Subjects')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}