{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The distinct iterative prior updating process in ASD and TD individuals \n",
    "\n",
    "Research has shown individuals with autism spectrum disorder (ASD) display unique patterns in predictive processing, yet it remains controversial regarding what causes these atypical behaviors.  Both ASD individuals and typically developing (TD) counterparts participated in a task where they reproduced time durations over two sessions, one characterized by high volatility and the other by predictable sequence. Both sessions involved the same time durations, but the sequences differed in volatility. A visual stimulus (a disk) appeared for a given duration, and participants were asked to reproduce the duration by pressing a key.\n",
    "\n",
    "This repository contains the data and analysis scripts for this study.   The codes and data are organized as follows:\n",
    "\n",
    "## 1. Folder Structure\n",
    "\n",
    "1. `/experiments`: Experimental codes and instructions\n",
    "\n",
    "This sub-folder contains Matlab codes and instructions for the duration reproduction task. The sequences of the duration reproductions are stored in the sub-folder `/experiments/seqs`. Those sequences were used for matched participants. \n",
    "\n",
    "2. `/data`: raw data files\n",
    "\n",
    "- `rawdata.csv`: Raw reproduction trials of all participants\n",
    "- `parinfo.csv`: Participant information, including measured scores AQ, EQ, SQ, IQ etc. \n",
    "\n",
    "3. `/figures`: output figures. \n",
    "4. analysis scripts\n",
    "- `analysis-notebook.ipynb`: Jupyter notebook for data analysis\n",
    "- `kmodelY.py`: Python script for the Kalman filter two-state model\n",
    "- \n",
    "\n",
    "## 2. Data Analysis\n",
    "\n",
    "### 2.1 Import raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nothing done.\n"
     ]
    },
    {
     "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>Unnamed: 0</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>trlNo</th>\n",
       "      <th>blkNo</th>\n",
       "      <th>dur1</th>\n",
       "      <th>pdur</th>\n",
       "      <th>production</th>\n",
       "      <th>vrep</th>\n",
       "      <th>Reproduction</th>\n",
       "      <th>sub</th>\n",
       "      <th>group</th>\n",
       "      <th>itd</th>\n",
       "      <th>rep_err</th>\n",
       "      <th>sequence</th>\n",
       "      <th>preDuration</th>\n",
       "      <th>blk1st</th>\n",
       "      <th>Order</th>\n",
       "      <th>preErr</th>\n",
       "      <th>outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.400</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.409</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.374</td>\n",
       "      <td>A31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>LV First</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.342</td>\n",
       "      <td>A31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>0.100</td>\n",
       "      <td>-0.158</td>\n",
       "      <td>31</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0</td>\n",
       "      <td>LV First</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.400</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.326</td>\n",
       "      <td>A31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.074</td>\n",
       "      <td>31</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0</td>\n",
       "      <td>LV First</td>\n",
       "      <td>-0.158</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.400</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.406</td>\n",
       "      <td>A31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>31</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0</td>\n",
       "      <td>LV First</td>\n",
       "      <td>-0.074</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.500</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.398</td>\n",
       "      <td>A31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>0.100</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>31</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0</td>\n",
       "      <td>LV First</td>\n",
       "      <td>0.006</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Duration Volatility  trlNo  blkNo  dur1  pdur  production  \\\n",
       "0           1     0.400  Low Vola.      1      1 0.400 0.400       0.409   \n",
       "1           2     0.500  Low Vola.      2      1 0.500 0.506       0.507   \n",
       "2           3     0.400  Low Vola.      3      1 0.400 0.400       0.406   \n",
       "3           4     0.400  Low Vola.      4      1 0.400 0.400       0.397   \n",
       "4           5     0.500  Low Vola.      5      1 0.500 0.506       0.501   \n",
       "\n",
       "   vrep  Reproduction  sub group    itd  rep_err  sequence  preDuration  \\\n",
       "0 0.377         0.374  A31   ASD    NaN   -0.026        31          NaN   \n",
       "1 0.353         0.342  A31   ASD  0.100   -0.158        31        0.400   \n",
       "2 0.342         0.326  A31   ASD -0.100   -0.074        31        0.506   \n",
       "3 0.412         0.406  A31   ASD  0.000    0.006        31        0.400   \n",
       "4 0.412         0.398  A31   ASD  0.100   -0.102        31        0.400   \n",
       "\n",
       "   blk1st     Order  preErr  outlier  \n",
       "0       1  LV First     NaN     True  \n",
       "1       0  LV First  -0.026    False  \n",
       "2       0  LV First  -0.158    False  \n",
       "3       0  LV First  -0.074    False  \n",
       "4       0  LV First   0.006    False  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data analysis packages\n",
    "%reset\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.optimize import least_squares\n",
    "# pingouin ANOVA\n",
    "import pingouin as pg\n",
    "# keep output precision to 3 decimal places\n",
    "pd.options.display.float_format = '{:,.3f}'.format\n",
    "\n",
    "# read data from ./data/rawdata.csv\n",
    "rawdata = pd.read_csv('./data/rawdata.csv')\n",
    "# change rawdata.group to upper case\n",
    "rawdata['group'] = rawdata['group'].str.upper()\n",
    "# add a new column preErr, indicating the previous trial's error\n",
    "rawdata['preErr'] = rawdata.groupby(['sub', 'Volatility'])['rep_err'].shift(1)\n",
    "# mark the outliers that exceed [Duration/3, Duration * 3] or preErr is nan\n",
    "rawdata['outlier'] = (rawdata['Reproduction'] < rawdata['Duration']/3) | (rawdata['Reproduction'] > rawdata['Duration']*3) | (rawdata['preErr'].isna())\n",
    "\n",
    "# show the first 5 rows of rawdata\n",
    "rawdata.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The key columns in `rawdata.csv` are:\n",
    "1. Duration: the duration of the visual stimulus\n",
    "2. Reproduction: the reproduced duration\n",
    "3. rep_err: the reproduction error (Reproduction - Duration)\n",
    "4. preDuration: the duration of the previous visual stimulus\n",
    "5. sub: subject ID\n",
    "6. group: ASD or TD\n",
    "7. Volatility: Low Vola. or High Vola."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The outlier trials were generally low for both groups: 2.3% for the ASD group and 1% for the TD group. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "ASD   0.023\n",
       "TD    0.010\n",
       "Name: outlier, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the percentange of outliers in each group\n",
    "rawdata.groupby(['sub', 'group'])['outlier'].mean().reset_index().groupby('group')['outlier'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Duration sequences\n",
    "\n",
    "The experiment was structured into two distinct sessions, characterized by high and low volatility respectively. Each session included 500 trials, all following the same distribution, yet they varied in terms of sequential volatility. The provided figure illustrates a typical sequence of durations for a single participant.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns \n",
    "sns.set(style='ticks', context='paper', rc={'figure.figsize': (10, 5)})\n",
    "#sns.color_palette(palette='colorblind')\n",
    "sns.set_palette(\"Dark2\")\n",
    "\n",
    "# select a subset of rawdata (sub == 'ara27') for illustration\n",
    "subdata = rawdata.query('sub == \"ara27\"')\n",
    "# plot Duration as a function of trlNo, using different color for Volatility\n",
    "# use color palette 'colorblind' from seaborn\n",
    "#sns.set_palette('colorblind')\n",
    "sns.lineplot(x='trlNo', y='Duration', hue='Volatility', data=subdata)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Central tendency and autocorrelation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  calculate the mean and standard deviation of Duration for each sub, group, Volatility, and Duration\n",
    "mdata = rawdata.query('outlier == False').groupby(\n",
    "    ['sub', 'group', 'Volatility', 'Duration','sequence','Order']).agg(\n",
    "        {'rep_err': ['mean', 'std']}).reset_index()\n",
    "mdata.columns = ['sub', 'group', 'Volatility', 'Duration', 'sequence','Order','rep_err', 'rep_err_std']\n",
    "\n",
    "# visualize the mean reproduction error as a function of Duration\n",
    "ax = sns.lmplot(x='Duration', y='rep_err', hue='Volatility', col='group', \n",
    "                scatter_kws = {'alpha':0.3, 's':5}, legend = False, height = 3.5, \n",
    "                data=mdata, hue_order = ['Low Vola.', 'High Vola.'])\n",
    "\n",
    "# add dashed line 0 to each subplot\n",
    "for ax1 in ax.axes.flat:\n",
    "    ax1.axhline(0, ls='--')\n",
    "plt.ylim(-0.5, 1.)\n",
    "# save the figure to ./figures/rep_err_vs_Duration.png\n",
    "plt.savefig('./figures/rep_err_vs_Duration_b.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In both group, the high volatility session showed a larger central tendency than the low volatility session. Let's do linear regression and durbin-watson test for the autocorrelation in two environments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>sub</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>sequence</th>\n",
       "      <th>group</th>\n",
       "      <th>level_4</th>\n",
       "      <th>r2</th>\n",
       "      <th>intercept</th>\n",
       "      <th>slope</th>\n",
       "      <th>cti</th>\n",
       "      <th>ar_dw</th>\n",
       "      <th>aic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A31</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>0</td>\n",
       "      <td>0.389</td>\n",
       "      <td>0.284</td>\n",
       "      <td>-0.451</td>\n",
       "      <td>0.451</td>\n",
       "      <td>1.710</td>\n",
       "      <td>9.836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A31</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>0</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.279</td>\n",
       "      <td>-0.506</td>\n",
       "      <td>0.506</td>\n",
       "      <td>1.017</td>\n",
       "      <td>-4.688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A32</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>32</td>\n",
       "      <td>ASD</td>\n",
       "      <td>0</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.550</td>\n",
       "      <td>-0.904</td>\n",
       "      <td>0.904</td>\n",
       "      <td>1.405</td>\n",
       "      <td>-86.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A32</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>32</td>\n",
       "      <td>ASD</td>\n",
       "      <td>0</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.222</td>\n",
       "      <td>-0.227</td>\n",
       "      <td>0.227</td>\n",
       "      <td>1.701</td>\n",
       "      <td>166.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A33</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>33</td>\n",
       "      <td>ASD</td>\n",
       "      <td>0</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.180</td>\n",
       "      <td>-0.239</td>\n",
       "      <td>0.239</td>\n",
       "      <td>1.783</td>\n",
       "      <td>-113.065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sub  Volatility  sequence group  level_4    r2  intercept  slope   cti  \\\n",
       "0  A31  High Vola.        31   ASD        0 0.389      0.284 -0.451 0.451   \n",
       "1  A31   Low Vola.        31   ASD        0 0.459      0.279 -0.506 0.506   \n",
       "2  A32  High Vola.        32   ASD        0 0.606      0.550 -0.904 0.904   \n",
       "3  A32   Low Vola.        32   ASD        0 0.020      0.222 -0.227 0.227   \n",
       "4  A33  High Vola.        33   ASD        0 0.182      0.180 -0.239 0.239   \n",
       "\n",
       "   ar_dw      aic  \n",
       "0  1.710    9.836  \n",
       "1  1.017   -4.688  \n",
       "2  1.405  -86.745  \n",
       "3  1.701  166.866  \n",
       "4  1.783 -113.065  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define a regression function with input of a dataframe\n",
    "from statsmodels.stats.stattools import durbin_watson\n",
    "# define a regression function, input is a dataframe and y and x \n",
    "def reg_func(data, x='Duration'):\n",
    "   # Check and remove NaNs or infinite values\n",
    "    data = data.replace([np.inf, -np.inf], np.nan).dropna(subset=[x, 'rep_err'])\n",
    "    \n",
    "    if data.empty or data[x].isnull().any() or data['rep_err'].isnull().any():\n",
    "        # Return None or some indication that the data was not valid for regression\n",
    "        return pd.DataFrame({'r2': [None], 'intercept': [None], 'slope': [None],\n",
    "            'cti': [None], 'ar_dw': [None], 'aic': [None]})\n",
    "\n",
    "    reg = sm.OLS(data['rep_err'], sm.add_constant(data[x])).fit()\n",
    "    dw = durbin_watson(reg.resid)\n",
    "    aic = reg.aic\n",
    "    # return goodness of fit, intercept, slope, and dw\n",
    "    return pd.DataFrame({'r2': reg.rsquared, 'intercept': reg.params.iloc[0], 'slope':reg.params.iloc[1],\n",
    "        'cti': -reg.params.iloc[1], 'ar_dw': dw, 'aic':reg.aic}, index=[0])\n",
    "# apply the regression function to each sub, Volatility, and group\n",
    "df_coef = rawdata.query('outlier == False').groupby(['sub', 'Volatility', 'sequence', 'group']).apply(reg_func).reset_index()\n",
    "# show the first 5 rows of df_coef\n",
    "df_coef.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>1.611</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>1.611</td>\n",
       "      <td>80.105</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.564</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.003    1   62 0.003  0.071  0.790 0.001   NaN\n",
       "1   Volatility 1.611    1   62 1.611 80.105  0.000 0.564 1.000\n",
       "2  Interaction 0.005    1   62 0.005  0.228  0.634 0.004   NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mixed ANOVA on the central tendency effect cti\n",
    "# use pingouin to perform mixed ANOVA\n",
    "aov = pg.mixed_anova(data=df_coef, dv='cti', within='Volatility', between='group', subject='sub')\n",
    "# show the ANOVA table\n",
    "aov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>1.370</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>1.370</td>\n",
       "      <td>84.976</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.578</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.000    1   62 0.000  0.009  0.926 0.000   NaN\n",
       "1   Volatility 1.370    1   62 1.370 84.976  0.000 0.578 1.000\n",
       "2  Interaction 0.010    1   62 0.010  0.650  0.423 0.010   NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mixed ANOVA on the intercept\n",
    "aov = pg.mixed_anova(data=df_coef, \n",
    "                     dv='intercept', within='Volatility', between='group', subject='sub')\n",
    "# show the ANOVA table\n",
    "aov"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, autoregressive durbins-watson test values were significant different between the two groups, even with the outliers. This suggests that we need to consider the inter-trial updating of the prior information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.314</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.314</td>\n",
       "      <td>4.498</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.068</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.059</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.059</td>\n",
       "      <td>2.142</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.033</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.060</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.060</td>\n",
       "      <td>2.183</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.034</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS     F  p-unc   np2   eps\n",
       "0        group 0.314    1   62 0.314 4.498  0.038 0.068   NaN\n",
       "1   Volatility 0.059    1   62 0.059 2.142  0.148 0.033 1.000\n",
       "2  Interaction 0.060    1   62 0.060 2.183  0.145 0.034   NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.mixed_anova(data=df_coef, \n",
    "               dv='ar_dw', within='Volatility', between = 'group', subject='sub')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot mean and error bar of aw_dw as a function of group, Volatility\n",
    "#sns.barplot(data=df_coef.query(\"sequence not in @outliers_regress\"), \n",
    "#            x='group', y='ar_dw', hue='Volatility',capsize = .1,\n",
    "#            zorder = 5, errorbar=('ci', 68))\n",
    "# change y axis as 'DW index'\n",
    "# y axis from 1 to 2\n",
    "#plt.ylim(1, 2.4)\n",
    "# add stripplot\n",
    "sns.boxplot(data=df_coef, y='group', x='ar_dw', hue='Volatility', orient = 'h', hue_order=['Low Vola.', 'High Vola.'])\n",
    "# only show the last two legends\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "#plt.legend(handles[2:], labels[2:])\n",
    "# add dashed line 2 to indicate the 0 autocorrelation\n",
    "plt.axvline(2, ls='--', c='k')\n",
    "# remove box around the plot\n",
    "sns.despine()\n",
    "plt.xlabel('DW index')\n",
    "\n",
    "# save the figure to vector file ./figures/ar_dw.pdf\n",
    "plt.savefig('./figures/ar_dw.pdf', dpi=300)\n",
    "# save the figure to ./figures/ar_dw.png\n",
    "plt.savefig('./figures/ar_dw.png', dpi=300)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>-7.913</td>\n",
       "      <td>63</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000</td>\n",
       "      <td>[1.72, 1.83]</td>\n",
       "      <td>0.989</td>\n",
       "      <td>1.893e+08</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            T  dof alternative  p-val         CI95%  cohen-d       BF10  power\n",
       "T-test -7.913   63   two-sided  0.000  [1.72, 1.83]    0.989  1.893e+08  1.000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's compare if ar_dw was significantly lower than 2 in the two groups. \n",
    "# We will use a one-sample t-test to compare the mean of ar_dw to 2.\n",
    "# t-test for ar_dw\n",
    "pg.ttest(df_coef.query(\"group == 'ASD'\")['ar_dw'], 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>-11.877</td>\n",
       "      <td>63</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000</td>\n",
       "      <td>[1.62, 1.73]</td>\n",
       "      <td>1.485</td>\n",
       "      <td>5.748e+14</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             T  dof alternative  p-val         CI95%  cohen-d       BF10  \\\n",
       "T-test -11.877   63   two-sided  0.000  [1.62, 1.73]    1.485  5.748e+14   \n",
       "\n",
       "        power  \n",
       "T-test  1.000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for TD group\n",
    "pg.ttest(df_coef.query(\"group == 'TD'\")['ar_dw'], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4. Outliers \n",
    "There was no difference in cti between groups, partly because there were three outliers in the ASD groups, as the CTI > 0.9. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>sub</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>sequence</th>\n",
       "      <th>group</th>\n",
       "      <th>level_4</th>\n",
       "      <th>r2</th>\n",
       "      <th>intercept</th>\n",
       "      <th>slope</th>\n",
       "      <th>cti</th>\n",
       "      <th>ar_dw</th>\n",
       "      <th>aic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A32</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>32</td>\n",
       "      <td>ASD</td>\n",
       "      <td>0</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.550</td>\n",
       "      <td>-0.904</td>\n",
       "      <td>0.904</td>\n",
       "      <td>1.405</td>\n",
       "      <td>-86.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>aril02</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>2</td>\n",
       "      <td>ASD</td>\n",
       "      <td>0</td>\n",
       "      <td>0.647</td>\n",
       "      <td>1.051</td>\n",
       "      <td>-0.997</td>\n",
       "      <td>0.997</td>\n",
       "      <td>1.313</td>\n",
       "      <td>-131.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>arm13</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>13</td>\n",
       "      <td>ASD</td>\n",
       "      <td>0</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.669</td>\n",
       "      <td>-0.907</td>\n",
       "      <td>0.907</td>\n",
       "      <td>1.650</td>\n",
       "      <td>-133.904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sub  Volatility  sequence group  level_4    r2  intercept  slope   cti  \\\n",
       "2      A32  High Vola.        32   ASD        0 0.606      0.550 -0.904 0.904   \n",
       "36  aril02  High Vola.         2   ASD        0 0.647      1.051 -0.997 0.997   \n",
       "50   arm13  High Vola.        13   ASD        0 0.617      0.669 -0.907 0.907   \n",
       "\n",
       "    ar_dw      aic  \n",
       "2   1.405  -86.745  \n",
       "36  1.313 -131.330  \n",
       "50  1.650 -133.904  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_coef.query(\"cti > 0.9\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize these three outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x350 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outliers_regress = df_coef.query(\"cti > 0.9\").sequence\n",
    "# from mdata, select rows with sequence in outliers_regress, and plot rep_err vs. Duration\n",
    "# separate for individual sub, Volatility\n",
    "moutliers = rawdata.query('sequence in @outliers_regress and group == \"ASD\" and outlier == False')\n",
    "sns.set_palette('Dark2')\n",
    "ax = sns.lmplot(x='Duration', y='Reproduction', hue='Volatility', col='sub',\n",
    "                scatter_kws = {'alpha':0.3, 's':5}, legend = False, height = 3.5,\n",
    "                data=moutliers)\n",
    "ax.set(ylabel = 'Reproduction (s)')\n",
    "# remove subplots titles\n",
    "ax.set_titles('')\n",
    "# add diagonal dashed line to each subplot\n",
    "for ax in ax.axes.flat:\n",
    "    ax.plot(ax.get_xlim(), ax.get_xlim(), ls='--', c='k')\n",
    "\n",
    "# put legend in the upper right corner\n",
    "plt.legend(loc='upper right')\n",
    "# save the figure to ./figures/rep_err_vs_Duration_outliers.png\n",
    "plt.savefig('./figures/outliers.png', dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.047</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.047</td>\n",
       "      <td>1.734</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.030</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>1.204</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>1.204</td>\n",
       "      <td>81.366</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.592</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.049</td>\n",
       "      <td>3.324</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.056</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.047    1   56 0.047  1.734  0.193 0.030   NaN\n",
       "1   Volatility 1.204    1   56 1.204 81.366  0.000 0.592 1.000\n",
       "2  Interaction 0.049    1   56 0.049  3.324  0.074 0.056   NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see what happens if we remove the outliers from the regression analysis.\n",
    "# ANOVA for slopes with group as between-subject factor and Volatility as within-subject factor\n",
    "pg.mixed_anova(data=df_coef.query(\"sequence not in @outliers_regress\"), \n",
    "               dv='cti', within='Volatility', between = 'group', subject='sub')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It remains not significant between groups, suggesting both groups had similar acquisition of the prior (Volatility) information. But let's check the residual autocorrelation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.559</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.559</td>\n",
       "      <td>8.357</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.130</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.087</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.087</td>\n",
       "      <td>3.338</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.056</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.081</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.081</td>\n",
       "      <td>3.103</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.052</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS     F  p-unc   np2   eps\n",
       "0        group 0.559    1   56 0.559 8.357  0.005 0.130   NaN\n",
       "1   Volatility 0.087    1   56 0.087 3.338  0.073 0.056 1.000\n",
       "2  Interaction 0.081    1   56 0.081 3.103  0.084 0.052   NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.mixed_anova(data=df_coef.query(\"sequence not in @outliers_regress\"), \n",
    "               dv='ar_dw', within='Volatility', between = 'group', subject='sub')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By excluding the outliers, the difference between groups became even more significant, p = .005. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5 Visualize the behavioral results \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now we combine plots together for the paper\n",
    "sns.set(style='ticks', context='paper')\n",
    "fig, axes = plt.subplots(nrows = 2, ncols = 2, figsize = (7, 6))\n",
    "sns.set_palette('Dark2')\n",
    "# for each group, on seperate subplot, plot rep_err as a function of Duration, using different color for Volatility with regplot \n",
    "for i, group in enumerate(df_coef.group.unique()):\n",
    "    for j, vol in enumerate(df_coef.Volatility.unique()):\n",
    "        sns.regplot(data=mdata.query(\"group == @group and Volatility == @vol\"), \n",
    "                    x='Duration', y='rep_err', ax = axes[0,i], color = sns.color_palette('Dark2')[1-j], label = vol, \n",
    "                    ci = None, scatter_kws = {'alpha':0.3, 's':5})\n",
    "    if i == 1:\n",
    "        axes[0,i].legend(loc='lower left')\n",
    "    else:\n",
    "        # remove legend\n",
    "        axes[0,i].legend().remove()\n",
    "    # add dashed line 0 to each subplot\n",
    "    axes[0,i].axhline(0, ls='--', c='k')\n",
    "    # set y axis limit\n",
    "    axes[0,i].set_ylim(-0.6, 0.6)\n",
    "    # set x label to 'Duration (s)', y label to 'Reproduction error (s)'\n",
    "    axes[0,i].set(xlabel = 'Duration (s)', ylabel = 'Reproduction error (s)')\n",
    "    # set title to group\n",
    "    axes[0,i].set_title(group)\n",
    "# add horizontal boxplot for cti from df_coef on the second row first column\n",
    "sns.boxplot(data=df_coef, \n",
    "            y='group', x='cti', hue='Volatility', ax = axes[1,0],  width = 0.5, \n",
    "            orient = 'h', hue_order=['Low Vola.', 'High Vola.'])\n",
    "axes[1,0].set(xlabel = 'Central Tendency Index')\n",
    "axes[1,0].legend().remove()\n",
    "# add a vertical line at 0.0\n",
    "axes[1,0].axvline(0, ls='--', c='k')\n",
    "# remove y axis label\n",
    "axes[1,0].set(ylabel = '')\n",
    "# add horizontal boxplot for ar_dw from df_coef on the second row second column\n",
    "sns.boxplot(data=df_coef, y='group', x='ar_dw', hue='Volatility', width = 0.5, \n",
    "            ax = axes[1,1], orient = 'h', hue_order=['Low Vola.', 'High Vola.'])\n",
    "# legend off\n",
    "#axes[1,1].legend().remove()\n",
    "axes[1,1].legend(loc='lower right')\n",
    "axes[1,1].set(xlabel = 'Autocorrelation (DW) Index')\n",
    "# add a vertical line at 2.0\n",
    "axes[1,1].axvline(2, ls='--', c='k')\n",
    "# x axis from 0.9 to 3\n",
    "axes[1,1].set_xlim(0.9, 3)\n",
    "axes[1,1].set(ylabel = '')\n",
    "# remove box around the plot\n",
    "sns.despine()\n",
    "# add labels to subplots a, b, c, d\n",
    "for i, label in enumerate(['a', 'b', 'c', 'd']):\n",
    "    axes[int(i/2),i%2].text(-0.1, 1.1, label, transform=axes[int(i/2),i%2].transAxes, \n",
    "                            fontsize=16, fontweight='bold', va='top', ha='right')\n",
    "    \n",
    "\n",
    "# Adjust layout and show plot\n",
    "plt.tight_layout()\n",
    "\n",
    "# save fig to vector file ./figures/rep_err_vs_Duration.png\n",
    "plt.savefig('./figures/rep_err_vs_Duration.png', dpi=300, bbox_inches='tight', facecolor='white')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.6 General bias (over- or under-reproduction) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.006</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.010</td>\n",
       "      <td>5.573</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.082</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.011</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS     F  p-unc   np2   eps\n",
       "0        group 0.005    1   62 0.005 0.385  0.537 0.006   NaN\n",
       "1   Volatility 0.010    1   62 0.010 5.573  0.021 0.082 1.000\n",
       "2  Interaction 0.001    1   62 0.001 0.698  0.407 0.011   NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average the reproduction errors for general biases\n",
    "mrep_err = rawdata.query(\"outlier == False\").groupby(['sub','group', 'Volatility'])['rep_err'].mean().reset_index()\n",
    "# pingouin mixed ANOVA on mrep_err\n",
    "aov = pg.mixed_anova(data=mrep_err, dv='rep_err', within='Volatility', between='group', subject='sub')\n",
    "# show the ANOVA table\n",
    "aov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>group</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ASD</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TD</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group  mean   std\n",
       "0   ASD 0.024 0.099\n",
       "1    TD 0.036 0.062"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the mean reproduction error and standard deviation for each group and Volatility\n",
    "mrep_err.groupby(['group'])['rep_err'].agg(['mean', 'std']).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.7 Two-state Iterative model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# include in one notebook (the following code from KmodelY.py)\n",
    "from scipy.optimize import least_squares\n",
    "from statsmodels.stats.stattools import durbin_watson\n",
    "import numpy as np\n",
    "\n",
    "def fitKmodel(subdata, nolog=None, pfit=None, p0=None):\n",
    "    \"\"\"\n",
    "   Parameters:\n",
    "    - subdata : subject data\n",
    "    - nolog : If zero, logarithm is not used (default is 0)\n",
    "    - pfit : A list of logical values to indicate which parameters to fit (default is [True, True, True])\n",
    "    - p0 : Initial parameters (must always have length 3)\n",
    "    \n",
    "    Returns:\n",
    "    - px : Parameters of the model\n",
    "\n",
    "    By S.Glasauer 2019 (matlab), translated to Python by Strongway\n",
    "    # add AIC and DW\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    # Handle default arguments\n",
    "    if p0 is None:\n",
    "        p0 = [1., 1, 0]\n",
    "    if pfit is None:\n",
    "        pfit = [True, True, True]\n",
    "    if nolog is None:\n",
    "        nolog = 0\n",
    "\n",
    "    # Convert pfit to logical and filter p0\n",
    "    pfit = np.array(pfit, dtype=bool)\n",
    "    p0 = np.array(p0)[pfit]\n",
    "\n",
    "    # Lower bounds (lb) for the optimization\n",
    "    lb = np.array([0, 0, -np.inf])[pfit]\n",
    "    \n",
    "\n",
    "    # extract Duration and Reproduction from subdata as 2d array\n",
    "    x = subdata['Duration'].values\n",
    "    y = subdata['Reproduction'].values\n",
    "    # replace extreme y with nan with y > 3 * x or y < x/3\n",
    "    y[(y > 3 * x) | (y < x/3)] = np.nan\n",
    "    # combine x,y as 2d array\n",
    "    stimrep = np.vstack([x,y]).T\n",
    "    # Perform the optimization using least_squares (equivalent to lsqnonlin in MATLAB)\n",
    "    result = least_squares(kmodelY,  p0, args = (stimrep, 1),\n",
    "                           bounds=(lb, np.inf), method='trf')\n",
    "    \n",
    "    # calculate kalmann filter parameters\n",
    "    q11 = result.x[0]\n",
    "    q22 = result.x[1]\n",
    "    r = 1\n",
    "    # calculate residual sum of squares\n",
    "    rss = np.sum(result.fun**2)\n",
    "    dw = durbin_watson(result.fun)\n",
    "    # number of parameters\n",
    "    k = len(result.x)\n",
    "    # number of observations\n",
    "    n = len(stimrep)\n",
    "    # calculate the log-likelihood\n",
    "    ll = -n/2*(np.log(2*np.pi) + np.log(rss/n) + 1)\n",
    "    # calculate the Akaike information criterion (AIC)\n",
    "    aic = 2*k - 2*ll\n",
    "    # steady state solution\n",
    "    p22 = (q22+np.sqrt(q22*q22+4*(q11+r)*q22))/2\n",
    "    K = np.array([p22 + q11, p22])/(p22+q11+r)\n",
    "    # return the optimized parameters, steady state solution, and AIC\n",
    "    return np.append(np.append(result.x, K), [aic, dw])  # Optimized parameters\n",
    "\n",
    "\n",
    "def kmodelY(par, stimrep, nolog=1, pfit=[1, 1, 1]):\n",
    "    \"\"\"\n",
    "    Function to perform Kalman filter-based estimation.\n",
    "    \n",
    "    Parameters:\n",
    "    - par: Model parameters (if pfit = [1,1,1], then par = [q1/r, q2/r, cost-related parameter (0 for median)])\n",
    "    - stimrep: Stimulus representation\n",
    "    - nolog: Flag to decide if logarithm transformation is needed\n",
    "    - pfit: Parameter fitting list (note: len(par) = sum(pfit))\n",
    "    \n",
    "    Returns:\n",
    "    - sres: Stimulus residuals\n",
    "    - xest: Estimated state\n",
    "    - pest: Estimate error covariance\n",
    "    - resp: Response\n",
    "    - perr: Prediction error\n",
    "\n",
    "    S.Glasauer 2019/2023, translated to Python by Strongway\n",
    "    \"\"\"\n",
    "\n",
    "    # Convert pfit to a boolean array\n",
    "    pfit = np.array(pfit, dtype=bool)\n",
    "\n",
    "    # Adjust pfit based on the size of par\n",
    "    if len(par) < 3:\n",
    "        pfit[len(par):] = False\n",
    "    \n",
    "    # Adjust stimrep's shape for further processing\n",
    "    if stimrep.shape[1] == 1:\n",
    "        stimrep = np.tile(stimrep, (1, 2))\n",
    "    # the first column is the stimulus, the second column is the response, \n",
    "    # and add the third column to indicate the start of a new sequence\n",
    "    #if stimrep.shape[1] == 2:\n",
    "    #    stimrep = np.hstack((stimrep, np.zeros((stimrep.shape[0], 1))))\n",
    "    #    stimrep[0, 2] = 1\n",
    "    \n",
    "    # Initialize pars and overwrite with provided parameters based on pfit\n",
    "    pars = np.array([0.0, 0.0, 0.0])\n",
    "    pars[pfit] = par\n",
    "    par = pars\n",
    "\n",
    "    # Constants for the model\n",
    "    a = 10.0\n",
    "    off = 1.\n",
    "    r = 1.\n",
    "    q1 = par[0] * r\n",
    "    q2 = par[1] * r\n",
    "\n",
    "    # Define matrices Q, P, H, and F for the Kalman filter of two-state model\n",
    "    # details see Glasauer & Shi, 2022, Sci. Rep., https://doi.org/10.1038/s41598-022-14939-8\n",
    "    Q = np.array([[q1, 0], [0, q2]])\n",
    "    P = np.array([[r, 0], [0, r]])\n",
    "    H = np.array([[1., 0]])\n",
    "    F = np.array([[0, 1.], [0, 1.]])\n",
    "\n",
    "    # Apply logarithm transformation if nolog is false\n",
    "    if nolog:\n",
    "        z = stimrep[:, 0]\n",
    "    else: # log transformation\n",
    "        z = np.log(a * stimrep[:, 0] + off)\n",
    "\n",
    "    # Initialize state vector x\n",
    "    x = np.array([[z[0]], [z[0]]])\n",
    "\n",
    "    # Initialize matrices for storing results\n",
    "    xest = np.zeros((len(z), 2))\n",
    "    pest = np.zeros((len(z), 2))\n",
    "    perr = np.zeros(len(z))\n",
    "\n",
    "    # Kalman filter estimation loop\n",
    "    for i in range(len(z)):\n",
    "        \n",
    "        x = F@x\n",
    "        P = F@P@F.T + Q\n",
    "        K = P@H.T/(H@P@H.T + r)\n",
    "        perr[i] = z[i] - H@x\n",
    "        x = x + K*perr[i]\n",
    "        P = (np.eye(2) - K@H)@P\n",
    "\n",
    "        pest[i, :] = np.diag(P)\n",
    "        xest[i, :] = x.reshape(-1)\n",
    "\n",
    "    # Adjust for third parameter, if present\n",
    "    if len(par) == 3:\n",
    "        sh = par[2]\n",
    "    else:\n",
    "        sh = 0\n",
    "\n",
    "    # Compute response, adjusting for logarithm if needed\n",
    "    if nolog:\n",
    "        resp = xest[:, 0] + sh\n",
    "    else: # log transformation\n",
    "        resp = (np.exp(xest[:, 0] + sh) - off)/a \n",
    "        \n",
    "\n",
    "    # Calculate stimulus residuals\n",
    "    sres = stimrep[:, 1] - resp\n",
    "\n",
    "    # Remove NaNs from sres\n",
    "    sres = sres[np.isfinite(sres)]\n",
    "\n",
    "    return sres\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dc/hksrz0yj5bb8n7f4_yptkcmw0000gn/T/ipykernel_74932/1675682148.py:146: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  perr[i] = z[i] - H@x\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>sequence</th>\n",
       "      <th>group</th>\n",
       "      <th>par</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>tau</th>\n",
       "      <th>K1</th>\n",
       "      <th>K2</th>\n",
       "      <th>AIC_2S</th>\n",
       "      <th>DW_2S</th>\n",
       "      <th>Kd</th>\n",
       "      <th>kseq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A31</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[0.9185124640252414, 0.05243938464301662, -0.1...</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.052</td>\n",
       "      <td>-0.170</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.152</td>\n",
       "      <td>-1.177</td>\n",
       "      <td>1.728</td>\n",
       "      <td>-0.406</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A31</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[0.7563162466196017, 2.049556565459247e-05, -0...</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.290</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.003</td>\n",
       "      <td>-42.879</td>\n",
       "      <td>1.181</td>\n",
       "      <td>-0.429</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A32</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>32</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[0.09834201160694395, 1.1492562435182896e-18, ...</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.330</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-132.762</td>\n",
       "      <td>1.382</td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A32</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>32</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[3.342491279429646e-10, 0.4012756813450186, -0...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.401</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.464</td>\n",
       "      <td>158.794</td>\n",
       "      <td>1.669</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A33</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>33</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[1.0307068777913497, 1.3110276998645982, -0.06...</td>\n",
       "      <td>1.031</td>\n",
       "      <td>1.311</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.543</td>\n",
       "      <td>-134.099</td>\n",
       "      <td>1.838</td>\n",
       "      <td>-0.232</td>\n",
       "      <td>0.122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sub  Volatility  sequence group  \\\n",
       "0  A31  High Vola.        31   ASD   \n",
       "1  A31   Low Vola.        31   ASD   \n",
       "2  A32  High Vola.        32   ASD   \n",
       "3  A32   Low Vola.        32   ASD   \n",
       "4  A33  High Vola.        33   ASD   \n",
       "\n",
       "                                                 par    p1    p2    tau    K1  \\\n",
       "0  [0.9185124640252414, 0.05243938464301662, -0.1... 0.919 0.052 -0.170 0.558   \n",
       "1  [0.7563162466196017, 2.049556565459247e-05, -0... 0.756 0.000 -0.290 0.433   \n",
       "2  [0.09834201160694395, 1.1492562435182896e-18, ... 0.098 0.000 -0.330 0.090   \n",
       "3  [3.342491279429646e-10, 0.4012756813450186, -0... 0.000 0.401 -0.001 0.464   \n",
       "4  [1.0307068777913497, 1.3110276998645982, -0.06... 1.031 1.311 -0.061 0.775   \n",
       "\n",
       "     K2   AIC_2S  DW_2S     Kd  kseq  \n",
       "0 0.152   -1.177  1.728 -0.406 0.067  \n",
       "1 0.003  -42.879  1.181 -0.429 0.002  \n",
       "2 0.000 -132.762  1.382 -0.090 0.000  \n",
       "3 0.464  158.794  1.669 -0.000 0.249  \n",
       "4 0.543 -134.099  1.838 -0.232 0.122  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit two-state model\n",
    "# from kmodelY import * #(when use kmodelY.py)\n",
    "\n",
    "# fit two-state model\n",
    "# subsecting each subject, volatility, and estimate two-state model parameters\n",
    "df_kmodel = rawdata.groupby(\n",
    "    ['sub', 'Volatility', 'sequence', 'group']).apply(\n",
    "        fitKmodel).reset_index()\n",
    "df_kmodel.columns = ['sub', 'Volatility', 'sequence', 'group', 'par']\n",
    "# split the parameters to columns\n",
    "df_kmodel[['p1','p2','tau','K1', 'K2','AIC_2S','DW_2S']] = pd.DataFrame(df_kmodel['par'].tolist(), index=df_kmodel.index)\n",
    "# add a column for the difference between K1 and K2\n",
    "df_kmodel['Kd'] = df_kmodel['K2'] - df_kmodel['K1']\n",
    "# sequential dependence analytical results for randomized sequences\n",
    "df_kmodel['kseq'] = df_kmodel['K2']*(1-df_kmodel['K1'])\n",
    "df_kmodel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>sub</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>sequence</th>\n",
       "      <th>group</th>\n",
       "      <th>par</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>tau</th>\n",
       "      <th>K1</th>\n",
       "      <th>K2</th>\n",
       "      <th>...</th>\n",
       "      <th>Kd</th>\n",
       "      <th>kseq</th>\n",
       "      <th>level_4</th>\n",
       "      <th>r2</th>\n",
       "      <th>intercept</th>\n",
       "      <th>slope</th>\n",
       "      <th>cti</th>\n",
       "      <th>ar_dw</th>\n",
       "      <th>aic</th>\n",
       "      <th>dAIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A31</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[0.9185124640252414, 0.05243938464301662, -0.1...</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.052</td>\n",
       "      <td>-0.170</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.152</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.406</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0</td>\n",
       "      <td>0.389</td>\n",
       "      <td>0.284</td>\n",
       "      <td>-0.451</td>\n",
       "      <td>0.451</td>\n",
       "      <td>1.710</td>\n",
       "      <td>9.836</td>\n",
       "      <td>-11.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A31</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[0.7563162466196017, 2.049556565459247e-05, -0...</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.290</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.003</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.429</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.279</td>\n",
       "      <td>-0.506</td>\n",
       "      <td>0.506</td>\n",
       "      <td>1.017</td>\n",
       "      <td>-4.688</td>\n",
       "      <td>-38.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A32</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>32</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[0.09834201160694395, 1.1492562435182896e-18, ...</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.330</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.550</td>\n",
       "      <td>-0.904</td>\n",
       "      <td>0.904</td>\n",
       "      <td>1.405</td>\n",
       "      <td>-86.745</td>\n",
       "      <td>-46.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A32</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>32</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[3.342491279429646e-10, 0.4012756813450186, -0...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.401</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.464</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.222</td>\n",
       "      <td>-0.227</td>\n",
       "      <td>0.227</td>\n",
       "      <td>1.701</td>\n",
       "      <td>166.866</td>\n",
       "      <td>-8.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A33</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>33</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[1.0307068777913497, 1.3110276998645982, -0.06...</td>\n",
       "      <td>1.031</td>\n",
       "      <td>1.311</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.543</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.232</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.180</td>\n",
       "      <td>-0.239</td>\n",
       "      <td>0.239</td>\n",
       "      <td>1.783</td>\n",
       "      <td>-113.065</td>\n",
       "      <td>-21.033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   sub  Volatility  sequence group  \\\n",
       "0  A31  High Vola.        31   ASD   \n",
       "1  A31   Low Vola.        31   ASD   \n",
       "2  A32  High Vola.        32   ASD   \n",
       "3  A32   Low Vola.        32   ASD   \n",
       "4  A33  High Vola.        33   ASD   \n",
       "\n",
       "                                                 par    p1    p2    tau    K1  \\\n",
       "0  [0.9185124640252414, 0.05243938464301662, -0.1... 0.919 0.052 -0.170 0.558   \n",
       "1  [0.7563162466196017, 2.049556565459247e-05, -0... 0.756 0.000 -0.290 0.433   \n",
       "2  [0.09834201160694395, 1.1492562435182896e-18, ... 0.098 0.000 -0.330 0.090   \n",
       "3  [3.342491279429646e-10, 0.4012756813450186, -0... 0.000 0.401 -0.001 0.464   \n",
       "4  [1.0307068777913497, 1.3110276998645982, -0.06... 1.031 1.311 -0.061 0.775   \n",
       "\n",
       "     K2  ...     Kd  kseq  level_4    r2  intercept  slope   cti  ar_dw  \\\n",
       "0 0.152  ... -0.406 0.067        0 0.389      0.284 -0.451 0.451  1.710   \n",
       "1 0.003  ... -0.429 0.002        0 0.459      0.279 -0.506 0.506  1.017   \n",
       "2 0.000  ... -0.090 0.000        0 0.606      0.550 -0.904 0.904  1.405   \n",
       "3 0.464  ... -0.000 0.249        0 0.020      0.222 -0.227 0.227  1.701   \n",
       "4 0.543  ... -0.232 0.122        0 0.182      0.180 -0.239 0.239  1.783   \n",
       "\n",
       "       aic    dAIC  \n",
       "0    9.836 -11.013  \n",
       "1   -4.688 -38.192  \n",
       "2  -86.745 -46.017  \n",
       "3  166.866  -8.072  \n",
       "4 -113.065 -21.033  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine df_kmodel and df_seq by sub, Volatility, and group\n",
    "kpars = df_kmodel.merge(df_coef, on=['sub', 'Volatility', 'sequence', 'group'])\n",
    "# show the first 5 rows of df_kmodel\n",
    "kpars['dAIC'] = kpars['AIC_2S'] - kpars['aic']\n",
    "kpars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-10.75263200035032"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean benefits of two-state model in terms of AIC\n",
    "kpars['dAIC'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the AIC difference between the two models was significant, suggesting the two-state model was a better fit to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x15c4308f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols = 2, figsize = (7, 3.5))\n",
    "# barplot for DW_2S as a function of group, Volatility\n",
    "sns.barplot(data=kpars, x='group', y='DW_2S', hue='Volatility', capsize = .1,\n",
    "            zorder = 5, errorbar=('ci', 68), ax = axes[0])\n",
    "# change y axis to 1.5 to 2\n",
    "axes[0].set_ylim(1.5, 2)\n",
    "# add dashed line 2 to indicate the 0 autocorrelation\n",
    "axes[0].axhline(2, ls='--', c='k')\n",
    "axes[0].axhline(1.7, ls='--', c='k')\n",
    "# second subplot for the ar_dw as a function of group, Volatility\n",
    "sns.barplot(data=kpars, x='group', y='ar_dw', hue='Volatility', capsize = .1,\n",
    "            zorder = 5, errorbar=('ci', 68), ax = axes[1])\n",
    "# change y axis to 1.5 to 2\n",
    "axes[1].set_ylim(1.5, 2)\n",
    "# add dashed line 2 to indicate the 0 autocorrelation\n",
    "axes[1].axhline(2, ls='--', c='k')\n",
    "axes[1].axhline(1.7, ls='--', c='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">K1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">K2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">kseq</th>\n",
       "      <th colspan=\"2\" halign=\"left\">tau</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th>Volatility</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ASD</th>\n",
       "      <th>High Vola.</th>\n",
       "      <td>0.681</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Vola.</th>\n",
       "      <td>0.642</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TD</th>\n",
       "      <th>High Vola.</th>\n",
       "      <td>0.682</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Vola.</th>\n",
       "      <td>0.568</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    K1          K2        kseq         tau      \n",
       "                  mean   sem  mean   sem  mean   sem  mean   sem\n",
       "group Volatility                                                \n",
       "ASD   High Vola. 0.681 0.043 0.446 0.047 0.100 0.013 0.013 0.019\n",
       "      Low Vola.  0.642 0.036 0.344 0.057 0.089 0.016 0.041 0.017\n",
       "TD    High Vola. 0.682 0.026 0.396 0.039 0.113 0.012 0.031 0.010\n",
       "      Low Vola.  0.568 0.035 0.198 0.038 0.088 0.016 0.057 0.010"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# average K1, K2, and tau for each Volatility, group, with standard error\n",
    "df_kmodel.groupby(['group', 'Volatility']).agg({'K1': ['mean', 'sem'], 'K2': ['mean', 'sem'], 'kseq': ['mean', 'sem'], 'tau': ['mean', 'sem']})\n",
    "\n",
    "# save kmodel_v to csv file ./data/kmodel_v.csv\n",
    "#kmodel_v.to_csv('./data/kmodel_v.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics for those parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.043</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.011</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.188</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.188</td>\n",
       "      <td>10.613</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.146</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.046</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.046</td>\n",
       "      <td>2.620</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.041</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.043    1   62 0.043  0.685  0.411 0.011   NaN\n",
       "1   Volatility 0.188    1   62 0.188 10.613  0.002 0.146 1.000\n",
       "2  Interaction 0.046    1   62 0.046  2.620  0.111 0.041   NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistics for K1\n",
    "pg.mixed_anova(data=df_kmodel, dv='K1', within='Volatility', between = 'group', subject='sub')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.307</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.307</td>\n",
       "      <td>4.232</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.064</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.725</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.725</td>\n",
       "      <td>11.754</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.159</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.075</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.075</td>\n",
       "      <td>1.209</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.019</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.307    1   62 0.307  4.232  0.044 0.064   NaN\n",
       "1   Volatility 0.725    1   62 0.725 11.754  0.001 0.159 1.000\n",
       "2  Interaction 0.075    1   62 0.075  1.209  0.276 0.019   NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistics for K2\n",
    "pg.mixed_anova(data=df_kmodel, dv='K2', within='Volatility', between = 'group', subject='sub')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K2 shows a significant difference between groups!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.009</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.013</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.024</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.024</td>\n",
       "      <td>11.835</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.160</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.009    1   62 0.009  0.794  0.376 0.013   NaN\n",
       "1   Volatility 0.024    1   62 0.024 11.835  0.001 0.160 1.000\n",
       "2  Interaction 0.000    1   62 0.000  0.013  0.911 0.000   NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistics for tau\n",
    "pg.mixed_anova(data=df_kmodel, dv='tau', within='Volatility', between = 'group', subject='sub')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exclude outliers\n",
    "kmodel_v = df_kmodel.query(\"sequence not in @outliers_regress\")\n",
    "#kmodel_v = df_kmodel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x350 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the mean and standard error of K1, K2, and tau as a function of group, Volatility\n",
    "sns.set(style='ticks', context='paper')\n",
    "sns.set_palette('Dark2')\n",
    "\n",
    "# three subplots side by side\n",
    "fig, axes = plt.subplots(ncols = 3, figsize = (9, 3.5))\n",
    "# barplot for K1 as a function of group, Volatility\n",
    "sns.barplot(data=df_kmodel, x='group', y='K1', hue='Volatility', capsize = .1,\n",
    "            zorder = 5, errorbar=('se'), ax = axes[0], hue_order=['Low Vola.', 'High Vola.'])\n",
    "# y axis from 0.5 to 1\n",
    "axes[0].set_ylim(0.1, .8)\n",
    "# legend top right\n",
    "axes[0].legend(loc='upper right')\n",
    "axes[0].legend().remove()\n",
    "# remove x axis label\n",
    "axes[0].set(xlabel = '')\n",
    "# barplot for K2 as a function of group, Volatility\n",
    "sns.barplot(data=df_kmodel, x='group', y='K2', hue='Volatility', capsize = .1,\n",
    "            zorder = 5, errorbar=('se'), ax = axes[1], hue_order=['Low Vola.', 'High Vola.'])\n",
    "# legend off\n",
    "#axes[1].legend().remove()\n",
    "axes[1].set_ylim(0.1, .8)\n",
    "axes[1].set(xlabel = '')\n",
    "\n",
    "# barplot for tau as a function of group, Volatility\n",
    "# change the tau from seconds to milliseconds\n",
    "df_kmodel['Tau'] = df_kmodel['tau']*1000\n",
    "sns.barplot(data=df_kmodel, x='group', y='Tau', hue='Volatility', capsize = .1,\n",
    "            zorder = 5, errorbar=('se'), ax = axes[2], hue_order=['Low Vola.', 'High Vola.'])\n",
    "# y label to 'tau (ms)'\n",
    "axes[2].set_ylabel('General Bias (ms)')\n",
    "# legend off\n",
    "axes[2].legend().remove()\n",
    "axes[2].set(xlabel = '')\n",
    "axes[2].set_ylim(0, 70)\n",
    "\n",
    "# remove box around the plot\n",
    "sns.despine()\n",
    "# add labels to subplots a, b, c, d\n",
    "for i, label in enumerate(['a', 'b', 'c']):\n",
    "    axes[i].text(-0.1, 1.1, label, transform=axes[i].transAxes, \n",
    "                            fontsize=16, fontweight='bold', va='top', ha='right')\n",
    "# tight layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# save the figure to vector file ./figures/kmodel.png\n",
    "plt.savefig('./figures/kmodel.png', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">K1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">K2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">kseq</th>\n",
       "      <th colspan=\"2\" halign=\"left\">tau</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th>Volatility</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ASD</th>\n",
       "      <th>High Vola.</th>\n",
       "      <td>0.681</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Vola.</th>\n",
       "      <td>0.642</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TD</th>\n",
       "      <th>High Vola.</th>\n",
       "      <td>0.682</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Vola.</th>\n",
       "      <td>0.568</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    K1          K2        kseq         tau      \n",
       "                  mean   sem  mean   sem  mean   sem  mean   sem\n",
       "group Volatility                                                \n",
       "ASD   High Vola. 0.681 0.043 0.446 0.047 0.100 0.013 0.013 0.019\n",
       "      Low Vola.  0.642 0.036 0.344 0.057 0.089 0.016 0.041 0.017\n",
       "TD    High Vola. 0.682 0.026 0.396 0.039 0.113 0.012 0.031 0.010\n",
       "      Low Vola.  0.568 0.035 0.198 0.038 0.088 0.016 0.057 0.010"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display those outliers parameters\n",
    "df_kmodel.groupby(['group', 'Volatility']).agg({'K1': ['mean', 'sem'], 'K2': ['mean', 'sem'], 'kseq': ['mean', 'sem'], 'tau': ['mean', 'sem']})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.8 Would large K2 lead to a slow updating?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that K2 was significantly larger in the ASD group, we would expect a slower updating of the prior information. So we split the trials into two: the first half and the second half. Due to reduction of the sample trials, we excluded the outliers in this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's check the first half of the trials from each session from the rawdata\n",
    "# select the first half of the trials (trlNo 1-125 for the first session, 251-375 for the second) \n",
    "firsthalf_raw = rawdata.query('trlNo <= 125 or (trlNo >= 251 and trlNo <= 375)')\n",
    "# and second half of the trials (trlNo 126-250 for the first session, 376-500 for the second)\n",
    "secondhalf_raw = rawdata.query('trlNo >= 126 or trlNo >= 376')\n",
    "# calculate coefficient for the first half of the trials\n",
    "df_coef1 = firsthalf_raw.query('outlier == False and sequence not in @outliers_regress').groupby(['sub', 'Volatility', 'sequence', 'group']).apply(reg_func).reset_index()\n",
    "# calculate coefficient for the second half of the trials\n",
    "df_coef2 = secondhalf_raw.query('outlier == False and sequence not in @outliers_regress').groupby(['sub', 'Volatility', 'sequence', 'group']).apply(reg_func).reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set(style='ticks', context='paper')\n",
    "sns.set_palette('Dark2')\n",
    "# two subplots side by side\n",
    "fig, axes = plt.subplots(ncols = 2, figsize = (7, 3.5))\n",
    "# barplot for cti as a function of group, Volatility, and half\n",
    "sns.barplot(data=df_coef1, x='group', y='cti', hue='Volatility', capsize = .1,\n",
    "            zorder = 5, errorbar=('ci', 68), ax = axes[0], hue_order=['Low Vola.', 'High Vola.'])\n",
    "# legend off\n",
    "axes[0].legend().remove()\n",
    "# remove x axis label\n",
    "axes[0].set(xlabel = '')\n",
    "axes[0].set(ylabel = 'Central Tendency Index (CTI)')\n",
    "# add title to the subplot\n",
    "axes[0].set_title('The first half of trials')\n",
    "# second subplot for the ar_dw as a function of group, Volatility, and half\n",
    "sns.barplot(data=df_coef1, x='group', y='ar_dw', hue='Volatility', capsize = .1,\n",
    "            zorder = 5, errorbar=('ci', 68), ax = axes[1], hue_order=['Low Vola.', 'High Vola.'])\n",
    "axes[1].set(xlabel = '')\n",
    "axes[1].set(ylabel = 'DW index')\n",
    "# y axis from 1.5 to 2.5\n",
    "axes[1].set_ylim(1.5, 2.1)\n",
    "# add dashed line 2 to indicate the 0 autocorrelation\n",
    "axes[1].axhline(2, ls='--', c='k')\n",
    "axes[1].set_title('The first half of trials')\n",
    "# add title to the subplot\n",
    "#axes[1].set_title('Changes in CTI')\n",
    "# remove box around the plot\n",
    "sns.despine()\n",
    "# add labels to subplots a, b, c, d\n",
    "for i, label in enumerate(['a', 'b']):\n",
    "    axes[i].text(-0.1, 1.1, label, transform=axes[i].transAxes, \n",
    "                            fontsize=16, fontweight='bold', va='top', ha='right')\n",
    "# tight layout\n",
    "plt.tight_layout()\n",
    "# save the figure to vector file ./figures/cti_half.png\n",
    "plt.savefig('./figures/cti_half.png', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.222</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.222</td>\n",
       "      <td>6.236</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.480</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.480</td>\n",
       "      <td>24.738</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.306</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.013</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.012</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.222    1   56 0.222  6.236  0.015 0.100   NaN\n",
       "1   Volatility 0.480    1   56 0.480 24.738  0.000 0.306 1.000\n",
       "2  Interaction 0.013    1   56 0.013  0.682  0.412 0.012   NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.mixed_anova(data=df_coef1, \n",
    "               dv='cti', within='Volatility', between = 'group', subject='sub')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above statistics showed that the central tendency was significantly difference between groups in the first half! This suggests that the ASD group had a slower updating of the prior information, which might not have fully updated in the first half."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
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       "      <th>eps</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.544</td>\n",
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       "      <td>56</td>\n",
       "      <td>0.544</td>\n",
       "      <td>7.202</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.114</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.408</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.027</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS     F  p-unc   np2   eps\n",
       "0        group 0.544    1   56 0.544 7.202  0.010 0.114   NaN\n",
       "1   Volatility 0.036    1   56 0.036 0.694  0.408 0.012 1.000\n",
       "2  Interaction 0.027    1   56 0.027 0.522  0.473 0.009   NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.mixed_anova(data=df_coef1, \n",
    "               dv='ar_dw', within='Volatility', between = 'group', subject='sub')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the autocorrelation index (DW) for the first half of the trials was also significant between two groups. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compared to the entire sessions: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.047</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.047</td>\n",
       "      <td>1.734</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.030</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>1.204</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>1.204</td>\n",
       "      <td>81.366</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.592</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.049</td>\n",
       "      <td>3.324</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.056</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.047    1   56 0.047  1.734  0.193 0.030   NaN\n",
       "1   Volatility 1.204    1   56 1.204 81.366  0.000 0.592 1.000\n",
       "2  Interaction 0.049    1   56 0.049  3.324  0.074 0.056   NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.mixed_anova(data=df_coef.query(\"sequence not in @outliers_regress\"), \n",
    "               dv='cti', within='Volatility', between = 'group', subject='sub')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
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       "      <th>DF2</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.559</td>\n",
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       "      <td>56</td>\n",
       "      <td>0.559</td>\n",
       "      <td>8.357</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.130</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.087</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.087</td>\n",
       "      <td>3.338</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.056</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.081</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.081</td>\n",
       "      <td>3.103</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.052</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS     F  p-unc   np2   eps\n",
       "0        group 0.559    1   56 0.559 8.357  0.005 0.130   NaN\n",
       "1   Volatility 0.087    1   56 0.087 3.338  0.073 0.056 1.000\n",
       "2  Interaction 0.081    1   56 0.081 3.103  0.084 0.052   NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.mixed_anova(data=df_coef.query(\"sequence not in @outliers_regress\"), \n",
    "               dv='ar_dw', within='Volatility', between = 'group', subject='sub')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the two-state model to the first half of the trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dc/hksrz0yj5bb8n7f4_yptkcmw0000gn/T/ipykernel_74932/1675682148.py:146: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  perr[i] = z[i] - H@x\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>sequence</th>\n",
       "      <th>group</th>\n",
       "      <th>par</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>tau</th>\n",
       "      <th>K1</th>\n",
       "      <th>K2</th>\n",
       "      <th>AIC_2S</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A31</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[0.9714549830363363, 0.01693385718297349, -0.1...</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.199</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-7.870</td>\n",
       "      <td>1.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A31</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>31</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[2.0532000286870844e-22, 12.169718779060531, -...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.170</td>\n",
       "      <td>-0.438</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.929</td>\n",
       "      <td>66.842</td>\n",
       "      <td>0.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A33</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>33</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[1.204485814584274, 0.7122651732928083, -0.058...</td>\n",
       "      <td>1.204</td>\n",
       "      <td>0.712</td>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.429</td>\n",
       "      <td>-60.205</td>\n",
       "      <td>1.666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A33</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>33</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[5.735340853415004e-12, 0.4293106313521714, -0...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.429</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.475</td>\n",
       "      <td>-117.713</td>\n",
       "      <td>2.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A34</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>34</td>\n",
       "      <td>ASD</td>\n",
       "      <td>[0.06397846678007811, 0.4849299971316463, 0.00...</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.485</td>\n",
       "      <td>-133.310</td>\n",
       "      <td>1.814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sub  Volatility  sequence group  \\\n",
       "0  A31  High Vola.        31   ASD   \n",
       "1  A31   Low Vola.        31   ASD   \n",
       "2  A33  High Vola.        33   ASD   \n",
       "3  A33   Low Vola.        33   ASD   \n",
       "4  A34  High Vola.        34   ASD   \n",
       "\n",
       "                                                 par    p1     p2    tau  \\\n",
       "0  [0.9714549830363363, 0.01693385718297349, -0.1... 0.971  0.017 -0.199   \n",
       "1  [2.0532000286870844e-22, 12.169718779060531, -... 0.000 12.170 -0.438   \n",
       "2  [1.204485814584274, 0.7122651732928083, -0.058... 1.204  0.712 -0.058   \n",
       "3  [5.735340853415004e-12, 0.4293106313521714, -0... 0.000  0.429 -0.028   \n",
       "4  [0.06397846678007811, 0.4849299971316463, 0.00... 0.064  0.485  0.005   \n",
       "\n",
       "     K1    K2   AIC_2S  DW_2S  \n",
       "0 0.538 0.088   -7.870  1.933  \n",
       "1 0.929 0.929   66.842  0.850  \n",
       "2 0.741 0.429  -60.205  1.666  \n",
       "3 0.475 0.475 -117.713  2.117  \n",
       "4 0.516 0.485 -133.310  1.814  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now fit two-state model to the first half of the trials\n",
    "# subsecting each subject, volatility, and estimate two-state model parameters\n",
    "df_kmodel1 = firsthalf_raw.query(\"sequence not in @outliers_regress\").groupby(\n",
    "    ['sub', 'Volatility', 'sequence', 'group']).apply(\n",
    "        fitKmodel).reset_index()\n",
    "df_kmodel1.columns = ['sub', 'Volatility', 'sequence', 'group', 'par']\n",
    "# split the parameters to columns\n",
    "df_kmodel1[['p1','p2','tau','K1', 'K2','AIC_2S','DW_2S']] = pd.DataFrame(df_kmodel1['par'].tolist(), \n",
    "                                                                         index=df_kmodel1.index)\n",
    "df_kmodel1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">K1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">K2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">tau</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th>Volatility</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 rowspan=\"2\" valign=\"top\">ASD</th>\n",
       "      <th>High Vola.</th>\n",
       "      <td>0.750</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Vola.</th>\n",
       "      <td>0.610</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TD</th>\n",
       "      <th>High Vola.</th>\n",
       "      <td>0.685</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Vola.</th>\n",
       "      <td>0.554</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    K1          K2         tau      \n",
       "                  mean   sem  mean   sem  mean   sem\n",
       "group Volatility                                    \n",
       "ASD   High Vola. 0.750 0.029 0.466 0.057 0.028 0.016\n",
       "      Low Vola.  0.610 0.046 0.333 0.058 0.034 0.022\n",
       "TD    High Vola. 0.685 0.026 0.396 0.048 0.012 0.011\n",
       "      Low Vola.  0.554 0.042 0.178 0.047 0.052 0.013"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the average K1, K2 and tau for each group and Volatility\n",
    "df_kmodel1.groupby(['group', 'Volatility']).agg({'K1': ['mean', 'sem'], 'K2': ['mean', 'sem'], 'tau': ['mean', 'sem']})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">K1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">K2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">tau</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th>Volatility</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 rowspan=\"2\" valign=\"top\">ASD</th>\n",
       "      <th>High Vola.</th>\n",
       "      <td>0.681</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Vola.</th>\n",
       "      <td>0.642</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TD</th>\n",
       "      <th>High Vola.</th>\n",
       "      <td>0.682</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Vola.</th>\n",
       "      <td>0.568</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    K1          K2         tau      \n",
       "                  mean   sem  mean   sem  mean   sem\n",
       "group Volatility                                    \n",
       "ASD   High Vola. 0.681 0.043 0.446 0.047 0.013 0.019\n",
       "      Low Vola.  0.642 0.036 0.344 0.057 0.041 0.017\n",
       "TD    High Vola. 0.682 0.026 0.396 0.039 0.031 0.010\n",
       "      Low Vola.  0.568 0.035 0.198 0.038 0.057 0.010"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compared to df_kmodel\n",
    "df_kmodel.groupby(['group', 'Volatility']).agg({'K1': ['mean', 'sem'], 'K2': ['mean', 'sem'], 'tau': ['mean', 'sem']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">dK1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">dK2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">dtau</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th>Volatility</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ASD</th>\n",
       "      <th>High Vola.</th>\n",
       "      <td>0.006</td>\n",
       "      <td>0.010</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Vola.</th>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.034</td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.065</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TD</th>\n",
       "      <th>High Vola.</th>\n",
       "      <td>0.019</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.040</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Vola.</th>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.023</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.047</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    dK1          dK2         dtau      \n",
       "                   mean   sem   mean   sem   mean   sem\n",
       "group Volatility                                       \n",
       "ASD   High Vola.  0.006 0.010 -0.022 0.035 -0.004 0.004\n",
       "      Low Vola.  -0.058 0.034 -0.019 0.065 -0.012 0.010\n",
       "TD    High Vola.  0.019 0.009  0.007 0.040 -0.017 0.006\n",
       "      Low Vola.  -0.007 0.023 -0.015 0.047 -0.005 0.007"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine df_kmodel and df_kmodel1 by sub, Volatility, and group, for K1, K2, and tau, and add differences between two \n",
    "# K1, K2, and tau\n",
    "kmodel_v1 = df_kmodel1.merge(df_kmodel, on=['sub', 'Volatility', 'sequence', 'group'])\n",
    "kmodel_v1['dK1'] = kmodel_v1['K1_x'] - kmodel_v1['K1_y']\n",
    "kmodel_v1['dK2'] = kmodel_v1['K2_x'] - kmodel_v1['K2_y']\n",
    "kmodel_v1['dtau'] = kmodel_v1['tau_x'] - kmodel_v1['tau_y']\n",
    "# show the average dk1, dk2, and dtau for each group and Volatility\n",
    "kmodel_v1.groupby(['group', 'Volatility']).agg({'dK1': ['mean', 'sem'], 'dK2': ['mean', 'sem'], 'dtau': ['mean', 'sem']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.030</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.030</td>\n",
       "      <td>2.225</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.038</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.058</td>\n",
       "      <td>4.305</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.071</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS     F  p-unc   np2   eps\n",
       "0        group 0.030    1   56 0.030 2.225  0.141 0.038   NaN\n",
       "1   Volatility 0.058    1   56 0.058 4.305  0.043 0.071 1.000\n",
       "2  Interaction 0.010    1   56 0.010 0.772  0.383 0.014   NaN"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# anova test for dK1\n",
    "pg.mixed_anova(data=kmodel_v1, \n",
    "               dv='dK1', within='Volatility', between = 'group', subject='sub')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Source</th>\n",
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       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
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       "      <th>p-unc</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.105</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.105</td>\n",
       "      <td>1.914</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.033</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.532</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.532</td>\n",
       "      <td>21.887</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.281</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.105    1   56 0.105  1.914  0.172 0.033   NaN\n",
       "1   Volatility 0.532    1   56 0.532 21.887  0.000 0.281 1.000\n",
       "2  Interaction 0.001    1   56 0.001  0.021  0.885 0.000   NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.mixed_anova(data=df_kmodel1, \n",
    "               dv='K1', within='Volatility', between = 'group', subject='sub')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
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       "      <td>0.106</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS     F  p-unc   np2   eps\n",
       "0        group 0.008    1   56 0.008 0.106  0.745 0.002   NaN\n",
       "1   Volatility 0.002    1   56 0.002 0.039  0.845 0.001 1.000\n",
       "2  Interaction 0.004    1   56 0.004 0.070  0.792 0.001   NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# anova test for dK2\n",
    "pg.mixed_anova(data=kmodel_v1, \n",
    "               dv='dK2', within='Volatility', between = 'group', subject='sub')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.369</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.369</td>\n",
       "      <td>4.514</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.075</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.893</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.893</td>\n",
       "      <td>11.179</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.166</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.053</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.012</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.369    1   56 0.369  4.514  0.038 0.075   NaN\n",
       "1   Volatility 0.893    1   56 0.893 11.179  0.001 0.166 1.000\n",
       "2  Interaction 0.053    1   56 0.053  0.661  0.420 0.012   NaN"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# anova test for dK1\n",
    "pg.mixed_anova(data=df_kmodel1, \n",
    "               dv='K2', within='Volatility', between = 'group', subject='sub')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1.626</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.028</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS     F  p-unc   np2   eps\n",
       "0        group 0.000    1   56 0.000 0.217  0.643 0.004   NaN\n",
       "1   Volatility 0.000    1   56 0.000 0.078  0.781 0.001 1.000\n",
       "2  Interaction 0.003    1   56 0.003 1.626  0.208 0.028   NaN"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# anova test for dtau\n",
    "pg.mixed_anova(data=kmodel_v1, \n",
    "               dv='dtau', within='Volatility', between = 'group', subject='sub')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.9 General Linear Model with the current and previous trial durations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that the low-volatility session used the random walk sequence, the current and previous durations were highly correlated. So we use the difference between the current and previous durations as a regressor. This won't affect the fully random sequence in the high-volatility session.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dc/hksrz0yj5bb8n7f4_yptkcmw0000gn/T/ipykernel_74932/4187532016.py:34: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  glm_results = pd.concat([glm_results, res], ignore_index=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub</th>\n",
       "      <th>Volatility</th>\n",
       "      <th>group</th>\n",
       "      <th>Intercept</th>\n",
       "      <th>curDur</th>\n",
       "      <th>dDur</th>\n",
       "      <th>p_Intercept</th>\n",
       "      <th>p_CTI</th>\n",
       "      <th>p_dDur</th>\n",
       "      <th>r2</th>\n",
       "      <th>sequence</th>\n",
       "      <th>aic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A31</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>ASD</td>\n",
       "      <td>-0.167</td>\n",
       "      <td>-0.393</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.390</td>\n",
       "      <td>31.000</td>\n",
       "      <td>15.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A31</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>ASD</td>\n",
       "      <td>-0.224</td>\n",
       "      <td>-0.487</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.447</td>\n",
       "      <td>31.000</td>\n",
       "      <td>-4.166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A33</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>ASD</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>-0.131</td>\n",
       "      <td>-0.104</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.222</td>\n",
       "      <td>33.000</td>\n",
       "      <td>-117.297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A33</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>ASD</td>\n",
       "      <td>-0.032</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>-0.382</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.060</td>\n",
       "      <td>33.000</td>\n",
       "      <td>-216.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A34</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>ASD</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.379</td>\n",
       "      <td>-0.138</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.664</td>\n",
       "      <td>34.000</td>\n",
       "      <td>-284.612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sub  Volatility group  Intercept  curDur   dDur  p_Intercept  p_CTI  \\\n",
       "0  A31  High Vola.   ASD     -0.167  -0.393 -0.057        0.000  0.000   \n",
       "1  A31   Low Vola.   ASD     -0.224  -0.487 -0.130        0.000  0.000   \n",
       "4  A33  High Vola.   ASD     -0.056  -0.131 -0.104        0.000  0.007   \n",
       "5  A33   Low Vola.   ASD     -0.032  -0.061 -0.382        0.001  0.023   \n",
       "6  A34  High Vola.   ASD     -0.023  -0.379 -0.138        0.007  0.000   \n",
       "\n",
       "   p_dDur    r2  sequence      aic  \n",
       "0   0.122 0.390    31.000   15.538  \n",
       "1   0.450 0.447    31.000   -4.166  \n",
       "4   0.002 0.222    33.000 -117.297  \n",
       "5   0.004 0.060    33.000 -216.504  \n",
       "6   0.000 0.664    34.000 -284.612  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import OLS from statsmodels\n",
    "from statsmodels.regression.linear_model import OLS\n",
    "# creating an autorergressive model with lag 1, store the results in a new dataframe\n",
    "glm_results = pd.DataFrame(columns = ['sub', 'Volatility', 'group', \n",
    "                                      'Intercept', 'curDur', 'dDur',\n",
    "                                      'p_Intercept', 'p_CTI', 'p_dDur','r2'])\n",
    "\n",
    "# drop nan values in preDuration, preErr from vdata\n",
    "vdata = rawdata.query(\"outlier == False\").dropna(subset=['preDuration', 'preErr'])\n",
    "# center Duration to 1\n",
    "vdata['cDuration'] = vdata['Duration'] - 1\n",
    "vdata['cpreDuration'] = vdata['preDuration'] - 1\n",
    "vdata['dDuration'] = vdata['Duration'] - vdata['preDuration']\n",
    "groups = vdata.groupby(['sub', 'Volatility', 'group', 'sequence'])\n",
    "# drop nan values in preDuration, preErr\n",
    "\n",
    "for name, group in groups:\n",
    "    glm_mod = OLS(group['rep_err'], sm.add_constant(group[['cDuration', 'dDuration']])).fit()\n",
    "    summary = glm_mod.summary2()\n",
    "    r2 = glm_mod.rsquared\n",
    "    aic = glm_mod.aic\n",
    "    # estimate the prediction reproduced error\n",
    "    res = pd.DataFrame({\n",
    "        'sub': name[0], 'Volatility': name[1], 'group': name[2], 'sequence': name[3],\n",
    "        'Intercept': glm_mod.params['const'], \n",
    "        'curDur': glm_mod.params['cDuration'], #as CTI = -beta\n",
    "        'dDur': glm_mod.params['dDuration'],\n",
    "        'p_Intercept': summary.tables[1]['P>|t|']['const'],\n",
    "        'p_CTI': summary.tables[1]['P>|t|']['cDuration'],\n",
    "        'p_dDur': summary.tables[1]['P>|t|']['dDuration'],\n",
    "        'r2': r2,\n",
    "        'aic': aic\n",
    "        }, index=[0])\n",
    "    glm_results = pd.concat([glm_results, res], ignore_index=True)\n",
    "    \n",
    "# remove outliers\n",
    "glm_v = glm_results.query(\"sequence not in @outliers_regress\" )\n",
    "glm_v.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>Volatility</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Intercept</th>\n",
       "      <th colspan=\"2\" halign=\"left\">curDur</th>\n",
       "      <th colspan=\"2\" halign=\"left\">dDur</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ASD</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.015</td>\n",
       "      <td>-0.166</td>\n",
       "      <td>0.026</td>\n",
       "      <td>-0.104</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASD</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.015</td>\n",
       "      <td>-0.092</td>\n",
       "      <td>0.030</td>\n",
       "      <td>-0.281</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TD</td>\n",
       "      <td>High Vola.</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.011</td>\n",
       "      <td>-0.224</td>\n",
       "      <td>0.023</td>\n",
       "      <td>-0.123</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TD</td>\n",
       "      <td>Low Vola.</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.013</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.021</td>\n",
       "      <td>-0.394</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group  Volatility Intercept       curDur         dDur      \n",
       "                         mean   sem   mean   sem   mean   sem\n",
       "0   ASD  High Vola.     0.032 0.015 -0.166 0.026 -0.104 0.016\n",
       "1   ASD   Low Vola.     0.042 0.015 -0.092 0.030 -0.281 0.041\n",
       "2    TD  High Vola.     0.029 0.011 -0.224 0.023 -0.123 0.012\n",
       "3    TD   Low Vola.     0.038 0.013 -0.086 0.021 -0.394 0.049"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# some unknown error with sub column in groupby and agg, so drop it first\n",
    "# show the mean and standard errors of Intercept, Duration, preErr for each group, Volatility\n",
    "glm_v.drop('sub', axis =1).groupby(['group', 'Volatility']).agg(['mean', 'sem'])[['Intercept', 'curDur', 'dDur']].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.003</td>\n",
       "      <td>3.388</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.057</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS     F  p-unc   np2   eps\n",
       "0        group 0.000    1   56 0.000 0.039  0.845 0.001   NaN\n",
       "1   Volatility 0.003    1   56 0.003 3.388  0.071 0.057 1.000\n",
       "2  Interaction 0.000    1   56 0.000 0.010  0.922 0.000   NaN"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.mixed_anova(data=glm_v, dv='Intercept', within='Volatility', between='group', subject='sub')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>0.326</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.326</td>\n",
       "      <td>24.570</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.305</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.029</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.029</td>\n",
       "      <td>2.218</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.038</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.020    1   56 0.020  0.906  0.345 0.016   NaN\n",
       "1   Volatility 0.326    1   56 0.326 24.570  0.000 0.305 1.000\n",
       "2  Interaction 0.029    1   56 0.029  2.218  0.142 0.038   NaN"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pingouin ANOVA test for CTI\n",
    "pg.mixed_anova(data=glm_v, dv='curDur', within='Volatility', between='group', subject='sub')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF1</th>\n",
       "      <th>DF2</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group</td>\n",
       "      <td>0.126</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.126</td>\n",
       "      <td>3.335</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.056</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volatility</td>\n",
       "      <td>1.460</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>1.460</td>\n",
       "      <td>53.488</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.489</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interaction</td>\n",
       "      <td>0.063</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.063</td>\n",
       "      <td>2.323</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source    SS  DF1  DF2    MS      F  p-unc   np2   eps\n",
       "0        group 0.126    1   56 0.126  3.335  0.073 0.056   NaN\n",
       "1   Volatility 1.460    1   56 1.460 53.488  0.000 0.489 1.000\n",
       "2  Interaction 0.063    1   56 0.063  2.323  0.133 0.040   NaN"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# pingouin ANOVA test for preDur\n",
    "pg.mixed_anova(data=glm_v, dv='dDur', within='Volatility', between='group', subject='sub')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.10 Correlation with EQ, AQ\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>-8.878</td>\n",
       "      <td>120.558</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000</td>\n",
       "      <td>[-39.01, -24.78]</td>\n",
       "      <td>1.577</td>\n",
       "      <td>7.799e+11</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            T     dof alternative  p-val             CI95%  cohen-d  \\\n",
       "T-test -8.878 120.558   two-sided  0.000  [-39.01, -24.78]    1.577   \n",
       "\n",
       "             BF10  power  \n",
       "T-test  7.799e+11  1.000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "# load data/parinfo.csv as a dataframe\n",
    "pinfo = pd.read_csv('data/parinfo.csv')\n",
    "# add difference between EQ and SQ as a new column ES\n",
    "pinfo['ES'] = pinfo['EQ'] - pinfo['SQ'] # positive 'female brain', negative 'male brain'\n",
    "# merge glm_v and aq on sub\n",
    "res = pd.merge(kpars, pinfo, on=['group','sequence'])\n",
    "# t-test for DQ between ASD and TD\n",
    "pg.ttest(res.query(\"group == 'ASD'\")['ES'], res.query(\"group == 'TD'\")['ES'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>K1</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>        <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>        <td>0.0184</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>23.2472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>        <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>              <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.801</td>   <td>0.129</td>   <td>6.218</td> <td>0.000</td>  <td>0.548</td>  <td>1.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.097</td>   <td>0.083</td>  <td>-1.164</td> <td>0.245</td> <td>-0.261</td>  <td>0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.078</td>   <td>0.024</td>  <td>-3.224</td> <td>0.001</td> <td>-0.125</td> <td>-0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AQ</th>                      <td>-0.003</td>   <td>0.003</td>  <td>-0.804</td> <td>0.421</td> <td>-0.009</td>  <td>0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.023</td>   <td>0.060</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/strongway/miniforge3/envs/py312/lib/python3.12/site-packages/statsmodels/regression/mixed_linear_model.py:2238: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>    <td>K2</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>         <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>         <td>0.0611</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>-17.5335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.545</td>   <td>0.138</td>   <td>3.952</td> <td>0.000</td>  <td>0.275</td>  <td>0.815</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.148</td>   <td>0.089</td>  <td>-1.666</td> <td>0.096</td> <td>-0.321</td>  <td>0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.158</td>   <td>0.044</td>  <td>-3.587</td> <td>0.000</td> <td>-0.244</td> <td>-0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AQ</th>                      <td>-0.002</td>   <td>0.004</td>  <td>-0.538</td> <td>0.590</td> <td>-0.009</td>  <td>0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.005</td>   <td>0.037</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/strongway/miniforge3/envs/py312/lib/python3.12/site-packages/statsmodels/regression/mixed_linear_model.py:2238: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>    <td>tau</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>         <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>         <td>0.0020</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>143.1968</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>         <td>Yes</td>  \n",
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       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.111</td>   <td>0.054</td>   <td>2.038</td> <td>0.042</td>  <td>0.004</td>  <td>0.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.036</td>   <td>0.035</td>  <td>-1.024</td> <td>0.306</td> <td>-0.106</td>  <td>0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th>  <td>0.027</td>   <td>0.008</td>   <td>3.366</td> <td>0.001</td>  <td>0.011</td>  <td>0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AQ</th>                      <td>-0.003</td>   <td>0.001</td>  <td>-1.849</td> <td>0.064</td> <td>-0.005</td>  <td>0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.005</td>   <td>0.033</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
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       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>cti</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>        <td>REML</td>  \n",
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       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>        <td>0.0202</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>32.4618</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>        <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>              <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.286</td>   <td>0.106</td>   <td>2.706</td> <td>0.007</td>  <td>0.079</td>  <td>0.493</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>              <td>0.018</td>   <td>0.068</td>   <td>0.262</td> <td>0.793</td> <td>-0.116</td>  <td>0.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.225</td>   <td>0.025</td>  <td>-8.897</td> <td>0.000</td> <td>-0.275</td> <td>-0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AQ</th>                       <td>0.001</td>   <td>0.003</td>   <td>0.467</td> <td>0.641</td> <td>-0.004</td>  <td>0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.011</td>   <td>0.038</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "# fit the models with AQ\n",
    "display(HTML(smf.mixedlm(\"K1 ~ AQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"K2 ~ AQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"tau ~ AQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"cti ~ AQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AQ was only marginal correlated with tau. Due to the multiple comparison, we did not consider this correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>K1</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>        <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>        <td>0.0184</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>23.2224</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>        <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>              <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.636</td>   <td>0.056</td>  <td>11.287</td> <td>0.000</td>  <td>0.526</td>  <td>0.747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.095</td>   <td>0.059</td>  <td>-1.617</td> <td>0.106</td> <td>-0.211</td>  <td>0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.078</td>   <td>0.024</td>  <td>-3.224</td> <td>0.001</td> <td>-0.125</td> <td>-0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EQ</th>                       <td>0.002</td>   <td>0.002</td>   <td>1.415</td> <td>0.157</td> <td>-0.001</td>  <td>0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.022</td>   <td>0.059</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/strongway/miniforge3/envs/py312/lib/python3.12/site-packages/statsmodels/regression/mixed_linear_model.py:2238: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>    <td>K2</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>         <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>         <td>0.0611</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>-18.0032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.432</td>   <td>0.063</td>   <td>6.874</td> <td>0.000</td>  <td>0.309</td>  <td>0.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.143</td>   <td>0.063</td>  <td>-2.266</td> <td>0.023</td> <td>-0.266</td> <td>-0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.158</td>   <td>0.044</td>  <td>-3.587</td> <td>0.000</td> <td>-0.244</td> <td>-0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EQ</th>                       <td>0.002</td>   <td>0.002</td>   <td>0.854</td> <td>0.393</td> <td>-0.002</td>  <td>0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.005</td>   <td>0.037</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/strongway/miniforge3/envs/py312/lib/python3.12/site-packages/statsmodels/regression/mixed_linear_model.py:2238: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>    <td>tau</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>         <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>         <td>0.0020</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>141.0893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>-0.000</td>   <td>0.024</td>  <td>-0.010</td> <td>0.992</td> <td>-0.048</td>  <td>0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>              <td>0.007</td>   <td>0.026</td>   <td>0.272</td> <td>0.785</td> <td>-0.044</td>  <td>0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th>  <td>0.027</td>   <td>0.008</td>   <td>3.366</td> <td>0.001</td>  <td>0.011</td>  <td>0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EQ</th>                       <td>0.001</td>   <td>0.001</td>   <td>0.698</td> <td>0.485</td> <td>-0.001</td>  <td>0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.005</td>   <td>0.034</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>cti</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>        <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>        <td>0.0202</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>31.7572</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>        <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>              <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.349</td>   <td>0.047</td>   <td>7.387</td> <td>0.000</td>  <td>0.257</td>  <td>0.442</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>              <td>0.005</td>   <td>0.049</td>   <td>0.098</td> <td>0.922</td> <td>-0.091</td>  <td>0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.225</td>   <td>0.025</td>  <td>-8.897</td> <td>0.000</td> <td>-0.275</td> <td>-0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EQ</th>                      <td>-0.001</td>   <td>0.001</td>  <td>-0.429</td> <td>0.668</td> <td>-0.003</td>  <td>0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.011</td>   <td>0.038</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(smf.mixedlm(\"K1 ~ EQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"K2 ~ EQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"tau ~ EQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"cti ~ EQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>K1</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>        <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>        <td>0.0184</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>22.2862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>        <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>              <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.701</td>   <td>0.072</td>   <td>9.754</td> <td>0.000</td>  <td>0.560</td>  <td>0.841</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.041</td>   <td>0.048</td>  <td>-0.846</td> <td>0.398</td> <td>-0.135</td>  <td>0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.078</td>   <td>0.024</td>  <td>-3.224</td> <td>0.001</td> <td>-0.125</td> <td>-0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SQ</th>                       <td>0.000</td>   <td>0.002</td>   <td>0.001</td> <td>1.000</td> <td>-0.004</td>  <td>0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.023</td>   <td>0.061</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/strongway/miniforge3/envs/py312/lib/python3.12/site-packages/statsmodels/regression/mixed_linear_model.py:2238: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>    <td>K2</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>         <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>         <td>0.0611</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>-14.2098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.660</td>   <td>0.073</td>   <td>8.988</td> <td>0.000</td>  <td>0.516</td>  <td>0.803</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.155</td>   <td>0.047</td>  <td>-3.262</td> <td>0.001</td> <td>-0.248</td> <td>-0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.158</td>   <td>0.044</td>  <td>-3.587</td> <td>0.000</td> <td>-0.244</td> <td>-0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SQ</th>                      <td>-0.005</td>   <td>0.002</td>  <td>-2.967</td> <td>0.003</td> <td>-0.009</td> <td>-0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.001</td>   <td>0.033</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/strongway/miniforge3/envs/py312/lib/python3.12/site-packages/statsmodels/regression/mixed_linear_model.py:2238: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>    <td>tau</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>         <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>         <td>0.0020</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>141.3026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.038</td>   <td>0.031</td>   <td>1.248</td> <td>0.212</td> <td>-0.022</td>  <td>0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>              <td>0.013</td>   <td>0.021</td>   <td>0.610</td> <td>0.542</td> <td>-0.028</td>  <td>0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th>  <td>0.027</td>   <td>0.008</td>   <td>3.366</td> <td>0.001</td>  <td>0.011</td>  <td>0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SQ</th>                      <td>-0.001</td>   <td>0.001</td>  <td>-0.906</td> <td>0.365</td> <td>-0.002</td>  <td>0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.005</td>   <td>0.034</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>cti</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>        <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>        <td>0.0202</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>31.7151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>        <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>              <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.336</td>   <td>0.059</td>   <td>5.686</td> <td>0.000</td>  <td>0.220</td>  <td>0.452</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.010</td>   <td>0.039</td>  <td>-0.246</td> <td>0.805</td> <td>-0.086</td>  <td>0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.225</td>   <td>0.025</td>  <td>-8.897</td> <td>0.000</td> <td>-0.275</td> <td>-0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SQ</th>                      <td>-0.000</td>   <td>0.001</td>  <td>-0.053</td> <td>0.958</td> <td>-0.003</td>  <td>0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.011</td>   <td>0.038</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(smf.mixedlm(\"K1 ~ SQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"K2 ~ SQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"tau ~ SQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"cti ~ SQ + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SQ and K2 correlation was significant!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatterplot for K2 as a function of SQ, separated by group (different colors), Volatility (different symbols)\n",
    "#sns.scatterplot(data=res, x='SQ', y='K2', hue='group', style='Volatility', alpha=0.5)\n",
    "palette = sns.color_palette('Dark2')\n",
    "plt.figure(figsize=(6,4))\n",
    "# subplot 1\n",
    "#plt.subplot(1,2,1)\n",
    "plt.xlabel('SQ')\n",
    "plt.ylabel('K2')\n",
    "# now add regression lines for each group, Volatility\n",
    "sns.regplot(data=res.query(\"group == 'ASD' and Volatility == 'Low Vola.'\"), \n",
    "            x='SQ', y='K2', color = palette[0],  line_kws={'ls':'--'}, \n",
    "            scatter_kws ={'facecolors':'none','alpha':0.3}, marker='o', label = \"ASD/Low Vola.\") \n",
    "sns.regplot(data=res.query(\"group == 'ASD' and Volatility == 'High Vola.'\"),\n",
    "             x='SQ', y='K2', color = palette[1],  line_kws={'ls':'--'}, \n",
    "             scatter_kws ={'alpha':0.3}, marker='o', label = \"ASD/High Vola.\")\n",
    "sns.regplot(data=res.query(\"group == 'TD' and Volatility == 'Low Vola.'\"), \n",
    "            x='SQ', y='K2', color = palette[2], line_kws={'ls':'-'}, \n",
    "            scatter_kws ={'facecolors':'none','alpha':0.3}, marker = 's', label = \"TD/Low Vola.\")\n",
    "sns.regplot(data=res.query(\"group == 'TD' and Volatility == 'High Vola.'\"), \n",
    "            x='SQ', y='K2', color = palette[3],  line_kws={'ls':'-'}, \n",
    "            scatter_kws ={'alpha':0.3}, marker='s', label=\"TD/High Vola.\")\n",
    "# add legend\n",
    "plt.legend()\n",
    "# plot a label 'a' on the top left corner\n",
    "#plt.text(-0.1, 1.1, 'a', transform=plt.gca().transAxes, \n",
    "#                            fontsize=16, fontweight='bold', va='top', ha='right')\n",
    "# remove box around the plot\n",
    "sns.despine()\n",
    "# subplot 1\n",
    "#plt.subplot(1,2,2)\n",
    "#plt.xlabel('E-S')\n",
    "#plt.ylabel('K2')\n",
    "# now add regression lines for each group, Volatility\n",
    "#sns.regplot(data=res.query(\"group == 'ASD' and Volatility == 'Low Vola.'\"), \n",
    "#            x='ES', y='K2', color = palette[0], line_kws={'ls':'--'}, \n",
    "#            scatter_kws ={'facecolors':'none','alpha':0.3}, marker='o', label = \"ASD/Low Vola.\") \n",
    "#sns.regplot(data=res.query(\"group == 'ASD' and Volatility == 'High Vola.'\"),\n",
    "#             x='ES', y='K2', color = palette[1],  line_kws={'ls':'--'}, \n",
    "#             scatter_kws ={'alpha':0.3}, marker='o', label = \"ASD/High Vola.\")\n",
    "#sns.regplot(data=res.query(\"group == 'TD' and Volatility == 'Low Vola.'\"), \n",
    "#            x='ES', y='K2', color = palette[2], line_kws={'ls':'-'}, \n",
    "#            scatter_kws ={'facecolors':'none','alpha':0.3}, marker = 's', label = \"TD/Low Vola.\")\n",
    "#sns.regplot(data=res.query(\"group == 'TD' and Volatility == 'High Vola.'\"), \n",
    "#            x='ES', y='K2', color = palette[3],  line_kws={'ls':'-'}, \n",
    "#            scatter_kws ={'alpha':0.3}, marker='s', label=\"TD/High Vola.\")\n",
    "# add legend\n",
    "#plt.legend()\n",
    "# plot a label 'b' on the top left corner\n",
    "#plt.text(-0.1, 1.1, 'b', transform=plt.gca().transAxes, \n",
    "#                            fontsize=16, fontweight='bold', va='top', ha='right')\n",
    "# remove box around the plot\n",
    "sns.despine()\n",
    "# tight layout\n",
    "plt.tight_layout()\n",
    "# save the figure to vector file ./figures/K2_vs_SQ.pdf\n",
    "plt.savefig('./figures/K2_vs_SQ.png', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>K1</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>        <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>        <td>0.0184</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>23.3700</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>        <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>              <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.735</td>   <td>0.045</td>  <td>16.190</td> <td>0.000</td>  <td>0.646</td>  <td>0.823</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.057</td>   <td>0.047</td>  <td>-1.214</td> <td>0.225</td> <td>-0.150</td>  <td>0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.078</td>   <td>0.024</td>  <td>-3.224</td> <td>0.001</td> <td>-0.125</td> <td>-0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BDI</th>                     <td>-0.003</td>   <td>0.003</td>  <td>-1.112</td> <td>0.266</td> <td>-0.009</td>  <td>0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.022</td>   <td>0.059</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/strongway/miniforge3/envs/py312/lib/python3.12/site-packages/statsmodels/regression/mixed_linear_model.py:2238: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>    <td>K2</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>         <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>         <td>0.0611</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>-16.1921</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.532</td>   <td>0.051</td>  <td>10.531</td> <td>0.000</td>  <td>0.433</td>  <td>0.631</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.136</td>   <td>0.049</td>  <td>-2.776</td> <td>0.006</td> <td>-0.232</td> <td>-0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.158</td>   <td>0.044</td>  <td>-3.587</td> <td>0.000</td> <td>-0.244</td> <td>-0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BDI</th>                     <td>-0.005</td>   <td>0.003</td>  <td>-1.844</td> <td>0.065</td> <td>-0.011</td>  <td>0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.004</td>   <td>0.035</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/strongway/miniforge3/envs/py312/lib/python3.12/site-packages/statsmodels/regression/mixed_linear_model.py:2238: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>    <td>tau</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>         <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>         <td>0.0020</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>141.6521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>             <th>Coef.</th> <th>Std.Err.</th>   <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>0.004</td>   <td>0.019</td>  <td>0.186</td> <td>0.852</td> <td>-0.035</td>  <td>0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>0.024</td>   <td>0.021</td>  <td>1.158</td> <td>0.247</td> <td>-0.016</td>  <td>0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>0.027</td>   <td>0.008</td>  <td>3.366</td> <td>0.001</td>  <td>0.011</td>  <td>0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BDI</th>                     <td>0.001</td>   <td>0.001</td>  <td>0.756</td> <td>0.450</td> <td>-0.001</td>  <td>0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>               <td>0.005</td>   <td>0.034</td>    <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>cti</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>        <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>        <td>0.0202</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>32.6026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>        <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>              <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.310</td>   <td>0.038</td>   <td>8.183</td> <td>0.000</td>  <td>0.236</td>  <td>0.385</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>              <td>0.002</td>   <td>0.039</td>   <td>0.060</td> <td>0.952</td> <td>-0.073</td>  <td>0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.225</td>   <td>0.025</td>  <td>-8.897</td> <td>0.000</td> <td>-0.275</td> <td>-0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BDI</th>                      <td>0.002</td>   <td>0.002</td>   <td>0.916</td> <td>0.360</td> <td>-0.002</td>  <td>0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.011</td>   <td>0.038</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(smf.mixedlm(\"K1 ~ BDI + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"K2 ~ BDI + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"tau ~ BDI + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"cti ~ BDI + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>K1</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>        <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>        <td>0.0184</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>22.2399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>        <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>              <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.709</td>   <td>0.035</td>  <td>20.292</td> <td>0.000</td>  <td>0.641</td>  <td>0.778</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.073</td>   <td>0.057</td>  <td>-1.281</td> <td>0.200</td> <td>-0.186</td>  <td>0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.078</td>   <td>0.024</td>  <td>-3.224</td> <td>0.001</td> <td>-0.125</td> <td>-0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ES</th>                       <td>0.001</td>   <td>0.001</td>   <td>0.921</td> <td>0.357</td> <td>-0.001</td>  <td>0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.022</td>   <td>0.060</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/strongway/miniforge3/envs/py312/lib/python3.12/site-packages/statsmodels/regression/mixed_linear_model.py:2238: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>    <td>K2</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>         <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>         <td>0.0611</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>-16.0307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.496</td>   <td>0.040</td>  <td>12.412</td> <td>0.000</td>  <td>0.418</td>  <td>0.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.195</td>   <td>0.058</td>  <td>-3.336</td> <td>0.001</td> <td>-0.309</td> <td>-0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.158</td>   <td>0.044</td>  <td>-3.587</td> <td>0.000</td> <td>-0.244</td> <td>-0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ES</th>                       <td>0.003</td>   <td>0.001</td>   <td>2.402</td> <td>0.016</td>  <td>0.001</td>  <td>0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.002</td>   <td>0.034</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/strongway/miniforge3/envs/py312/lib/python3.12/site-packages/statsmodels/regression/mixed_linear_model.py:2238: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>    <td>tau</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>         <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>         <td>0.0020</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>140.9529</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>         <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>               <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>             <th>Coef.</th> <th>Std.Err.</th>   <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>               <td>0.018</td>   <td>0.015</td>  <td>1.207</td> <td>0.228</td> <td>-0.011</td>  <td>0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>0.003</td>   <td>0.025</td>  <td>0.120</td> <td>0.904</td> <td>-0.046</td>  <td>0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>0.027</td>   <td>0.008</td>  <td>3.366</td> <td>0.001</td>  <td>0.011</td>  <td>0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ES</th>                      <td>0.000</td>   <td>0.000</td>  <td>1.029</td> <td>0.303</td> <td>-0.000</td>  <td>0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>               <td>0.005</td>   <td>0.034</td>    <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td>   <td>cti</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>126</td>         <td>Method:</td>        <td>REML</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>63</td>          <td>Scale:</td>        <td>0.0202</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>     <td>2</td>      <td>Log-Likelihood:</td>   <td>31.2769</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>     <td>2</td>        <td>Converged:</td>        <td>Yes</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>    <td>2.0</td>            <td></td>              <td></td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>              <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td>0.331</td>   <td>0.030</td>  <td>11.144</td> <td>0.000</td>  <td>0.273</td>  <td>0.390</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>group[T.TD]</th>             <td>-0.002</td>   <td>0.047</td>  <td>-0.035</td> <td>0.972</td> <td>-0.094</td>  <td>0.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volatility[T.Low Vola.]</th> <td>-0.225</td>   <td>0.025</td>  <td>-8.897</td> <td>0.000</td> <td>-0.275</td> <td>-0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ES</th>                      <td>-0.000</td>   <td>0.001</td>  <td>-0.249</td> <td>0.804</td> <td>-0.002</td>  <td>0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>                <td>0.011</td>   <td>0.038</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(smf.mixedlm(\"K1 ~ ES + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"K2 ~ ES + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"tau ~ ES + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n",
    "display(HTML(smf.mixedlm(\"cti ~ ES + group + Volatility\", res, groups=res[\"sub\"]).fit().summary().as_html()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ES had a significant correlation with K2. Let's visualize it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "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>sub</th>\n",
       "      <th>sequence</th>\n",
       "      <th>SQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A32</td>\n",
       "      <td>32</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A32</td>\n",
       "      <td>32</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>C32</td>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>C32</td>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>aril02</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>aril02</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>arm13</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>arm13</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>crs02</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>crs02</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>crw13</td>\n",
       "      <td>13</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>crw13</td>\n",
       "      <td>13</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sub  sequence  SQ\n",
       "2       A32        32  44\n",
       "3       A32        32  44\n",
       "12      C32        32  10\n",
       "13      C32        32  10\n",
       "36   aril02         2   8\n",
       "37   aril02         2   8\n",
       "50    arm13        13  32\n",
       "51    arm13        13  32\n",
       "114   crs02         2  21\n",
       "115   crs02         2  21\n",
       "124   crw13        13  51\n",
       "125   crw13        13  51"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show those outliers SQ scores\n",
    "res.query(\"sequence in @outliers_regress\")[['sub', 'sequence', 'SQ']]"
   ]
  }
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