Robust EZ-CDM.ipynb

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    {
      "cell_type": "markdown",
      "source": "# Robust EZ-CDM",
      "metadata": {},
      "id": "c899f03c"
    },
    {
      "cell_type": "markdown",
      "source": "We will illustrate the fitting procedure of the robust EZ-CDM in the following steps:\n- importing data\n- calculating the statistics of data\n- calculating estimates of parameters from statistics of data\n- plotting fits",
      "metadata": {},
      "id": "d1becf63"
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    {
      "cell_type": "markdown",
      "source": "##  Importing data",
      "metadata": {},
      "id": "80600f99"
    },
    {
      "cell_type": "markdown",
      "source": "The data should be a couple in the format of a list or a NumPy array in which `CA` represents the choice angle and `RT` represents the response time data.  \nHere, we use an example for the data containing 200 trials which are simulated from the CDM with the following parameter values:\n\n| Parameter | Value |\n| ----------- | ----------- |\n| drift length | 2 |\n| drift angle | 0.2 |\n| threshold | 2 |\n| non-decision time | 0.6 |\n\nWe replace the last ten response time data with an outlier value of $10$.  \nThe choice angle is in radian and response time is in second. ",
      "metadata": {},
      "id": "9d70ea12"
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      "source": "CA = [  1.298, -0.374,  0.783,  0.491, -0.141, -0.089,  0.724,  0.356,\n        0.437, -0.304,  0.3  ,  0.   , -0.328,  0.98 , -0.457,  0.247,\n        0.56 ,  0.736,  0.092,  0.887,  1.022,  0.597, -0.537, -0.247,\n        0.454,  1.01 , -0.141, -0.217,  0.572,  0.12 , -0.507,  0.482,\n        1.007,  0.937,  0.046, -0.322, -0.034,  0.86 ,  0.128,  0.065,\n        1.467,  1.119, -0.009, -0.295, -0.34 , -0.617,  0.864, -0.429,\n        0.708, -0.296, -0.357, -0.366,  0.168,  1.419,  0.864, -0.142,\n        1.373, -0.268,  0.831,  0.609,  0.671, -0.799,  0.014,  0.448,\n        1.021,  0.67 ,  0.089,  1.217,  0.679,  0.654,  0.935,  1.347,\n       -0.265, -0.524,  0.056,  0.018,  0.413,  0.502,  0.183,  0.851,\n        0.156,  0.84 ,  0.556,  0.633,  1.443,  1.814, -0.067,  0.295,\n       -0.509, -0.234, -0.299, -0.538,  0.07 ,  0.045,  0.839,  0.075,\n        0.818,  0.63 ,  1.041, -0.156,  0.51 , -0.479, -0.184, -0.57 ,\n        0.302,  0.725, -0.668,  0.584,  0.097, -0.086,  0.168, -0.189,\n       -0.06 ,  0.594, -0.23 ,  0.109,  0.313,  0.734,  0.736,  0.478,\n        0.933,  0.112,  0.256,  0.05 ,  0.272,  0.336, -0.539, -0.337,\n        0.286,  0.301,  0.749, -0.292,  0.581, -0.134, -0.056,  1.034,\n        0.279, -0.735,  0.337,  1.091,  0.695, -0.126,  0.282,  0.377,\n       -0.341,  0.681,  0.592, -0.245, -0.137,  0.213, -0.162,  0.15 ,\n        0.599, -0.548, -0.836,  0.127,  0.245,  0.076,  0.204,  0.396,\n        0.604,  0.155,  0.277, -0.893,  0.222, -0.438,  0.147,  0.387,\n        0.021,  0.605, -0.085, -1.154, -0.247,  0.914,  0.754,  0.606,\n       -0.228,  0.044, -0.178,  0.754, -0.135, -0.43 ,  1.305,  0.17 ,\n        0.427,  0.158,  1.225,  0.373,  0.113,  1.009,  0.454,  0.874,\n        0.572,  0.68 ,  0.846,  0.642,  0.661,  0.064,  0.839,  0.345 ]\n\nRT = [ 1.779, 1.07 , 1.01 , 1.42 , 0.806, 1.491, 1.751, 1.473, 1.362,\n       1.264, 1.071, 1.029, 1.331, 1.633, 1.452, 1.403, 1.648, 1.385,\n       1.775, 2.531, 1.572, 3.21 , 1.313, 1.357, 2.074, 1.43 , 1.81 ,\n       1.504, 1.237, 1.199, 1.081, 1.736, 2.163, 1.564, 1.372, 1.047,\n       0.983, 1.295, 1.19 , 1.319, 1.008, 1.292, 1.814, 0.861, 0.987,\n       1.125, 1.298, 1.515, 1.118, 1.074, 1.172, 1.82 , 1.208, 1.164,\n       1.251, 1.313, 1.696, 1.409, 1.039, 1.558, 1.111, 1.667, 2.321,\n       1.451, 1.986, 1.781, 0.931, 1.014, 1.217, 1.35 , 1.326, 0.936,\n       1.308, 1.193, 1.433, 1.48 , 1.391, 1.307, 1.27 , 1.324, 1.535,\n       1.074, 1.221, 1.241, 1.016, 1.131, 1.651, 1.896, 1.498, 1.4  ,\n       1.392, 1.122, 1.21 , 0.949, 1.747, 1.357, 1.619, 1.019, 1.513,\n       1.319, 0.869, 1.151, 2.842, 3.566, 0.932, 0.989, 1.293, 1.482,\n       1.178, 1.718, 2.21 , 1.025, 1.741, 1.471, 1.546, 2.001, 1.706,\n       1.399, 1.283, 1.846, 1.576, 0.952, 1.363, 1.118, 1.163, 1.348,\n       1.026, 1.175, 1.034, 1.75 , 1.567, 1.397, 1.145, 2.005, 1.099,\n       1.1  , 2.153, 1.381, 1.38 , 1.3  , 0.967, 1.633, 1.774, 1.867,\n       1.954, 0.854, 1.228, 2.17 , 1.441, 3.017, 1.195, 1.582, 1.071,\n       1.138, 1.392, 1.526, 1.715, 1.595, 1.29 , 1.829, 1.186, 1.055,\n       1.39 , 3.263, 2.024, 1.567, 1.485, 1.329, 1.952, 2.184, 1.615,\n       1.268, 1.306, 1.   , 1.237, 1.559, 1.655, 1.582, 1.118, 1.661,\n       0.936, 1.527, 1.452, 1.095, 1.713, 1.247, 1.712, 1.808, 1.387,\n       1.304, 10, 10, 10, 10, 10, 10 , 10, 10,\n       10, 10 ]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": [],
      "id": "25f83828"
    },
    {
      "cell_type": "markdown",
      "source": "We convert the data to numpy array form.",
      "metadata": {},
      "id": "f116a0f6"
    },
    {
      "cell_type": "code",
      "source": "import numpy as np\n\nCA = np.array(CA)\nRT = np.array(RT)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": [],
      "id": "f5f5151c"
    },
    {
      "cell_type": "markdown",
      "source": "## Calculating the statistics of data",
      "metadata": {},
      "id": "bee1d79d"
    },
    {
      "cell_type": "markdown",
      "source": "First we should calculate the statistics of data (circular mean and variance of chaoice angle and mean and variance of response time):\n\\begin{equation*}\n  \\left\\{\n    \\begin{aligned}\n        & MCA = atan2 \\Big(\\frac{1}{N}\\sum_{n=1}^N \\sin \\theta_n,\\frac{1}{N}\\sum_{n=1}^N \\cos \\theta_n\\Big)  \\\\\n        & VCA = 1-\\sqrt{\\Big(\\frac{1}{N}\\sum_{n=1}^N \\cos\\theta_n\\Big)^2 + \\Big(\\frac{1}{N}\\sum_{n=1}^N \\sin\\theta_n\\Big)^2 }   \\\\\n        & MRT = median\\big[\\{t_n\\}_{n=1}^N\\big]  \\\\\n        & VRT = \\Big(IQR\\big[\\{t_n\\}_{n=1}^N\\big]/iqr\\Big)^2\n    \\end{aligned}\n  \\right.\n\\end{equation*}\n\nwhere $\\theta_n$ and $t_n$ are respectively the $n^{th}$ choice angle and response time data points, and $N$ is the number of data points. Also, $iqr$ is the interquartile range of standard normal distribution.",
      "metadata": {},
      "id": "6c15586b"
    },
    {
      "cell_type": "markdown",
      "source": "We can use the pecent point function (inverse of cumulative distribution function) in Scipy to calculate the $iqr$ .",
      "metadata": {},
      "id": "927d4a65-b652-44a2-bc33-8e8abb4b80f1"
    },
    {
      "cell_type": "code",
      "source": "from scipy import stats\n\nN = len(CA)\nMCA = np.arctan2(sum(np.sin(CA))/N, sum(np.cos(CA))/N)\nVCA = 1 - 1/N*np.sqrt(sum(np.cos(CA))**2+sum(np.sin(CA))**2)\nMRT = np.median(RT)\nVRT = (stats.iqr(RT)/stats.norm.ppf(.75)/2)**2",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": [],
      "id": "cee411b1"
    },
    {
      "cell_type": "markdown",
      "source": "## Calculating estimates of parameters from statistics of data ",
      "metadata": {},
      "id": "b36ce361"
    },
    {
      "cell_type": "markdown",
      "source": "The calculations are as follow:\n\\begin{equation*}\n  \\left\\{\n    \\begin{aligned}\n        & \\theta_v = MCA  \\\\\n        & R = 1-VCA \\\\\n        & \\kappa_0 = \\frac{R(2-R^2)}{1-R^2} \\\\\n        & \\kappa_1 = \\kappa_0 - \\frac{I_1(\\kappa_0)/I_0(\\kappa_0)-R}{1-I_1^2(\\kappa_0)/I_0^2(\\kappa_0)- I_1(\\kappa_0)/\\kappa_0 I_0(\\kappa_0)} \\\\\n        & v = \\sqrt[4]{\\frac{1}{VRT}\\Big( \\kappa_1^2R^2 + 2\\kappa_1R-\\kappa_1^2 \\Big)} \\\\\n        & a = \\kappa_1/v \\\\\n        & t_0 = MRT - \\frac{a}{v}R\n    \\end{aligned}\n  \\right.\n\\end{equation*}",
      "metadata": {},
      "id": "9b7b2d68"
    },
    {
      "cell_type": "markdown",
      "source": "We use the Scipy library for the Bessel functions.",
      "metadata": {},
      "id": "34cde38e"
    },
    {
      "cell_type": "code",
      "source": "from scipy.special import iv",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": [],
      "id": "77f1ef29"
    },
    {
      "cell_type": "code",
      "source": "theta_v = MCA\nR = 1 - VCA\nk0 = R*(2-R**2)/(1-R**2)\nk1 = k0 - (iv(1,k0)/iv(0,k0)-R)/(1-iv(1,k0)/iv(0,k0)*iv(1,k0)/iv(0,k0)-iv(1,k0)/iv(0,k0)/k0)\nv = ((k1**2*R**2+2*k1*R-k1**2)/VRT)**(1/4)\na = k1/v\nt0 = MRT-a/v*R",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": [],
      "id": "e71862a6"
    },
    {
      "cell_type": "code",
      "source": "v, theta_v, a, t0",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": [
        {
          "execution_count": 6,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(2.07769512923473, 0.2751222693050222, 1.9314803537301253, 0.5865962944060547)"
          },
          "metadata": {}
        }
      ],
      "id": "733901bc"
    },
    {
      "cell_type": "markdown",
      "source": "The estimated parameter values are:\n\n| Parameter | Generating Value | Estimated Value |\n| ----------- | ----------- | ----------- |\n| drift length | 2 | 2.08 |\n| drift angle | 0.2 | 0.275 |\n| threshold | 2 | 1.93 |\n| non-decision time | 0.6 | 0.587 |",
      "metadata": {},
      "id": "3b461b17"
    },
    {
      "cell_type": "markdown",
      "source": "## Plotting fits ",
      "metadata": {},
      "id": "686bc789"
    },
    {
      "cell_type": "markdown",
      "source": "We can use the following formulas for calculating the marginal distributions of choice angle and response time to plot the fits of the model on data:\n\n\\begin{equation*}\n    \\begin{aligned}\n        Pr(\\theta) = & \\frac{1}{2\\pi I_0(av)}\\exp\\Big(va\\cos(\\theta-\\theta_v)\\Big) \\\\\n        Pr(t) = &  \\frac{I_0(av)}{a^2}\\sum_{i =1}^{\\infty}\\frac{j_{0,i}}{J_1(j_{0,i})}\\exp\\big(-\\frac{(t-t_0)}{2}(v^2+\\frac{j_{0,i}^2}{a^2})\\big) \n    \\end{aligned}\n\\end{equation*}\n\n",
      "metadata": {},
      "id": "21591a49"
    },
    {
      "cell_type": "code",
      "source": "def PrCA(v, theta_v, a, t0, theta):\n    return np.exp(a*v*np.cos(theta-theta_v))/2/np.pi/iv(0,v*a)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 7,
      "outputs": [],
      "id": "f1449ef2"
    },
    {
      "cell_type": "code",
      "source": "import scipy\n\nj0 = scipy.special.jn_zeros(0, 200)\nJ1 = scipy.special.jv(1, j0)\n\ndef PrRT(v, theta_v, a, t0, t):\n    if t<=t0:\n        return 0\n    else:\n        ans = sum(j0/J1*np.exp(-j0**2*(t-t0)/2/a**2))/a**2*np.exp(-v**2*(t-t0)/2)*iv(0,v*a)\n        if ans<0: # returning 0 for negative probability\n            ans = 0\n        if j0[-1]/J1[-1]*np.exp(-j0[-1]**2*(t-t0)/2/a**2) != 0: # returning 0 for non-convereged series\n            ans = 0\n        return ans",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [],
      "id": "75d688d8"
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n\nfigure, axis = plt.subplots(1, 2, figsize=(20,10), tight_layout=True)\n\ntheta = np.linspace(min(CA), max(CA), 100)\nPr_theta = np.empty(len(theta))\nfor i in range(len(theta)):\n    Pr_theta[i] = PrCA(v, theta_v, a, t0, theta[i])\n    \nt = np.linspace(min(RT), max(RT), 1000)\nPr_t = np.empty(len(t))\nfor i in range(len(t)):\n    Pr_t[i] = PrRT(v, theta_v, a, t0, t[i])\n    \naxis[0].hist(CA, bins=20, density=True)\naxis[0].plot(theta, Pr_theta, linewidth=5)\naxis[1].hist(RT, bins=50, density=True)\naxis[1].plot(t, Pr_t, linewidth=5)\nplt.show()",
      "metadata": {
        "trusted": true
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      "execution_count": 9,
      "outputs": [
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}