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@deep-introspection
Created June 25, 2019 13:27
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Empirical simulations to explore the behavior of various effect size estimators on different distributions.
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.stats import mstats, trim_mean, mannwhitneyu\n",
"\n",
"simulations = 10000000\n",
"\n",
"def D(x, y):\n",
" \"\"\"Calculate Cohen D effect size.\"\"\"\n",
" x = x[np.isfinite(x)]\n",
" y = y[np.isfinite(y)]\n",
" nx = len(x)\n",
" ny = len(y)\n",
" dof = nx + ny - 2\n",
" poolsd = np.sqrt(((nx - 1) * np.std(x, ddof=1) ** 2 +\n",
" (ny - 1) * np.std(y, ddof=1) ** 2) / dof)\n",
" return ((np.mean(x) - np.mean(y)) / poolsd)\n",
"\n",
"\n",
"def Dr(x, y):\n",
" \"\"\"Calculate Robust ds (Dr) effect size.\"\"\"\n",
" x = x[np.isfinite(x)]\n",
" y = y[np.isfinite(y)]\n",
" nx = len(x)\n",
" ny = len(y)\n",
" dof = nx + ny - 2\n",
" stp = np.sqrt(((nx - 1) * np.std(mstats.winsorize(x, 0.2), ddof=1) ** 2 +\n",
" (ny - 1) * np.std(mstats.winsorize(y, 0.2), ddof=1) ** 2) / dof)\n",
" drs = (trim_mean(x, 0.2) - trim_mean(y, 0.2)) / stp\n",
" return 0.642 * drs\n",
"\n",
"\n",
"def Aw(x, y):\n",
" \"\"\"Calculate the probability of superiority (A) effect size.\"\"\"\n",
" U, p = mannwhitneyu(x,y)\n",
" ns = np.min([len(x), len(y)])\n",
" n1 = len(x)\n",
" n2 = len(y)\n",
" #U = W - (ns * (ns + 1))/ 2\n",
" return ((n1 * n2) - U) / (n1 * n2)\n",
"\n",
"\n",
"def IQR(x,y):\n",
" \"\"\"Inter-Quartile Ranges.\"\"\"\n",
" bins = [2.3, 15.9, 25, 50, 75, 84.1, 97.7]\n",
" qx = np.percentile(x, bins)\n",
" qy = np.percentile(y, bins)\n",
" # Classic IQR between 25% and 75%\n",
" IQR = np.mean((x>=qy[2])*(x<=qy[4]))\n",
" # 15.9% and 84.1%; corresponds to within 1 SD in the normal distribution\n",
" SD1 = np.mean((x>=qy[1])*(x<=qy[5]))\n",
" # 2.3% and 97.7%; corresponds to within 2 SD in the normal distribution\n",
" SD2 = np.mean((x>=qy[0])*(x<=qy[6]))\n",
" return IQR, SD1, SD2\n",
"\n",
"\n",
"def AUC(dist_ctr, dist_asd, plot=False):\n",
" # Adapted from https://tinyurl.com/ydflzyf8\n",
" x = np.linspace(mu_ctr - 4*sigma_ctr, mu_ctr + 4*sigma_ctr, 100)\n",
" good_pdf = dist_ctr(x)\n",
" bad_pdf = dist_asd(x)\n",
" # Total\n",
" total_bad = np.sum(bad_pdf)\n",
" total_good = np.sum(good_pdf)\n",
" # Cumulative sum\n",
" cum_TP = 0\n",
" cum_FP = 0\n",
" # TPR and FPR list initialization\n",
" TPR_list=[]\n",
" FPR_list=[]\n",
" # Iteratre through all values of x\n",
" for i in range(len(x)):\n",
" #We are only interested in non-zero values of bad\n",
" if bad_pdf[i]>0:\n",
" cum_TP+=bad_pdf[len(x)-1-i]\n",
" cum_FP+=good_pdf[len(x)-1-i]\n",
" FPR=cum_FP/total_good\n",
" TPR=cum_TP/total_bad\n",
" TPR_list.append(TPR)\n",
" FPR_list.append(FPR)\n",
" # Calculating AUC, taking the 100 timesteps into account\n",
" auc= np.sum(TPR_list)/100\n",
" \n",
" if plot:\n",
" # Plotting ROC curve\n",
" fig, ax = plt.subplots(1,1, figsize=(10,5))\n",
" ax.plot(FPR_list, TPR_list)\n",
" ax.plot(x,x, \"--\")\n",
" ax.set_xlim([0,1])\n",
" ax.set_ylim([0,1])\n",
" ax.set_title(\"ROC Curve\", fontsize=14)\n",
" ax.set_ylabel('TPR', fontsize=12)\n",
" ax.set_xlabel('FPR', fontsize=12)\n",
" ax.grid()\n",
" ax.legend([\"AUC=%.3f\"%auc])\n",
" return auc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Normal distributions with various effect sizes"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "df5cbcfbd8a440689e7392157fb93e70",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"from tqdm import tqdm_notebook\n",
"from scipy.stats import norm\n",
"\n",
"mu_ctr = mu_asd = 0\n",
"sigma_ctr = sigma_asd = 1\n",
"\n",
"measures = [\"D\", \"Dr\", \"IQR(25%-50%)\", \"IQR(15.9%-84.1%)\", \"IQR(2.3%-97.7%)\", \"Aw\", \"AUC\", \"1SD%\", \"2SD%\"]\n",
"effect_sizes = [.2, .5, 1, 1.5, 2, 2.7]\n",
"distributions = ['Normal_D={}'.format(es) for es in effect_sizes]\n",
"\n",
"accu = []\n",
"for mu_asd in tqdm_notebook(effect_sizes):\n",
" def dist_ctr(x):\n",
" return norm.pdf(x=x, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
" def dist_asd(x):\n",
" return norm.pdf(x=x, loc=mu_asd, scale=sigma_asd)\n",
"\n",
" def data_ctr(n=simulations):\n",
" return norm.rvs(size=n, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
" def data_asd(n=simulations):\n",
" return norm.rvs(size=n, loc=mu_asd, scale=sigma_asd)\n",
"\n",
" val_ctr = data_ctr()\n",
" val_asd = data_asd()\n",
" \n",
" IQR_2550, IQR_SD1, IQR_SD2 = IQR(val_asd, val_ctr)\n",
" accu.append([\n",
" D(val_asd, val_ctr),\n",
" Dr(val_asd, val_ctr),\n",
" IQR_2550, IQR_SD1, IQR_SD2,\n",
" Aw(val_asd, val_ctr),\n",
" AUC(dist_ctr, dist_asd),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+1*np.nanstd(val_ctr))),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+2*np.nanstd(val_ctr))),\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"def plot_dist(filename, x = np.linspace(mu_ctr - 6*sigma_ctr, mu_ctr + 6*sigma_ctr, 100)):\n",
" plt.figure(figsize=[12,5])\n",
" \n",
" plt.plot(x,dist_ctr(x), 'blue')\n",
" plt.plot(x,dist_asd(x), 'red')\n",
"\n",
" #All\n",
" plt.fill_between(x, dist_asd(x), color='red', alpha=0.1)\n",
" centiles = [5, 25, 50, 75, 95]\n",
" centiles_values_ctr = np.percentile(data_ctr(n=1000), centiles)\n",
" centiles_values_asd = np.percentile(data_asd(n=1000), centiles)\n",
"\n",
" #95%\n",
" x= np.linspace(centiles_values_ctr[0], centiles_values_ctr[4], 100)\n",
" plt.fill_between(x, dist_asd(x), color='blue', alpha=0.1)\n",
" x= np.linspace(centiles_values_ctr[1], centiles_values_ctr[3], 100)\n",
" plt.fill_between(x, dist_asd(x), color='blue', alpha=0.1)\n",
"\n",
" for b in centiles_values_ctr:\n",
" M = max(dist_ctr(b), dist_asd(b))\n",
" plt.plot(b * np.array([1, 1]), [0, M], 'black', linestyle='--', linewidth=1)\n",
"\n",
" plt.plot(centiles_values_ctr[2] * np.array([1, 1]), [0, dist_ctr(centiles_values_ctr[2])], 'blue', linestyle='-', linewidth=2) \n",
"\n",
" plt.tight_layout()\n",
" plt.gca()\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
" plt.xticks(centiles_values_ctr, [str(u)+'%' for u in centiles])\n",
" plt.savefig(filename)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CTR Normal - ASD Skewed"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import exponnorm\n",
"\n",
"mu_ctr = 0\n",
"sigma_ctr = 1\n",
"\n",
"mu_asd = 0\n",
"K_asd = 1.421\n",
"\n",
"def dist_ctr(x):\n",
" return norm.pdf(x=x, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def dist_asd(x):\n",
" return exponnorm.pdf(x=x, K=K_asd, loc=mu_asd, scale=sigma_asd)\n",
"\n",
"def data_ctr(n=simulations):\n",
" return norm.rvs(size=n, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def data_asd(n=simulations):\n",
" return exponnorm.rvs(size=n, K=K_asd, loc=mu_asd, scale=sigma_asd)\n",
"\n",
"val_asd, val_ctr = data_asd(), data_ctr()\n",
"IQR_2550, IQR_SD1, IQR_SD2 = IQR(val_asd, val_ctr)\n",
"accu.append([\n",
" D(val_asd, val_ctr),\n",
" Dr(val_asd, val_ctr),\n",
" IQR_2550, IQR_SD1, IQR_SD2,\n",
" Aw(val_asd, val_ctr),\n",
" AUC(dist_ctr, dist_asd),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+1*np.nanstd(val_ctr))),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+2*np.nanstd(val_ctr))),\n",
" ])\n",
"distributions.append(\"OneSkewed_D=1\")\n",
"\n",
"plot_dist(filename=\"OneSkewed_D=1.pdf\",\n",
" x=np.linspace(mu_ctr - 5*sigma_ctr, mu_ctr + 5*sigma_ctr, 100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CTR Skewed - ASD Skewed"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import exponnorm\n",
"\n",
"mu_ctr = 0\n",
"sigma_ctr = 1\n",
"K_ctr = 1.421\n",
"\n",
"mu_asd = 1\n",
"sigma_asd = 1\n",
"K_asd = 1.421\n",
"\n",
"def dist_ctr(x):\n",
" return exponnorm.pdf(x=x, K=K_ctr, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def dist_asd(x):\n",
" return exponnorm.pdf(x=x, K=K_asd, loc=mu_asd, scale=sigma_asd)\n",
"\n",
"def data_ctr(n=simulations):\n",
" return exponnorm.rvs(size=n, K=K_ctr, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def data_asd(n=simulations):\n",
" return exponnorm.rvs(size=n, K=K_asd, loc=mu_asd, scale=sigma_asd)\n",
"\n",
"val_asd, val_ctr = data_asd(), data_ctr()\n",
"IQR_2550, IQR_SD1, IQR_SD2 = IQR(val_asd, val_ctr)\n",
"accu.append([\n",
" D(val_asd, val_ctr),\n",
" Dr(val_asd, val_ctr),\n",
" IQR_2550, IQR_SD1, IQR_SD2,\n",
" Aw(val_asd, val_ctr),\n",
" AUC(dist_ctr, dist_asd),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+1*np.nanstd(val_ctr))),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+2*np.nanstd(val_ctr))),\n",
" ])\n",
"distributions.append(\"BothSkewed_D=1\")\n",
"\n",
"plot_dist(filename=\"BothSkewed_D=1.pdf\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CTR Gamma a=3 - ASD Gamma a=1.5"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import gamma\n",
"\n",
"mu_ctr = 0\n",
"mu_asd = 0\n",
"\n",
"sigma_ctr = 1.\n",
"sigma_asd = 1.\n",
"\n",
"a_ctr = 3\n",
"a_asd = 1.5\n",
"\n",
"def dist_ctr(x):\n",
" return gamma.pdf(x=x, a=a_ctr, loc=mu_ctr-a_ctr, scale=sigma_ctr)\n",
"\n",
"def dist_asd(x, a=a_asd):\n",
" return gamma.pdf(x=x, a=a_asd, loc=mu_asd-a_asd, scale=sigma_asd)\n",
"\n",
"def data_ctr(n=simulations):\n",
" return gamma.rvs(size=n, a=a_ctr, loc=mu_ctr-a_ctr, scale=sigma_ctr)\n",
"\n",
"def data_asd(n=simulations, a=a_asd):\n",
" return gamma.rvs(size=n, a=a_asd, loc=mu_asd-a_asd, scale=sigma_asd)\n",
"\n",
"val_asd, val_ctr = data_asd(), data_ctr()\n",
"IQR_2550, IQR_SD1, IQR_SD2 = IQR(val_asd, val_ctr)\n",
"accu.append([\n",
" D(val_asd, val_ctr),\n",
" Dr(val_asd, val_ctr),\n",
" IQR_2550, IQR_SD1, IQR_SD2,\n",
" Aw(val_asd, val_ctr),\n",
" AUC(dist_ctr, dist_asd),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+1*np.nanstd(val_ctr))),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+2*np.nanstd(val_ctr))),\n",
" ])\n",
"distributions.append(\"BothGamma_(a=3)vs(a=1.5)\")\n",
"\n",
"plot_dist(filename=\"BothGamma_(a=3)vs(a=1.5).pdf\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CTR Gamma a=3 - ASD Gamma a=2"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import gamma\n",
"\n",
"mu_ctr = 0\n",
"mu_asd = 0\n",
"\n",
"sigma_ctr = 1.\n",
"sigma_asd = 1.\n",
"\n",
"a_ctr = 3\n",
"a_asd = 2\n",
"\n",
"def dist_ctr(x):\n",
" return gamma.pdf(x=x, a=a_ctr, loc=mu_ctr-a_ctr, scale=sigma_ctr)\n",
"\n",
"def dist_asd(x, a=a_asd):\n",
" return gamma.pdf(x=x, a=a_asd, loc=mu_asd-a_asd, scale=sigma_asd)\n",
"\n",
"def data_ctr(n=simulations):\n",
" return gamma.rvs(size=n, a=a_ctr, loc=mu_ctr-a_ctr, scale=sigma_ctr)\n",
"\n",
"def data_asd(n=simulations, a=a_asd):\n",
" return gamma.rvs(size=n, a=a_asd, loc=mu_asd-a_asd, scale=sigma_asd)\n",
"\n",
"val_asd, val_ctr = data_asd(), data_ctr()\n",
"IQR_2550, IQR_SD1, IQR_SD2 = IQR(val_asd, val_ctr)\n",
"accu.append([\n",
" D(val_asd, val_ctr),\n",
" Dr(val_asd, val_ctr),\n",
" IQR_2550, IQR_SD1, IQR_SD2,\n",
" Aw(val_asd, val_ctr),\n",
" AUC(dist_ctr, dist_asd),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+1*np.nanstd(val_ctr))),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+2*np.nanstd(val_ctr))),\n",
" ])\n",
"distributions.append(\"BothGamma_(a=3)vs(a=2)\")\n",
"\n",
"plot_dist(filename=\"BothGamma_(a=3)vs(a=2).pdf\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Platikurtic - ASD Cosine"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import cosine\n",
"\n",
"mu_ctr = 0\n",
"mu_asd = 0\n",
"\n",
"sigma_ctr = 1.\n",
"sigma_asd = 1.\n",
"\n",
"def dist_ctr(x):\n",
" return norm.pdf(x=x, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def dist_asd(x):\n",
" return cosine.pdf(x=x, loc=mu_asd, scale=sigma_asd)\n",
"\n",
"def data_ctr(n=1000):\n",
" return norm.rvs(size=n, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def data_asd(n=1000):\n",
" return cosine.rvs(size=n, loc=mu_asd, scale=sigma_asd)\n",
"\n",
"val_asd, val_ctr = data_asd(), data_ctr()\n",
"IQR_2550, IQR_SD1, IQR_SD2 = IQR(val_asd, val_ctr)\n",
"accu.append([\n",
" D(val_asd, val_ctr),\n",
" Dr(val_asd, val_ctr),\n",
" IQR_2550, IQR_SD1, IQR_SD2,\n",
" Aw(val_asd, val_ctr),\n",
" AUC(dist_ctr, dist_asd),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+1*np.nanstd(val_ctr))),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+2*np.nanstd(val_ctr))),\n",
" ])\n",
"distributions.append(\"Platikurtic\")\n",
"\n",
"plot_dist(filename=\"Platikurtic.pdf\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Leptokurtic - ASD Hypsecant"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import hypsecant\n",
"\n",
"mu_ctr = 0\n",
"mu_asd = 0\n",
"sigma_asd = 1.\n",
"sigma_asd = 1.\n",
"\n",
"def dist_ctr(x):\n",
" return norm.pdf(x=x, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def dist_asd(x):\n",
" return hypsecant.pdf(x=x)\n",
"\n",
"def data_ctr(n=simulations):\n",
" return norm.rvs(size=n, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def data_asd(n=simulations):\n",
" return hypsecant.rvs(size=n)\n",
"\n",
"val_asd, val_ctr = data_asd(), data_ctr()\n",
"IQR_2550, IQR_SD1, IQR_SD2 = IQR(val_asd, val_ctr)\n",
"accu.append([\n",
" D(val_asd, val_ctr),\n",
" Dr(val_asd, val_ctr),\n",
" IQR_2550, IQR_SD1, IQR_SD2,\n",
" Aw(val_asd, val_ctr),\n",
" AUC(dist_ctr, dist_asd),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+1*np.nanstd(val_ctr))),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+2*np.nanstd(val_ctr))),\n",
" ])\n",
"distributions.append(\"Leptokurtic\")\n",
"\n",
"plot_dist(filename=\"Leptokurtic.pdf\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bimodal symmetric"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mu_ctr = 0\n",
"sigma_asd = 1.\n",
"mu_asd = 1.5\n",
"sigma_asd = 1.\n",
"\n",
"def dist_ctr(x):\n",
" return norm.pdf(x=x, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def dist_asd(x):\n",
" return (norm.pdf(x=x, loc=-mu_asd, scale=sigma_asd) + norm.pdf(x=x, loc=+mu_asd, scale=sigma_asd))/2\n",
"\n",
"def data_ctr(n=simulations):\n",
" return norm.rvs(size=n, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def data_asd(n=simulations):\n",
" return norm.rvs(size=n, loc=mu_asd, scale=sigma_asd) * ((np.random.randn(n)>0).astype(int)*2-1)\n",
"\n",
"val_asd, val_ctr = data_asd(), data_ctr()\n",
"IQR_2550, IQR_SD1, IQR_SD2 = IQR(val_asd, val_ctr)\n",
"accu.append([\n",
" D(val_asd, val_ctr),\n",
" Dr(val_asd, val_ctr),\n",
" IQR_2550, IQR_SD1, IQR_SD2,\n",
" Aw(val_asd, val_ctr),\n",
" AUC(dist_ctr, dist_asd),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+1*np.nanstd(val_ctr))),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+2*np.nanstd(val_ctr))),\n",
" ])\n",
"distributions.append(\"Bimodal_Symmetric\")\n",
"\n",
"plot_dist(filename=\"Bimodal_Symmetric.pdf\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bimodal asymmetric"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mu_ctr = 0\n",
"sigma_asd = 1.\n",
"mu_asd = 1.5\n",
"sigma_asd = 1.\n",
"\n",
"def dist_ctr(x):\n",
" return norm.pdf(x=x, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def dist_asd(x):\n",
" return (3*norm.pdf(x=x, loc=1-mu_asd, scale=sigma_asd)/4 + 1*norm.pdf(x=x, loc=1+1.*mu_asd, scale=sigma_asd)/4)\n",
"\n",
"def data_ctr(n=10000):\n",
" return norm.rvs(size=n, loc=mu_ctr, scale=sigma_ctr)\n",
"\n",
"def data_asd(n=10000):\n",
" mod1 = (np.random.rand(n)>1./4).astype(int)\n",
" mod2 = 1-mod1\n",
" gen1 = norm.rvs(size=n, loc=1-mu_asd, scale=sigma_asd)\n",
" gen2 = norm.rvs(size=n, loc=1+1*mu_asd, scale=sigma_asd)\n",
" return gen1*mod1 + gen2*mod2\n",
"\n",
"val_asd, val_ctr = data_asd(), data_ctr()\n",
"IQR_2550, IQR_SD1, IQR_SD2 = IQR(val_asd, val_ctr)\n",
"accu.append([\n",
" D(val_asd, val_ctr),\n",
" Dr(val_asd, val_ctr),\n",
" IQR_2550, IQR_SD1, IQR_SD2,\n",
" Aw(val_asd, val_ctr),\n",
" AUC(dist_ctr, dist_asd),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+1*np.nanstd(val_ctr))),\n",
" np.mean(val_asd>(np.nanmean(val_ctr)+2*np.nanstd(val_ctr))),\n",
" ])\n",
"distributions.append(\"Bimodal_Asymmetric\")\n",
"\n",
"plot_dist(filename=\"Bimodal_Asymmetric.pdf\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generate a summary CSV"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>D</th>\n",
" <th>Dr</th>\n",
" <th>IQR(25%-50%)</th>\n",
" <th>IQR(15.9%-84.1%)</th>\n",
" <th>IQR(2.3%-97.7%)</th>\n",
" <th>Aw</th>\n",
" <th>AUC</th>\n",
" <th>1SD%</th>\n",
" <th>2SD%</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Distribution</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",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Normal_D=0.2</th>\n",
" <td>2.001022e-01</td>\n",
" <td>0.200325</td>\n",
" <td>0.491797</td>\n",
" <td>0.672645</td>\n",
" <td>0.949622</td>\n",
" <td>0.556294</td>\n",
" <td>0.529725</td>\n",
" <td>0.211802</td>\n",
" <td>0.035907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Normal_D=0.5</th>\n",
" <td>5.004607e-01</td>\n",
" <td>0.500757</td>\n",
" <td>0.448783</td>\n",
" <td>0.623632</td>\n",
" <td>0.926442</td>\n",
" <td>0.638319</td>\n",
" <td>0.566783</td>\n",
" <td>0.308736</td>\n",
" <td>0.066848</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Normal_D=1</th>\n",
" <td>9.999342e-01</td>\n",
" <td>0.999760</td>\n",
" <td>0.325461</td>\n",
" <td>0.476610</td>\n",
" <td>0.838727</td>\n",
" <td>0.760210</td>\n",
" <td>0.628265</td>\n",
" <td>0.499920</td>\n",
" <td>0.158801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Normal_D=1.5</th>\n",
" <td>1.499740e+00</td>\n",
" <td>1.499995</td>\n",
" <td>0.189688</td>\n",
" <td>0.301823</td>\n",
" <td>0.689361</td>\n",
" <td>0.855532</td>\n",
" <td>0.688658</td>\n",
" <td>0.691480</td>\n",
" <td>0.308728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Normal_D=2</th>\n",
" <td>2.000157e+00</td>\n",
" <td>1.999947</td>\n",
" <td>0.088707</td>\n",
" <td>0.156909</td>\n",
" <td>0.497901</td>\n",
" <td>0.921391</td>\n",
" <td>0.746218</td>\n",
" <td>0.841429</td>\n",
" <td>0.500012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Normal_D=2.7</th>\n",
" <td>2.700649e+00</td>\n",
" <td>2.700781</td>\n",
" <td>0.021042</td>\n",
" <td>0.044223</td>\n",
" <td>0.240269</td>\n",
" <td>0.971921</td>\n",
" <td>0.817036</td>\n",
" <td>0.955499</td>\n",
" <td>0.758239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OneSkewed_D=1</th>\n",
" <td>1.002512e+00</td>\n",
" <td>0.946828</td>\n",
" <td>0.283635</td>\n",
" <td>0.405523</td>\n",
" <td>0.688106</td>\n",
" <td>0.762292</td>\n",
" <td>0.640554</td>\n",
" <td>0.549691</td>\n",
" <td>0.306039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BothSkewed_D=1</th>\n",
" <td>5.757442e-01</td>\n",
" <td>0.651333</td>\n",
" <td>0.463928</td>\n",
" <td>0.653629</td>\n",
" <td>0.951450</td>\n",
" <td>0.676931</td>\n",
" <td>0.737903</td>\n",
" <td>0.275457</td>\n",
" <td>0.082631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BothGamma_(a=3)vs(a=1.5)</th>\n",
" <td>6.736811e-05</td>\n",
" <td>0.005574</td>\n",
" <td>0.744962</td>\n",
" <td>0.900639</td>\n",
" <td>0.991388</td>\n",
" <td>0.529149</td>\n",
" <td>0.419610</td>\n",
" <td>0.091096</td>\n",
" <td>0.019159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BothGamma_(a=3)vs(a=2)</th>\n",
" <td>-9.918817e-07</td>\n",
" <td>0.002715</td>\n",
" <td>0.623250</td>\n",
" <td>0.824119</td>\n",
" <td>0.986979</td>\n",
" <td>0.517200</td>\n",
" <td>0.430809</td>\n",
" <td>0.113292</td>\n",
" <td>0.027418</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Platikurtic</th>\n",
" <td>-6.523541e-02</td>\n",
" <td>-0.029267</td>\n",
" <td>0.411000</td>\n",
" <td>0.584000</td>\n",
" <td>0.918000</td>\n",
" <td>0.514301</td>\n",
" <td>0.505000</td>\n",
" <td>0.183000</td>\n",
" <td>0.031000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Leptokurtic</th>\n",
" <td>-3.368797e-04</td>\n",
" <td>-0.000641</td>\n",
" <td>0.400353</td>\n",
" <td>0.550829</td>\n",
" <td>0.828225</td>\n",
" <td>0.500138</td>\n",
" <td>0.505000</td>\n",
" <td>0.224335</td>\n",
" <td>0.085482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bimodal_Symmetric</th>\n",
" <td>6.268947e-05</td>\n",
" <td>0.000057</td>\n",
" <td>0.189733</td>\n",
" <td>0.301808</td>\n",
" <td>0.689436</td>\n",
" <td>0.500009</td>\n",
" <td>0.505000</td>\n",
" <td>0.348891</td>\n",
" <td>0.154463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bimodal_Asymmetric</th>\n",
" <td>1.864647e-01</td>\n",
" <td>0.029425</td>\n",
" <td>0.344800</td>\n",
" <td>0.486300</td>\n",
" <td>0.770900</td>\n",
" <td>0.512126</td>\n",
" <td>0.527834</td>\n",
" <td>0.282900</td>\n",
" <td>0.179400</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" D Dr IQR(25%-50%) \\\n",
"Distribution \n",
"Normal_D=0.2 2.001022e-01 0.200325 0.491797 \n",
"Normal_D=0.5 5.004607e-01 0.500757 0.448783 \n",
"Normal_D=1 9.999342e-01 0.999760 0.325461 \n",
"Normal_D=1.5 1.499740e+00 1.499995 0.189688 \n",
"Normal_D=2 2.000157e+00 1.999947 0.088707 \n",
"Normal_D=2.7 2.700649e+00 2.700781 0.021042 \n",
"OneSkewed_D=1 1.002512e+00 0.946828 0.283635 \n",
"BothSkewed_D=1 5.757442e-01 0.651333 0.463928 \n",
"BothGamma_(a=3)vs(a=1.5) 6.736811e-05 0.005574 0.744962 \n",
"BothGamma_(a=3)vs(a=2) -9.918817e-07 0.002715 0.623250 \n",
"Platikurtic -6.523541e-02 -0.029267 0.411000 \n",
"Leptokurtic -3.368797e-04 -0.000641 0.400353 \n",
"Bimodal_Symmetric 6.268947e-05 0.000057 0.189733 \n",
"Bimodal_Asymmetric 1.864647e-01 0.029425 0.344800 \n",
"\n",
" IQR(15.9%-84.1%) IQR(2.3%-97.7%) Aw \\\n",
"Distribution \n",
"Normal_D=0.2 0.672645 0.949622 0.556294 \n",
"Normal_D=0.5 0.623632 0.926442 0.638319 \n",
"Normal_D=1 0.476610 0.838727 0.760210 \n",
"Normal_D=1.5 0.301823 0.689361 0.855532 \n",
"Normal_D=2 0.156909 0.497901 0.921391 \n",
"Normal_D=2.7 0.044223 0.240269 0.971921 \n",
"OneSkewed_D=1 0.405523 0.688106 0.762292 \n",
"BothSkewed_D=1 0.653629 0.951450 0.676931 \n",
"BothGamma_(a=3)vs(a=1.5) 0.900639 0.991388 0.529149 \n",
"BothGamma_(a=3)vs(a=2) 0.824119 0.986979 0.517200 \n",
"Platikurtic 0.584000 0.918000 0.514301 \n",
"Leptokurtic 0.550829 0.828225 0.500138 \n",
"Bimodal_Symmetric 0.301808 0.689436 0.500009 \n",
"Bimodal_Asymmetric 0.486300 0.770900 0.512126 \n",
"\n",
" AUC 1SD% 2SD% \n",
"Distribution \n",
"Normal_D=0.2 0.529725 0.211802 0.035907 \n",
"Normal_D=0.5 0.566783 0.308736 0.066848 \n",
"Normal_D=1 0.628265 0.499920 0.158801 \n",
"Normal_D=1.5 0.688658 0.691480 0.308728 \n",
"Normal_D=2 0.746218 0.841429 0.500012 \n",
"Normal_D=2.7 0.817036 0.955499 0.758239 \n",
"OneSkewed_D=1 0.640554 0.549691 0.306039 \n",
"BothSkewed_D=1 0.737903 0.275457 0.082631 \n",
"BothGamma_(a=3)vs(a=1.5) 0.419610 0.091096 0.019159 \n",
"BothGamma_(a=3)vs(a=2) 0.430809 0.113292 0.027418 \n",
"Platikurtic 0.505000 0.183000 0.031000 \n",
"Leptokurtic 0.505000 0.224335 0.085482 \n",
"Bimodal_Symmetric 0.505000 0.348891 0.154463 \n",
"Bimodal_Asymmetric 0.527834 0.282900 0.179400 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"results = pd.DataFrame(accu, columns=measures, index=distributions)\n",
"results.index.name = 'Distribution'\n",
"results.to_csv('Effect-Sizes-Estimates_({}-simulations).csv'.format(simulations))\n",
"results"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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