tutorial_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enter your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import / Create Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm\n",
    "import pandas as pd\n",
    "import dabest\n",
    "control = norm.rvs(loc=0, size=30, random_state=12345)\n",
    "test    = norm.rvs(loc=0.5, size=30, random_state=12345)\n",
    "my_df   = pd.DataFrame({\"control\": control,\n",
    "                            \"test\": test})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the width of the confidence interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ci = 90"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select type of analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "paired = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"), ci=ci, paired=paired)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose your effect size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DABEST v0.3.1\n",
       "=============\n",
       "             \n",
       "Good afternoon!\n",
       "The current time is Tue Jan 18 16:12:21 2022.\n",
       "\n",
       "The unpaired mean difference between control and test is 0.5 [90%CI 0.0439, 0.938].\n",
       "The p-value of the two-sided permutation t-test is 0.0758. \n",
       "\n",
       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
       "The p-value(s) reported are the likelihood(s) of observing the effect size(s),\n",
       "if the null hypothesis of zero difference is true.\n",
       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
       "\n",
       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dabest_object.mean_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cohens' d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DABEST v0.3.1\n",
       "=============\n",
       "             \n",
       "Good afternoon!\n",
       "The current time is Tue Jan 18 16:12:24 2022.\n",
       "\n",
       "The unpaired Cohen's d between control and test is 0.471 [90%CI 0.00307, 0.887].\n",
       "The p-value of the two-sided permutation t-test is 0.0758. \n",
       "\n",
       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
       "The p-value(s) reported are the likelihood(s) of observing the effect size(s),\n",
       "if the null hypothesis of zero difference is true.\n",
       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
       "\n",
       "To get the results of all valid statistical tests, use `.cohens_d.statistical_tests`"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dabest_object.cohens_d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hedges’ g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DABEST v0.3.1\n",
       "=============\n",
       "             \n",
       "Good afternoon!\n",
       "The current time is Tue Jan 18 16:12:27 2022.\n",
       "\n",
       "The unpaired Hedges' g between control and test is 0.465 [90%CI 0.00303, 0.876].\n",
       "The p-value of the two-sided permutation t-test is 0.0758. \n",
       "\n",
       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
       "The p-value(s) reported are the likelihood(s) of observing the effect size(s),\n",
       "if the null hypothesis of zero difference is true.\n",
       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
       "\n",
       "To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dabest_object.hedges_g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cliffs_delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DABEST v0.3.1\n",
       "=============\n",
       "             \n",
       "Good afternoon!\n",
       "The current time is Tue Jan 18 16:12:32 2022.\n",
       "\n",
       "The unpaired Cliff's delta between control and test is 0.28 [90%CI 0.0267, 0.498].\n",
       "The p-value of the two-sided permutation t-test is 0.061. \n",
       "\n",
       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
       "The p-value(s) reported are the likelihood(s) of observing the effect size(s),\n",
       "if the null hypothesis of zero difference is true.\n",
       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
       "\n",
       "To get the results of all valid statistical tests, use `.cliffs_delta.statistical_tests`"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dabest_object.cliffs_delta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_dabest_object.mean_diff.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "3cf36b79b29f749df5c5a6da00d65c1ccc82e42df605e7d02ef896d3b4bcb990"
  },
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}