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@@ -1,127 +0,0 @@
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-{
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": 11,
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "(4.877804138352662, 0.02720420624699369, 1, array([[58.13207547, 20.86792453],\n",
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- " [19.86792453, 7.13207547]]))\n",
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- "Ttest_indResult(statistic=2.2558785269477974, pvalue=0.02686599410805479)\n",
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- "(9.417431088992272, 0.009016351249902618, 2, array([[58.13207547, 19.86792453],\n",
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- " [ 2.23584906, 0.76415094],\n",
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- " [18.63207547, 6.36792453]]))\n",
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- "Ttest_indResult(statistic=-8.26430309338257, pvalue=4.402977956690523e-09)\n"
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- ]
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- },
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- {
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- "data": {
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- "text/plain": [
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- "(inf, 0.013361462728551347)"
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- ]
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- },
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- "execution_count": 11,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "import numpy\n",
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- "import scipy.stats\n",
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- "gender=numpy.array([[63,16],[15,12]])\n",
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- "print(scipy.stats.chi2_contingency(gender))\n",
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- "print(scipy.stats.ttest_ind_from_stats(39.48, 38.26, 79, 25.78, 22.26, 27, equal_var=False))\n",
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- "cvar=numpy.array([[61,17],[0,3],[18,7]])\n",
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- "cvar1=numpy.array([[61,17],[0,3]])\n",
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- "print(scipy.stats.chi2_contingency(cvar))\n",
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- "print(scipy.stats.ttest_ind_from_stats(1.72, 1.07, 79, 6.19, 2.74, 27, equal_var=False))\n",
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- "scipy.stats.fisher_exact(cvar1)\n"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 27,
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "(5.742882730015083, 0.016555599001289927, 1, array([[48.13483146, 14.86516854],\n",
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- " [19.86516854, 6.13483146]]))\n",
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- "Ttest_indResult(statistic=0.507116003909028, pvalue=0.6140574223641995)\n",
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- "(0.5543478260869565, 0.5384774958157479)\n",
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- "\n",
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- "\tFisher's Exact Test for Count Data\n",
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- "\n",
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- "data: \n",
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- "p-value = 0.4703\n",
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- "alternative hypothesis: two.sided\n",
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- "\n",
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- "\n",
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- "(inf, 0.017701863354037183)\n",
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- "Ttest_indResult(statistic=-7.670035520868973, pvalue=1.8453617356830517e-08)\n"
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- ]
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- }
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- ],
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- "source": [
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- "import numpy\n",
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- "import scipy.stats\n",
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- "gender=numpy.array([[53,10],[15,11]])\n",
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- "print(scipy.stats.chi2_contingency(gender))\n",
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- "print(scipy.stats.ttest_ind_from_stats(26.85,25.49, 63, 24.2 , 21.02 , 26, equal_var=False))\n",
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- "cvar=numpy.array([[51,12],[23,3]])\n",
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- "print(scipy.stats.fisher_exact(cvar))\n",
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- "\n",
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- "import rpy2.robjects\n",
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- "rpy2.robjects.r['pi']\n",
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- "dataframe=rpy2.robjects.DataFrame({'hidrokolon':rpy2.robjects.IntVector([2,3,3,4]),'operacija':rpy2.robjects.IntVector([1,0,2,0])})\n",
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- "dataframe.rownames=rpy2.robjects.StrVector(['<12hr','12-24h','24-48h','>48h'])\n",
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- "#print(dataframe)\n",
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- "#import rpy2.robjects.packages\n",
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- "#base=rpy2.robjects.packages.importr('base')\n",
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- "#utils=rpy2.robjects.packages.importr('utils')\n",
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- "#dir(utils)\n",
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- "fisherTest=rpy2.robjects.r['fisher.test']\n",
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- "res=fisherTest(dataframe)\n",
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- "print(res)\n",
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- "cvar1=numpy.array([[51,16],[0,3]])\n",
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- "print(scipy.stats.fisher_exact(cvar1))\n",
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- "print(scipy.stats.ttest_ind_from_stats(1.98,1.19 , 63, 6.23,2.72 , 26, equal_var=False))\n",
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- "\n"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": []
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- }
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- ],
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- "metadata": {
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- "kernelspec": {
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- "display_name": "Python 3",
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- "language": "python",
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- "name": "python3"
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- },
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- "language_info": {
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- "codemirror_mode": {
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- "name": "ipython",
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- "version": 3
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- },
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- "file_extension": ".py",
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- "mimetype": "text/x-python",
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- "name": "python",
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- "nbconvert_exporter": "python",
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- "pygments_lexer": "ipython3",
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- "version": "3.8.5"
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- }
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- },
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- "nbformat": 4,
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- "nbformat_minor": 4
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-}
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