{"id":3527,"date":"2023-07-17T00:49:18","date_gmt":"2023-07-17T00:49:18","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e7%bb%9f%e8%ae%a1%e6%a8%a1%e5%9e%8b%e9%80%bb%e8%be%91%e5%9b%9e%e5%bd%92\/"},"modified":"2023-07-17T00:49:18","modified_gmt":"2023-07-17T00:49:18","slug":"%e7%bb%9f%e8%ae%a1%e6%a8%a1%e5%9e%8b%e9%80%bb%e8%be%91%e5%9b%9e%e5%bd%92","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e7%bb%9f%e8%ae%a1%e6%a8%a1%e5%9e%8b%e9%80%bb%e8%be%91%e5%9b%9e%e5%bd%92\/","title":{"rendered":"\u5982\u4f55\u4f7f\u7528\u7edf\u8ba1\u6a21\u578b\u6267\u884c\u903b\u8f91\u56de\u5f52"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">Python \u7684<a href=\"https:\/\/www.statsmodels.org\/stable\/index.html\" target=\"_blank\" rel=\"noopener\">statsmodels<\/a>\u6a21\u5757\u63d0\u4f9b\u4e86\u5404\u79cd\u51fd\u6570\u548c\u7c7b\uff0c\u53ef\u8ba9\u60a8\u9002\u5e94\u5404\u79cd\u7edf\u8ba1\u6a21\u578b\u3002<\/span><\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528 statsmodels \u51fd\u6570\u6267\u884c<a href=\"https:\/\/statorials.org\/cn\/\u903b\u8f91\u56de\u5f521\/\" target=\"_blank\" rel=\"noopener\">\u903b\u8f91\u56de\u5f52<\/a>\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u7b2c 1 \u6b65\uff1a\u521b\u5efa\u6570\u636e<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u4e09\u4e2a\u53d8\u91cf\u7684 pandas DataFrame\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5b66\u4e60\u65f6\u6570\uff08\u5168\u90e8\u503c\uff09<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7814\u7a76\u65b9\u6cd5\uff08\u65b9\u6cd5A\u6216B\uff09<\/span><\/li>\n<li><span style=\"color: #000000;\">\u8003\u8bd5\u7ed3\u679c\uff08\u901a\u8fc7\u6216\u5931\u8d25\uff09<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u5c06\u4f7f\u7528\u5b66\u4e60\u65f6\u95f4\u548c\u5b66\u4e60\u65b9\u6cd5\u62df\u5408\u903b\u8f91\u56de\u5f52\u6a21\u578b\u6765\u9884\u6d4b\u5b66\u751f\u662f\u5426\u901a\u8fc7\u7ed9\u5b9a\u7684\u8003\u8bd5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u521b\u5efa pandas DataFrame\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">result<\/span> ': [0, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n                              0, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n                   ' <span style=\"color: #ff0000;\">hours<\/span> ': [1, 2, 2, 2, 3, 2, 5, 4, 3, 6,\n                            5, 8, 8, 7, 6, 7, 5, 4, 8, 9],\n                   ' <span style=\"color: #ff0000;\">method<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'B',\n                             'B', 'B', 'A', 'B', 'A', 'B', 'B',\n                             'A', 'A', 'B', 'A', 'B', 'A']})\n\n<span style=\"color: #008080;\">#view first five rows of DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n\tresult hours method\n0 0 1 A\n1 1 2 A\n2 0 2 A\n3 0 2 B\n4 0 3 B<\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 2\uff1a\u62df\u5408\u903b\u8f91\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528<strong>logit()<\/strong>\u51fd\u6570\u62df\u5408\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">formula<\/span> . <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> smf\n\n<span style=\"color: #008080;\">#fit logistic regression model\n<\/span>model = smf. <span style=\"color: #3366ff;\">logit<\/span> (' <span style=\"color: #ff0000;\">result~hours+method<\/span> ', data=df). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view model summary\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">model.summary<\/span> ())\n\nOptimization completed successfully.\n         Current function value: 0.557786\n         Iterations 5\n                           Logit Regression Results                           \n==================================================== ============================\nDept. Variable: result No. Observations: 20\nModel: Logit Df Residuals: 17\nMethod: MLE Df Model: 2\nDate: Mon, 22 Aug 2022 Pseudo R-squ.: 0.1894\nTime: 09:53:35 Log-Likelihood: -11.156\nconverged: True LL-Null: -13.763\nCovariance Type: nonrobust LLR p-value: 0.07375\n==================================================== ============================\n                  coef std err z P&gt;|z| [0.025 0.975]\n-------------------------------------------------- -----------------------------\nIntercept -2.1569 1.416 -1.523 0.128 -4.932 0.618\nmethod[TB] 0.0875 1.051 0.083 0.934 -1.973 2.148\nhours 0.4909 0.245 2.002 0.045 0.010 0.972\n==================================================== ============================\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u8f93\u51fa\u7684<strong>coef<\/strong>\u5217\u4e2d\u7684\u503c\u544a\u8bc9\u6211\u4eec\u901a\u8fc7\u8003\u8bd5\u7684\u5bf9\u6570\u51e0\u7387\u7684\u5e73\u5747\u53d8\u5316\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u4f8b\u5982\uff1a<\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u4e0e\u4f7f\u7528\u5b66\u4e60\u65b9\u6cd5 A \u76f8\u6bd4\uff0c\u4f7f\u7528\u5b66\u4e60\u65b9\u6cd5 B \u7684\u901a\u8fc7\u8003\u8bd5\u5bf9\u6570\u51e0\u7387\u5e73\u5747\u589e\u52a0<strong>0.0875<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6bcf\u591a\u5b66\u4e60\u4e00\u5c0f\u65f6\uff0c\u901a\u8fc7\u8003\u8bd5\u7684\u5bf9\u6570\u51e0\u7387\u5c31\u4f1a\u5e73\u5747\u589e\u52a0<strong>0.4909<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>P&gt;|z|<\/strong>\u4e2d\u7684\u503c\u8be5\u5217\u8868\u793a\u6bcf\u4e2a\u7cfb\u6570\u7684 p \u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u8be5\u7814\u7a76\u65b9\u6cd5\u7684 p \u503c\u4e3a<strong>0.934<\/strong> \u3002\u7531\u4e8e\u8be5\u503c\u4e0d\u5c0f\u4e8e0.05\uff0c\u56e0\u6b64\u610f\u5473\u7740\u5b66\u4e60\u65f6\u95f4\u4e0e\u5b66\u751f\u662f\u5426\u901a\u8fc7\u8003\u8bd5\u4e4b\u95f4\u4e0d\u5b58\u5728\u7edf\u8ba1\u4e0a\u7684\u663e\u7740\u5173\u7cfb\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7814\u7a76\u65f6\u95f4\u7684 p \u503c\u4e3a<strong>0.045<\/strong> \u3002\u7531\u4e8e\u8be5\u503c\u5c0f\u4e8e 0.05\uff0c\u8fd9\u610f\u5473\u7740\u5b66\u4e60\u65f6\u95f4\u4e0e\u5b66\u751f\u662f\u5426\u901a\u8fc7\u8003\u8bd5\u4e4b\u95f4\u5b58\u5728\u7edf\u8ba1\u4e0a\u663e\u7740\u7684\u5173\u7cfb\u3002<\/span><\/li>\n<\/ul>\n<h2><span style=\"color: #000000;\"><strong>\u7b2c 3 \u6b65\uff1a\u8bc4\u4f30\u6a21\u578b\u6027\u80fd<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u8bc4\u4f30\u903b\u8f91\u56de\u5f52\u6a21\u578b\u7684\u8d28\u91cf\uff0c\u6211\u4eec\u53ef\u4ee5\u67e5\u770b\u8f93\u51fa\u4e2d\u7684\u4e24\u4e2a\u6307\u6807\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1.\u6635\u79f0R\u5e73\u65b9<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u8be5\u503c\u53ef\u88ab\u89c6\u4e3a\u7ebf\u6027\u56de\u5f52\u6a21\u578b R \u5e73\u65b9\u503c\u7684\u66ff\u4ee3\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5b83\u88ab\u8ba1\u7b97\u4e3a\u96f6\u6a21\u578b\u4e0e\u5b8c\u6574\u6a21\u578b\u7684\u6700\u5927\u5316\u5bf9\u6570\u4f3c\u7136\u51fd\u6570\u7684\u6bd4\u7387\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8be5\u503c\u7684\u8303\u56f4\u53ef\u4ee5\u4ece 0 \u5230 1\uff0c\u503c\u8d8a\u9ad8\u8868\u793a\u6a21\u578b\u62df\u5408\u6548\u679c\u8d8a\u597d\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u4f2a R \u5e73\u65b9\u503c\u4e3a<strong>0.1894<\/strong> \uff0c\u8be5\u503c\u76f8\u5f53\u4f4e\u3002\u8fd9\u544a\u8bc9\u6211\u4eec\uff0c\u6a21\u578b\u7684\u9884\u6d4b\u53d8\u91cf\u5728\u9884\u6d4b\u54cd\u5e94\u53d8\u91cf\u7684\u503c\u65b9\u9762\u8868\u73b0\u4e0d\u4f73\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. LLR p \u503c<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u8be5\u503c\u53ef\u88ab\u89c6\u4e3a\u7ebf\u6027\u56de\u5f52\u6a21\u578b<a href=\"https:\/\/statorials.org\/cn\/\u4e86\u89e3\u56de\u5f52\u4e2d\u603b\u4f53\u663e\u7740\u6027\u7684-f-\u68c0\u9a8c\u7684\u7b80\u5355\u6307\u5357\/\" target=\"_blank\" rel=\"noopener\">\u6574\u4f53 F \u503c<\/a>\u7684 p \u503c\u7684\u66ff\u4ee3\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5982\u679c\u8fd9\u4e2a\u503c\u4f4e\u4e8e\u67d0\u4e2a\u9608\u503c\uff08\u4f8b\u5982\u03b1 = 0.05\uff09\uff0c\u90a3\u4e48\u6211\u4eec\u5c31\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff0c\u6a21\u578b\u4f5c\u4e3a\u4e00\u4e2a\u6574\u4f53\u662f\u201c\u6709\u7528\u7684\u201d\uff0c\u5e76\u4e14\u4e0e\u6ca1\u6709\u9884\u6d4b\u53d8\u91cf\u7684\u6a21\u578b\u76f8\u6bd4\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u9884\u6d4b\u54cd\u5e94\u53d8\u91cf\u7684\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728\u6b64\u793a\u4f8b\u4e2d\uff0cLLR \u7684 p \u503c\u4e3a<strong>0.07375<\/strong> \u3002\u6839\u636e\u6211\u4eec\u9009\u62e9\u7684\u663e\u7740\u6027\u6c34\u5e73\uff08\u4f8b\u59820.01\u30010.05\u30010.1\uff09\uff0c\u6211\u4eec\u53ef\u80fd\u4f1a\u4e5f\u53ef\u80fd\u4e0d\u4f1a\u5f97\u51fa\u6a21\u578b\u6574\u4f53\u6709\u7528\u7684\u7ed3\u8bba\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u7ebf\u6027\u56de\u5f52-python\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u7ebf\u6027\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u5bf9\u6570\u56de\u5f52-python\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5bf9\u6570\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/python-\u4e2d\u7684\u5206\u4f4d\u6570\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5206\u4f4d\u6570\u56de\u5f52<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Python \u7684statsmodels\u6a21\u5757\u63d0\u4f9b\u4e86\u5404\u79cd\u51fd\u6570\u548c\u7c7b\uff0c\u53ef\u8ba9\u60a8\u9002\u5e94\u5404\u79cd\u7edf\u8ba1\u6a21\u578b\u3002 \u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-3527","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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