{"id":1316,"date":"2023-07-26T22:01:06","date_gmt":"2023-07-26T22:01:06","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e5%8e%a8%e6%88%bf%e8%bf%9c%e7%a8%8bpython\/"},"modified":"2023-07-26T22:01:06","modified_gmt":"2023-07-26T22:01:06","slug":"%e5%8e%a8%e6%88%bf%e8%bf%9c%e7%a8%8bpython","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e5%8e%a8%e6%88%bf%e8%bf%9c%e7%a8%8bpython\/","title":{"rendered":"\u5982\u4f55\u7528 python \u8ba1\u7b97\u5e93\u514b\u8ddd\u79bb"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u5e93\u514b\u8ddd\u79bb<\/strong>\u7528\u4e8e\u8bc6\u522b\u56de\u5f52\u6a21\u578b\u4e2d\u6709\u5f71\u54cd\u7684<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u89c2\u5bdf\/\" target=\"_blank\" rel=\"noopener\">\u89c2\u6d4b\u503c<\/a>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5e93\u514b\u8ddd\u79bb\u7684\u516c\u5f0f\u4e3a\uff1a<\/span><\/p>\n<p style=\"text-align: center;\"> <span style=\"color: #000000;\"><strong>d <sub>i<\/sub><\/strong> = (r <sub>i<\/sub> <sup>2<\/sup> \/ p*MSE) * (h <sub>ii<\/sub> \/ (1-h <sub>ii<\/sub> ) <sup>2<\/sup> )<\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>r<\/strong> <sub><strong>i<\/strong><\/sub>\u662f\u7b2c i<sup>\u4e2a<\/sup>\u6b8b\u57fa<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>p<\/strong>\u662f\u56de\u5f52\u6a21\u578b\u4e2d\u7684\u7cfb\u6570\u6570\u91cf<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>MSE<\/strong>\u662f\u5747\u65b9\u8bef\u5dee<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>h<\/strong> <sub>ii<\/sub>\u4e3a<sup>\u7b2c i<\/sup>\u4e2a\u6760\u6746\u503c<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u672c\u8d28\u4e0a\uff0c\u5e93\u514b\u8ddd\u79bb\u8861\u91cf\u7684\u662f\u5f53\u7b2c i<sup>\u4e2a<\/sup>\u89c2\u6d4b\u503c\u88ab\u79fb\u9664\u65f6\u6a21\u578b\u7684\u6240\u6709\u62df\u5408\u503c\u53d1\u751f\u4e86\u591a\u5c11\u53d8\u5316\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5e93\u514b\u8ddd\u79bb\u7684\u503c\u8d8a\u5927\uff0c\u7ed9\u5b9a\u89c2\u6d4b\u7684\u5f71\u54cd\u529b\u5c31\u8d8a\u5927\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f5c\u4e3a\u4e00\u822c\u89c4\u5219\uff0c\u5e93\u514b\u8ddd\u79bb\u5927\u4e8e 4\/n\uff08\u5176\u4e2d<em>n<\/em> = \u603b\u89c2\u6d4b\u503c\uff09\u7684\u4efb\u4f55\u89c2\u6d4b\u503c\u90fd\u88ab\u8ba4\u4e3a\u5177\u6709\u8f83\u5927\u5f71\u54cd\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u7ed9\u5b9a\u56de\u5f52\u6a21\u578b\u7684\u5e93\u514b\u8ddd\u79bb\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 1 \u6b65\uff1a\u8f93\u5165\u6570\u636e<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u5c06\u521b\u5efa\u4e00\u4e2a\u5728 Python \u4e2d\u4f7f\u7528\u7684\u5c0f\u6570\u636e\u96c6\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;\">#create dataset\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #008000;\">x<\/span> ': [8, 12, 12, 13, 14, 16, 17, 22, 24, 26, 29, 30],\n                   ' <span style=\"color: #008000;\">y<\/span> ': [41, 42, 39, 37, 35, 39, 45, 46, 39, 49, 55, 57]})\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 2\uff1a\u62df\u5408\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u62df\u5408\u4e00\u4e2a<a href=\"https:\/\/statorials.org\/cn\/python-\u4e2d\u7684\u7b80\u5355\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u7b80\u5355\u7684\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\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;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm\n\n<span style=\"color: #008080;\">#define response variable\n<\/span>y = df[' <span style=\"color: #008000;\">y<\/span> ']\n\n<span style=\"color: #008080;\">#define explanatory variable\n<\/span>x = df[' <span style=\"color: #008000;\">x<\/span> ']\n\n<span style=\"color: #008080;\">#add constant to predictor variables\n<\/span>x = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (x)\n\n<span style=\"color: #008080;\">#fit linear regression model\n<\/span>model = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()<\/strong><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 3 \u6b65\uff1a\u8ba1\u7b97\u70f9\u996a\u8ddd\u79bb<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8ba1\u7b97\u6a21\u578b\u4e2d\u6bcf\u4e2a\u89c2\u6d4b\u503c\u7684\u5e93\u514b\u8ddd\u79bb\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#suppress scientific notation\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\nn.p. <span style=\"color: #3366ff;\">set_printoptions<\/span> (suppress= <span style=\"color: #008000;\">True<\/span> )\n\n<span style=\"color: #008080;\">#create instance of influence\n<\/span>influence = model. <span style=\"color: #3366ff;\">get_influence<\/span> ()\n\n<span style=\"color: #008080;\">#obtain Cook's distance for each observation\n<\/span>cooks = influence. <span style=\"color: #3366ff;\">cooks_distance<\/span>\n\n<span style=\"color: #008080;\">#display Cook's distances\n<\/span><span style=\"color: #993300;\">print<\/span> (cooks)\n\n(array([0.368, 0.061, 0.001, 0.028, 0.105, 0.022, 0.017, 0. , 0.343,\n        0. , 0.15 , 0.349]),\n array([0.701, 0.941, 0.999, 0.973, 0.901, 0.979, 0.983, 1. , 0.718,\n        1. , 0.863, 0.713]))\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c <strong>cooks_distance()<\/strong>\u51fd\u6570\u663e\u793a\u6bcf\u4e2a\u89c2\u6d4b\u503c\u7684 Cook \u8ddd\u79bb\u503c\u6570\u7ec4\uff0c\u540e\u8ddf\u76f8\u5e94\u7684 p \u503c\u6570\u7ec4\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u89c2\u5bdf #1 \u7684\u5e93\u514b\u8ddd\u79bb\uff1a <strong>0.368<\/strong> \uff08p \u503c\uff1a0.701\uff09<\/span><\/li>\n<li><span style=\"color: #000000;\">\u89c2\u5bdf #2 \u7684\u5e93\u514b\u8ddd\u79bb\uff1a <strong>0.061<\/strong> \uff08p \u503c\uff1a0.941\uff09<\/span><\/li>\n<li><span style=\"color: #000000;\">\u89c2\u5bdf #3 \u7684\u5e93\u514b\u8ddd\u79bb\uff1a <strong>0.001<\/strong> \uff08p \u503c\uff1a0.999\uff09<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u7b49\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c\u56db\u6b65\uff1a\u53ef\u89c6\u5316\u53a8\u5e08\u7684\u8ddd\u79bb<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u6563\u70b9\u56fe\uff0c\u5c06\u9884\u6d4b\u53d8\u91cf\u7684\u503c\u53ef\u89c6\u5316\u4e3a\u6bcf\u6b21\u89c2\u6d4b\u7684\u5e93\u514b\u8ddd\u79bb\u7684\u51fd\u6570\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n\nplt. <span style=\"color: #3366ff;\">scatter<\/span> (df.x, cooks[0])\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #008000;\">x<\/span> ')\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #008000;\">Cooks Distance<\/span> ')\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/span><\/span><\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12869 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/cuisinierspython1.png\" alt=\"Python \u4e2d\u7684\u5e93\u514b\u8ddd\u79bb\" width=\"420\" height=\"284\" srcset=\"\" sizes=\"auto, \"><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u6700\u540e\u7684\u60f3\u6cd5<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u5e93\u514b\u8ddd\u79bb\u5e94\u7528\u4e8e<em>\u8bc6\u522b<\/em>\u6f5c\u5728\u5f71\u54cd\u7684\u89c2\u6d4b\u7ed3\u679c\u3002<\/span><span style=\"color: #000000;\">\u4ec5\u4ec5\u56e0\u4e3a\u89c2\u5bdf\u6709\u5f71\u54cd\u5e76\u4e0d\u610f\u5473\u7740\u5b83\u5e94\u8be5\u4ece\u6570\u636e\u96c6\u4e2d\u5220\u9664\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u60a8\u9700\u8981\u9a8c\u8bc1\u89c2\u5bdf\u7ed3\u679c\u4e0d\u662f\u6570\u636e\u8f93\u5165\u9519\u8bef\u6216\u5176\u4ed6\u5947\u602a\u4e8b\u4ef6\u7684\u7ed3\u679c\u3002\u5982\u679c\u7ed3\u679c\u8bc1\u660e\u5b83\u662f\u5408\u6cd5\u503c\uff0c\u60a8\u53ef\u4ee5\u51b3\u5b9a\u662f\u5426\u9002\u5f53\u5c06\u5176\u5220\u9664\u3001\u4fdd\u7559\u539f\u6837\uff0c\u6216\u8005\u7b80\u5355\u5730\u5c06\u5176\u66ff\u6362\u4e3a\u4e2d\u4f4d\u6570\u7b49\u66ff\u4ee3\u503c\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5e93\u514b\u8ddd\u79bb\u7528\u4e8e\u8bc6\u522b\u56de\u5f52\u6a21\u578b\u4e2d\u6709\u5f71\u54cd\u7684\u89c2\u6d4b\u503c\u3002 \u5e93\u514b\u8ddd\u79bb\u7684\u516c\u5f0f\u4e3a\uff1a d i = (r i 2 \/ p*MSE) * [&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-1316","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u7528 Python \u8ba1\u7b97\u5e93\u514b\u8ddd\u79bb<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u4e00\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u5e93\u514b\u8ddd\u79bb\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/\u53a8\u623f\u8fdc\u7a0bpython\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta 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