{"id":3015,"date":"2023-07-19T15:30:27","date_gmt":"2023-07-19T15:30:27","guid":{"rendered":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e4%bc%98%e5%8c%96%e5%87%bd%e6%95%b0\/"},"modified":"2023-07-19T15:30:27","modified_gmt":"2023-07-19T15:30:27","slug":"r%e4%b8%ad%e7%9a%84%e4%bc%98%e5%8c%96%e5%87%bd%e6%95%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e4%bc%98%e5%8c%96%e5%87%bd%e6%95%b0\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u4f7f\u7528 optim \u51fd\u6570\uff082 \u4e2a\u793a\u4f8b\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u4f7f\u7528 R \u4e2d\u7684<strong>optim<\/strong>\u51fd\u6570\u8fdb\u884c\u4e00\u822c\u4f18\u5316\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>optim(by, fn, data, ...)\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>by<\/strong> : \u8981\u4f18\u5316\u7684\u53c2\u6570\u7684\u521d\u59cb\u503c<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>fn<\/strong> \uff1a\u6700\u5c0f\u5316\u6216\u6700\u5927\u5316\u7684\u51fd\u6570<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>data<\/strong> \uff1aR \u4e2d\u5305\u542b\u6570\u636e\u7684\u5bf9\u8c61\u7684\u540d\u79f0<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u4ee5\u4e0b\u573a\u666f\u4e2d\u4f7f\u7528\u8be5\u529f\u80fd\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1.<\/strong>\u6c42\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u7cfb\u6570\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2.<\/strong>\u6c42\u4e8c\u6b21\u56de\u5f52\u6a21\u578b\u7684\u7cfb\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8d70\u5427\uff01<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a\u67e5\u627e\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u7cfb\u6570<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u5982\u4f55\u4f7f\u7528<strong>optim()<\/strong>\u51fd\u6570\u901a\u8fc7\u6700\u5c0f\u5316\u6b8b\u5dee\u5e73\u65b9\u548c\u6765\u67e5\u627e\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u7cfb\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: #008080;\">#create data frame\n<\/span>df &lt;- data.frame(x=c(1, 3, 3, 5, 6, 7, 9, 12),\n                 y=c(4, 5, 8, 6, 9, 10, 13, 17))\n\n<span style=\"color: #008080;\">#define function to minimize residual sum of squares\n<\/span>min_residuals &lt;- <span style=\"color: #008000;\">function<\/span> (data, par) {\n                   <span style=\"color: #008000;\">with<\/span> (data, sum((par[1] + par[2] * x - y)^2))\n}\n\n<span style=\"color: #008080;\">#find coefficients of linear regression model\n<\/span>optim(par=c(0, 1), fn=min_residuals, data=df)\n\n$by\n[1] 2.318592 1.162012\n\n$value\n[1] 11.15084\n\n$counts\nfunction gradient \n      79 NA \n\n$convergence\n[1] 0\n\n$message\nNULL\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4f7f\u7528<strong>$par<\/strong>\u4e0b\u8fd4\u56de\u7684\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u7f16\u5199\u4ee5\u4e0b\u62df\u5408\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = 2.318 + 1.162x<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528 R \u4e2d\u5185\u7f6e\u7684<strong>lm()<\/strong>\u51fd\u6570\u8ba1\u7b97\u56de\u5f52\u7cfb\u6570\u6765\u9a8c\u8bc1\u8fd9\u662f\u6b63\u786e\u7684\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#find coefficients of linear regression model using lm() function\n<span style=\"color: #000000;\">lm(y ~ x, data=df)\n\nCall:\nlm(formula = y ~ x, data = df)\n\nCoefficients:\n(Intercept) x  \n      2,318 1,162\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8fd9\u4e9b\u7cfb\u6570\u503c\u4e0e\u6211\u4eec\u4f7f\u7528<strong>optim()<\/strong>\u51fd\u6570\u8ba1\u7b97\u7684\u503c\u76f8\u5bf9\u5e94\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b 2\uff1a\u67e5\u627e\u4e8c\u6b21\u56de\u5f52\u6a21\u578b\u7684\u7cfb\u6570<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u5982\u4f55\u4f7f\u7528<strong>optim()<\/strong>\u51fd\u6570\u901a\u8fc7\u6700\u5c0f\u5316\u6b8b\u5dee\u5e73\u65b9\u548c\u6765\u67e5\u627e<a href=\"https:\/\/statorials.org\/cn\/\u4e8c\u6b21\u56de\u5f52-r\/\" target=\"_blank\" rel=\"noopener\">\u4e8c\u6b21\u56de\u5f52\u6a21\u578b<\/a>\u7684\u7cfb\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: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create data frame<\/span>\ndf &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (x=c(6, 9, 12, 14, 30, 35, 40, 47, 51, 55, 60),\n                 y=c(14, 28, 50, 70, 89, 94, 90, 75, 59, 44, 27))<\/span>\n\n<span style=\"color: #000000;\"><span style=\"color: #008080;\">#define function to minimize residual sum of squares\n<\/span>min_residuals &lt;- <span style=\"color: #008000;\">function<\/span> (data, par) {\n                   <span style=\"color: #008000;\">with<\/span> (data, sum((par[1] + par[2]*x + par[3]*x^2 - y)^2))\n}\n\n<span style=\"color: #008080;\">#find coefficients of quadratic regression model\n<\/span>optim(par=c(0, 0, 0), fn=min_residuals, data=df)\n\n$by\n[1] -18.261320 6.744531 -0.101201\n\n$value\n[1] 309.3412\n\n$counts\nfunction gradient \n     218 NA \n\n$convergence\n[1] 0\n\n$message\nNULL<\/span><\/span><\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4f7f\u7528<strong>$par<\/strong>\u4e0b\u8fd4\u56de\u7684\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u7f16\u5199\u4ee5\u4e0b\u62df\u5408\u4e8c\u6b21\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = -18.261 + 6.744x \u2013 0.101x <sup>2<\/sup><\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 R \u4e2d\u7684\u5185\u7f6e<strong>lm()<\/strong>\u51fd\u6570\u9a8c\u8bc1\u8fd9\u662f\u6b63\u786e\u7684\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create data frame\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (x=c(6, 9, 12, 14, 30, 35, 40, 47, 51, 55, 60),\n                 y=c(14, 28, 50, 70, 89, 94, 90, 75, 59, 44, 27))\n\n<span style=\"color: #008080;\">#create a new variable for x^2\n<\/span>df$x2 &lt;- df$x^2\n\n<span style=\"color: #008080;\">#fit quadratic regression model\n<\/span>quadraticModel &lt;- lm(y ~ x + x2, data=df)\n\n<span style=\"color: #008080;\">#display coefficients of quadratic regression model\n<\/span>summary(quadraticModel)$coef\n\n               Estimate Std. Error t value Pr(&gt;|t|)\n(Intercept) -18.2536400 6.185069026 -2.951243 1.839072e-02\nx 6.7443581 0.485515334 13.891133 6.978849e-07\nx2 -0.1011996 0.007460089 -13.565470 8.378822e-07<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8fd9\u4e9b\u7cfb\u6570\u503c\u4e0e\u6211\u4eec\u4f7f\u7528<strong>optim()<\/strong>\u51fd\u6570\u8ba1\u7b97\u7684\u503c\u76f8\u5bf9\u5e94\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u64cd\u4f5c\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/r-\u4e2d\u7684\u7b80\u5355\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u591a\u5143\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u89e3\u91ca-r-\u4e2d\u7684\u56de\u5f52\u8f93\u51fa\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u89e3\u91ca R \u4e2d\u7684\u56de\u5f52\u8f93\u51fa<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u60a8\u53ef\u4ee5\u4f7f\u7528 R \u4e2d\u7684optim\u51fd\u6570\u8fdb\u884c\u4e00\u822c\u4f18\u5316\u3002 \u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\uff1a optim(by, fn, dat [&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-3015","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\/ 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