{"id":1642,"date":"2023-07-25T13:26:30","date_gmt":"2023-07-25T13:26:30","guid":{"rendered":"https:\/\/statorials.org\/cn\/r%e6%9b%b2%e7%ba%bf%e8%b0%83%e6%95%b4\/"},"modified":"2023-07-25T13:26:30","modified_gmt":"2023-07-25T13:26:30","slug":"r%e6%9b%b2%e7%ba%bf%e8%b0%83%e6%95%b4","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r%e6%9b%b2%e7%ba%bf%e8%b0%83%e6%95%b4\/","title":{"rendered":"R \u4e2d\u7684\u66f2\u7ebf\u62df\u5408\uff08\u5e26\u6709\u793a\u4f8b\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u901a\u5e38\uff0c\u60a8\u53ef\u80fd\u5e0c\u671b\u627e\u5230\u6700\u9002\u5408 R \u66f2\u7ebf\u7684\u65b9\u7a0b\u3002<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16195\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courberaccordr1.png\" alt=\"R\u4e2d\u7684\u66f2\u7ebf\u62df\u5408\" width=\"405\" height=\"368\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u8bf4\u660e\u4e86\u5982\u4f55\u4f7f\u7528<strong>poly()<\/strong>\u51fd\u6570\u5c06\u66f2\u7ebf\u62df\u5408\u5230 R \u4e2d\u7684\u6570\u636e\uff0c\u4ee5\u53ca\u5982\u4f55\u786e\u5b9a\u54ea\u6761\u66f2\u7ebf\u6700\u9002\u5408\u6570\u636e\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 1 \u6b65\uff1a\u521b\u5efa\u6570\u636e\u5e76\u53ef\u89c6\u5316<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u8ba9\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e00\u4e2a\u5047\u6570\u636e\u96c6\uff0c\u7136\u540e\u521b\u5efa\u4e00\u4e2a\u6563\u70b9\u56fe\u6765\u53ef\u89c6\u5316\u6570\u636e\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data frame\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (x=1:15,\n                 y=c(3, 14, 23, 25, 23, 15, 9, 5, 9, 13, 17, 24, 32, 36, 46))\n\n<span style=\"color: #008080;\">#create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch= <span style=\"color: #008000;\">19<\/span> , xlab=' <span style=\"color: #ff0000;\">x<\/span> ', ylab=' <span style=\"color: #ff0000;\">y<\/span> ')<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16197 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courbeajustementr0.png\" alt=\"\" width=\"411\" height=\"374\" srcset=\"\" sizes=\"auto, \"><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c2\u6b65\uff1a\u8c03\u6574\u591a\u6761\u66f2\u7ebf<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u7136\u540e\uff0c\u8ba9\u6211\u4eec\u5c06\u51e0\u4e2a\u591a\u9879\u5f0f\u56de\u5f52\u6a21\u578b\u62df\u5408\u5230\u6570\u636e\uff0c\u5e76\u5728\u540c\u4e00\u56fe\u4e2d\u53ef\u89c6\u5316\u6bcf\u4e2a\u6a21\u578b\u7684\u66f2\u7ebf\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;\">#fit polynomial regression models up to degree 5\n<\/span>fit1 &lt;- lm(y~x, data=df)\nfit2 &lt;- lm(y~poly(x,2,raw= <span style=\"color: #008000;\">TRUE<\/span> ), data=df)\nfit3 &lt;- lm(y~poly(x,3,raw= <span style=\"color: #008000;\">TRUE<\/span> ), data=df)\nfit4 &lt;- lm(y~poly(x,4,raw= <span style=\"color: #008000;\">TRUE<\/span> ), data=df)\nfit5 &lt;- lm(y~poly(x,5,raw= <span style=\"color: #008000;\">TRUE<\/span> ), data=df)<\/span>\n\n#create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch=19, xlab=' <span style=\"color: #ff0000;\">x<\/span> ', ylab=' <span style=\"color: #ff0000;\">y<\/span> ')\n\n<span style=\"color: #008080;\">#define x-axis values\n<\/span>x_axis &lt;- seq(1, 15, length= <span style=\"color: #008000;\">15<\/span> )\n\n<span style=\"color: #008080;\">#add curve of each model to plot\n<\/span>lines(x_axis, predict(fit1, data. <span style=\"color: #3366ff;\">frame<\/span> (x=x_axis)), col=' <span style=\"color: #ff0000;\">green<\/span> ')\nlines(x_axis, predict(fit2, data. <span style=\"color: #3366ff;\">frame<\/span> (x=x_axis)), col=' <span style=\"color: #ff0000;\">red<\/span> ')\nlines(x_axis, predict(fit3, data. <span style=\"color: #3366ff;\">frame<\/span> (x=x_axis)), col=' <span style=\"color: #ff0000;\">purple<\/span> ')\nlines(x_axis, predict(fit4, data. <span style=\"color: #3366ff;\">frame<\/span> (x=x_axis)), col=' <span style=\"color: #ff0000;\">blue<\/span> ')\nlines(x_axis, predict(fit5, data. <span style=\"color: #3366ff;\">frame<\/span> (x=x_axis)), col=' <span style=\"color: #ff0000;\">orange<\/span> ')\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16198 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courbeajustementr2.png\" alt=\"\" width=\"416\" height=\"375\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u786e\u5b9a\u54ea\u6761\u66f2\u7ebf\u6700\u9002\u5408\u6570\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u67e5\u770b\u6bcf\u4e2a\u6a21\u578b\u7684<a href=\"https:\/\/statorials.org\/cn\/r-\u62df\u5408\u4e2d\u7684-r-\u65b9\/\" target=\"_blank\" rel=\"noopener\">\u8c03\u6574\u540e\u7684 R \u65b9<\/a>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8be5\u503c\u544a\u8bc9\u6211\u4eec\u53ef\u4ee5\u7531\u6a21\u578b\u4e2d\u7684\u9884\u6d4b\u53d8\u91cf\u89e3\u91ca\u7684\u54cd\u5e94\u53d8\u91cf\u7684\u53d8\u5316\u767e\u5206\u6bd4\uff0c\u5e76\u6839\u636e\u9884\u6d4b\u53d8\u91cf\u7684\u6570\u91cf\u8fdb\u884c\u8c03\u6574\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculated adjusted R-squared of each model\n<\/span>summary(fit1)$adj. <span style=\"color: #3366ff;\">r<\/span> . <span style=\"color: #3366ff;\">squared<\/span>\nsummary(fit2)$adj. <span style=\"color: #3366ff;\">r<\/span> . <span style=\"color: #3366ff;\">squared<\/span>\nsummary(fit3)$adj. <span style=\"color: #3366ff;\">r<\/span> . <span style=\"color: #3366ff;\">squared<\/span>\nsummary(fit4)$adj. <span style=\"color: #3366ff;\">r<\/span> . <span style=\"color: #3366ff;\">squared<\/span>\nsummary(fit5)$adj. <span style=\"color: #3366ff;\">r<\/span> . <span style=\"color: #3366ff;\">squared<\/span>\n\n[1] 0.3144819\n[1] 0.5186706\n[1] 0.7842864\n[1] 0.9590276\n[1] 0.9549709\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u8c03\u6574 R \u5e73\u65b9\u6700\u9ad8\u7684\u6a21\u578b\u662f\u56db\u6b21\u591a\u9879\u5f0f\uff0c\u5176\u8c03\u6574 R \u5e73\u65b9\u4e3a<strong>0.959<\/strong> \u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 3 \u6b65\uff1a\u53ef\u89c6\u5316\u6700\u7ec8\u66f2\u7ebf<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u56db\u6b21\u591a\u9879\u5f0f\u6a21\u578b\u7684\u66f2\u7ebf\u521b\u5efa\u6563\u70b9\u56fe\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch=19, xlab=' <span style=\"color: #ff0000;\">x<\/span> ', ylab=' <span style=\"color: #ff0000;\">y<\/span> ')\n\n<span style=\"color: #008080;\">#define x-axis values\n<\/span>x_axis &lt;- seq(1, 15, length= <span style=\"color: #008000;\">15<\/span> )\n\n<span style=\"color: #008080;\">#add curve of fourth-degree polynomial model\n<\/span>lines(x_axis, predict(fit4, data. <span style=\"color: #3366ff;\">frame<\/span> (x=x_axis)), col=' <span style=\"color: #ff0000;\">blue<\/span> ')\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-16195\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courberaccordr1.png\" alt=\"R\u4e2d\u7684\u66f2\u7ebf\u62df\u5408\" width=\"405\" height=\"368\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528<strong>summary()<\/strong>\u51fd\u6570\u5f97\u5230\u8fd9\u6761\u7ebf\u7684\u65b9\u7a0b\uff1a<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>summary(fit4)\n\nCall:\nlm(formula = y ~ poly(x, 4, raw = TRUE), data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-3.4490 -1.1732 0.6023 1.4899 3.0351 \n\nCoefficients:\n                         Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) -26.51615 4.94555 -5.362 0.000318 ***\npoly(x, 4, raw = TRUE)1 35.82311 3.98204 8.996 4.15e-06 ***\npoly(x, 4, raw = TRUE)2 -8.36486 0.96791 -8.642 5.95e-06 ***\npoly(x, 4, raw = TRUE)3 0.70812 0.08954 7.908 1.30e-05 ***\npoly(x, 4, raw = TRUE)4 -0.01924 0.00278 -6.922 4.08e-05 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.424 on 10 degrees of freedom\nMultiple R-squared: 0.9707, Adjusted R-squared: 0.959 \nF-statistic: 82.92 on 4 and 10 DF, p-value: 1.257e-07\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u66f2\u7ebf\u65b9\u7a0b\u5982\u4e0b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = -0.0192x <sup>4<\/sup> + 0.7081x <sup>3<\/sup> \u2013 8.3649x <sup>2<\/sup> + 35.823x \u2013 26.516<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8be5\u65b9\u7a0b\u6839\u636e\u6a21\u578b\u4e2d\u7684\u9884\u6d4b\u53d8\u91cf\u6765\u9884\u6d4b<a href=\"https:\/\/statorials.org\/cn\/\u53d8\u91cf\u89e3\u91ca\u6027\u53cd\u5e94\/\" target=\"_blank\" rel=\"noopener\">\u54cd\u5e94\u53d8\u91cf<\/a>\u7684\u503c\u3002\u4f8b\u5982\uff0c\u5982\u679c<em>x<\/em> = 4 \u90a3\u4e48\u6211\u4eec\u5c06\u9884\u6d4b<em>y<\/em> = <strong>23.34<\/strong> \uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = -0.0192(4) <sup>4<\/sup> + 0.7081(4) <sup>3<\/sup> \u2013 8.3649(4) <sup>2<\/sup> + 35.823(4) \u2013 26.516 = 23.34<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u591a\u9879\u5f0f\u56de\u5f521\/\" target=\"_blank\" rel=\"noopener\">\u591a\u9879\u5f0f\u56de\u5f52\u7b80\u4ecb<br \/><\/a><a href=\"https:\/\/statorials.org\/cn\/\u591a\u9879\u5f0f\u56de\u5f52-r\/\" target=\"_blank\" rel=\"noopener\">R \u4e2d\u7684\u591a\u9879\u5f0f\u56de\u5f52\uff08\u9010\u6b65\uff09<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/r\u4e2d\u7684seq\u51fd\u6570\/\">\u5982\u4f55\u4f7f\u7528R\u4e2d\u7684seq\u51fd\u6570<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u901a\u5e38\uff0c\u60a8\u53ef\u80fd\u5e0c\u671b\u627e\u5230\u6700\u9002\u5408 R \u66f2\u7ebf\u7684\u65b9\u7a0b\u3002 \u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u8bf4\u660e\u4e86\u5982\u4f55\u4f7f\u7528poly()\u51fd\u6570\u5c06\u66f2\u7ebf\u62df\u5408\u5230 R \u4e2d\u7684 [&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-1642","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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