{"id":4619,"date":"2023-07-09T14:43:59","date_gmt":"2023-07-09T14:43:59","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e6%a0%87%e5%87%86%e5%8c%96%e5%9b%9e%e5%bd%92%e7%b3%bb%e6%95%b0\/"},"modified":"2023-07-09T14:43:59","modified_gmt":"2023-07-09T14:43:59","slug":"r-%e4%b8%ad%e7%9a%84%e6%a0%87%e5%87%86%e5%8c%96%e5%9b%9e%e5%bd%92%e7%b3%bb%e6%95%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e6%a0%87%e5%87%86%e5%8c%96%e5%9b%9e%e5%bd%92%e7%b3%bb%e6%95%b0\/","title":{"rendered":"\u5982\u4f55\u8ba1\u7b97 r \u4e2d\u7684\u6807\u51c6\u5316\u56de\u5f52\u7cfb\u6570"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u901a\u5e38\uff0c\u5f53\u6211\u4eec\u6267\u884c<a href=\"https:\/\/statorials.org\/cn\/\u591a\u5143\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/a>\u65f6\uff0c\u6a21\u578b\u8f93\u51fa\u4e2d\u5f97\u5230\u7684\u56de\u5f52\u7cfb\u6570<strong>\u4e0d\u662f\u6807\u51c6\u5316\u7684<\/strong>\uff0c\u8fd9\u610f\u5473\u7740\u5b83\u4eec\u4f7f\u7528\u539f\u59cb\u6570\u636e\u6765\u627e\u5230\u6700\u4f73\u62df\u5408\u7ebf\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>model &lt;- lm(price ~ age + sqfeet, data=df)\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4f46\u662f\uff0c\u53ef\u4ee5<strong>\u6807\u51c6\u5316<\/strong>\u6bcf\u4e2a\u9884\u6d4b\u53d8\u91cf\u548c\u54cd\u5e94\u53d8\u91cf\uff08\u901a\u8fc7\u4ece\u539f\u59cb\u503c\u4e2d\u51cf\u53bb\u6bcf\u4e2a\u53d8\u91cf\u7684\u5e73\u5747\u503c\uff0c\u7136\u540e\u9664\u4ee5\u53d8\u91cf\u7684\u6807\u51c6\u5dee\uff09\uff0c\u7136\u540e\u8fd0\u884c\u56de\u5f52\uff0c\u8fd9\u4f1a\u5bfc\u81f4<strong>\u6807\u51c6\u5316\u56de\u5f52\u7cfb\u6570<\/strong>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728 R \u4e2d\u8ba1\u7b97\u6807\u51c6\u5316\u56de\u5f52\u7cfb\u6570\u7684\u6700\u7b80\u5355\u65b9\u6cd5\u662f\u4f7f\u7528<a href=\"https:\/\/statorials.org\/cn\/r\u4e2d\u7684\u7f29\u653e\u51fd\u6570\/\" target=\"_blank\" rel=\"noopener\">scale()<\/a>\u51fd\u6570\u6765\u6807\u51c6\u5316\u6a21\u578b\u4e2d\u7684\u6bcf\u4e2a\u53d8\u91cf\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>model &lt;- lm(scale(price) ~ scale(age) + scale(sqfeet), data=df)<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4e0b\u9762\u7684\u4f8b\u5b50\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u8ba1\u7b97\u6807\u51c6\u5316\u56de\u5f52\u7cfb\u6570\u3002<\/span><\/p>\n<h2><strong>\u793a\u4f8b\uff1a\u5982\u4f55\u8ba1\u7b97 R \u4e2d\u7684\u6807\u51c6\u5316\u56de\u5f52\u7cfb\u6570<\/strong><\/h2>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u5305\u542b 12 \u680b\u623f\u5c4b\u7684\u623f\u9f84\u3001\u9762\u79ef\u548c\u9500\u552e\u4ef7\u683c\u4fe1\u606f\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> (age=c(4, 7, 10, 15, 16, 18, 24, 28, 30, 35, 40, 44),\n                 sqfeet=c(2600, 2800, 1700, 1300, 1500, 1800,\n                          1200, 2200, 1800, 1900, 2100, 1300),\n                 price=c(280000, 340000, 195000, 180000, 150000, 200000,\n                         180000, 240000, 200000, 180000, 260000, 140000))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\n   age square feet price\n1 4 2600 280000\n2 7 2800 340000\n3 10 1700 195000\n4 15 1300 180000\n5 16 1500 150000\n6 18 1800 200000\n7 24 1200 180000\n8 28 2200 240000\n9 30 1800 200000\n10 35 1900 180000\n11 40 2100 260000\n12 44 1300 140000\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u4f7f\u7528<strong>\u5e74\u9f84<\/strong>\u548c<strong>\u5e73\u65b9\u82f1\u5c3a<\/strong>\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0c<strong>\u4ef7\u683c<\/strong>\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\u6765\u6267\u884c\u591a\u5143\u7ebf\u6027\u56de\u5f52\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit regression model\n<\/span>model &lt;- lm(price ~ age + sqfeet, data=df)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = price ~ age + sqfeet, data = df)\n\nResiduals:\n   Min 1Q Median 3Q Max \n-32038 -10526 -6139 21641 34060 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 34736.54 37184.32 0.934 0.374599    \nage -409.83 612.46 -0.669 0.520187    \nsqfeet 100.87 15.75 6.405 0.000125 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 24690 on 9 degrees of freedom\nMultiple R-squared: 0.8508, Adjusted R-squared: 0.8176 \nF-statistic: 25.65 on 2 and 9 DF, p-value: 0.0001916\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u6a21\u578b\u7ed3\u679c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230<strong>\u975e\u6807\u51c6\u5316\u56de\u5f52\u7cfb\u6570<\/strong>\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u62e6\u622a\uff1a <strong>34736.54<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\">\u5e74\u9f84\uff1a <strong>-409.83<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\">\u5e73\u65b9\u82f1\u5c3a\uff1a <strong>100.87<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4e4d\u4e00\u770b\uff0c\u5e74\u9f84\u5bf9\u623f\u5730\u4ea7\u4ef7\u683c\u7684\u5f71\u54cd\u4f3c\u4e4e\u66f4\u5927\uff0c\u56e0\u4e3a\u56de\u5f52\u8868\u4e2d\u7684\u7cfb\u6570\u4e3a<strong>-409.833<\/strong> \uff0c\u800c\u5e73\u65b9\u82f1\u5c3a\u9884\u6d4b\u53d8\u91cf\u7684\u7cfb\u6570\u4ec5\u4e3a<strong>100.866<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7136\u800c\uff0c\u5e74\u9f84\u7684\u6807\u51c6\u8bef\u5dee\u6bd4\u5e73\u65b9\u82f1\u5c3a\u7684\u6807\u51c6\u8bef\u5dee\u5927\u5f97\u591a\uff0c\u8fd9\u5c31\u662f\u4e3a\u4ec0\u4e48\u5e74\u9f84\u7684\u76f8\u5e94 p \u503c\u5b9e\u9645\u4e0a\u8f83\u5927 (p = 0.520)\uff0c\u800c\u5e73\u65b9\u82f1\u5c3a\u7684\u6807\u51c6\u8bef\u5dee\u8f83\u5c0f (p = 0.000)\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u56de\u5f52\u7cfb\u6570\u5b58\u5728\u6781\u5927\u5dee\u5f02\u7684\u539f\u56e0\u662f\u4e24\u4e2a\u53d8\u91cf\u7684\u5c3a\u5ea6\u5b58\u5728\u6781\u5927\u5dee\u5f02\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u5e74\u9f84<\/strong>\u503c\u8303\u56f4\u4e3a 4 \u81f3 44 \u5c81\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u5e73\u65b9\u82f1\u5c3a<\/strong>\u503c\u8303\u56f4\u4ece 1,200 \u5230 2,800\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u5bf9\u539f\u59cb\u6570\u636e<strong>\u8fdb\u884c\u6807\u51c6\u5316<\/strong>\u5e76\u62df\u5408\u65b0\u7684\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#standardize each variable and fit regression model\n<\/span>model_std &lt;- lm(scale(price) ~ scale(age) + scale(sqfeet), data=df)\n\n<span style=\"color: #008080;\">#turn off scientific notation<\/span>\noptions(scipen= <span style=\"color: #008000;\">999<\/span> )\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model_std)\n\nCall:\nlm(formula = scale(price) ~ scale(age) + scale(sqfeet), data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-0.5541 -0.1820 -0.1062 0.3743 0.5891 \n\nCoefficients:\n                            Estimate Std. Error t value Pr(&gt;|t|)\n(Intercept) -0.0000000000000002253 0.1232881457926768426 0.000 1.000000\nscale(age) -0.0924421263946849786 0.1381464029075653854 -0.669 0.520187\nscale(sqfeet) 0.8848591938302141635 0.1381464029075653577 6.405 0.000125\n                 \n(Intercept)      \nscale(age)       \nscale(sqfeet)***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.4271 on 9 degrees of freedom\nMultiple R-squared: 0.8508, Adjusted R-squared: 0.8176 \nF-statistic: 25.65 on 2 and 9 DF, p-value: 0.0001916\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8be5\u8868\u4e2d\u7684\u56de\u5f52\u7cfb\u6570\u662f<strong>\u6807\u51c6\u5316\u7684<\/strong>\uff0c\u8fd9\u610f\u5473\u7740\u4ed6\u4eec\u4f7f\u7528\u6807\u51c6\u5316\u6570\u636e\u6765\u62df\u5408\u8be5\u56de\u5f52\u6a21\u578b\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8868\u4e2d\u7cfb\u6570\u7684\u89e3\u91ca\u65b9\u5f0f\u5982\u4e0b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5047\u8bbe\u9762\u79ef\u4fdd\u6301\u4e0d\u53d8\uff0c<strong>\u5e74\u9f84<\/strong>\u589e\u52a0 1 \u4e2a\u6807\u51c6\u5dee\u4e0e\u623f\u4ef7\u4e0b\u964d<strong>0.092 \u4e2a<\/strong>\u6807\u51c6\u5dee\u76f8\u5173\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5047\u8bbe\u5e74\u9f84\u4fdd\u6301\u4e0d\u53d8<strong>\uff0c\u5e73\u65b9\u82f1\u5c3a\u6bcf<\/strong>\u589e\u52a0 1 \u4e2a\u6807\u51c6\u5dee\uff0c\u623f\u4ef7\u5c31\u4f1a\u589e\u52a0<strong>0.885 \u4e2a<\/strong>\u6807\u51c6\u5dee\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u73b0\u5728\u53ef\u4ee5\u770b\u5230\uff0c\u9762\u79ef\u5bf9\u623f\u4ef7\u7684\u5f71\u54cd\u6bd4\u5e74\u9f84\u66f4\u5927\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1a\u6bcf\u4e2a\u9884\u6d4b\u53d8\u91cf\u7684 p \u503c\u4e0e\u4e4b\u524d\u7684\u56de\u5f52\u6a21\u578b\u4e2d\u7684 p \u503c\u5b8c\u5168\u76f8\u540c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728\u51b3\u5b9a\u4f7f\u7528\u54ea\u79cd\u6700\u7ec8\u6a21\u578b\u65f6\uff0c\u6211\u4eec\u73b0\u5728\u77e5\u9053\uff0c\u5728\u9884\u6d4b\u623f\u5c4b\u4ef7\u683c\u65b9\u9762\uff0c<strong>\u5e73\u65b9\u82f1\u5c3a<\/strong>\u6bd4\u623f\u5c4b<strong>\u5e74\u9f84<\/strong>\u66f4\u91cd\u8981\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\u63d0\u4f9b\u6709\u5173\u56de\u5f52\u6a21\u578b\u7684\u5176\u4ed6\u4fe1\u606f\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/cn\/\u9605\u8bfb\u56de\u5f52\u89e3\u91ca\u8868\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u5982\u4f55\u9605\u8bfb\u548c\u89e3\u91ca\u56de\u5f52\u8868<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u5982\u4f55\u89e3\u91ca\u56de\u5f52\u7cfb\u6570\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u5982\u4f55\u89e3\u91ca\u56de\u5f52\u7cfb\u6570<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u7ebf\u6027\u56de\u5f52-p-\u503c\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u89e3\u91ca\u7ebf\u6027\u56de\u5f52\u4e2d\u7684P\u503c<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u901a\u5e38\uff0c\u5f53\u6211\u4eec\u6267\u884c\u591a\u5143\u7ebf\u6027\u56de\u5f52\u65f6\uff0c\u6a21\u578b\u8f93\u51fa\u4e2d\u5f97\u5230\u7684\u56de\u5f52\u7cfb\u6570\u4e0d\u662f\u6807\u51c6\u5316\u7684\uff0c\u8fd9\u610f\u5473\u7740\u5b83\u4eec\u4f7f\u7528\u539f\u59cb\u6570\u636e\u6765\u627e\u5230\u6700\u4f73\u62df\u5408\u7ebf [&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-4619","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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