{"id":2482,"date":"2023-07-22T02:13:43","date_gmt":"2023-07-22T02:13:43","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e5%88%86%e6%ae%b5%e5%9b%9e%e5%bd%92\/"},"modified":"2023-07-22T02:13:43","modified_gmt":"2023-07-22T02:13:43","slug":"r-%e4%b8%ad%e7%9a%84%e5%88%86%e6%ae%b5%e5%9b%9e%e5%bd%92","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e5%88%86%e6%ae%b5%e5%9b%9e%e5%bd%92\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u6267\u884c\u5206\u6bb5\u56de\u5f52\uff08\u9010\u6b65\uff09"},"content":{"rendered":"

<\/p>\n

\n

\u5206\u6bb5\u56de\u5f52<\/strong>\u662f\u5f53\u6570\u636e\u96c6\u4e2d\u5b58\u5728\u660e\u786e\u7684\u201c\u65ad\u70b9\u201d\u65f6\u6211\u4eec\u7ecf\u5e38\u4f7f\u7528\u7684\u56de\u5f52\u65b9\u6cd5\u3002<\/span><\/p>\n

\u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u5206\u6bb5\u56de\u5f52\u3002<\/span><\/p>\n

\u7b2c 1 \u6b65\uff1a\u521b\u5efa\u6570\u636e<\/strong><\/span><\/h3>\n

\u9996\u5148\uff0c\u6211\u4eec\u521b\u5efa\u4ee5\u4e0b\u6570\u636e\u6846\uff1a<\/span><\/p>\n

 #view DataFrame\n<\/span>df <- data. frame<\/span> (x=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16),\n                 y=c(2, 4, 5, 6, 8, 10, 12, 13, 15, 19, 24, 28, 31, 34, 39, 44))\n\n#view first six rows of data frame<\/span>\nhead(df)\n\n  xy\n1 1 2\n2 2 4\n3 3 5\n4 4 6\n5 5 8\n6 6 10\n<\/strong><\/pre>\n
\u7b2c 2 \u6b65\uff1a\u53ef\u89c6\u5316\u6570\u636e<\/strong><\/span><\/h3>\n
\u63a5\u4e0b\u6765\uff0c\u8ba9\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u6563\u70b9\u56fe\u6765\u53ef\u89c6\u5316\u6570\u636e\uff1a<\/span> <\/p>\n
 #create scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch= 16<\/span> , col=' steelblue<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u770b\u5230 x \u548c y \u4e4b\u95f4\u7684\u5173\u7cfb\u4f3c\u4e4e\u5728x = 9<\/strong>\u9644\u8fd1\u7a81\u7136\u53d1\u751f\u53d8\u5316\u3002<\/span><\/p>\n
\u6b65\u9aa4 3\uff1a\u62df\u5408\u5206\u6bb5\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h3>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528R\u4e2dsegmented<\/a>\u5305\u4e2d\u7684segmented()<\/strong>\u51fd\u6570\u6765\u5c06\u5206\u6bb5\u56de\u5f52\u6a21\u578b\u62df\u5408\u5230\u6211\u4eec\u7684\u6570\u636e\u96c6\uff1a<\/span><\/p>\n
 library<\/span> (segmented)\n\n#fit simple linear regression model<\/span>\nfit <- lm(y ~ x, data=df)\n\n#fit piecewise regression model to original model, estimating a breakpoint at x=9\n<\/span>segmented. fit<\/span> <- segmented(fit, seg.Z = ~x, psi= 9<\/span> )\n\n#view summary of segmented model\n<\/span>summary( segmented.fit<\/span> )\n\nCall: \nsegmented.lm(obj = fit, seg.Z = ~x, psi = 9)\n\nEstimated Break-Point(s):\n         East. St.Err\npsi1.x 8.762 0.26\n\nMeaningful coefficients of the linear terms:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 0.32143 0.48343 0.665 0.519    \nx 1.59524 0.09573 16.663 1.16e-09 ***\nU1.x 2.40476 0.13539 17.762 NA    \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.6204 on 12 degrees of freedom\nMultiple R-Squared: 0.9983, Adjusted R-squared: 0.9978 \n\nConvergence achieved in 2 iter. (rel. changes 0)\n<\/strong><\/pre>\n
 segmented()<\/strong>\u51fd\u6570\u5728 x = 8.762 \u5904\u68c0\u6d4b\u5230\u65ad\u70b9\u3002<\/span><\/p>\n
\u62df\u5408\u7684\u5206\u6bb5\u56de\u5f52\u6a21\u578b\u4e3a\uff1a<\/span><\/p>\n
\u5982\u679c x \u2264 8.762\uff1ay = 0.32143 + 1.59524*(x)<\/span><\/p>\n
\u5982\u679c x > 8.762\uff1a y = 0.32143 + 1.59524*(8.762) + (1.59524+2.40476)*(x-8.762)<\/span><\/p>\n
\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u7684\u503c\u4e3ax = 5<\/strong> \u3002\u4f30\u8ba1\u503c\u4e3a\uff1a<\/span><\/p>\n