{"id":3152,"date":"2023-07-18T23:26:33","date_gmt":"2023-07-18T23:26:33","guid":{"rendered":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e9%bb%84%e5%9c%9f%e5%9b%9e%e5%bd%92\/"},"modified":"2023-07-18T23:26:33","modified_gmt":"2023-07-18T23:26:33","slug":"r%e4%b8%ad%e7%9a%84%e9%bb%84%e5%9c%9f%e5%9b%9e%e5%bd%92","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e9%bb%84%e5%9c%9f%e5%9b%9e%e5%bd%92\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u6267\u884c loess \u56de\u5f52\uff08\u9644\u793a\u4f8b\uff09"},"content":{"rendered":"

<\/p>\n

\n

LOESS \u56de\u5f52<\/strong>\uff0c\u6709\u65f6\u79f0\u4e3a\u5c40\u90e8\u56de\u5f52\uff0c\u662f\u4e00\u79cd\u4f7f\u7528\u5c40\u90e8\u8c03\u6574\u5c06\u56de\u5f52\u6a21\u578b\u62df\u5408\u5230\u4e00\u7ec4\u6570\u636e\u7684\u65b9\u6cd5\u3002<\/span><\/p>\n

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

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

\u9996\u5148\uff0c\u6211\u4eec\u5728 R \u4e2d\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),\n                 y=c(1, 4, 7, 13, 19, 24, 20, 15, 13, 11, 15, 18, 22, 27))\n\n#view first six rows of data frame<\/span>\nhead(df)\n\n  xy\n1 1 1\n2 2 4\n3 3 7\n4 4 13\n5 5 19\n6 6 24\n<\/strong><\/pre>\n
\u6b65\u9aa4 2\uff1a\u62df\u5408\u591a\u4e2a LOESS \u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h3>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528loess()<\/strong>\u51fd\u6570\u5c06\u591a\u4e2a LOESS \u56de\u5f52\u6a21\u578b\u62df\u5408\u5230\u8be5\u6570\u636e\u96c6\uff0c\u5e76\u4f7f\u7528\u4e0d\u540c\u7684\u8de8\u5ea6<\/strong>\u53c2\u6570\u503c\uff1a<\/span> <\/p>\n
 #fit several LOESS regression models to dataset\n<\/span>loess50 <- loess(y ~ x, data=df, span= .5<\/span> )\nsmooth50 <- predict(loess50) \n\nloess75 <- loess(y ~ x, data=df, span= .75<\/span> )\nsmooth75 <- predict(loess75) \n\nloess90 <- loess(y ~ x, data=df, span= .9<\/span> )\nsmooth90 <- predict(loess90) \n\n#create scatterplot with each regression line overlaid\n<\/span>plot(df$x, df$y, pch= 19<\/span> , main=' Loess Regression Models<\/span> ')\nlines(smooth50, x=df$x, col=' red<\/span> ')\nlines(smooth75, x=df$x, col=' purple<\/span> ')\nlines(smooth90, x=df$x, col=' blue<\/span> ')\nlegend(' bottomright<\/span> ', legend=c(' .5<\/span> ', ' .75<\/span> ', ' .9<\/span> '),\n        col=c(' red<\/span> ', ' purple<\/span> ', ' blue<\/span> '), pch= 19<\/span> , title=' Smoothing Span<\/span> ')\n<\/strong><\/span><\/pre>\n
\"R<\/p>\n
\u8bf7\u6ce8\u610f\uff0c\u6211\u4eec\u4f7f\u7528\u7684\u8de8\u5ea6<\/strong>\u503c\u8d8a\u4f4e\uff0c\u56de\u5f52\u6a21\u578b\u5c31\u8d8a\u4e0d\u201c\u5e73\u6ed1\u201d\uff0c\u6a21\u578b\u5c31\u8d8a\u4f1a\u5c1d\u8bd5\u62df\u5408\u6570\u636e\u70b9\u3002<\/span><\/p>\n
\u7b2c 3 \u6b65\uff1a\u4f7f\u7528 K \u6298\u4ea4\u53c9\u9a8c\u8bc1\u627e\u5230\u6700\u4f73\u6a21\u578b<\/strong><\/span><\/h3>\n
\u4e3a\u4e86\u627e\u5230\u8981\u4f7f\u7528\u7684\u6700\u4f73\u8303\u56f4<\/strong>\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528caret<\/strong>\u5305\u4e2d\u7684\u51fd\u6570\u6267\u884ck \u500d\u4ea4\u53c9\u9a8c\u8bc1<\/a>\uff1a<\/span><\/p>\n
 library<\/span> (caret)\n\n#define k-fold cross validation method\n<\/span>ctrl <- trainControl(method = \" cv<\/span> \", number = 5<\/span> )\ngrid <- expand. grid<\/span> (span = seq( 0.5<\/span> , 0.9<\/span> , len = 5<\/span> ), degree = 1<\/span> )\n\n#perform cross-validation using smoothing spans ranging from 0.5 to 0.9\n<\/span>model <- train(y ~ x, data = df, method = \" gamLoess<\/span> \", tuneGrid=grid, trControl = ctrl)\n\n#print results of k-fold cross-validation\n<\/span>print<\/span> (model)\n\n14 samples\n 1 predictor\n\nNo pre-processing\nResampling: Cross-Validated (5 fold) \nSummary of sample sizes: 12, 11, 11, 11, 11 \nResampling results across tuning parameters:\n\n  span RMSE Rsquared MAE      \n  0.5 10.148315 0.9570137 6.467066\n  0.6 7.854113 0.9350278 5.343473\n  0.7 6.113610 0.8150066 4.769545\n  0.8 17.814105 0.8202561 11.875943\n  0.9 26.705626 0.7384931 17.304833\n\nTuning parameter 'degree' was held constant at a value of 1\nRMSE was used to select the optimal model using the smallest value.\nThe final values used for the model were span = 0.7 and degree = 1.<\/strong><\/span><\/pre>\n
\u56e0\u6b64\uff0c\u5bf9\u4e8e\u6700\u7ec8\u7684 LOESS \u56de\u5f52\u6a21\u578b\uff0c\u6211\u4eec\u4f1a\u9009\u62e9\u5728loess()<\/strong>\u51fd\u6570\u4e2d\u4f7f\u7528\u503c0.7<\/strong>\u4f5c\u4e3a\u8de8\u5ea6<\/strong>\u53c2\u6570\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u6559\u7a0b\u63d0\u4f9b\u6709\u5173 R \u56de\u5f52\u6a21\u578b\u7684\u5176\u4ed6\u4fe1\u606f\uff1a<\/span><\/p>\n
\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/a>
\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/a>
\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u903b\u8f91\u56de\u5f52
\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u5206\u4f4d\u6570\u56de\u5f52<\/a>
 \u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u52a0\u6743\u56de\u5f52<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
LOESS \u56de\u5f52\uff0c\u6709\u65f6\u79f0\u4e3a\u5c40\u90e8\u56de\u5f52\uff0c\u662f\u4e00\u79cd\u4f7f\u7528\u5c40\u90e8\u8c03\u6574\u5c06\u56de\u5f52\u6a21\u578b\u62df\u5408\u5230\u4e00\u7ec4\u6570\u636e\u7684\u65b9\u6cd5\u3002 \u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55 […]<\/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-3152","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u5728 R \u4e2d\u6267\u884c LOESS \u56de\u5f52\uff08\u9644\u793a\u4f8b\uff09-Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c loess \u56de\u5f52\uff0c\u5305\u62ec\u4e00\u4e2a\u5b8c\u6574\u7684\u793a\u4f8b\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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