{"id":3149,"date":"2023-07-18T23:26:33","date_gmt":"2023-07-18T23:26:33","guid":{"rendered":"https:\/\/statorials.org\/ru\/%d0%bb%d0%b5%d1%81%d1%81%d0%be%d0%b2%d0%b0%d1%8f-%d1%80%d0%b5%d0%b3%d1%80%d0%b5%d1%81%d1%81%d0%b8%d1%8f-%d0%b2-r\/"},"modified":"2023-07-18T23:26:33","modified_gmt":"2023-07-18T23:26:33","slug":"%d0%bb%d0%b5%d1%81%d1%81%d0%be%d0%b2%d0%b0%d1%8f-%d1%80%d0%b5%d0%b3%d1%80%d0%b5%d1%81%d1%81%d0%b8%d1%8f-%d0%b2-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/%d0%bb%d0%b5%d1%81%d1%81%d0%be%d0%b2%d0%b0%d1%8f-%d1%80%d0%b5%d0%b3%d1%80%d0%b5%d1%81%d1%81%d0%b8%d1%8f-%d0%b2-r\/","title":{"rendered":"\u041a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c loess-\u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u0432 r (\u0441 \u043f\u0440\u0438\u043c\u0435\u0440\u043e\u043c)"},"content":{"rendered":"

<\/p>\n

\n

LOESS-\u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044f<\/strong> , \u0438\u043d\u043e\u0433\u0434\u0430 \u043d\u0430\u0437\u044b\u0432\u0430\u0435\u043c\u0430\u044f \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u043e\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0435\u0439, \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0441\u043e\u0431\u043e\u0439 \u043c\u0435\u0442\u043e\u0434, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u0443\u044e \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u043a\u0443 \u0434\u043b\u044f \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u043d\u044b\u0445.<\/span><\/p>\n

\u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c \u043f\u043e\u0448\u0430\u0433\u043e\u0432\u043e\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u043a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e LOESS \u0432 R.<\/span><\/p>\n

\u0428\u0430\u0433 1. \u0421\u043e\u0437\u0434\u0430\u0439\u0442\u0435 \u0434\u0430\u043d\u043d\u044b\u0435<\/strong><\/span><\/h3>\n

\u0421\u043d\u0430\u0447\u0430\u043b\u0430 \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u0441\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 R:<\/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
 \u0428\u0430\u0433 2. \u041f\u043e\u0434\u0431\u0435\u0440\u0438\u0442\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 LOESS.<\/strong><\/span><\/h3>\n
 \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e loess()<\/strong> , \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u0434\u043e\u0433\u043d\u0430\u0442\u044c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 LOESS \u043a \u044d\u0442\u043e\u043c\u0443 \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u043d\u044b\u0445, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u0440\u0430\u0437\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430 \u0438\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u0430<\/strong> :<\/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
\"\u041b\u0451\u0441\u0441\u043e\u0432\u0430\u044f<\/p>\n
 \u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435: \u0447\u0435\u043c \u043c\u0435\u043d\u044c\u0448\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u043c\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c \u0434\u043b\u044f \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0430<\/strong> , \u0442\u0435\u043c \u043c\u0435\u043d\u0435\u0435 \u00ab\u0433\u043b\u0430\u0434\u043a\u043e\u0439\u00bb \u0431\u0443\u0434\u0435\u0442 \u043c\u043e\u0434\u0435\u043b\u044c \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0438 \u0442\u0435\u043c \u0431\u043e\u043b\u044c\u0448\u0435 \u043e\u043d\u0430 \u0431\u0443\u0434\u0435\u0442 \u043f\u044b\u0442\u0430\u0442\u044c\u0441\u044f \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u043e\u0432\u0430\u0442\u044c \u0442\u043e\u0447\u043a\u0430\u043c \u0434\u0430\u043d\u043d\u044b\u0445.<\/span><\/p>\n
 \u0428\u0430\u0433 3. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u043f\u0435\u0440\u0435\u043a\u0440\u0435\u0441\u0442\u043d\u0443\u044e \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0443 K-Fold, \u0447\u0442\u043e\u0431\u044b \u043d\u0430\u0439\u0442\u0438 \u043b\u0443\u0447\u0448\u0443\u044e \u043c\u043e\u0434\u0435\u043b\u044c<\/strong><\/span><\/h3>\n
 \u0427\u0442\u043e\u0431\u044b \u043d\u0430\u0439\u0442\u0438 \u043e\u043f\u0442\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u044d\u043a\u0441\u0442\u0435\u043d\u0442\u0430<\/strong> , \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043f\u0435\u0440\u0435\u043a\u0440\u0435\u0441\u0442\u043d\u0443\u044e \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0443 \u0432 k-\u043a\u0440\u0430\u0442\u043d\u043e\u043c \u0440\u0430\u0437\u043c\u0435\u0440\u0435<\/a> , \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u0438 \u0438\u0437 \u043f\u0430\u043a\u0435\u0442\u0430 Caret<\/strong> :<\/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
 \u0418\u0442\u0430\u043a, \u0434\u043b\u044f \u043d\u0430\u0448\u0435\u0439 \u043e\u043a\u043e\u043d\u0447\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 LOESS \u043c\u044b \u0431\u044b \u0432\u044b\u0431\u0440\u0430\u043b\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 0,7<\/strong> \u0434\u043b\u044f \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0430 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0430<\/strong> \u0432 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 loess()<\/strong> .<\/span><\/p>\n
 \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0435\u0441\u0443\u0440\u0441\u044b<\/strong><\/span><\/h3>\n
 \u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0430 \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u044e\u0442 \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u043c\u043e\u0434\u0435\u043b\u044f\u0445 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0432 R:<\/span><\/p>\n
 \u041a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u0441\u0442\u0443\u044e \u043b\u0438\u043d\u0435\u0439\u043d\u0443\u044e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u0432 R<\/a>
 \u041a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u0443\u044e \u043b\u0438\u043d\u0435\u0439\u043d\u0443\u044e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u0432 R<\/a>
 \u041a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043b\u043e\u0433\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0443\u044e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u0432 R
 \u041a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043a\u0432\u0430\u043d\u0442\u0438\u043b\u044c\u043d\u0443\u044e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u0432 R<\/a>
 \u041a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u0432\u0437\u0432\u0435\u0448\u0435\u043d\u043d\u0443\u044e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u0432 R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
LOESS-\u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044f , \u0438\u043d\u043e\u0433\u0434\u0430 \u043d\u0430\u0437\u044b\u0432\u0430\u0435\u043c\u0430\u044f \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u043e\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0435\u0439, \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0441\u043e\u0431\u043e\u0439 \u043c\u0435\u0442\u043e\u0434, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u0443\u044e \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u043a\u0443 \u0434\u043b\u044f \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u043d\u044b\u0445. \u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c \u043f\u043e\u0448\u0430\u0433\u043e\u0432\u043e\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u043a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e LOESS \u0432 R. \u0428\u0430\u0433 1. \u0421\u043e\u0437\u0434\u0430\u0439\u0442\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0421\u043d\u0430\u0447\u0430\u043b\u0430 \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u0441\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 R: #view DataFrame df <- data. frame (x=c(1, 2, 3, 4, 5, 6, 7, […]<\/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-3149","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u041a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c LOESS-\u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u0432 R (\u0441 \u043f\u0440\u0438\u043c\u0435\u0440\u043e\u043c) - Statorials<\/title>\n<meta name=\"description\" content=\"\u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043b\u0435\u0441\u0441\u043e\u0432\u0443\u044e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u0432 R, \u0432\u043a\u043b\u044e\u0447\u0430\u044f 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 \u041c\u044b \u0432\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0430<\/strong> , \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0434\u0430\u043b\u043e \u043d\u0430\u0438\u043c\u0435\u043d\u044c\u0448\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u043d\u043e\u0439 \u043e\u0448\u0438\u0431\u043a\u0438<\/a> (RMSE), \u0440\u0430\u0432\u043d\u043e 0,7<\/strong> .<\/span><\/p>\n