{"id":3150,"date":"2023-07-18T23:26:33","date_gmt":"2023-07-18T23:26:33","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d9%84%d9%88%d8%b3-%d9%81%d9%8a-%d8%b5\/"},"modified":"2023-07-18T23:26:33","modified_gmt":"2023-07-18T23:26:33","slug":"%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d9%84%d9%88%d8%b3-%d9%81%d9%8a-%d8%b5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d9%84%d9%88%d8%b3-%d9%81%d9%8a-%d8%b5\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0646\u062d\u062f\u0627\u0631 loess \u0641\u064a r (\u0645\u0639 \u0645\u062b\u0627\u0644)"},"content":{"rendered":"

<\/p>\n

\n

\u0627\u0646\u062d\u062f\u0627\u0631 LOESS<\/strong> \u060c \u0627\u0644\u0630\u064a \u064a\u064f\u0637\u0644\u0642 \u0639\u0644\u064a\u0647 \u0623\u062d\u064a\u0627\u0646\u064b\u0627 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u062d\u0644\u064a\u060c \u0647\u0648 \u0623\u0633\u0644\u0648\u0628 \u064a\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u062a\u0639\u062f\u064a\u0644 \u0627\u0644\u0645\u062d\u0644\u064a \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u0639 \u0645\u062c\u0645\u0648\u0639\u0629 \u0645\u0646 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a.<\/span><\/p>\n

\u064a\u0648\u0636\u062d \u0627\u0644\u0645\u062b\u0627\u0644 \u0627\u0644\u062a\u0627\u0644\u064a \u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0646\u062d\u062f\u0627\u0631 LOESS \u0641\u064a R.<\/span><\/p>\n

\u0627\u0644\u062e\u0637\u0648\u0629 1: \u0625\u0646\u0634\u0627\u0621 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a<\/strong><\/span><\/h3>\n

\u0623\u0648\u0644\u0627\u064b\u060c \u0644\u0646\u0642\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 \u0625\u0637\u0627\u0631 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a \u0641\u064a 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
 \u0627\u0644\u062e\u0637\u0648\u0629 2: \u062a\u0646\u0627\u0633\u0628 \u0646\u0645\u0627\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 LOESS \u0627\u0644\u0645\u062a\u0639\u062f\u062f\u0629<\/strong><\/span><\/h3>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 loess()<\/strong> \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0627\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 LOESS \u0627\u0644\u0645\u062a\u0639\u062f\u062f\u0629 \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0647\u0630\u0647\u060c \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0642\u064a\u0645 \u0645\u062e\u062a\u0644\u0641\u0629 \u0644\u0645\u0639\u0644\u0645\u0629 \u0627\u0644\u0627\u0645\u062a\u062f\u0627\u062f<\/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
\"\u0627\u0646\u062d\u062f\u0627\u0631<\/p>\n
 \u0644\u0627\u062d\u0638 \u0623\u0646\u0647 \u0643\u0644\u0645\u0627 \u0627\u0646\u062e\u0641\u0636\u062a \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u062a\u064a \u0646\u0633\u062a\u062e\u062f\u0645\u0647\u0627 \u0644\u0644\u0646\u0637\u0627\u0642<\/strong> \u060c \u0643\u0644\u0645\u0627 \u0643\u0627\u0646 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0623\u0642\u0644 “\u0633\u0644\u0627\u0633\u0629” \u0648\u0643\u0644\u0645\u0627 \u062d\u0627\u0648\u0644 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0642\u0627\u0637 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a.<\/span><\/p>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 3: \u0627\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0642\u0627\u0637\u0639 \u0645\u0646 K-Fold \u0644\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0623\u0641\u0636\u0644 \u0646\u0645\u0648\u0630\u062c<\/strong><\/span><\/h3>\n
 \u0644\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0642\u064a\u0645\u0629 \u0627\u0644\u0645\u062f\u0649<\/strong> \u0627\u0644\u0623\u0645\u062b\u0644 \u0644\u0644\u0627\u0633\u062a\u062e\u062f\u0627\u0645\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0635\u062d\u0629 k-fold<\/a> \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0648\u0638\u0627\u0626\u0641 \u0627\u0644\u0645\u0648\u062c\u0648\u062f\u0629 \u0641\u064a \u062d\u0632\u0645\u0629 \u0639\u0644\u0627\u0645\u0629 \u0627\u0644\u0625\u0642\u062d\u0627\u0645<\/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
 \u0644\u0630\u0627\u060c \u0628\u0627\u0644\u0646\u0633\u0628\u0629 \u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 LOESS \u0627\u0644\u0646\u0647\u0627\u0626\u064a \u0627\u0644\u062e\u0627\u0635 \u0628\u0646\u0627\u060c \u0633\u0646\u062e\u062a\u0627\u0631 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0642\u064a\u0645\u0629 0.7<\/strong> \u0644\u0644\u0648\u0633\u064a\u0637\u0629 span<\/strong> \u0641\u064a \u062f\u0627\u0644\u0629 loess()<\/strong> .<\/span><\/p>\n
 \u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h3>\n
 \u062a\u0648\u0641\u0631 \u0627\u0644\u0628\u0631\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0625\u0636\u0627\u0641\u064a\u0629 \u062d\u0648\u0644 \u0646\u0645\u0627\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0641\u064a R:<\/span><\/p>\n
 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0628\u0633\u064a\u0637 \u0641\u064a R<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0645\u062a\u0639\u062f\u062f \u0641\u064a R<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0641\u064a R
 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0643\u0645\u064a \u0641\u064a R<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0631\u062c\u062d \u0641\u064a R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0627\u0646\u062d\u062f\u0627\u0631 LOESS \u060c \u0627\u0644\u0630\u064a \u064a\u064f\u0637\u0644\u0642 \u0639\u0644\u064a\u0647 \u0623\u062d\u064a\u0627\u0646\u064b\u0627 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u062d\u0644\u064a\u060c \u0647\u0648 \u0623\u0633\u0644\u0648\u0628 \u064a\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u062a\u0639\u062f\u064a\u0644 \u0627\u0644\u0645\u062d\u0644\u064a \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u0639 \u0645\u062c\u0645\u0648\u0639\u0629 \u0645\u0646 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a. \u064a\u0648\u0636\u062d \u0627\u0644\u0645\u062b\u0627\u0644 \u0627\u0644\u062a\u0627\u0644\u064a \u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0646\u062d\u062f\u0627\u0631 LOESS \u0641\u064a R. \u0627\u0644\u062e\u0637\u0648\u0629 1: \u0625\u0646\u0634\u0627\u0621 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0623\u0648\u0644\u0627\u064b\u060c \u0644\u0646\u0642\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 \u0625\u0637\u0627\u0631 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a \u0641\u064a R: #view DataFrame df <- data. frame (x=c(1, 2, 3, 4, 5, […]<\/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":[],"yoast_head":"\n\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0646\u062d\u062f\u0627\u0631 LOESS \u0641\u064a R (\u0645\u0639 \u0645\u062b\u0627\u0644) - 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