{"id":3151,"date":"2023-07-18T23:26:33","date_gmt":"2023-07-18T23:26:33","guid":{"rendered":"https:\/\/statorials.org\/hi\/%e0%a4%86%e0%a4%b0-%e0%a4%ae%e0%a5%87%e0%a4%82-%e0%a4%b2%e0%a5%8b%e0%a4%8f%e0%a4%b8-%e0%a4%aa%e0%a5%8d%e0%a4%b0%e0%a4%a4%e0%a4%bf%e0%a4%97%e0%a4%ae%e0%a4%a8\/"},"modified":"2023-07-18T23:26:33","modified_gmt":"2023-07-18T23:26:33","slug":"%e0%a4%86%e0%a4%b0-%e0%a4%ae%e0%a5%87%e0%a4%82-%e0%a4%b2%e0%a5%8b%e0%a4%8f%e0%a4%b8-%e0%a4%aa%e0%a5%8d%e0%a4%b0%e0%a4%a4%e0%a4%bf%e0%a4%97%e0%a4%ae%e0%a4%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/hi\/%e0%a4%86%e0%a4%b0-%e0%a4%ae%e0%a5%87%e0%a4%82-%e0%a4%b2%e0%a5%8b%e0%a4%8f%e0%a4%b8-%e0%a4%aa%e0%a5%8d%e0%a4%b0%e0%a4%a4%e0%a4%bf%e0%a4%97%e0%a4%ae%e0%a4%a8\/","title":{"rendered":"R \u092e\u0947\u0902 loess \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902 (\u0909\u0926\u093e\u0939\u0930\u0923 \u0915\u0947 \u0938\u093e\u0925)"},"content":{"rendered":"

<\/p>\n

\n

LOESS \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928<\/strong> , \u091c\u093f\u0938\u0947 \u0915\u092d\u0940-\u0915\u092d\u0940 \u0938\u094d\u0925\u093e\u0928\u0940\u092f \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u092d\u0940 \u0915\u0939\u093e \u091c\u093e\u0924\u093e \u0939\u0948, \u090f\u0915 \u0910\u0938\u0940 \u0935\u093f\u0927\u093f \u0939\u0948 \u091c\u094b \u0921\u0947\u091f\u093e \u0915\u0947 \u090f\u0915 \u0938\u0947\u091f \u092e\u0947\u0902 \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u092e\u0949\u0921\u0932 \u0915\u094b \u092b\u093f\u091f \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0938\u094d\u0925\u093e\u0928\u0940\u092f \u0938\u092e\u093e\u092f\u094b\u091c\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0924\u0940 \u0939\u0948\u0964<\/span><\/p>\n

\u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u091a\u0930\u0923-\u0926\u0930-\u091a\u0930\u0923 \u0909\u0926\u093e\u0939\u0930\u0923 \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948 \u0915\u093f R \u092e\u0947\u0902 LOESS \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902\u0964<\/span><\/p>\n

\u091a\u0930\u0923 1: \u0921\u0947\u091f\u093e \u092c\u0928\u093e\u090f\u0902<\/strong><\/span><\/h3>\n

\u0938\u092c\u0938\u0947 \u092a\u0939\u0932\u0947, \u0906\u0907\u090f R \u092e\u0947\u0902 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0921\u0947\u091f\u093e \u092b\u093c\u094d\u0930\u0947\u092e \u092c\u0928\u093e\u090f\u0902:<\/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
 \u091a\u0930\u0923 2: \u090f\u0915\u093e\u0927\u093f\u0915 LOESS \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u092e\u0949\u0921\u0932 \u092b\u093c\u093f\u091f \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n
 \u0939\u092e \u0938\u094d\u092a\u0948\u0928<\/strong> \u092a\u0948\u0930\u093e\u092e\u0940\u091f\u0930 \u0915\u0947 \u0932\u093f\u090f \u0935\u093f\u092d\u093f\u0928\u094d\u0928 \u092e\u093e\u0928\u094b\u0902 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0915\u0947, \u0907\u0938 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u092e\u0947\u0902 \u090f\u0915\u093e\u0927\u093f\u0915 LOESS \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u092e\u0949\u0921\u0932 \u0915\u094b \u092b\u093f\u091f \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f loess()<\/strong> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902:<\/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
\"\u0906\u0930<\/p>\n
 \u0927\u094d\u092f\u093e\u0928 \u0926\u0947\u0902 \u0915\u093f \u0939\u092e \u0938\u094d\u092a\u0948\u0928<\/strong> \u0915\u0947 \u0932\u093f\u090f \u091c\u093f\u0924\u0928\u093e \u0915\u092e \u092e\u0942\u0932\u094d\u092f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902\u0917\u0947, \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u092e\u0949\u0921\u0932 \u0909\u0924\u0928\u093e \u0939\u0940 \u0915\u092e “\u0938\u0941\u091a\u093e\u0930\u0942” \u0939\u094b\u0917\u093e \u0914\u0930 \u0909\u0924\u0928\u093e \u0939\u0940 \u0905\u0927\u093f\u0915 \u092e\u0949\u0921\u0932 \u0921\u0947\u091f\u093e \u092c\u093f\u0902\u0926\u0941\u0913\u0902 \u0915\u094b \u092b\u093f\u091f \u0915\u0930\u0928\u0947 \u0915\u093e \u092a\u094d\u0930\u092f\u093e\u0938 \u0915\u0930\u0947\u0917\u093e\u0964<\/span><\/p>\n
 \u091a\u0930\u0923 3: \u0938\u0930\u094d\u0935\u0936\u094d\u0930\u0947\u0937\u094d\u0920 \u092e\u0949\u0921\u0932 \u0916\u094b\u091c\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u0947-\u092b\u094b\u0932\u094d\u0921 \u0915\u094d\u0930\u0949\u0938-\u0935\u0948\u0932\u093f\u0921\u0947\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n
 \u0909\u092a\u092f\u094b\u0917 \u0915\u0947 \u0932\u093f\u090f \u0907\u0937\u094d\u091f\u0924\u092e \u0938\u0940\u092e\u093e<\/strong> \u092e\u093e\u0928 \u0916\u094b\u091c\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f, \u0939\u092e \u0915\u0948\u0930\u0947\u091f<\/strong> \u092a\u0948\u0915\u0947\u091c \u092e\u0947\u0902 \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0915\u0947 \u0915\u0947-\u092b\u093c\u094b\u0932\u094d\u0921 \u0915\u094d\u0930\u0949\u0938-\u0938\u0924\u094d\u092f\u093e\u092a\u0928<\/a> \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902:<\/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
 \u0907\u0938\u0932\u093f\u090f, \u0939\u092e\u093e\u0930\u0947 \u0905\u0902\u0924\u093f\u092e LOESS \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u092e\u0949\u0921\u0932 \u0915\u0947 \u0932\u093f\u090f, \u0939\u092e loess()<\/strong> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u092e\u0947\u0902 \u0938\u094d\u092a\u0948\u0928<\/strong> \u0924\u0930\u094d\u0915 \u0915\u0947 \u0932\u093f\u090f 0.7<\/strong> \u0915\u0947 \u092e\u093e\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u093e \u091a\u0941\u0928\u0947\u0902\u0917\u0947\u0964<\/span><\/p>\n
 \u0905\u0924\u093f\u0930\u093f\u0915\u094d\u0924 \u0938\u0902\u0938\u093e\u0927\u0928<\/strong><\/span><\/h3>\n
 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 \u0906\u0930 \u092e\u0947\u0902 \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u092e\u0949\u0921\u0932 \u0915\u0947 \u092c\u093e\u0930\u0947 \u092e\u0947\u0902 \u0905\u0924\u093f\u0930\u093f\u0915\u094d\u0924 \u091c\u093e\u0928\u0915\u093e\u0930\u0940 \u092a\u094d\u0930\u0926\u093e\u0928 \u0915\u0930\u0924\u0947 \u0939\u0948\u0902:<\/span><\/p>\n
 \u0906\u0930 \u092e\u0947\u0902 \u0938\u0930\u0932 \u0930\u0948\u0916\u093f\u0915 \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902<\/a>
 \u0906\u0930 \u092e\u0947\u0902 \u092e\u0932\u094d\u091f\u0940\u092a\u0932 \u0932\u0940\u0928\u093f\u092f\u0930 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902<\/a>
 \u0906\u0930 \u092e\u0947\u0902 \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902
 \u0906\u0930 \u092e\u0947\u0902 \u0915\u094d\u0935\u093e\u0902\u091f\u093e\u0907\u0932 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902<\/a>
 \u0906\u0930 \u092e\u0947\u0902 \u092d\u093e\u0930\u093f\u0924 \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
LOESS \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 , \u091c\u093f\u0938\u0947 \u0915\u092d\u0940-\u0915\u092d\u0940 \u0938\u094d\u0925\u093e\u0928\u0940\u092f \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u092d\u0940 \u0915\u0939\u093e \u091c\u093e\u0924\u093e \u0939\u0948, \u090f\u0915 \u0910\u0938\u0940 \u0935\u093f\u0927\u093f \u0939\u0948 \u091c\u094b \u0921\u0947\u091f\u093e \u0915\u0947 \u090f\u0915 \u0938\u0947\u091f \u092e\u0947\u0902 \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u092e\u0949\u0921\u0932 \u0915\u094b \u092b\u093f\u091f \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0938\u094d\u0925\u093e\u0928\u0940\u092f \u0938\u092e\u093e\u092f\u094b\u091c\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0924\u0940 \u0939\u0948\u0964 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u091a\u0930\u0923-\u0926\u0930-\u091a\u0930\u0923 \u0909\u0926\u093e\u0939\u0930\u0923 \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948 \u0915\u093f R \u092e\u0947\u0902 LOESS \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902\u0964 \u091a\u0930\u0923 1: \u0921\u0947\u091f\u093e \u092c\u0928\u093e\u090f\u0902 \u0938\u092c\u0938\u0947 \u092a\u0939\u0932\u0947, \u0906\u0907\u090f R \u092e\u0947\u0902 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-3151","post","type-post","status-publish","format-standard","hentry","category-3"],"yoast_head":"\nR \u092e\u0947\u0902 LOESS \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902 (\u0909\u0926\u093e\u0939\u0930\u0923 \u0915\u0947 \u0938\u093e\u0925) - \u0938\u093e\u0902\u0916\u094d\u092f\u093f\u0915\u0940<\/title>\n<meta name=\"description\" content=\"\u092f\u0939 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