{"id":1311,"date":"2023-07-26T22:17:32","date_gmt":"2023-07-26T22:17:32","guid":{"rendered":"https:\/\/statorials.org\/id\/residu-distandarisasi-di-r\/"},"modified":"2023-07-26T22:17:32","modified_gmt":"2023-07-26T22:17:32","slug":"residu-distandarisasi-di-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/residu-distandarisasi-di-r\/","title":{"rendered":"Cara menghitung residu standar di r"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Residual<\/strong> adalah selisih antara nilai observasi dan nilai prediksi dalam <a href=\"https:\/\/statorials.org\/id\/regresi-linier-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">model regresi<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ini dihitung sebagai berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Sisa = Nilai yang diamati \u2013 Nilai yang diprediksi<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Jika kita memplot nilai observasi dan menempatkan garis regresi yang dipasang, residu untuk setiap <a href=\"https:\/\/statorials.org\/id\/pengamatan-dalam-statistik\/\" target=\"_blank\" rel=\"noopener\">observasi<\/a> akan menjadi jarak vertikal antara observasi dan garis regresi:<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12422 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/residus1-1.png\" alt=\"Contoh residu dalam statistik\" width=\"487\" height=\"382\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Salah satu jenis residu yang sering kita gunakan untuk mengidentifikasi outlier dalam model regresi disebut <strong>residu terstandarisasi<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ini dihitung sebagai berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>r <sub>i<\/sub> = e <sub>i<\/sub> \/ s( <sub>ei<\/sub> )<\/strong> = <strong>e <sub>i<\/sub> \/ RSE\u221a <span style=\"border-top: 1px solid black;\">1-h <sub>ii<\/sub><\/span><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>e <sub>i<\/sub> :<\/strong> Residu <sup>ke<\/sup> -i<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>RSE:<\/strong> kesalahan standar sisa model<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>h <sub>ii<\/sub><\/strong> : Meningkatnya observasi <sup>ke-i<\/sup><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Dalam praktiknya, kita sering menganggap residu terstandar yang nilai absolutnya lebih besar dari 3 sebagai outlier.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tutorial ini memberikan contoh langkah demi langkah tentang cara menghitung residu terstandar di R.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 1: Masukkan datanya<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Pertama, kita akan membuat kumpulan data kecil untuk digunakan di R:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data<\/span>\ndata &lt;- data.frame(x=c(8, 12, 12, 13, 14, 16, 17, 22, 24, 26, 29, 30),\n                   y=c(41, 42, 39, 37, 35, 39, 45, 46, 39, 49, 55, 57))\n\n<span style=\"color: #008080;\">#viewdata\n<\/span>data\n\n    xy\n1 8 41\n2 12 42\n3 12 39\n4 13 37\n5 14 35\n6 16 39\n7 17 45\n8 22 46\n9 24 39\n10 26 49\n11 29 55\n12 30 57<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 2: Sesuaikan model regresi<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Selanjutnya, kita akan menggunakan fungsi <strong>lm()<\/strong> agar sesuai dengan <a href=\"https:\/\/statorials.org\/id\/regresi-linier-sederhana-di-r\/\" target=\"_blank\" rel=\"noopener\">model regresi linier sederhana<\/a> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit model\n<\/span>model &lt;- lm(y ~ x, data=data)\n\n<span style=\"color: #008080;\">#view model summary\n<span style=\"color: #000000;\">summary(model)<\/span> \n\n<span style=\"color: #000000;\">Call:\nlm(formula = y ~ x, data = data)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-8.7578 -2.5161 0.0292 3.3457 5.3268 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 29.6309 3.6189 8.188 9.6e-06 ***\nx 0.7553 0.1821 4.148 0.00199 ** \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 4.442 on 10 degrees of freedom\nMultiple R-squared: 0.6324, Adjusted R-squared: 0.5956 \nF-statistic: 17.2 on 1 and 10 DF, p-value: 0.001988<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 3: Hitung residu standar<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">Selanjutnya, kita akan menggunakan fungsi bawaan <strong>rstandard()<\/strong> untuk menghitung residu standar model:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate the standardized residuals\n<\/span>standard_res &lt;- rstandard(model)\n\n<span style=\"color: #008080;\">#view the standardized residuals<\/span>\nstandard_res\n\n          1 2 3 4 5 6 \n 1.40517322 0.81017562 0.07491009 -0.59323342 -1.24820530 -0.64248883 \n          7 8 9 10 11 12 \n 0.59610905 -0.05876884 -2.11711982 -0.06655600 0.91057211 1.26973888\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kita dapat menambahkan sisa standar ke bingkai data asli jika kita mau:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#column bind standardized residuals back to original data frame\n<\/span>final_data &lt;- cbind(data, standard_res)\n\n<span style=\"color: #008080;\">#view data frame<\/span>\n    xy standard_res\n1 8 41 1.40517322\n2 12 42 0.81017562\n3 12 39 0.07491009\n4 13 37 -0.59323342\n5 14 35 -1.24820530\n6 16 39 -0.64248883\n7 17 45 0.59610905\n8 22 46 -0.05876884\n9 24 39 -2.11711982\n10 26 49 -0.06655600\n11 29 55 0.91057211\n12 30 57 1.26973888\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kami kemudian dapat mengurutkan setiap observasi dari yang terbesar hingga yang terkecil berdasarkan residu standarnya untuk mendapatkan gambaran observasi mana yang paling dekat dengan outlier:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#sort standardized residuals descending\n<span style=\"color: #000000;\">final_data[ <span style=\"color: #3366ff;\">order<\/span> (-standard_res),]\n\n    xy standard_res\n1 8 41 1.40517322\n12 30 57 1.26973888\n11 29 55 0.91057211\n2 12 42 0.81017562\n7 17 45 0.59610905\n3 12 39 0.07491009\n8 22 46 -0.05876884\n10 26 49 -0.06655600\n4 13 37 -0.59323342\n6 16 39 -0.64248883\n5 14 35 -1.24820530\n9 24 39 -2.11711982<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Dari hasil tersebut, kita dapat melihat bahwa tidak ada satu pun residu terstandar yang melebihi nilai absolut 3. Dengan demikian, tidak ada satu pun observasi yang tampak outlier.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 4: Visualisasikan residu standar<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Terakhir, kita dapat membuat plot sebar untuk memvisualisasikan nilai variabel prediktor terhadap residu standar:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#plot predictor variable vs. standardized residuals\n<\/span>plot(final_data$x, standard_res, ylab=' <span style=\"color: #008000;\">Standardized Residuals<\/span> ', xlab=' <span style=\"color: #008000;\">x<\/span> ') \n\n<span style=\"color: #008080;\">#add horizontal line at 0\n<\/span>abline(0, 0)<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/id\/residu\/\" target=\"_blank\" rel=\"noopener\">Apa itu residu?<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/residu-terstandar\/\">Apa yang dimaksud dengan residu terstandar?<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/regresi-linier-berganda\/\" target=\"_blank\" rel=\"noopener\">Pengantar Regresi Linier Berganda<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Residual adalah selisih antara nilai observasi dan nilai prediksi dalam model regresi . Ini dihitung sebagai berikut: Sisa = Nilai yang diamati \u2013 Nilai yang diprediksi Jika kita memplot nilai observasi dan menempatkan garis regresi yang dipasang, residu untuk setiap observasi akan menjadi jarak vertikal antara observasi dan garis regresi: Salah satu jenis residu yang [&hellip;]<\/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":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Cara menghitung residu standar di R<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menghitung residu terstandar di R, termasuk contoh langkah demi langkah.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/id\/residu-distandarisasi-di-r\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara menghitung residu standar di R\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menghitung residu terstandar di R, termasuk contoh langkah demi langkah.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/residu-distandarisasi-di-r\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-26T22:17:32+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/residus1-1.png\" \/>\n<meta name=\"author\" content=\"Benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Ditulis oleh\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimasi waktu membaca\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/residu-distandarisasi-di-r\/\",\"url\":\"https:\/\/statorials.org\/id\/residu-distandarisasi-di-r\/\",\"name\":\"Cara menghitung residu standar di R\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-26T22:17:32+00:00\",\"dateModified\":\"2023-07-26T22:17:32+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menghitung residu terstandar di R, termasuk contoh langkah demi langkah.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/residu-distandarisasi-di-r\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/residu-distandarisasi-di-r\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/residu-distandarisasi-di-r\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menghitung residu standar di r\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/id\/#website\",\"url\":\"https:\/\/statorials.org\/id\/\",\"name\":\"Statorials\",\"description\":\"Panduan anda untuk kompetensi statistik!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/id\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"id\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\",\"name\":\"Benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"id\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/image\/\",\"url\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin anderson\"},\"description\":\"Halo, saya Benjamin, pensiunan profesor statistika yang menjadi guru Statorial yang berdedikasi. 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