{"id":1181,"date":"2023-07-27T09:26:41","date_gmt":"2023-07-27T09:26:41","guid":{"rendered":"https:\/\/statorials.org\/id\/bootstrap-di-r\/"},"modified":"2023-07-27T09:26:41","modified_gmt":"2023-07-27T09:26:41","slug":"bootstrap-di-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/bootstrap-di-r\/","title":{"rendered":"Cara bootstrapping di r (dengan contoh)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Bootstrapping<\/strong> adalah metode yang dapat digunakan untuk memperkirakan kesalahan standar suatu <a href=\"https:\/\/statorials.org\/id\/statistik-vs-parameter\/\" target=\"_blank\" rel=\"noopener noreferrer\">statistik<\/a> dan menghasilkan interval kepercayaan untuk statistik tersebut.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Proses dasar untuk bootstrap adalah sebagai berikut:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Ambil <em>k<\/em> sampel replikasi dengan penggantian dari kumpulan data tertentu.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Untuk setiap sampel, hitung statistik minat.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Hal ini memberikan <em>k<\/em> perkiraan berbeda untuk statistik tertentu, yang kemudian dapat Anda gunakan untuk menghitung kesalahan standar statistik dan membuat interval kepercayaan untuk statistik tersebut.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Kita dapat melakukan bootstrap di R menggunakan fungsi berikut dari <a href=\"https:\/\/cran.r-project.org\/web\/packages\/boot\/boot.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">perpustakaan bootstrap<\/a> :<\/span><\/p>\n<p> <span style=\"color: #000000;\">1. Hasilkan sampel bootstrap.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>boot(data, statistik, R,\u2026)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>data:<\/strong> vektor, matriks, atau blok data<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>statistik:<\/strong> fungsi yang menghasilkan statistik yang akan dimulai<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>A:<\/strong> Jumlah pengulangan bootstrap<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">2. Hasilkan interval kepercayaan bootstrap.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>boot.ci(objek boot, conf, ketik)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>bootobject:<\/strong> Objek yang dikembalikan oleh fungsi boot()<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>conf :<\/strong> Interval kepercayaan yang akan dihitung. Nilai defaultnya adalah 0,95<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>type :<\/strong> Jenis interval kepercayaan yang akan dihitung. Pilihannya mencakup &#8216;standar&#8217;, &#8216;basic&#8217;, &#8216;stud&#8217;, &#8216;perc&#8217;, &#8216;bca&#8217; dan &#8216;all&#8217; &#8211; defaultnya adalah &#8216;all&#8217;<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menggunakan fungsi-fungsi ini dalam praktik.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 1: bootstrap statistik tunggal<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menghitung kesalahan standar untuk <a href=\"https:\/\/statorials.org\/id\/nilai-r-kuadrat-yang-bagus\/\" target=\"_blank\" rel=\"noopener noreferrer\">R-kuadrat<\/a> dari model regresi linier sederhana:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>set.seed(0)\n<span style=\"color: #993300;\">library<\/span> (boot)\n\n<span style=\"color: #008080;\">#define function to calculate R-squared\n<\/span>rsq_function &lt;- <span style=\"color: #008000;\">function<\/span> (formula, data, indices) {\n  d &lt;- data[indices,] <span style=\"color: #008080;\">#allows boot to select sample<\/span>\n  fit &lt;- lm(formula, data=d) <span style=\"color: #008080;\">#fit regression model<\/span>\n  <span style=\"color: #008000;\">return<\/span> (summary(fit)$r.square) <span style=\"color: #008080;\">#return R-squared of model<\/span>\n}\n<span style=\"color: #008080;\">#perform bootstrapping with 2000 replications\n<\/span>reps &lt;- boot(data=mtcars, statistic=rsq_function, R=2000, formula=mpg~disp)\n\n<span style=\"color: #008080;\">#view results of boostrapping\n<\/span>reps\n\nORDINARY NONPARAMETRIC BOOTSTRAP\n\n\nCall:\nboot(data = mtcars, statistic = rsq_function, R = 2000, formula = mpg ~ \n    available)\n\n\nBootstrap Statistics:\n     original bias std. error\nt1* 0.7183433 0.002164339 0.06513426<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Dari hasilnya kita dapat melihat:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Estimasi R-squared untuk model regresi ini adalah <strong>0.7183433<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">Kesalahan standar untuk perkiraan ini adalah <strong>0,06513426<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Kami juga dapat dengan cepat memvisualisasikan distribusi sampel bootstrap:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>plot(reps)\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11750 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/demarrage1.png\" alt=\"Histogram sampel bootstrap di R\" width=\"456\" height=\"437\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Kita juga dapat menggunakan kode berikut untuk menghitung interval kepercayaan 95% untuk estimasi R-kuadrat model:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate adjusted bootstrap percentile (BCa) interval\n<\/span>boot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \")\n\nCALL: \nboot.ci(boot.out = reps, type = \"bca\")\n\nIntervals: \nLevel BCa          \n95% (0.5350, 0.8188)  \nCalculations and Intervals on Original Scale<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Dari hasilnya terlihat bahwa interval kepercayaan 95% bootstrap untuk nilai R-kuadrat sebenarnya adalah (0,5350, 0,8188).<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 2: bootstrap beberapa statistik<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menghitung kesalahan standar untuk setiap koefisien dalam model regresi linier berganda:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>set.seed(0)\n<span style=\"color: #993300;\">library<\/span> (boot)\n\n<span style=\"color: #008080;\">#define function to calculate fitted regression coefficients\n<\/span>coef_function &lt;- <span style=\"color: #008000;\">function<\/span> (formula, data, indices) {\n  d &lt;- data[indices,] <span style=\"color: #008080;\">#allows boot to select sample<\/span>\n  fit &lt;- lm(formula, data=d) <span style=\"color: #008080;\">#fit regression model<\/span>\n  <span style=\"color: #008000;\">return<\/span> (coef(fit)) <span style=\"color: #008080;\">#return coefficient estimates of model<\/span>\n}\n\n<span style=\"color: #008080;\">#perform bootstrapping with 2000 replications\n<\/span>reps &lt;- boot(data=mtcars, statistic=coef_function, R=2000, formula=mpg~disp)\n\n<span style=\"color: #008080;\">#view results of boostrapping\n<\/span>reps\n\nORDINARY NONPARAMETRIC BOOTSTRAP\n\n\nCall:\nboot(data = mtcars, statistic = coef_function, R = 2000, formula = mpg ~ \n    available)\n\n\nBootstrap Statistics:\n       original bias std. error\nt1* 29.59985476 -5.058601e-02 1.49354577\nt2* -0.04121512 6.549384e-05 0.00527082<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Dari hasilnya kita dapat melihat:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Koefisien perkiraan untuk intersep model adalah <strong>29.59985476<\/strong> dan kesalahan standar perkiraan ini adalah <strong>1.49354577<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">Koefisien estimasi <em>disp<\/em> variabel prediktor dalam model adalah <strong>-0.04121512<\/strong> dan standar error estimasi ini adalah <strong>0.00527082<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Kami juga dapat dengan cepat memvisualisasikan distribusi sampel bootstrap:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>plot(reps, index=1) <span style=\"color: #008080;\">#intercept of model<\/span>\nplot(reps, index=2) <span style=\"color: #008080;\">#disp predictor variable\n<\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11752 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/demarrage2.png\" alt=\"Bootstrap di R\" width=\"669\" height=\"325\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Kita juga dapat menggunakan kode berikut untuk menghitung interval kepercayaan 95% untuk setiap koefisien:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate adjusted bootstrap percentile (BCa) intervals\n<\/span>boot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \", index=1) <span style=\"color: #008080;\">#intercept of model<\/span>\nboot.ci(reps, type=\" <span style=\"color: #008000;\">bca<\/span> \", index=2) <span style=\"color: #008080;\">#disp predictor variable\n<\/span>\nCALL: \nboot.ci(boot.out = reps, type = \"bca\", index = 1)\n\nIntervals: \nLevel BCa          \n95% (26.78, 32.66)  \nCalculations and Intervals on Original Scale\nBOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS\nBased on 2000 bootstrap replicates\n\nCALL: \nboot.ci(boot.out = reps, type = \"bca\", index = 2)\n\nIntervals: \nLevel BCa          \n95% (-0.0520, -0.0312)  \nCalculations and Intervals on Original Scale<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Dari hasil tersebut terlihat bahwa interval kepercayaan 95% bootstrap untuk koefisien model adalah sebagai berikut:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">IC untuk intersepsi: (26.78, 32.66)<\/span><\/li>\n<li> <span style=\"color: #000000;\">CI untuk <em>tampilan<\/em> : (-.0520, -.0312)<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/id\/regresi-linier-sederhana-di-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">Cara melakukan regresi linier sederhana di R<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/regresi-linier-berganda-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">Cara melakukan regresi linier berganda di R<\/a><br \/> Pengantar Interval Keyakinan<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Bootstrapping adalah metode yang dapat digunakan untuk memperkirakan kesalahan standar suatu statistik dan menghasilkan interval kepercayaan untuk statistik tersebut. Proses dasar untuk bootstrap adalah sebagai berikut: Ambil k sampel replikasi dengan penggantian dari kumpulan data tertentu. Untuk setiap sampel, hitung statistik minat. Hal ini memberikan k perkiraan berbeda untuk statistik tertentu, yang kemudian dapat Anda [&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 Bootstrapping di R (dengan Contoh) - Statologi<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara bootstrap di R, dengan beberapa contoh.\" \/>\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\/bootstrap-di-r\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Bootstrapping di R (dengan Contoh) - Statologi\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara bootstrap di R, dengan beberapa contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/bootstrap-di-r\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-27T09:26:41+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/demarrage1.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=\"3 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/bootstrap-di-r\/\",\"url\":\"https:\/\/statorials.org\/id\/bootstrap-di-r\/\",\"name\":\"Cara Bootstrapping di R (dengan Contoh) - Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-27T09:26:41+00:00\",\"dateModified\":\"2023-07-27T09:26:41+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara bootstrap di R, dengan beberapa contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/bootstrap-di-r\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/bootstrap-di-r\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/bootstrap-di-r\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara bootstrapping di r (dengan contoh)\"}]},{\"@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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