{"id":3585,"date":"2023-07-16T16:59:09","date_gmt":"2023-07-16T16:59:09","guid":{"rendered":"https:\/\/statorials.org\/id\/penskalaan-multidimensi-di-r\/"},"modified":"2023-07-16T16:59:09","modified_gmt":"2023-07-16T16:59:09","slug":"penskalaan-multidimensi-di-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/penskalaan-multidimensi-di-r\/","title":{"rendered":"Cara melakukan penskalaan multidimensi di r (dengan contoh)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Dalam statistik, <strong>penskalaan multidimensi<\/strong> adalah cara untuk memvisualisasikan kesamaan observasi dalam kumpulan data dalam ruang Cartesian abstrak (biasanya ruang 2D).<\/span><\/p>\n<p> <span style=\"color: #000000;\">Cara termudah untuk melakukan penskalaan multidimensi di R adalah dengan menggunakan fungsi <strong>cmdscale()<\/strong> bawaan, yang menggunakan sintaks dasar berikut:<\/span><\/p>\n<p> <strong><span style=\"color: #000000;\">cmdscale(d, eig = SALAH, k = 2, \u2026)<\/span><\/strong><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>d<\/strong> : Matriks jarak umumnya dihitung dengan fungsi <strong>dist()<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>eig<\/strong> : apakah akan mengembalikan nilai eigen atau tidak.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>k<\/strong> : Jumlah dimensi untuk melihat data. Standarnya adalah <strong>2<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menggunakan fungsi ini dalam praktiknya.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Contoh: Penskalaan Multidimensi di R<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Misalkan kita memiliki kerangka data berikut di R yang berisi informasi tentang berbagai pemain bola basket:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data frame\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (points=c(4, 4, 6, 7, 8, 14, 16, 19, 25, 25, 28),\n                 assists=c(3, 2, 2, 5, 4, 8, 7, 6, 8, 10, 11),\n                 blocks=c(7, 3, 6, 7, 5, 8, 8, 4, 2, 2, 1),\n                 rebounds=c(4, 5, 5, 6, 5, 8, 10, 4, 3, 2, 2))\n\n<span style=\"color: #008080;\">#add row names\n<\/span>row. <span style=\"color: #3366ff;\">names<\/span> (df) &lt;- LETTERS[1:11]\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\n  points assists blocks rebounds\nA 4 3 7 4\nB 4 2 3 5\nC 6 2 6 5\nD 7 5 7 6\nE 8 4 5 5\nF 14 8 8 8\nG 16 7 8 10\nH 19 6 4 4\nI 25 8 2 3\nD 25 10 2 2\nK 28 11 1 2<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">Kita dapat menggunakan kode berikut untuk melakukan penskalaan multidimensi dengan fungsi <strong>cmdscale()<\/strong> dan memvisualisasikan hasilnya dalam ruang 2D:<\/span><\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#calculate distance matrix\n<\/span>d &lt;- dist(df)\n\n<span style=\"color: #008080;\">#perform multidimensional scaling\n<\/span>fit &lt;- cmdscale(d, eig= <span style=\"color: #008000;\">TRUE<\/span> , k= <span style=\"color: #008000;\">2<\/span> )\n\n<span style=\"color: #008080;\">#extract (x, y) coordinates of multidimensional scaling\n<\/span>x &lt;- fit$points[,1]\ny &lt;- fit$points[,2]\n\n<span style=\"color: #008080;\">#create scatterplot\n<\/span>plot(x, y, xlab=\" <span style=\"color: #ff0000;\">Coordinate 1<\/span> \", ylab=\" <span style=\"color: #ff0000;\">Coordinate 2<\/span> \",\n     main=\" <span style=\"color: #ff0000;\">Multidimensional Scaling Results<\/span> \", type=\" <span style=\"color: #ff0000;\">n<\/span> \")\n\n<span style=\"color: #008080;\">#add row names of data frame as labels\n<\/span>text(x, y, labels=row. <span style=\"color: #3366ff;\">names<\/span> (df))\n<\/span><\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-29590\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/mds1.jpg\" alt=\"penskalaan multidimensi di R\" width=\"481\" height=\"402\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Pemain dalam bingkai data asli yang memiliki nilai serupa di empat kolom asli (poin, assist, blok, dan rebound) berada berdekatan satu sama lain dalam plot.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Misal pemain <strong>A<\/strong> dan <strong>C<\/strong> saling tertutup. Berikut nilainya dari bingkai data asli:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view data frame values for players A and C<\/span>\ndf[rownames(df) <span style=\"color: #800080;\">%in%<\/span> c(' <span style=\"color: #ff0000;\">A<\/span> ', ' <span style=\"color: #ff0000;\">C<\/span> '), ]\n\n  points assists blocks rebounds\nA 4 3 7 4\nC 6 2 6 5<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Nilai poin, assist, blok, dan reboundnya sangat mirip, itulah sebabnya keduanya begitu dekat satu sama lain dalam plot 2D.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Sebaliknya, pertimbangkan pemain <strong>B<\/strong> dan <strong>K<\/strong> yang berjauhan dalam plot.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Jika kita mengacu pada nilainya pada data asli, kita dapat melihat bahwa keduanya sangat berbeda:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view data frame values for players B and K<\/span>\ndf[rownames(df) <span style=\"color: #800080;\">%in%<\/span> c(' <span style=\"color: #ff0000;\">B<\/span> ', ' <span style=\"color: #ff0000;\">K<\/span> '), ]\n\n  points assists blocks rebounds\nB 4 2 3 5\nK 28 11 1 2<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Jadi plot 2D adalah cara yang baik untuk memvisualisasikan seberapa mirip setiap pemain di semua variabel dalam bingkai data.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Pemain dengan statistik serupa dikelompokkan berdekatan sementara pemain dengan statistik sangat berbeda ditempatkan lebih jauh satu sama lain dalam plot.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Perhatikan bahwa Anda juga dapat mengekstrak koordinat yang tepat (x, y) dari masing-masing pemain dalam plot dengan mengetikkan <strong>fit<\/strong> , yang merupakan nama variabel tempat kita menyimpan hasil fungsi <strong>cmdscale()<\/strong> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view (x, y) coordinates of points in the plot<\/span>\nfit\n\n         [,1] [,2]\nA -10.6617577 -1.2511291\nB -10.3858237 -3.3450473\nC -9.0330408 -1.1968116\nD -7.4905743 1.0578445\nE -6.4021114 -1.0743669\nF -0.4618426 4.7392534\nG 0.8850934 6.1460850\nH 4.7352436 -0.6004609\nI 11.3793381 -1.3563398\nJ 12.0844168 -1.5494108\nK 15.3510585 -1.5696166\n<\/strong><\/pre>\n<h2> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Tutorial berikut menjelaskan cara melakukan tugas umum lainnya di R:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/id\/cara-menormalkan-data-di-r\/\" target=\"_blank\" rel=\"noopener\">Cara menormalkan data di R<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/pusat-data-di-sungai\/\" target=\"_blank\" rel=\"noopener\">Cara pusat data di R<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/menghapus-outlier-r\/\" target=\"_blank\" rel=\"noopener\">Cara menghilangkan outlier di R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Dalam statistik, penskalaan multidimensi adalah cara untuk memvisualisasikan kesamaan observasi dalam kumpulan data dalam ruang Cartesian abstrak (biasanya ruang 2D). Cara termudah untuk melakukan penskalaan multidimensi di R adalah dengan menggunakan fungsi cmdscale() bawaan, yang menggunakan sintaks dasar berikut: cmdscale(d, eig = SALAH, k = 2, \u2026) Emas: d : Matriks jarak umumnya dihitung dengan [&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 Melakukan Penskalaan Multidimensi di R (dengan Contoh) - Statologi<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara melakukan penskalaan multidimensi di R, dengan sebuah 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\/penskalaan-multidimensi-di-r\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Melakukan Penskalaan Multidimensi di R (dengan Contoh) - Statologi\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara melakukan penskalaan multidimensi di R, dengan sebuah contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/penskalaan-multidimensi-di-r\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-16T16:59:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/mds1.jpg\" \/>\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\/penskalaan-multidimensi-di-r\/\",\"url\":\"https:\/\/statorials.org\/id\/penskalaan-multidimensi-di-r\/\",\"name\":\"Cara Melakukan Penskalaan Multidimensi di R (dengan Contoh) - Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-16T16:59:09+00:00\",\"dateModified\":\"2023-07-16T16:59:09+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara melakukan penskalaan multidimensi di R, dengan sebuah contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/penskalaan-multidimensi-di-r\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/penskalaan-multidimensi-di-r\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/penskalaan-multidimensi-di-r\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara melakukan penskalaan multidimensi 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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