{"id":1977,"date":"2023-07-24T05:44:05","date_gmt":"2023-07-24T05:44:05","guid":{"rendered":"https:\/\/statorials.org\/id\/fungsi-lm-di-r\/"},"modified":"2023-07-24T05:44:05","modified_gmt":"2023-07-24T05:44:05","slug":"fungsi-lm-di-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/fungsi-lm-di-r\/","title":{"rendered":"Cara menggunakan fungsi lm() di r agar sesuai dengan model linier"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Fungsi <strong>lm()<\/strong> di R digunakan untuk menyesuaikan model regresi linier.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Fungsi ini menggunakan sintaks dasar berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>lm(rumus, data,\u2026)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>rumus :<\/strong> Rumus model linier (misal y ~ x1 + x2)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>data:<\/strong> Nama blok data yang berisi data<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menggunakan fungsi ini di R untuk melakukan hal berikut:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Cocokkan model regresi<\/span><\/li>\n<li> <span style=\"color: #000000;\">Lihat ringkasan kesesuaian model regresi<\/span><\/li>\n<li> <span style=\"color: #000000;\">Lihat plot diagnostik model<\/span><\/li>\n<li> <span style=\"color: #000000;\">Plot model regresi yang sesuai<\/span><\/li>\n<li> <span style=\"color: #000000;\">Buat prediksi menggunakan model regresi<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>Sesuaikan model regresi<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menggunakan fungsi <strong>lm()<\/strong> agar sesuai dengan model regresi linier di R:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define data<\/span>\ndf = data. <span style=\"color: #3366ff;\">frame<\/span> (x=c(1, 3, 3, 4, 5, 5, 6, 8, 9, 12),\n                y=c(12, 14, 14, 13, 17, 19, 22, 26, 24, 22))\n\n<span style=\"color: #008080;\">#fit linear regression model using 'x' as predictor and 'y' as response variable<\/span>\nmodel &lt;- lm(y ~ x, data=df)\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Tampilkan ringkasan model regresi<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kita kemudian dapat menggunakan fungsi <strong>ringkasan()<\/strong> untuk menampilkan ringkasan kesesuaian model regresi:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view summary of regression model<\/span>\nsummary(model)\n\nCall:\nlm(formula = y ~ x, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-4.4793 -0.9772 -0.4772 1.4388 4.6328 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 11.1432 1.9104 5.833 0.00039 ***\nx 1.2780 0.2984 4.284 0.00267 ** \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.929 on 8 degrees of freedom\nMultiple R-squared: 0.6964, Adjusted R-squared: 0.6584 \nF-statistic: 18.35 on 1 and 8 DF, p-value: 0.002675\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Berikut cara menginterpretasikan nilai terpenting dalam model:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>F-statistik<\/strong> = 18,35, <strong>nilai p<\/strong> yang sesuai = 0,002675. Karena nilai p ini kurang dari 0,05, model secara keseluruhan signifikan secara statistik.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Kelipatan R kuadrat<\/strong> = 0,6964. Hal ini menunjukkan bahwa 69,64% variasi variabel respon y dapat dijelaskan oleh variabel prediktor x.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Estimasi koefisien x<\/strong> : 1,2780. Hal ini menunjukkan bahwa setiap kenaikan unit tambahan pada x dikaitkan dengan peningkatan rata-rata sebesar 1,2780 pada y.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Kita kemudian dapat menggunakan estimasi koefisien dari output untuk menulis persamaan regresi estimasi:<\/span><\/p>\n<p> <span style=\"color: #000000;\">kamu = 11,1432 + 1,2780*(x)<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Bonus<\/strong> : Anda dapat menemukan panduan lengkap untuk menafsirkan setiap nilai keluaran regresi di R <a href=\"https:\/\/statorials.org\/id\/menafsirkan-keluaran-regresi-di-r\/\" target=\"_blank\" rel=\"noopener\">di sini<\/a> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Lihat plot diagnostik model<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kita kemudian dapat menggunakan fungsi <strong>plot()<\/strong> untuk memplot plot diagnostik model regresi:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create diagnostic plots\n<span style=\"color: #000000;\">plot(model)<\/span><\/span><\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-18677 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lm2.png\" alt=\"\" width=\"653\" height=\"649\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Grafik ini memungkinkan kita menganalisis <a href=\"https:\/\/statorials.org\/id\/residu\/\" target=\"_blank\" rel=\"noopener\">sisa<\/a> model regresi untuk menentukan apakah model tersebut sesuai digunakan untuk data.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Lihat <a href=\"https:\/\/statorials.org\/id\/diagram-diagnostik-di-sungai\/\" target=\"_blank\" rel=\"noopener\">tutorial ini<\/a> untuk penjelasan lengkap tentang cara menafsirkan plot diagnostik model di R.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Plot model regresi yang sesuai<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kita dapat menggunakan fungsi <strong>abline()<\/strong> untuk memplot model regresi yang sesuai:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create scatterplot of raw data<\/span>\nplot(df$x, df$y, col=' <span style=\"color: #ff0000;\">red<\/span> ', main=' <span style=\"color: #ff0000;\">Summary of Regression Model<\/span> ', xlab=' <span style=\"color: #ff0000;\">x<\/span> ', ylab=' <span style=\"color: #ff0000;\">y<\/span> ')\n\n<span style=\"color: #008080;\">#add fitted regression line\n<span style=\"color: #000000;\">abline(model)\n<\/span><\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-18678\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lm3.png\" alt=\"plot lm() di R\" width=\"448\" height=\"442\" srcset=\"\" sizes=\"\"><\/p>\n<h3> <strong>Gunakan model regresi untuk membuat prediksi<\/strong><\/h3>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">Kita dapat menggunakan fungsi <strong>prediksi()<\/strong> untuk memprediksi nilai respons observasi baru:<\/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;\">#define new observation\n<\/span>new &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (x=c(5))\n\n<span style=\"color: #008080;\">#use the fitted model to predict the value for the new observation\n<\/span>predict(model, newdata = new)\n\n      1 \n17.5332<\/span>\n<\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Model tersebut memperkirakan observasi baru ini akan memiliki nilai respon sebesar <strong>17.5332<\/strong> .<\/span><\/p>\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\">Cara melakukan regresi linier sederhana di R<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/regresi-linier-berganda-r\/\" target=\"_blank\" rel=\"noopener\">Cara melakukan regresi linier berganda di R<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/regresi-langkah-demi-langkah-r\/\" target=\"_blank\" rel=\"noopener\">Bagaimana melakukan regresi bertahap di R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Fungsi lm() di R digunakan untuk menyesuaikan model regresi linier. Fungsi ini menggunakan sintaks dasar berikut: lm(rumus, data,\u2026) Emas: rumus : Rumus model linier (misal y ~ x1 + x2) data: Nama blok data yang berisi data Contoh berikut menunjukkan cara menggunakan fungsi ini di R untuk melakukan hal berikut: Cocokkan model regresi Lihat ringkasan [&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 menggunakan fungsi lm() di R agar sesuai dengan model linier - Statorial<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menggunakan fungsi lm() di R agar sesuai dengan model regresi linier, 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\/fungsi-lm-di-r\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara menggunakan fungsi lm() di R agar sesuai dengan model linier - Statorial\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menggunakan fungsi lm() di R agar sesuai dengan model regresi linier, dengan beberapa contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/fungsi-lm-di-r\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-24T05:44:05+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lm2.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\/fungsi-lm-di-r\/\",\"url\":\"https:\/\/statorials.org\/id\/fungsi-lm-di-r\/\",\"name\":\"Cara menggunakan fungsi lm() di R agar sesuai dengan model linier - Statorial\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-24T05:44:05+00:00\",\"dateModified\":\"2023-07-24T05:44:05+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menggunakan fungsi lm() di R agar sesuai dengan model regresi linier, dengan beberapa contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/fungsi-lm-di-r\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/fungsi-lm-di-r\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/fungsi-lm-di-r\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menggunakan fungsi lm() di r agar sesuai dengan model linier\"}]},{\"@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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