{"id":4439,"date":"2023-07-11T02:36:29","date_gmt":"2023-07-11T02:36:29","guid":{"rendered":"https:\/\/statorials.org\/id\/r-prediksi-regresi-logistik\/"},"modified":"2023-07-11T02:36:29","modified_gmt":"2023-07-11T02:36:29","slug":"r-prediksi-regresi-logistik","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/r-prediksi-regresi-logistik\/","title":{"rendered":"Cara menggunakan predict() dengan model regresi logistik di r"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Setelah kita memasang <a href=\"https:\/\/statorials.org\/id\/regresi-logistik-1\/\" target=\"_blank\" rel=\"noopener\">model regresi logistik<\/a> di R, kita dapat menggunakan fungsi <strong>prediksi()<\/strong> untuk memprediksi nilai respons dari observasi baru yang belum pernah dilihat model tersebut sebelumnya.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Fungsi ini menggunakan sintaks berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>prediksi(objek, data baru, tipe = \u201crespons\u201d)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>objek :<\/strong> Nama model regresi logistik<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>data baru:<\/strong> Nama bingkai data baru yang akan dijadikan prediksi<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>type :<\/strong> Jenis prediksi yang akan dibuat<\/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: Menggunakan Predict() dengan Model Regresi Logistik di R<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Untuk contoh ini, kita akan menggunakan dataset R bawaan yang disebut <a href=\"https:\/\/statorials.org\/id\/kumpulan-data-mtcars-r\/\" target=\"_blank\" rel=\"noopener\">mtcars<\/a> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view first six rows of <em>mtcars<\/em> dataset<\/span>\nhead(mtcars)\n\n                   mpg cyl disp hp drat wt qsec vs am gear carb\nMazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\nMazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\nDatsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\nHornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\nHornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\nValiant 18.1 6 225 105 2.76 3,460 20.22 1 0 3 1<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kami akan menyesuaikan model regresi logistik berikut di mana kami menggunakan variabel <strong>disp<\/strong> dan <strong>hp<\/strong> untuk memprediksi variabel respon <strong>am<\/strong> (tipe transmisi mobil: 0 = otomatis, 1 = manual):<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit logistic regression model<\/span>\nmodel &lt;- glm(am ~ disp + hp, data=mtcars, family=binomial)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = am ~ disp + hp, family = binomial, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-1.9665 -0.3090 -0.0017 0.3934 1.3682  \n\nCoefficients:\n            Estimate Std. Error z value Pr(&gt;|z|)  \n(Intercept) 1.40342 1.36757 1.026 0.3048  \navailable -0.09518 0.04800 -1.983 0.0474 *\nhp 0.12170 0.06777 1.796 0.0725 .\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 43,230 on 31 degrees of freedom\nResidual deviance: 16,713 on 29 degrees of freedom\nAIC: 22,713\n\nNumber of Fisher Scoring iterations: 8\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kita kemudian dapat membuat bingkai data baru yang berisi informasi tentang delapan mobil yang modelnya belum pernah dilihat sebelumnya dan menggunakan fungsi <strong>prediksi()<\/strong> untuk memprediksi kemungkinan bahwa sebuah mobil baru akan memiliki transmisi otomatis (am=0) atau transmisi manual ( pagi =1):<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define new data frame\n<span style=\"color: #000000;\">newdata = data. <span style=\"color: #3366ff;\">frame<\/span> (disp=c(200, 180, 160, 140, 120, 120, 100, 160),\n                     hp=c(100, 90, 108, 90, 80, 90, 80, 90),\n                     am=c(0, 0, 0, 1, 0, 1, 1, 1))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>newdata\n\n<span style=\"color: #008080;\">#use model to predict value of am for all new cars\n<\/span>newdata$am_prob &lt;- predict(model, newdata, type=\" <span style=\"color: #ff0000;\">response<\/span> \")\n\n<span style=\"color: #008080;\">#view updated data frame\n<\/span>newdata\n\n  disp hp am am_prob\n1 200 100 0 0.004225640\n2 180 90 0 0.008361069\n3 160 108 0 0.335916069\n4 140 90 1 0.275162866\n5 120 80 0 0.429961894\n6 120 90 1 0.718090728\n7 100 80 1 0.835013994\n8 160 90 1 0.053546152<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Berikut cara menafsirkan hasilnya:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Peluang terambilnya mobil 1 bertransmisi manual adalah <strong>0,004<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">Peluang terambilnya mobil 2 bertransmisi manual adalah <strong>0,008<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">Peluang terambilnya mobil 3 bertransmisi manual adalah <strong>0,336<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Dan seterusnya.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kita juga dapat menggunakan fungsi <strong>table()<\/strong> untuk membuat matriks konfusi yang menampilkan nilai am aktual versus nilai yang diprediksi oleh model:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create vector that contains 0 or 1 depending on predicted value of am\n<span style=\"color: #000000;\">am_pred = rep(0, dim(newdata)[1])\nam_pred[newdata$am_prob &gt; .5] = 1\n\n<span style=\"color: #008080;\">#create confusion matrix\n<\/span>table(am_pred, newdata$am)\n\nam_pred 0 1\n      0 4 2\n      1 0 2\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Terakhir, kita dapat menggunakan fungsi <strong>Mean()<\/strong> untuk menghitung persentase observasi dalam database baru yang modelnya memprediksi nilai <strong>am<\/strong> dengan benar :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate percentage of observations the model correctly predicted response value for\n<span style=\"color: #000000;\">mean(am_pred == newdata$am)\n\n[1] 0.75\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kita dapat melihat bahwa model tersebut dengan tepat memprediksi nilai <strong>am<\/strong> untuk <strong>75%<\/strong> mobil di database baru.<\/span><\/p>\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\/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-polinomial-r\/\" target=\"_blank\" rel=\"noopener\">Bagaimana melakukan regresi polinomial di R<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/interval-prediksi-r\/\" target=\"_blank\" rel=\"noopener\">Cara membuat interval prediksi di R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Setelah kita memasang model regresi logistik di R, kita dapat menggunakan fungsi prediksi() untuk memprediksi nilai respons dari observasi baru yang belum pernah dilihat model tersebut sebelumnya. Fungsi ini menggunakan sintaks berikut: prediksi(objek, data baru, tipe = \u201crespons\u201d) Emas: objek : Nama model regresi logistik data baru: Nama bingkai data baru yang akan dijadikan prediksi [&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 prediksi() dengan model regresi logistik di R - Statorials<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara membuat prediksi pada data baru menggunakan model regresi logistik di R, beserta 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\/r-prediksi-regresi-logistik\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara menggunakan prediksi() dengan model regresi logistik di R - Statorials\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara membuat prediksi pada data baru menggunakan model regresi logistik di R, beserta sebuah contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/r-prediksi-regresi-logistik\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-11T02:36:29+00:00\" \/>\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\/r-prediksi-regresi-logistik\/\",\"url\":\"https:\/\/statorials.org\/id\/r-prediksi-regresi-logistik\/\",\"name\":\"Cara menggunakan prediksi() dengan model regresi logistik di R - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-11T02:36:29+00:00\",\"dateModified\":\"2023-07-11T02:36:29+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara membuat prediksi pada data baru menggunakan model regresi logistik di R, beserta sebuah contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/r-prediksi-regresi-logistik\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/r-prediksi-regresi-logistik\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/r-prediksi-regresi-logistik\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menggunakan predict() dengan model regresi logistik 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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