{"id":1276,"date":"2023-07-27T01:24:14","date_gmt":"2023-07-27T01:24:14","guid":{"rendered":"https:\/\/statorials.org\/id\/dfbetas-di-r\/"},"modified":"2023-07-27T01:24:14","modified_gmt":"2023-07-27T01:24:14","slug":"dfbetas-di-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/dfbetas-di-r\/","title":{"rendered":"Cara menghitung dfbetas di r"},"content":{"rendered":"

<\/p>\n

\n

Dalam statistik, kita sering kali ingin mengetahui pengaruh observasi<\/a> yang berbeda terhadap model regresi.<\/span><\/p>\n

Salah satu cara untuk menghitung pengaruh observasi adalah dengan menggunakan metrik yang dikenal sebagai DFBETAS<\/strong> , yang memberi tahu kita efek standar pada setiap koefisien penghapusan setiap observasi individual.<\/span><\/p>\n

Metrik ini memberi kita gambaran tentang pengaruh setiap observasi terhadap setiap estimasi koefisien dalam model regresi tertentu.<\/span><\/p>\n

Tutorial ini menunjukkan contoh langkah demi langkah cara menghitung dan memvisualisasikan DFBETAS untuk setiap observasi dalam model di R.<\/span><\/p>\n

Langkah 1: Buat model regresi<\/strong><\/span><\/h3>\n

Pertama, kita akan membuat model regresi linier berganda<\/a> menggunakan kumpulan data mtcars<\/strong> yang ada di R:<\/span><\/p>\n

 #fit a regression model<\/span>\nmodel <- lm(mpg~disp+hp, data=mtcars)\n\n#view model summary\n<\/span>summary(model)\n\nCoefficients:\n             Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 30.735904 1.331566 23.083 < 2nd-16 ***\navailable -0.030346 0.007405 -4.098 0.000306 ***\nhp -0.024840 0.013385 -1.856 0.073679 .  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.127 on 29 degrees of freedom\nMultiple R-squared: 0.7482, Adjusted R-squared: 0.7309 \nF-statistic: 43.09 on 2 and 29 DF, p-value: 2.062e-09\n<\/strong><\/pre>\n
 Langkah 2: Hitung DFBETAS untuk setiap observasi<\/strong><\/span><\/h3>\n
 Selanjutnya, kita akan menggunakan fungsi dfbetas()<\/strong> bawaan untuk menghitung nilai DFBETAS untuk setiap observasi dalam model:<\/span><\/p>\n
 #calculate DFBETAS for each observation in the model\n<\/span>dfbetas <- as<\/span> . data<\/span> . frame<\/span> (dfbetas(model))\n\n#display DFBETAS for each observation\n<\/span>dfbetas\n\n                      (Intercept) disp hp\nMazda RX4 -0.1174171253 0.030760632 1.748143e-02\nMazda RX4 Wag -0.1174171253 0.030760632 1.748143e-02\nDatsun 710 -0.1694989349 0.086630144 -3.332781e-05\nHornet 4 Drive 0.0577309674 0.078971334 -8.705488e-02\nHornet Sportabout -0.0204333878 0.237526523 -1.366155e-01\nValiant -0.1711908285 -0.139135639 1.829038e-01\nDuster 360 -0.0312338677 -0.005356209 3.581378e-02\nMerc 240D -0.0312259577 -0.010409922 2.433256e-02\nMerc 230 -0.0865872595 0.016428917 2.287867e-02\nMerc 280 -0.1560683502 0.078667906 -1.911180e-02\nMerc 280C -0.2254489597 0.113639937 -2.760800e-02\nMerc 450SE 0.0022844093 0.002966155 -2.855985e-02\nMerc 450SL 0.0009062022 0.001176644 -1.132941e-02\nMerc 450SLC 0.0041566755 0.005397169 -5.196706e-02\nCadillac Fleetwood 0.0388832216 -0.134511133 7.277283e-02\nLincoln Continental 0.0483781688 -0.121146607 5.326220e-02\nChrysler Imperial -0.1645266331 0.236634429 -3.917771e-02\nFiat 128 0.5720358325 -0.181104179 -1.265475e-01\nHonda Civic 0.3490872162 -0.053660545 -1.326422e-01\nToyota Corolla 0.7367058819 -0.268512348 -1.342384e-01\nToyota Corona -0.2181110386 0.101336902 5.945352e-03\nDodge Challenger -0.0270169005 -0.123610713 9.441241e-02\nAMC Javelin -0.0406785103 -0.141711468 1.074514e-01\nCamaro Z28 0.0390139262 0.012846225 -5.031588e-02\nPontiac Firebird -0.0549059340 0.574544346 -3.689584e-01\nFiat X1-9 0.0565157245 -0.017751582 -1.262221e-02\nPorsche 914-2 0.0839169111 -0.028670987 -1.240452e-02\nLotus Europa 0.3444562478 -0.402678927 2.135224e-01\nFord Pantera L -0.1598854695 -0.094184733 2.320845e-01\nFerrari Dino -0.0343997122 0.248642444 -2.344154e-01\nMaserati Bora -0.3436265545 -0.511285637 7.319066e-01\nVolvo 142E -0.1784974091 0.132692956 -4.433915e-02\n<\/strong><\/pre>\n
 Untuk setiap observasi, kita dapat melihat perbedaan estimasi koefisien titik asal, variabel disp<\/em> , dan variabel hp<\/em> yang muncul ketika kita menghapus observasi tersebut.<\/span><\/p>\n
 Secara umum, kami menganggap bahwa suatu observasi mempunyai pengaruh yang kuat terhadap estimasi koefisien tertentu jika observasi tersebut mempunyai nilai DBETAS yang lebih besar dari ambang batas 2\/\u221a n<\/span> dimana n<\/em> adalah jumlah observasi.<\/span><\/p>\n
 Dalam contoh ini, ambang batasnya adalah 0.3535534<\/strong> :<\/span><\/p>\n
 #find number of observations<\/span>\nn <- nrow<\/span> (mtcars)\n\n#calculate DFBETAS threshold value<\/span>\nthresh <- 2\/ sqrt<\/span> (n)\n\nthresh\n\n[1] 0.3535534\n<\/strong><\/pre>\n
 Langkah 3: Visualisasikan DFBETAS<\/strong><\/p>\n
 Terakhir, kita dapat membuat plot untuk memvisualisasikan nilai DFBETAS untuk setiap observasi dan setiap prediktor dalam model:<\/span> <\/p>\n
 #specify 2 rows and 1 column in plotting region<\/span>\nby(mfrow=c(2,1))\n\n#plot DFBETAS for disp<\/em> with threshold lines<\/span>\nplot(dfbetas$disp, type=' h<\/span> ')\nabline(h = thresh, lty = 2)\nabline(h = -thresh, lty = 2)\n\n#plot DFBETAS for hp<\/em> with threshold lines<\/span> \nplot(dfbetas$hp, type=' h<\/span> ')\nabline(h = thresh, lty = 2)\nabline(h = -thresh, lty = 2)\n<\/strong><\/pre>\n
\"DFBETAS<\/p>\n
 Di setiap plot, sumbu x menampilkan indeks setiap observasi dalam kumpulan data dan nilai y menampilkan DFBETAS yang sesuai untuk setiap observasi dan setiap prediktor.<\/span><\/p>\n
 Pada plot pertama terlihat tiga observasi melebihi nilai ambang batas absolut sebesar 0,3535534<\/strong> dan pada plot kedua terlihat dua observasi melebihi nilai ambang batas absolut.<\/span><\/p>\n
 Kita dapat memilih untuk mempelajari observasi ini lebih dekat untuk menentukan apakah observasi tersebut mempunyai pengaruh yang tidak semestinya terhadap estimasi koefisien model.<\/span><\/p>\n
 Sumber daya tambahan<\/strong><\/span><\/h3>\n
 Cara melakukan regresi linier sederhana di R<\/a>
 Cara melakukan regresi linier berganda di R<\/a>
 Cara menghitung statistik leverage di R<\/a>
 Cara menghitung DFFITS di R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Dalam statistik, kita sering kali ingin mengetahui pengaruh observasi yang berbeda terhadap model regresi. Salah satu cara untuk menghitung pengaruh observasi adalah dengan menggunakan metrik yang dikenal sebagai DFBETAS , yang memberi tahu kita efek standar pada setiap koefisien penghapusan setiap observasi individual. Metrik ini memberi kita gambaran tentang pengaruh setiap observasi terhadap setiap estimasi […]<\/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":"\nCara menghitung DFBETAS di R - Statologi<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menghitung DFBETAS 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\/dfbetas-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 DFBETAS di R - Statologi\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menghitung DFBETAS di R, dengan sebuah contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/dfbetas-di-r\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-27T01:24:14+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/dfbetas1.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\/dfbetas-di-r\/\",\"url\":\"https:\/\/statorials.org\/id\/dfbetas-di-r\/\",\"name\":\"Cara menghitung DFBETAS di R - Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-27T01:24:14+00:00\",\"dateModified\":\"2023-07-27T01:24:14+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menghitung DFBETAS di R, dengan sebuah contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/dfbetas-di-r\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/dfbetas-di-r\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/dfbetas-di-r\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menghitung dfbetas 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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