{"id":1547,"date":"2023-07-25T22:47:19","date_gmt":"2023-07-25T22:47:19","guid":{"rendered":"https:\/\/statorials.org\/id\/glm-vs-lm-di-r\/"},"modified":"2023-07-25T22:47:19","modified_gmt":"2023-07-25T22:47:19","slug":"glm-vs-lm-di-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/glm-vs-lm-di-r\/","title":{"rendered":"Perbedaan antara glm dan lm di r"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Bahasa pemrograman R menyediakan fungsi berikut untuk menyesuaikan model linier:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. lm \u2013 Digunakan untuk menyesuaikan model linier<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Fungsi ini menggunakan sintaks 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;\"><strong>2. glm \u2013 Digunakan untuk menyesuaikan model linier umum<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Fungsi ini menggunakan sintaks berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>glm(rumus, keluarga=Gaussian, 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>keluarga:<\/strong> keluarga statistik yang digunakan agar sesuai dengan model. Standarnya adalah Gaussian, tetapi opsi lainnya antara lain Binomial, Gamma, dan Poisson.<\/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;\">Perhatikan bahwa satu-satunya perbedaan antara kedua fungsi ini adalah argumen <strong>keluarga<\/strong> yang disertakan dalam fungsi <strong>glm()<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Jika Anda menggunakan lm() atau glm() agar sesuai dengan model regresi linier, <strong>keduanya akan memberikan hasil yang persis sama<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Namun, fungsi glm() juga dapat digunakan untuk menyesuaikan model yang lebih kompleks seperti:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Regresi logistik (keluarga=binomial)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/id\/regresi-ikan\/\" target=\"_blank\" rel=\"noopener\">Regresi Poisson<\/a> (keluarga=ikan)<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menggunakan fungsi lm() dan glm() dalam praktiknya.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh penggunaan fungsi lm()<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menyesuaikan <strong>model regresi linier<\/strong> menggunakan fungsi lm():<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>model &lt;- lm(mpg ~ disp + hp, data=mtcars)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = mpg ~ disp + hp, data = mtcars)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-4.7945 -2.3036 -0.8246 1.8582 6.9363 \n\nCoefficients:\n             Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 30.735904 1.331566 23.083 &lt; 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<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Contoh penggunaan fungsi glm()<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menyesuaikan <strong>model regresi linier<\/strong> yang sama menggunakan fungsi glm():<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>model &lt;- glm(mpg ~ disp + hp, data=mtcars)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = mpg ~ disp + hp, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-4.7945 -2.3036 -0.8246 1.8582 6.9363  \n\nCoefficients:\n             Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 30.735904 1.331566 23.083 &lt; 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\n(Dispersion parameter for gaussian family taken to be 9.775636)\n\n    Null deviance: 1126.05 on 31 degrees of freedom\nResidual deviance: 283.49 on 29 degrees of freedom\nAIC: 168.62\n\nNumber of Fisher Scoring iterations: 2<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Perhatikan bahwa estimasi koefisien dan kesalahan standar estimasi koefisien sama persis dengan yang dihasilkan oleh fungsi lm().<\/span><\/p>\n<p> <span style=\"color: #000000;\">Perhatikan bahwa kita juga dapat menggunakan fungsi glm() agar sesuai dengan <strong>model regresi logistik<\/strong> dengan menentukan family=binomial sebagai berikut:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit logistic regression model\n<\/span>model &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 juga dapat menggunakan fungsi glm() agar sesuai dengan <strong>model regresi Poisson<\/strong> dengan menentukan family=poisson sebagai berikut:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit Poisson regression model\n<\/span>model &lt;- glm(am ~ disp + hp, data=mtcars, family=fish)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = am ~ disp + hp, family = fish, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-1.1266 -0.4629 -0.2453 0.1797 1.5428  \n\nCoefficients:\n             Estimate Std. Error z value Pr(&gt;|z|)   \n(Intercept) 0.214255 0.593463 0.361 0.71808   \navailable -0.018915 0.007072 -2.674 0.00749 **\nhp 0.016522 0.007163 2.307 0.02107 * \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for fish family taken to be 1)\n\n    Null deviance: 23,420 on 31 degrees of freedom\nResidual deviance: 10,526 on 29 degrees of freedom\nAIC: 42,526\n\nNumber of Fisher Scoring iterations: 6\n<\/strong><\/pre>\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\/r-glm-memprediksi\/\" target=\"_blank\" rel=\"noopener\">Cara menggunakan fungsi prediksi dengan glm di R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Bahasa pemrograman R menyediakan fungsi berikut untuk menyesuaikan model linier: 1. lm \u2013 Digunakan untuk menyesuaikan model linier Fungsi ini menggunakan sintaks berikut: lm(rumus, data,\u2026) Emas: rumus : Rumus model linier (misal y ~ x1 + x2) data: Nama blok data yang berisi data 2. glm \u2013 Digunakan untuk menyesuaikan model linier umum Fungsi ini [&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 - 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