{"id":1544,"date":"2023-07-25T22:47:19","date_gmt":"2023-07-25T22:47:19","guid":{"rendered":"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/"},"modified":"2023-07-25T22:47:19","modified_gmt":"2023-07-25T22:47:19","slug":"glm-vs-lm-in-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/","title":{"rendered":"R\u306eglm\u3068lm\u306e\u9055\u3044"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">R \u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u8a00\u8a9e\u306f\u3001\u7dda\u5f62\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u305f\u3081\u306e\u6b21\u306e\u95a2\u6570\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. lm \u2013 \u7dda\u5f62\u30e2\u30c7\u30eb\u306e\u8fd1\u4f3c\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>lm(\u5f0f\u3001\u30c7\u30fc\u30bf\u3001\u2026)<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u5f0f:<\/strong>\u7dda\u5f62\u30e2\u30c7\u30eb\u306e\u5f0f (\u4f8b: y ~ x1 + x2)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>data:<\/strong>\u30c7\u30fc\u30bf\u3092\u542b\u3080\u30c7\u30fc\u30bf \u30d6\u30ed\u30c3\u30af\u306e\u540d\u524d<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\"><strong>2. glm \u2013 \u4e00\u822c\u5316\u7dda\u5f62\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>glm(\u5f0f\u3001\u30d5\u30a1\u30df\u30ea\u30fc=\u30ac\u30a6\u30b9\u3001\u30c7\u30fc\u30bf\u3001\u2026)<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u5f0f:<\/strong>\u7dda\u5f62\u30e2\u30c7\u30eb\u306e\u5f0f (\u4f8b: y ~ x1 + x2)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>family:<\/strong>\u30e2\u30c7\u30eb\u306e\u9069\u5408\u306b\u4f7f\u7528\u3059\u308b\u7d71\u8a08\u7684\u30d5\u30a1\u30df\u30ea\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306f\u30ac\u30a6\u30b9\u3067\u3059\u304c\u3001\u4ed6\u306e\u30aa\u30d7\u30b7\u30e7\u30f3\u306b\u306f\u4e8c\u9805\u3001\u30ac\u30f3\u30de\u3001\u30dd\u30a2\u30bd\u30f3\u306a\u3069\u304c\u542b\u307e\u308c\u307e\u3059\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>data:<\/strong>\u30c7\u30fc\u30bf\u3092\u542b\u3080\u30c7\u30fc\u30bf \u30d6\u30ed\u30c3\u30af\u306e\u540d\u524d<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3089 2 \u3064\u306e\u95a2\u6570\u306e\u552f\u4e00\u306e\u9055\u3044\u306f\u3001 <strong>glm()<\/strong>\u95a2\u6570\u306b\u542b\u307e\u308c\u308b<strong>family<\/strong>\u5f15\u6570\u3067\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">lm() \u307e\u305f\u306f glm() \u3092\u4f7f\u7528\u3057\u3066\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b\u3068\u3001<strong>\u307e\u3063\u305f\u304f\u540c\u3058\u7d50\u679c\u304c\u5f97\u3089\u308c\u307e\u3059<\/strong>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3060\u3057\u3001 glm() \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u6b21\u306e\u3088\u3046\u306a\u3088\u308a\u8907\u96d1\u306a\u30e2\u30c7\u30eb\u3092\u9069\u5408\u3055\u305b\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30 (\u30d5\u30a1\u30df\u30ea\u30fc=\u4e8c\u9805)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/\u9b5a\u306e\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener\">\u30dd\u30a2\u30bd\u30f3\u56de\u5e30<\/a>(\u30d5\u30a1\u30df\u30ea\u30fc=\u9b5a)<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001lm() \u95a2\u6570\u3068 glm() \u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>lm()\u95a2\u6570\u306e\u4f7f\u7528\u4f8b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001lm() \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066<strong>\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb<\/strong>\u3092\u8fd1\u4f3c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/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>glm() \u95a2\u6570\u306e\u4f7f\u7528\u4f8b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001glm() \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u307e\u3063\u305f\u304f\u540c\u3058<strong>\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092<\/strong>\u8fd1\u4f3c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/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;\">\u4fc2\u6570\u63a8\u5b9a\u5024\u3068\u4fc2\u6570\u63a8\u5b9a\u5024\u306e\u6a19\u6e96\u8aa4\u5dee\u306f\u3001lm() \u95a2\u6570\u306b\u3088\u3063\u3066\u751f\u6210\u3055\u308c\u308b\u3082\u306e\u3068\u307e\u3063\u305f\u304f\u540c\u3058\u3067\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u3088\u3046\u306b family=binomial \u3092\u6307\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001 glm() \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066<strong>\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb<\/strong>\u3092\u8fd1\u4f3c\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/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;\">\u6b21\u306e\u3088\u3046\u306b\u3001glm() \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066 family=poisson \u3092\u6307\u5b9a\u3059\u308b\u3053\u3068\u306b\u3088\u308a\u3001<strong>\u30dd\u30a2\u30bd\u30f3\u56de\u5e30\u30e2\u30c7\u30eb\u3092<\/strong>\u8fd1\u4f3c\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/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>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/ja\/r-\u3066\u3099\u306e\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u91cd\u7dda\u5f62\u56de\u5e30r\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u91cd\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/r-glm-\u4e88\u6e2c\/\" target=\"_blank\" rel=\"noopener\">R\u306eglm\u3067predict\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>R \u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u8a00\u8a9e\u306f\u3001\u7dda\u5f62\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u305f\u3081\u306e\u6b21\u306e\u95a2\u6570\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002 1. lm \u2013 \u7dda\u5f62\u30e2\u30c7\u30eb\u306e\u8fd1\u4f3c\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 \u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002 lm(\u5f0f\u3001\u30c7\u30fc\u30bf\u3001\u2026) \u91d1\uff1a \u5f0f:\u7dda\u5f62\u30e2\u30c7\u30eb\u306e\u5f0f (\u4f8b: y  [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-1544","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>R\u306eglm\u3068lm\u306e\u9055\u3044<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e glm \u95a2\u6570\u3068 lm \u95a2\u6570\u306e\u9055\u3044\u306b\u3064\u3044\u3066\u3001\u3044\u304f\u3064\u304b\u306e\u4f8b\u3092\u793a\u3057\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\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\/ja\/glm-vs-lm-in-r\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"R\u306eglm\u3068lm\u306e\u9055\u3044\" \/>\n<meta property=\"og:description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e glm \u95a2\u6570\u3068 lm \u95a2\u6570\u306e\u9055\u3044\u306b\u3064\u3044\u3066\u3001\u3044\u304f\u3064\u304b\u306e\u4f8b\u3092\u793a\u3057\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-25T22:47:19+00:00\" \/>\n<meta name=\"author\" content=\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u57f7\u7b46\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u63a8\u5b9a\u8aad\u307f\u53d6\u308a\u6642\u9593\" \/>\n\t<meta name=\"twitter:data2\" content=\"2\u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/\",\"url\":\"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/\",\"name\":\"R\u306eglm\u3068lm\u306e\u9055\u3044\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/ja\/#website\"},\"datePublished\":\"2023-07-25T22:47:19+00:00\",\"dateModified\":\"2023-07-25T22:47:19+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83\"},\"description\":\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e glm \u95a2\u6570\u3068 lm \u95a2\u6570\u306e\u9055\u3044\u306b\u3064\u3044\u3066\u3001\u3044\u304f\u3064\u304b\u306e\u4f8b\u3092\u793a\u3057\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/#breadcrumb\"},\"inLanguage\":\"ja\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"\u5bb6\",\"item\":\"https:\/\/statorials.org\/ja\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"R\u306eglm\u3068lm\u306e\u9055\u3044\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/ja\/#website\",\"url\":\"https:\/\/statorials.org\/ja\/\",\"name\":\"Statorials\",\"description\":\"\u7d71\u8a08\u80fd\u529b\u3078\u306e\u30ac\u30a4\u30c9\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/ja\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"ja\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83\",\"name\":\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"ja\",\"@id\":\"https:\/\/statorials.org\/ja\/#\/schema\/person\/image\/\",\"url\":\"http:\/\/statorials.org\/ja\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"http:\/\/statorials.org\/ja\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\"},\"description\":\"\u79c1\u306f\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u3067\u3059\u3002\u9000\u8077\u3057\u305f\u7d71\u8a08\u6559\u6388\u304b\u3089\u3001\u5c02\u4efb\u306e Statorials \u6559\u80b2\u8005\u306b\u306a\u308a\u307e\u3057\u305f\u3002 \u7d71\u8a08\u5206\u91ce\u306b\u304a\u3051\u308b\u8c4a\u5bcc\u306a\u7d4c\u9a13\u3068\u5c02\u9580\u77e5\u8b58\u3092\u6d3b\u304b\u3057\u3066\u3001\u79c1\u306f Statorials \u3092\u901a\u3058\u3066\u5b66\u751f\u306b\u529b\u3092\u4e0e\u3048\u308b\u305f\u3081\u306b\u81ea\u5206\u306e\u77e5\u8b58\u3092\u5171\u6709\u3059\u308b\u3053\u3068\u306b\u5c3d\u529b\u3057\u3066\u3044\u307e\u3059\u3002\u3082\u3063\u3068\u77e5\u308b\",\"sameAs\":[\"http:\/\/statorials.org\/ja\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"R\u306eglm\u3068lm\u306e\u9055\u3044","description":"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e glm \u95a2\u6570\u3068 lm \u95a2\u6570\u306e\u9055\u3044\u306b\u3064\u3044\u3066\u3001\u3044\u304f\u3064\u304b\u306e\u4f8b\u3092\u793a\u3057\u3066\u8aac\u660e\u3057\u307e\u3059\u3002","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/","og_locale":"ja_JP","og_type":"article","og_title":"R\u306eglm\u3068lm\u306e\u9055\u3044","og_description":"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e glm \u95a2\u6570\u3068 lm \u95a2\u6570\u306e\u9055\u3044\u306b\u3064\u3044\u3066\u3001\u3044\u304f\u3064\u304b\u306e\u4f8b\u3092\u793a\u3057\u3066\u8aac\u660e\u3057\u307e\u3059\u3002","og_url":"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/","og_site_name":"Statorials","article_published_time":"2023-07-25T22:47:19+00:00","author":"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb","twitter_card":"summary_large_image","twitter_misc":{"\u57f7\u7b46\u8005":"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb","\u63a8\u5b9a\u8aad\u307f\u53d6\u308a\u6642\u9593":"2\u5206"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/","url":"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/","name":"R\u306eglm\u3068lm\u306e\u9055\u3044","isPartOf":{"@id":"https:\/\/statorials.org\/ja\/#website"},"datePublished":"2023-07-25T22:47:19+00:00","dateModified":"2023-07-25T22:47:19+00:00","author":{"@id":"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83"},"description":"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e glm \u95a2\u6570\u3068 lm \u95a2\u6570\u306e\u9055\u3044\u306b\u3064\u3044\u3066\u3001\u3044\u304f\u3064\u304b\u306e\u4f8b\u3092\u793a\u3057\u3066\u8aac\u660e\u3057\u307e\u3059\u3002","breadcrumb":{"@id":"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/#breadcrumb"},"inLanguage":"ja","potentialAction":[{"@type":"ReadAction","target":["https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/statorials.org\/ja\/glm-vs-lm-in-r\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"\u5bb6","item":"https:\/\/statorials.org\/ja\/"},{"@type":"ListItem","position":2,"name":"R\u306eglm\u3068lm\u306e\u9055\u3044"}]},{"@type":"WebSite","@id":"https:\/\/statorials.org\/ja\/#website","url":"https:\/\/statorials.org\/ja\/","name":"Statorials","description":"\u7d71\u8a08\u80fd\u529b\u3078\u306e\u30ac\u30a4\u30c9","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/statorials.org\/ja\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"ja"},{"@type":"Person","@id":"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83","name":"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb","image":{"@type":"ImageObject","inLanguage":"ja","@id":"https:\/\/statorials.org\/ja\/#\/schema\/person\/image\/","url":"http:\/\/statorials.org\/ja\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg","contentUrl":"http:\/\/statorials.org\/ja\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg","caption":"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb"},"description":"\u79c1\u306f\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u3067\u3059\u3002\u9000\u8077\u3057\u305f\u7d71\u8a08\u6559\u6388\u304b\u3089\u3001\u5c02\u4efb\u306e Statorials \u6559\u80b2\u8005\u306b\u306a\u308a\u307e\u3057\u305f\u3002 \u7d71\u8a08\u5206\u91ce\u306b\u304a\u3051\u308b\u8c4a\u5bcc\u306a\u7d4c\u9a13\u3068\u5c02\u9580\u77e5\u8b58\u3092\u6d3b\u304b\u3057\u3066\u3001\u79c1\u306f Statorials \u3092\u901a\u3058\u3066\u5b66\u751f\u306b\u529b\u3092\u4e0e\u3048\u308b\u305f\u3081\u306b\u81ea\u5206\u306e\u77e5\u8b58\u3092\u5171\u6709\u3059\u308b\u3053\u3068\u306b\u5c3d\u529b\u3057\u3066\u3044\u307e\u3059\u3002\u3082\u3063\u3068\u77e5\u308b","sameAs":["http:\/\/statorials.org\/ja"]}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"_links":{"self":[{"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/posts\/1544","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/comments?post=1544"}],"version-history":[{"count":0,"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/posts\/1544\/revisions"}],"wp:attachment":[{"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/media?parent=1544"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/categories?post=1544"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/tags?post=1544"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}