{"id":3898,"date":"2023-07-14T21:02:11","date_gmt":"2023-07-14T21:02:11","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e7%a0%82%e7%b3%96%e6%bc%ac%e3%81%91%e3%81%aer\/"},"modified":"2023-07-14T21:02:11","modified_gmt":"2023-07-14T21:02:11","slug":"%e7%a0%82%e7%b3%96%e6%bc%ac%e3%81%91%e3%81%aer","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e7%a0%82%e7%b3%96%e6%bc%ac%e3%81%91%e3%81%aer\/","title":{"rendered":"R \u3067 confit() \u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">R \u306e<strong>confint()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u8fd1\u4f3c\u56de\u5e30\u30e2\u30c7\u30eb\u5185\u306e 1 \u3064\u4ee5\u4e0a\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u306e\u4fe1\u983c\u533a\u9593\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <strong><span style=\"color: #000000;\">confint(\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3001\u30d1\u30e9\u30e1\u30fc\u30bf\u3001\u30ec\u30d9\u30eb=0.95)<\/span><\/strong><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>object<\/strong> : \u8fd1\u4f3c\u3055\u308c\u305f\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u540d\u524d<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>parm<\/strong> : \u4fe1\u983c\u533a\u9593\u3092\u8a08\u7b97\u3059\u308b\u30d1\u30e9\u30e1\u30fc\u30bf (\u30c7\u30d5\u30a9\u30eb\u30c8\u306f all)<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>level<\/strong> : \u4f7f\u7528\u3059\u308b\u4fe1\u983c\u30ec\u30d9\u30eb (\u30c7\u30d5\u30a9\u30eb\u30c8\u5024\u306f 0.95)<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b: R \u3067 confit() \u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">R \u306b\u3001\u30af\u30e9\u30b9\u306e 10 \u4eba\u306e\u751f\u5f92\u306e\u5b66\u7fd2\u6642\u9593\u6570\u3001\u53d7\u9a13\u3057\u305f\u6a21\u64ec\u8a66\u9a13\u306e\u6570\u3001\u304a\u3088\u3073\u6700\u7d42\u8a66\u9a13\u306e\u5f97\u70b9\u3092\u793a\u3059\u6b21\u306e\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data frame\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (score=c(77, 79, 84, 85, 88, 99, 95, 90, 92, 94),\n                 hours=c(1, 1, 2, 3, 2, 4, 4, 2, 3, 3),\n                 prac_exams=c(2, 3, 3, 2, 4, 5, 4, 3, 5, 4))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\n   score hours prac_exams\n1 77 1 2\n2 79 1 3\n3 84 2 3\n4 85 3 2\n5 88 2 4\n6 99 4 5\n7 95 4 4\n8 90 2 3\n9 92 3 5\n10 94 3 4\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u3053\u3067\u3001\u6b21\u306e\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092 R \u306b\u5f53\u3066\u306f\u3081\u305f\u3044\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8a66\u9a13\u306e\u30b9\u30b3\u30a2 = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> (\u6642\u9593\u6570) + \u03b2 <sub>2<\/sub> (\u5b9f\u6280\u8a66\u9a13)<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/r\u306elm\u95a2\u6570\/\" target=\"_blank\" rel=\"noopener\">lm()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u306e\u30e2\u30c7\u30eb\u3092\u9069\u5fdc\u3055\u305b\u308b\u3053\u3068\u304c\u3067\u304d\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>fit &lt;- lm(score ~ hours + prac_exams, data=df)\n\n<span style=\"color: #008080;\">#view summary of model\n<\/span>summary(fit)\n\nCall:\nlm(formula = score ~ hours + prac_exams, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-2.4324 -1.2632 -0.8956 0.4316 5.1412 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 68.4029 2.8723 23.815 5.85e-08 ***\nhours 4.1912 0.9961 4.207 0.0040 ** \nprac_exams 2.6912 0.9961 2.702 0.0306 *  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.535 on 7 degrees of freedom\nMultiple R-squared: 0.9005, Adjusted R-squared: 0.8721 \nF-statistic: 31.68 on 2 and 7 DF, p-value: 0.0003107\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u306e\u6982\u8981\u306b\u306f\u3001\u8fd1\u4f3c\u3055\u308c\u305f\u56de\u5e30\u4fc2\u6570\u304c\u8868\u793a\u3055\u308c\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5207\u7247 = 68.4029<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6642\u9593 = 4.1912<\/span><\/li>\n<li> <span style=\"color: #000000;\">prac_exams = 2.6912<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3089\u306e\u5404\u4fc2\u6570\u306e 95% \u4fe1\u983c\u533a\u9593\u3092\u53d6\u5f97\u3059\u308b\u306b\u306f\u3001 <strong>confint()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate 95% confidence interval for each coefficient in model<\/span>\nconfined(fit)\n\n                 2.5% 97.5%\n(Intercept) 61.6111102 75.194772\nhours 1.8357237 6.546629\nprac_exams 0.3357237 5.046629\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u5404\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u306e 95% \u4fe1\u983c\u533a\u9593\u306f\u6b21\u306e\u3088\u3046\u306b\u793a\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5207\u7247\u306e 95% CI = [61.61, 75.19]<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6642\u9593\u306e 95% CI = [1.84, 6.55]<\/span><\/li>\n<li> <span style=\"color: #000000;\">prac_exams \u306e 95% CI = [0.34, 5.05]<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">99% \u4fe1\u983c\u533a\u9593\u3092\u8a08\u7b97\u3059\u308b\u306b\u306f\u3001 <strong>level<\/strong>\u5f15\u6570\u306e\u5024\u3092\u5909\u66f4\u3059\u308b\u3060\u3051\u3067\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate 99% confidence interval for each coefficient in model\n<\/span>confint(fit, level= <span style=\"color: #008000;\">0.99<\/span> )\n\n                 0.5% 99.5%\n(Intercept) 58.3514926 78.454390\nhours 0.7052664 7.677087\nprac_exams -0.7947336 6.177087\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u307e\u305f\u3001\u7279\u5b9a\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u306e\u4fe1\u983c\u533a\u9593\u306e\u307f\u3092\u8a08\u7b97\u3059\u308b\u306b\u306f\u3001 <strong>parm<\/strong>\u5f15\u6570\u3092\u4f7f\u7528\u3057\u3066\u4fc2\u6570\u3092\u6307\u5b9a\u3059\u308b\u3060\u3051\u3067\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate 99% confidence interval for hours\n<\/span>confint(fit, parm=' <span style=\"color: #ff0000;\">hours<\/span> ', level= <span style=\"color: #008000;\">0.99<\/span> )\n\n          0.5% 99.5%\nhours 0.7052664 7.677087\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">99% \u4fe1\u983c\u533a\u9593\u306f\u6642\u9593\u5909\u6570\u306b\u3064\u3044\u3066\u306e\u307f\u8868\u793a\u3055\u308c\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e\u7dda\u5f62\u56de\u5e30\u306b\u95a2\u3059\u308b\u8ffd\u52a0\u60c5\u5831\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ja\/r-\u306e\u56de\u5e30\u51fa\u529b\u3092\u89e3\u91c8\u3059\u308b\/\" target=\"_blank\" rel=\"noopener\">R \u3067\u56de\u5e30\u51fa\u529b\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5<\/a><br \/><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 \/>R \u3067\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/p>\n","protected":false},"excerpt":{"rendered":"<p>R \u306econfint()\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u8fd1\u4f3c\u56de\u5e30\u30e2\u30c7\u30eb\u5185\u306e 1 \u3064\u4ee5\u4e0a\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u306e\u4fe1\u983c\u533a\u9593\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002 \u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002 confint(\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3001\u30d1\u30e9\u30e1\u30fc\u30bf\u3001\u30ec\u30d9\u30eb=0.95) \u91d1\uff1a  [&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-3898","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 \u3067 confint() 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