{"id":937,"date":"2023-07-28T06:10:31","date_gmt":"2023-07-28T06:10:31","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e4%bf%a1%e9%a0%bc%e5%8c%ba%e9%96%93-r-%e3%82%92%e3%83%95%e3%82%9a%e3%83%ad%e3%83%83%e3%83%88%e3%81%99%e3%82%8b\/"},"modified":"2023-07-28T06:10:31","modified_gmt":"2023-07-28T06:10:31","slug":"%e4%bf%a1%e9%a0%bc%e5%8c%ba%e9%96%93-r-%e3%82%92%e3%83%95%e3%82%9a%e3%83%ad%e3%83%83%e3%83%88%e3%81%99%e3%82%8b","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e4%bf%a1%e9%a0%bc%e5%8c%ba%e9%96%93-r-%e3%82%92%e3%83%95%e3%82%9a%e3%83%ad%e3%83%83%e3%83%88%e3%81%99%e3%82%8b\/","title":{"rendered":"R \u3067\u4fe1\u983c\u533a\u9593\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

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\u4fe1\u983c\u533a\u9593\u306f<\/a>\u3001\u4e00\u5b9a\u306e\u4fe1\u983c\u30ec\u30d9\u30eb\u3067\u6bcd\u96c6\u56e3\u30d1\u30e9\u30e1\u30fc\u30bf\u304c\u542b\u307e\u308c\u308b\u53ef\u80fd\u6027\u304c\u9ad8\u3044\u5024\u306e\u7bc4\u56f2\u3067\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4fe1\u983c\u533a\u9593\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u4f8b: R \u3067\u306e\u4fe1\u983c\u533a\u9593\u306e\u30d7\u30ed\u30c3\u30c8<\/strong><\/h3>\n

R \u306b 100 \u884c 2 \u5217\u306e\u6b21\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n

 #make this example reproducible<\/span>\nset.seed(0)\n\n#create dataset<\/span>\nx <- rnorm(100)\ny <- x*2 + rnorm(100)\ndf <- data.frame(x = x, y = y)\n\n#view first six rows of dataset<\/span>\nhead(df)\n\n           xy\n1 1.2629543 3.3077678\n2 -0.3262334 -1.4292433\n3 1.3297993 2.0436086\n4 1.2724293 2.5914389\n5 0.4146414 -0.3011029\n6 -1.5399500 -2.5031813\n<\/strong><\/pre>\n
 x \u3068 y \u306e\u95a2\u4fc2\u306e\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3059\u308b\u306b\u306f\u3001\u307e\u305a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u307e\u3059\u3002<\/span><\/p>\n
 model <- lm(y ~ x, data = df)\n<\/strong><\/pre>\n
\u6b21\u306b\u3001 abline()<\/a>\u95a2\u6570\u3068 Lines() \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u63a8\u5b9a\u7dda\u5f62\u56de\u5e30\u76f4\u7dda\u306e\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3057\u3001\u5b9f\u969b\u306e\u4fe1\u983c\u5e2f\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span> <\/p>\n
 #get predicted y values using regression equation<\/span>\nnewx <- seq(min(df$x), max(df$x), length.out=100)\npreds <- predict(model, newdata = data.frame(x=newx), interval = 'confidence')\n\n#create plot of x vs. y, but don't display individual points (type='n')<\/span> \nplot(y ~ x, data = df, type = 'n')\n\n#add fitted regression line<\/span>\nabline(model)\n\n#add dashed lines for confidence bands\n<\/span>lines(newx, preds[,3], lty = 'dashed', col = 'blue')\nlines(newx, preds[,2], lty = 'dashed', col = 'blue')<\/strong> <\/pre>\n
\"R<\/p>\n
\u9ed2\u3044\u7dda\u306f\u8fd1\u4f3c\u3055\u308c\u305f\u7dda\u5f62\u56de\u5e30\u76f4\u7dda\u3092\u793a\u3057\u30012 \u3064\u306e\u9752\u3044\u7834\u7dda\u306f\u4fe1\u983c\u533a\u9593\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u4fe1\u983c\u533a\u9593\u306e\u7dda\u3068\u63a8\u5b9a\u7dda\u5f62\u56de\u5e30\u76f4\u7dda\u306e\u9593\u306e\u9818\u57df\u3092\u57cb\u3081\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n
 #create plot of x vs. y<\/span>\nplot(y ~ x, data = df, type = 'n')\n\n#fill in area between regression line and confidence interval\npolygon(c(rev(newx), newx), c(rev(preds[,3]), preds[,2]), col = 'grey', border = NA)<\/span>\n\n#add fitted regression line<\/span>\nabline(model)\n\n#add dashed lines for confidence bands\n<\/span>lines(newx, preds[,3], lty = 'dashed', col = 'blue')\nlines(newx, preds[,2], lty = 'dashed', col = 'blue')<\/strong> <\/pre>\n
\"R<\/p>\n
\u6700\u521d\u304b\u3089\u6700\u5f8c\u307e\u3067\u306e\u5b8c\u5168\u306a\u30b3\u30fc\u30c9\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n
 #make this example reproducible\nset.seed(0)<\/span>\n\n#create dataset\nx <- rnorm(100)<\/span>\ny <- x*2 + rnorm(100)<\/span>\ndf <- data.frame(x = x, y = y)<\/span>\n\n#fit linear regression model\nmodel <- lm(y ~ x, data = df)<\/span>\n\n#get predicted y values using regression equation\nnewx <- seq(min(df$x), max(df$x), length.out=100)<\/span>\npreds <- predict(model, newdata = data.frame(x=newx), interval = 'confidence')<\/span>\n\n#create plot of x vs. y<\/span>\nplot(y ~ x, data = df, type = 'n')\n\n#fill in area between regression line and confidence interval\npolygon(c(rev(newx), newx), c(rev(preds[,3]), preds[,2]), col = 'grey', border = NA)<\/span>\n\n#add fitted regression line<\/span>\nabline(model)\n\n#add dashed lines for confidence bands\n<\/span>lines(newx, preds[,3], lty = 'dashed', col = 'blue')\nlines(newx, preds[,2], lty = 'dashed', col = 'blue')<\/strong><\/pre>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
\u4fe1\u983c\u533a\u9593\u3068\u306f\u4f55\u3067\u3059\u304b?<\/a>
 R \u3067 abline() \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30d7\u30ed\u30c3\u30c8\u306b\u76f4\u7dda\u3092\u8ffd\u52a0\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u4fe1\u983c\u533a\u9593\u306f\u3001\u4e00\u5b9a\u306e\u4fe1\u983c\u30ec\u30d9\u30eb\u3067\u6bcd\u96c6\u56e3\u30d1\u30e9\u30e1\u30fc\u30bf\u304c\u542b\u307e\u308c\u308b\u53ef\u80fd\u6027\u304c\u9ad8\u3044\u5024\u306e\u7bc4\u56f2\u3067\u3059\u3002 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4fe1\u983c\u533a\u9593\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002 \u4f8b: R \u3067\u306e\u4fe1\u983c\u533a\u9593\u306e\u30d7\u30ed\u30c3\u30c8 R \u306b 1 […]<\/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-937","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nR - Statorials \u3067\u4fe1\u983c\u533a\u9593\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5<\/title>\n<meta name=\"description\" content=\"R \u3067\u4fe1\u983c\u533a\u9593\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3068\u3068\u3082\u306b\u7c21\u5358\u306b\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\/\u4fe1\u983c\u533a\u9593-r-\u3092\u30d5\u309a\u30ed\u30c3\u30c8\u3059\u308b\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"R - 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