{"id":1639,"date":"2023-07-25T13:26:30","date_gmt":"2023-07-25T13:26:30","guid":{"rendered":"https:\/\/statorials.org\/ja\/r%e3%82%ab%e3%83%bc%e3%83%95%e3%82%99%e8%aa%bf%e6%95%b4\/"},"modified":"2023-07-25T13:26:30","modified_gmt":"2023-07-25T13:26:30","slug":"r%e3%82%ab%e3%83%bc%e3%83%95%e3%82%99%e8%aa%bf%e6%95%b4","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r%e3%82%ab%e3%83%bc%e3%83%95%e3%82%99%e8%aa%bf%e6%95%b4\/","title":{"rendered":"R \u3067\u306e\u66f2\u7dda\u8fd1\u4f3c (\u4f8b\u4ed8\u304d)"},"content":{"rendered":"

<\/p>\n

\n

\u591a\u304f\u306e\u5834\u5408\u3001R \u306e\u66f2\u7dda\u306b\u6700\u3082\u3088\u304f\u9069\u5408\u3059\u308b\u65b9\u7a0b\u5f0f\u3092\u898b\u3064\u3051\u305f\u3044\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/span> <\/p>\n

\"R<\/p>\n

\u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3067\u306f\u3001 poly()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u66f2\u7dda\u3092 R \u306e\u30c7\u30fc\u30bf\u306b\u9069\u5408\u3055\u305b\u308b\u65b9\u6cd5\u3068\u3001\u3069\u306e\u66f2\u7dda\u304c\u30c7\u30fc\u30bf\u306b\u6700\u3082\u9069\u5408\u3059\u308b\u304b\u3092\u5224\u65ad\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u306e\u4f5c\u6210\u3068\u8996\u899a\u5316<\/strong><\/span><\/h3>\n

\u307e\u305a\u507d\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f5c\u6210\u3057\u3001\u6b21\u306b\u6563\u5e03\u56f3\u3092\u4f5c\u6210\u3057\u3066\u30c7\u30fc\u30bf\u3092\u8996\u899a\u5316\u3057\u307e\u3057\u3087\u3046\u3002<\/span> <\/p>\n

 #create data frame\n<\/span>df <- data. frame<\/span> (x=1:15,\n                 y=c(3, 14, 23, 25, 23, 15, 9, 5, 9, 13, 17, 24, 32, 36, 46))\n\n#create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch= 19<\/span> , xlab=' x<\/span> ', ylab=' y<\/span> ')<\/strong> <\/pre>\n
\"\"<\/p>\n
\u30b9\u30c6\u30c3\u30d7 2: \u8907\u6570\u306e\u30ab\u30fc\u30d6\u3092\u8abf\u6574\u3059\u308b<\/strong><\/span><\/h3>\n
\u6b21\u306b\u3001\u3044\u304f\u3064\u304b\u306e\u591a\u9805\u5f0f\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u30c7\u30fc\u30bf\u306b\u9069\u5408\u3055\u305b\u3001\u540c\u3058\u30d7\u30ed\u30c3\u30c8\u3067\u5404\u30e2\u30c7\u30eb\u306e\u66f2\u7dda\u3092\u8996\u899a\u5316\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/span> <\/p>\n
 #fit polynomial regression models up to degree 5\n<\/span>fit1 <- lm(y~x, data=df)\nfit2 <- lm(y~poly(x,2,raw= TRUE<\/span> ), data=df)\nfit3 <- lm(y~poly(x,3,raw= TRUE<\/span> ), data=df)\nfit4 <- lm(y~poly(x,4,raw= TRUE<\/span> ), data=df)\nfit5 <- lm(y~poly(x,5,raw= TRUE<\/span> ), data=df)<\/span>\n\n#create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch=19, xlab=' x<\/span> ', ylab=' y<\/span> ')\n\n#define x-axis values\n<\/span>x_axis <- seq(1, 15, length= 15<\/span> )\n\n#add curve of each model to plot\n<\/span>lines(x_axis, predict(fit1, data. frame<\/span> (x=x_axis)), col=' green<\/span> ')\nlines(x_axis, predict(fit2, data. frame<\/span> (x=x_axis)), col=' red<\/span> ')\nlines(x_axis, predict(fit3, data. frame<\/span> (x=x_axis)), col=' purple<\/span> ')\nlines(x_axis, predict(fit4, data. frame<\/span> (x=x_axis)), col=' blue<\/span> ')\nlines(x_axis, predict(fit5, data. frame<\/span> (x=x_axis)), col=' orange<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
\u3069\u306e\u66f2\u7dda\u304c\u30c7\u30fc\u30bf\u306b\u6700\u3082\u3088\u304f\u9069\u5408\u3059\u308b\u304b\u3092\u5224\u65ad\u3059\u308b\u306b\u306f\u3001\u5404\u30e2\u30c7\u30eb\u306e\u8abf\u6574\u3055\u308c\u305f R \u4e8c\u4e57<\/a>\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u306e\u5024\u306f\u3001\u4e88\u6e2c\u5909\u6570\u306e\u6570\u3092\u8abf\u6574\u3057\u305f\u3001\u30e2\u30c7\u30eb\u5185\u306e\u4e88\u6e2c\u5909\u6570\u306b\u3088\u3063\u3066\u8aac\u660e\u3067\u304d\u308b\u5fdc\u7b54\u5909\u6570\u306e\u5909\u52d5\u306e\u30d1\u30fc\u30bb\u30f3\u30c6\u30fc\u30b8\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #calculated adjusted R-squared of each model\n<\/span>summary(fit1)$adj. r<\/span> . squared<\/span>\nsummary(fit2)$adj. r<\/span> . squared<\/span>\nsummary(fit3)$adj. r<\/span> . squared<\/span>\nsummary(fit4)$adj. r<\/span> . squared<\/span>\nsummary(fit5)$adj. r<\/span> . squared<\/span>\n\n[1] 0.3144819\n[1] 0.5186706\n[1] 0.7842864\n[1] 0.9590276\n[1] 0.9549709\n<\/strong><\/pre>\n
\u7d50\u679c\u304b\u3089\u3001\u8abf\u6574\u3055\u308c\u305f R \u4e8c\u4e57\u304c\u6700\u3082\u9ad8\u3044\u30e2\u30c7\u30eb\u306f\u3001\u8abf\u6574\u3055\u308c\u305f R \u4e8c\u4e57\u304c0.959<\/strong>\u3067\u3042\u308b 4 \u6b21\u591a\u9805\u5f0f\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u30b9\u30c6\u30c3\u30d7 3: \u6700\u7d42\u7684\u306a\u66f2\u7dda\u3092\u8996\u899a\u5316\u3059\u308b<\/strong><\/span><\/h3>\n
\u6700\u5f8c\u306b\u30014 \u6b21\u591a\u9805\u5f0f\u30e2\u30c7\u30eb\u306e\u66f2\u7dda\u3092\u4f7f\u7528\u3057\u3066\u6563\u5e03\u56f3\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n
 #create a scatterplot of x vs. y\n<\/span>plot(df$x, df$y, pch=19, xlab=' x<\/span> ', ylab=' y<\/span> ')\n\n#define x-axis values\n<\/span>x_axis <- seq(1, 15, length= 15<\/span> )\n\n#add curve of fourth-degree polynomial model\n<\/span>lines(x_axis, predict(fit4, data. frame<\/span> (x=x_axis)), col=' blue<\/span> ')\n<\/strong><\/pre>\n
\"R<\/p>\n
 summary()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u306e\u884c\u306e\u65b9\u7a0b\u5f0f\u3092\u53d6\u5f97\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/span><\/p>\n
 summary(fit4)\n\nCall:\nlm(formula = y ~ poly(x, 4, raw = TRUE), data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-3.4490 -1.1732 0.6023 1.4899 3.0351 \n\nCoefficients:\n                         Estimate Std. Error t value Pr(>|t|)    \n(Intercept) -26.51615 4.94555 -5.362 0.000318 ***\npoly(x, 4, raw = TRUE)1 35.82311 3.98204 8.996 4.15e-06 ***\npoly(x, 4, raw = TRUE)2 -8.36486 0.96791 -8.642 5.95e-06 ***\npoly(x, 4, raw = TRUE)3 0.70812 0.08954 7.908 1.30e-05 ***\npoly(x, 4, raw = TRUE)4 -0.01924 0.00278 -6.922 4.08e-05 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.424 on 10 degrees of freedom\nMultiple R-squared: 0.9707, Adjusted R-squared: 0.959 \nF-statistic: 82.92 on 4 and 10 DF, p-value: 1.257e-07\n<\/strong><\/pre>\n
\u66f2\u7dda\u306e\u65b9\u7a0b\u5f0f\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n
 y = -0.0192x 4<\/sup> + 0.7081x 3<\/sup> \u2013 8.3649x 2<\/sup> + 35.823x \u2013 26.516<\/span><\/p>\n
\u3053\u306e\u65b9\u7a0b\u5f0f\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u30e2\u30c7\u30eb\u5185\u306e\u4e88\u6e2c\u5909\u6570\u306b\u57fa\u3065\u3044\u3066\u5fdc\u7b54\u5909\u6570<\/a>\u306e\u5024\u3092\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001 x<\/em> = 4 \u306e\u5834\u5408\u3001 y<\/em> = 23.34<\/strong>\u3068\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n
 y = -0.0192(4) 4<\/sup> + 0.7081(4) 3<\/sup> \u2013 8.3649(4) 2<\/sup> + 35.823(4) \u2013 26.516 = 23.34<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
\u591a\u9805\u5f0f\u56de\u5e30\u306e\u6982\u8981
<\/a>R \u306e\u591a\u9805\u5f0f\u56de\u5e30 (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7)<\/a>
 R\u3067seq\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u591a\u304f\u306e\u5834\u5408\u3001R \u306e\u66f2\u7dda\u306b\u6700\u3082\u3088\u304f\u9069\u5408\u3059\u308b\u65b9\u7a0b\u5f0f\u3092\u898b\u3064\u3051\u305f\u3044\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002 \u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3067\u306f\u3001 poly()\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u66f2\u7dda\u3092 R \u306e\u30c7\u30fc\u30bf\u306b\u9069\u5408\u3055\u305b\u308b\u65b9\u6cd5\u3068\u3001\u3069\u306e\u66f2\u7dda\u304c\u30c7\u30fc\u30bf\u306b\u6700\u3082\u9069\u5408\u3059\u308b\u304b\u3092\u5224\u65ad\u3059\u308b […]<\/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-1639","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nR \u3067\u306e\u66f2\u7dda\u8fd1\u4f3c (\u4f8b\u4ed8\u304d) - \u7d71\u8a08<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u70b9\u7fa4\u306b\u66f2\u7dda\u3092\u5f53\u3066\u306f\u3081\u308b\u65b9\u6cd5\u3092\u3044\u304f\u3064\u304b\u306e\u4f8b\u3068\u3068\u3082\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\/r\u30ab\u30fc\u30d5\u3099\u8abf\u6574\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"R \u3067\u306e\u66f2\u7dda\u8fd1\u4f3c (\u4f8b\u4ed8\u304d) - 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