{"id":1963,"date":"2023-07-24T06:50:02","date_gmt":"2023-07-24T06:50:02","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e5%a4%9a%e9%a0%85%e5%bc%8f%e5%9b%9e%e5%b8%b0%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":"2023-07-24T06:50:02","modified_gmt":"2023-07-24T06:50:02","slug":"r-%e3%81%a6%e3%82%99%e5%a4%9a%e9%a0%85%e5%bc%8f%e5%9b%9e%e5%b8%b0%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\/r-%e3%81%a6%e3%82%99%e5%a4%9a%e9%a0%85%e5%bc%8f%e5%9b%9e%e5%b8%b0%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\u591a\u9805\u5f0f\u56de\u5e30\u66f2\u7dda\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u591a\u9805\u5f0f\u56de\u5e30\u306f\u3001<\/strong>\u4e88\u6e2c\u5909\u6570\u3068\u5fdc\u7b54\u5909\u6570<\/a>\u306e\u9593\u306e\u95a2\u4fc2\u304c\u975e\u7dda\u5f62\u3067\u3042\u308b\u5834\u5408\u306b\u4f7f\u7528\u3059\u308b\u56de\u5e30\u624b\u6cd5\u3067\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u591a\u9805\u5f0f\u56de\u5e30\u66f2\u7dda\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u95a2\u9023:<\/strong>\u6700\u3082\u4e00\u822c\u7684\u306a 7 \u7a2e\u985e\u306e\u56de\u5e30<\/a><\/span><\/p>\n

\u4f8b: R \u3067\u306e\u591a\u9805\u5f0f\u56de\u5e30\u66f2\u7dda\u306e\u30d7\u30ed\u30c3\u30c8<\/strong><\/span><\/h3>\n

\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u591a\u9805\u5f0f\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u8fd1\u4f3c\u3057\u3001\u6563\u5e03\u56f3\u306e\u751f\u30c7\u30fc\u30bf\u306b\u591a\u9805\u5f0f\u56de\u5e30\u66f2\u7dda\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span> <\/p>\n

 #define data\n<\/span>x <- runif(50, 5, 15)\ny <- 0.1*x^3 - 0.5 * x^2 - x + 5 + rnorm(length(x),0,10) \n \n#plot x vs. y\n<\/span>plot(x, y, pch= 16<\/span> , cex= 1.5<\/span> ) \n \n#fit polynomial regression model\n<\/span>fit <- lm(y ~ x + I(x^2) + I(x^3))\n \n#use model to get predicted values\n<\/span>pred <- predict(fit)\nix <- sort(x, index. return<\/span> = T<\/span> )$ix\n\n#add polynomial curve to plot\n<\/span>lines(x[ix], pred[ix], col=' red<\/span> ', lwd= 2<\/span> )\n<\/strong><\/pre>\n
\"R<\/p>\n
 text()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u8fd1\u4f3c\u3055\u308c\u305f\u591a\u9805\u5f0f\u56de\u5e30\u65b9\u7a0b\u5f0f\u3092\u30d7\u30ed\u30c3\u30c8\u306b\u8ffd\u52a0\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n
 #define data\n<\/span>x <- runif(50, 5, 15)\ny <- 0.1*x^3 - 0.5 * x^2 - x + 5 + rnorm(length(x),0,10) \n \n#plot x vs. y\n<\/span>plot(x, y, pch=16, cex=1.5) \n \n#fit polynomial regression model\n<\/span>fit <- lm(y ~ x + I(x^2) + I(x^3))\n \n#use model to get predicted values\n<\/span>pred <- predict(fit)\nix <- sort(x, index. return<\/span> = T<\/span> )$ix\n\n#add polynomial curve to plot\n<\/span>lines(x[ix], pred[ix], col=' red<\/span> ', lwd= 2<\/span> )\n\n#get model coefficients\n<\/span>coeff <- round(fit$coefficients, 2)\n\n#add fitted model equation to plot\n<\/span>text(9, 200 , paste(\"Model: \", coeff[1], \" + \", coeff[2],\n                    \"*x\", \"+\", coeff[3], \"*x^2\", \"+\", coeff[4], \"*x^3\"), cex= 1.3<\/span> )\n<\/strong><\/pre>\n
\"\"<\/p>\n
 cex<\/strong>\u5f15\u6570\u306f\u30c6\u30ad\u30b9\u30c8\u306e\u30d5\u30a9\u30f3\u30c8 \u30b5\u30a4\u30ba\u3092\u5236\u5fa1\u3059\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306f 1 \u306a\u306e\u3067\u3001\u30c6\u30ad\u30b9\u30c8\u3092\u8aad\u307f\u3084\u3059\u304f\u3059\u308b\u305f\u3081\u306b\u50241.3<\/strong>\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u306b\u3057\u307e\u3057\u305f\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
\u591a\u9805\u5f0f\u56de\u5e30\u306e\u6982\u8981<\/a>
Excel \u3067\u591a\u9805\u5f0f\u66f2\u7dda\u3092\u8fd1\u4f3c\u3059\u308b\u65b9\u6cd5<\/a>
Python \u3067\u591a\u9805\u5f0f\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u591a\u9805\u5f0f\u56de\u5e30\u306f\u3001\u4e88\u6e2c\u5909\u6570\u3068\u5fdc\u7b54\u5909\u6570\u306e\u9593\u306e\u95a2\u4fc2\u304c\u975e\u7dda\u5f62\u3067\u3042\u308b\u5834\u5408\u306b\u4f7f\u7528\u3059\u308b\u56de\u5e30\u624b\u6cd5\u3067\u3059\u3002 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u591a\u9805\u5f0f\u56de\u5e30\u66f2\u7dda\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002 \u95a2\u9023:\u6700\u3082\u4e00\u822c\u7684\u306a 7 \u7a2e\u985e\u306e\u56de\u5e30 \u4f8b: R \u3067\u306e\u591a\u9805\u5f0f […]<\/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-1963","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nR \u3067\u591a\u9805\u5f0f\u56de\u5e30\u66f2\u7dda\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5 - 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