{"id":453,"date":"2023-07-29T20:44:21","date_gmt":"2023-07-29T20:44:21","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%82%ab%e3%82%99%e3%83%b3%e3%83%9e%e5%88%86%e5%b8%83%e3%82%92-r-%e3%81%ae%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%82%bb%e3%83%83%e3%83%88%e3%81%ab%e9%81%a9%e5%90%88%e3%81%95%e3%81%9b%e3%81%be\/"},"modified":"2023-07-29T20:44:21","modified_gmt":"2023-07-29T20:44:21","slug":"%e3%82%ab%e3%82%99%e3%83%b3%e3%83%9e%e5%88%86%e5%b8%83%e3%82%92-r-%e3%81%ae%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%82%bb%e3%83%83%e3%83%88%e3%81%ab%e9%81%a9%e5%90%88%e3%81%95%e3%81%9b%e3%81%be","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%82%ab%e3%82%99%e3%83%b3%e3%83%9e%e5%88%86%e5%b8%83%e3%82%92-r-%e3%81%ae%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%82%bb%e3%83%83%e3%83%88%e3%81%ab%e9%81%a9%e5%90%88%e3%81%95%e3%81%9b%e3%81%be\/","title":{"rendered":"R \u3067\u30ac\u30f3\u30de\u5206\u5e03\u3092\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3055\u305b\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u30ac\u30f3\u30de\u5206\u5e03\u3092<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3055\u305b\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

R \u3067\u306e\u30ac\u30f3\u30de\u5206\u5e03\u306e\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0<\/span><\/strong><\/h2>\n

\u4ee5\u4e0b\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u4f7f\u7528\u3057\u3066\u751f\u6210\u3055\u308c\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8z<\/em>\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n

 #generate 50 random values that follow a gamma distribution with shape parameter = 3<\/span>\n#and shape parameter = 10 combined with some gaussian noise<\/span>\nz <- rgamma(50, 3, 10) + rnorm(50, 0, .02)\n\n#view first 6 values<\/span>\nhead(z)\n[1] 0.07730 0.02495 0.12788 0.15011 0.08839 0.09941<\/strong><\/pre>\n
\u30ac\u30f3\u30de\u5206\u5e03\u304c\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8z<\/em>\u306b\u3069\u306e\u7a0b\u5ea6\u9069\u5408\u3059\u308b\u304b\u3092\u78ba\u8a8d\u3059\u308b\u306b\u306f\u3001R \u306efitdistrplus<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #install 'fitdistrplus' package if not already installed<\/span>\ninstall. packages<\/span> ('fitdistrplus')\n\n#load package<\/span>\nlibrary(fitdistrplus)<\/strong><\/pre>\n
\u3053\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30a3\u30b9\u30c8\u30ea\u30d3\u30e5\u30fc\u30b7\u30e7\u30f3\u3092\u9069\u5fdc\u3055\u305b\u308b\u305f\u3081\u306b\u4f7f\u7528\u3059\u308b\u4e00\u822c\u7684\u306a\u69cb\u6587\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n
 fitdist(dataset, distr = “\u9078\u629e\u3057\u305f\u5206\u5e03”, Method = “\u30c7\u30fc\u30bf\u3092\u8fd1\u4f3c\u3059\u308b\u65b9\u6cd5”)<\/span><\/strong><\/p>\n
\u3053\u306e\u5834\u5408\u3001\u30ac\u30f3\u30de\u5206\u5e03\u3068\u6700\u5c24\u63a8\u5b9a\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u3092\u8fd1\u4f3c\u3059\u308b\u305f\u3081\u306b\u3001\u4ee5\u524d\u306b\u751f\u6210\u3057\u305fz<\/em>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8fd1\u4f3c\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #fit our dataset to a gamma distribution using mle<\/span>\nfit <- fitdist(z, distr = \"gamma\", method = \"male\")\n\n#view the summary of the fit<\/span>\nsummary(fit)<\/strong><\/pre>\n
\u3053\u308c\u306b\u3088\u308a\u3001\u6b21\u306e\u7d50\u679c\u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306b\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u30ac\u30f3\u30de\u5206\u5e03\u304c\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3069\u306e\u7a0b\u5ea6\u9069\u5408\u3057\u3066\u3044\u308b\u304b\u3092\u793a\u3059\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #produce plots<\/span>\nplot(fit)<\/strong><\/pre>\n
\u3053\u308c\u306b\u3088\u308a\u3001\u6b21\u306e\u30d7\u30ed\u30c3\u30c8\u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n
 R \u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u30ac\u30f3\u30de\u5206\u5e03\u3092\u9069\u5408\u3055\u305b\u308b\u305f\u3081\u306b\u4f7f\u7528\u3057\u305f\u5b8c\u5168\u306a\u30b3\u30fc\u30c9\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n
 #install 'fitdistrplus' package if not already installed\n<\/span>install. packages<\/span> ('fitdistrplus')\n\n#load package\n<\/span>library(fitdistrplus)\n\n#generate 50 random values that follow a gamma distribution with shape parameter = 3\n<\/span>#and shape parameter = 10 combined with some gaussian noise\n<\/span>z <- rgamma(50, 3, 10) + rnorm(50, 0, .02)\n\n#fit our dataset to a gamma distribution using mle\n<\/span>fit <- fitdist(z, distr = \"gamma\", method = \"male\")\n\n#view the summary of the fit\n<\/span>summary(fit)\n\n#produce plots to visualize the fit\n<\/span>plot(fit)\n<\/strong><\/pre>\n","protected":false},"excerpt":{"rendered":"
\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u30ac\u30f3\u30de\u5206\u5e03\u3092\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3055\u305b\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002 R \u3067\u306e\u30ac\u30f3\u30de\u5206\u5e03\u306e\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0 \u4ee5\u4e0b\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u4f7f\u7528\u3057\u3066\u751f\u6210\u3055\u308c\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8z\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002 #generate 50 […]<\/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-453","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nR \u3067\u30ac\u30f3\u30de\u5206\u5e03\u3092\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3055\u305b\u308b\u65b9\u6cd5 - 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