{"id":1218,"date":"2023-07-27T06:02:37","date_gmt":"2023-07-27T06:02:37","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%ae%e5%88%86%e9%a1%9e%e6%9c%a8%e3%81%a8%e5%9b%9e%e5%b8%b0%e6%9c%a8\/"},"modified":"2023-07-27T06:02:37","modified_gmt":"2023-07-27T06:02:37","slug":"r-%e3%81%ae%e5%88%86%e9%a1%9e%e6%9c%a8%e3%81%a8%e5%9b%9e%e5%b8%b0%e6%9c%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%ae%e5%88%86%e9%a1%9e%e6%9c%a8%e3%81%a8%e5%9b%9e%e5%b8%b0%e6%9c%a8\/","title":{"rendered":"R \u3067\u5206\u985e\u6728\u3068\u56de\u5e30\u6728\u3092\u5f53\u3066\u306f\u3081\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u4e00\u9023\u306e\u4e88\u6e2c\u5909\u6570\u3068\u5fdc\u7b54\u5909\u6570\u306e<\/a>\u9593\u306e\u95a2\u4fc2\u304c\u7dda\u5f62\u306e\u5834\u5408\u3001\u91cd\u7dda\u5f62\u56de\u5e30<\/a>\u306a\u3069\u306e\u65b9\u6cd5\u3067\u6b63\u78ba\u306a\u4e88\u6e2c\u30e2\u30c7\u30eb\u3092\u751f\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u305f\u3060\u3057\u3001\u4e00\u9023\u306e\u4e88\u6e2c\u5909\u6570\u3068\u5fdc\u7b54\u306e\u9593\u306e\u95a2\u4fc2\u304c\u3088\u308a\u8907\u96d1\u306a\u5834\u5408\u3001\u591a\u304f\u306e\u5834\u5408\u3001\u975e\u7dda\u5f62\u624b\u6cd5\u306e\u65b9\u304c\u3088\u308a\u6b63\u78ba\u306a\u30e2\u30c7\u30eb\u3092\u751f\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u305d\u306e\u3088\u3046\u306a\u65b9\u6cd5\u306e 1 \u3064\u304c\u5206\u985e\u56de\u5e30\u30c4\u30ea\u30fc<\/a>(CART) \u3067\u3059\u3002\u3053\u308c\u306f\u3001\u4e00\u9023\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u5fdc\u7b54\u5909\u6570\u306e\u5024\u3092\u4e88\u6e2c\u3059\u308b\u6c7a\u5b9a\u6728\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u5fdc\u7b54\u5909\u6570\u304c\u9023\u7d9a\u578b\u306e\u5834\u5408\u306f\u56de\u5e30\u30c4\u30ea\u30fc\u3092\u69cb\u7bc9\u3067\u304d\u3001\u5fdc\u7b54\u5909\u6570\u304c\u30ab\u30c6\u30b4\u30ea\u578b\u306e\u5834\u5408\u306f\u5206\u985e\u30c4\u30ea\u30fc\u3092\u69cb\u7bc9\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u56de\u5e30\u30c4\u30ea\u30fc\u3068\u5206\u985e\u30c4\u30ea\u30fc\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u4f8b 1: R \u3067\u306e\u56de\u5e30\u30c4\u30ea\u30fc\u306e\u69cb\u7bc9<\/strong><\/span><\/h3>\n

\u3053\u306e\u4f8b\u3067\u306f\u3001 ISLR<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u306eHitters<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u3053\u308c\u306b\u306f\u3001263 \u4eba\u306e\u30d7\u30ed\u91ce\u7403\u9078\u624b\u306b\u95a2\u3059\u308b\u3055\u307e\u3056\u307e\u306a\u60c5\u5831\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u3001\u30db\u30fc\u30e0\u30e9\u30f3\u3068\u51fa\u5834<\/em>\u5e74\u6570<\/em>\u306e\u4e88\u6e2c\u5909\u6570\u3092\u4f7f\u7528\u3057\u3066\u7279\u5b9a\u306e\u9078\u624b\u306e\u5e74\u4ff8<\/em>\u3092\u4e88\u6e2c\u3059\u308b\u56de\u5e30\u30c4\u30ea\u30fc\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u56de\u5e30\u30c4\u30ea\u30fc\u3092\u4f5c\u6210\u3059\u308b\u306b\u306f\u3001\u6b21\u306e\u624b\u9806\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u30b9\u30c6\u30c3\u30d7 1: \u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002<\/strong><\/span><\/p>\n

\u307e\u305a\u3001\u3053\u306e\u4f8b\u306b\u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002<\/span><\/p>\n

 library<\/span> (ISLR) #contains Hitters dataset<\/span>\nlibrary<\/span> (rpart) #for fitting decision trees<\/span>\nlibrary<\/span> (rpart.plot) #for plotting decision trees\n<\/span><\/strong><\/pre>\n
\u30b9\u30c6\u30c3\u30d7 2: \u521d\u671f\u56de\u5e30\u30c4\u30ea\u30fc\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/strong><\/span><\/p>\n
\u307e\u305a\u3001\u5927\u898f\u6a21\u306a\u521d\u671f\u56de\u5e30\u30c4\u30ea\u30fc\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002 \u300c\u8907\u96d1\u3055\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u300d\u3092\u8868\u3059cp<\/strong>\u306b\u5c0f\u3055\u306a\u5024\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u30c4\u30ea\u30fc\u304c\u5927\u304d\u3044\u3053\u3068\u3092\u4fdd\u8a3c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u306f\u3001\u30e2\u30c7\u30eb\u5168\u4f53\u306e R \u4e8c\u4e57\u304c\u5c11\u306a\u304f\u3068\u3082 cp \u3067\u6307\u5b9a\u3055\u308c\u305f\u5024\u3060\u3051\u5897\u52a0\u3059\u308b\u9650\u308a\u3001\u56de\u5e30\u30c4\u30ea\u30fc\u3067\u3055\u3089\u306b\u5206\u5272\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306b\u3001 printcp()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u3092\u51fa\u529b\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #build the initial tree\n<\/span>tree <- rpart(Salary ~ Years + HmRun, data=Hitters, control=rpart. control<\/span> (cp= .0001<\/span> ))\n\n#view results<\/span>\nprintcp(tree)\n\nVariables actually used in tree construction:\n[1] HmRun Years\n\nRoot node error: 53319113\/263 = 202734\n\nn=263 (59 observations deleted due to missingness)\n\n           CP nsplit rel error xerror xstd\n1 0.24674996 0 1.00000 1.00756 0.13890\n2 0.10806932 1 0.75325 0.76438 0.12828\n3 0.01865610 2 0.64518 0.70295 0.12769\n4 0.01761100 3 0.62652 0.70339 0.12337\n5 0.01747617 4 0.60891 0.70339 0.12337\n6 0.01038188 5 0.59144 0.66629 0.11817\n7 0.01038065 6 0.58106 0.65697 0.11687\n8 0.00731045 8 0.56029 0.67177 0.11913\n9 0.00714883 9 0.55298 0.67881 0.11960\n10 0.00708618 10 0.54583 0.68034 0.11988\n11 0.00516285 12 0.53166 0.68427 0.11997\n12 0.00445345 13 0.52650 0.68994 0.11996\n13 0.00406069 14 0.52205 0.68988 0.11940\n14 0.00264728 15 0.51799 0.68874 0.11916\n15 0.00196586 16 0.51534 0.68638 0.12043\n16 0.00016686 17 0.51337 0.67577 0.11635\n17 0.00010000 18 0.51321 0.67576 0.11615\nn=263 (59 observations deleted due to missingness)\n<\/span><\/span><\/strong><\/pre>\n
\u30b9\u30c6\u30c3\u30d7 3: \u6728\u3092\u526a\u5b9a\u3057\u307e\u3059\u3002<\/strong><\/span><\/p>\n
\u6b21\u306b\u3001\u56de\u5e30\u30c4\u30ea\u30fc\u3092\u679d\u5208\u308a\u3057\u3066\u3001\u30c6\u30b9\u30c8\u8aa4\u5dee\u3092\u6700\u5c0f\u306b\u3059\u308b cp (\u8907\u96d1\u3055\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc) \u306b\u4f7f\u7528\u3059\u308b\u6700\u9069\u306a\u5024\u3092\u898b\u3064\u3051\u307e\u3059\u3002<\/span><\/p>\n
 cp \u306e\u6700\u9069\u5024\u306f\u3001\u524d\u306e\u51fa\u529b\u3067\u6700\u5c0f\u306ex \u8aa4\u5dee<\/strong>\u3092\u3082\u305f\u3089\u3059\u5024\u3067\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u3053\u308c\u306f\u3001\u76f8\u4e92\u691c\u8a3c\u30c7\u30fc\u30bf\u304b\u3089\u306e\u89b3\u6e2c\u5024\u306e\u8aa4\u5dee\u3092\u8868\u3057\u307e\u3059\u3002<\/span> <\/p>\n
 #identify best cp value to use\nbest <- tree$cptable[which. min<\/span> (tree$cptable[,\" xerror<\/span> \"]),\" CP<\/span> \"]\n\n#produce a pruned tree based on the best cp value\n<\/span>pruned_tree <- prune<\/span> (tree, cp=best)\n\n#plot the pruned tree\n<\/span>prp(pruned_tree,\n    faclen= 0<\/span> , #use full names for factor labels<\/span>\n    extra= 1<\/span> , #display number of obs. for each terminal node<\/span>\n    roundint= F<\/span> , #don't round to integers in output<\/span>\n    digits= 5<\/span> ) #display 5 decimal places in output\n<\/span><\/span><\/span><\/span><\/strong><\/pre>\n
\"R<\/p>\n
\u6700\u7d42\u7684\u306b\u679d\u5208\u308a\u3055\u308c\u305f\u30c4\u30ea\u30fc\u306b\u306f 6 \u3064\u306e\u7d42\u7aef\u30ce\u30fc\u30c9\u304c\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u5404\u30ea\u30fc\u30d5 \u30ce\u30fc\u30c9\u306b\u306f\u3001\u305d\u306e\u30ce\u30fc\u30c9\u5185\u306e\u9078\u624b\u306e\u4e88\u6e2c\u5e74\u4ff8\u3068\u3001\u305d\u306e\u30b0\u30ec\u30fc\u30c9\u306b\u5c5e\u3059\u308b\u5143\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u306e\u89b3\u6e2c\u5024\u306e\u6570\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n
\u305f\u3068\u3048\u3070\u3001\u5143\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u3001\u7d4c\u9a13\u304c 4.5 \u5e74\u672a\u6e80\u306e\u30d7\u30ec\u30fc\u30e4\u30fc\u304c 90 \u4eba\u3044\u3066\u3001\u5e73\u5747\u7d66\u4e0e\u304c 225.83,000 \u30c9\u30eb\u3067\u3042\u3063\u305f\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span> <\/p>\n
\"R<\/p>\n
\u30b9\u30c6\u30c3\u30d7 4: \u30c4\u30ea\u30fc\u3092\u4f7f\u7528\u3057\u3066\u4e88\u6e2c\u3092\u884c\u3044\u307e\u3059\u3002<\/strong><\/span><\/p>\n
\u6700\u7d42\u7684\u306b\u526a\u5b9a\u3055\u308c\u305f\u6728\u3092\u4f7f\u7528\u3057\u3066\u3001\u7279\u5b9a\u306e\u9078\u624b\u306e\u5e74\u4ff8\u3068\u5e73\u5747\u30db\u30fc\u30e0\u30e9\u30f3\u306b\u57fa\u3065\u3044\u3066\u305d\u306e\u9078\u624b\u306e\u5e74\u4ff8\u3092\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u305f\u3068\u3048\u3070\u30017 \u5e74\u306e\u7d4c\u9a13\u3068\u5e73\u5747 4 \u672c\u5841\u6253\u306e\u30d7\u30ec\u30fc\u30e4\u30fc\u306e\u671f\u5f85\u5e74\u4ff8\u306f502.81,000 \u30c9\u30eb<\/strong>\u3067\u3059\u3002<\/span> <\/p>\n
\"R<\/p>\n
 R \u306edetect()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3053\u308c\u3092\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #define new player\nnew <- data.frame(Years=7, HmRun=4)\n\n<\/span>#use pruned tree to predict salary of this player\npredict(pruned_tree, newdata=new)\n\n502.8079<\/span><\/span><\/span><\/strong><\/pre>\n
\u4f8b 2: R \u3067\u5206\u985e\u30c4\u30ea\u30fc\u3092\u69cb\u7bc9\u3059\u308b<\/strong><\/span><\/h3>\n
\u3053\u306e\u4f8b\u3067\u306f\u3001 rpart.plot<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u306eptitanic<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u3053\u308c\u306b\u306f\u3001\u30bf\u30a4\u30bf\u30cb\u30c3\u30af\u53f7\u306e\u4e57\u5ba2\u306b\u95a2\u3059\u308b\u3055\u307e\u3056\u307e\u306a\u60c5\u5831\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u3001\u4e88\u6e2c\u5b50\u5909\u6570class<\/em> \u3001\u6027\u5225<\/em>\u3001\u304a\u3088\u3073\u5e74\u9f62<\/em>\u3092\u4f7f\u7528\u3057\u3066\u7279\u5b9a\u306e\u4e57\u5ba2\u304c\u751f\u5b58\u3057\u305f\u304b\u3069\u3046\u304b\u3092\u4e88\u6e2c\u3059\u308b\u5206\u985e\u30c4\u30ea\u30fc\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u306e\u5206\u985e\u30c4\u30ea\u30fc\u3092\u4f5c\u6210\u3059\u308b\u306b\u306f\u3001\u6b21\u306e\u624b\u9806\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u30b9\u30c6\u30c3\u30d7 1: \u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002<\/strong><\/span><\/p>\n
\u307e\u305a\u3001\u3053\u306e\u4f8b\u306b\u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002<\/span><\/p>\n
 library<\/span> (rpart) #for fitting decision trees<\/span>\nlibrary<\/span> (rpart.plot) #for plotting decision trees\n<\/span><\/strong><\/pre>\n
\u30b9\u30c6\u30c3\u30d7 2: \u521d\u671f\u5206\u985e\u30c4\u30ea\u30fc\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/strong><\/span><\/p>\n
\u307e\u305a\u3001\u5927\u898f\u6a21\u306a\u521d\u671f\u5206\u985e\u30c4\u30ea\u30fc\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002 \u300c\u8907\u96d1\u3055\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u300d\u3092\u8868\u3059cp<\/strong>\u306b\u5c0f\u3055\u306a\u5024\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u30c4\u30ea\u30fc\u304c\u5927\u304d\u3044\u3053\u3068\u3092\u4fdd\u8a3c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u306f\u3001\u30e2\u30c7\u30eb\u5168\u4f53\u306e\u9069\u5408\u5ea6\u304c\u5c11\u306a\u304f\u3068\u3082 cp \u3067\u6307\u5b9a\u3055\u308c\u305f\u5024\u3060\u3051\u5897\u52a0\u3059\u308b\u9650\u308a\u3001\u5206\u985e\u30c4\u30ea\u30fc\u3067\u3055\u3089\u306b\u5206\u5272\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306b\u3001 printcp()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u3092\u51fa\u529b\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #build the initial tree\n<\/span>tree <- rpart(survived~pclass+sex+age, data=ptitanic, control=rpart. control<\/span> (cp= .0001<\/span> ))\n\n#view results<\/span>\nprintcp(tree)\n\nVariables actually used in tree construction:\n[1] age pclass sex   \n\nRoot node error: 500\/1309 = 0.38197\n\nn=1309 \n\n      CP nsplit rel error xerror xstd\n1 0.4240 0 1.000 1.000 0.035158\n2 0.0140 1 0.576 0.576 0.029976\n3 0.0095 3 0.548 0.578 0.030013\n4 0.0070 7 0.510 0.552 0.029517\n5 0.0050 9 0.496 0.528 0.029035\n6 0.0025 11 0.486 0.532 0.029117\n7 0.0020 19 0.464 0.536 0.029198\n8 0.0001 22 0.458 0.528 0.029035\n<\/span><\/span><\/strong><\/pre>\n
\u30b9\u30c6\u30c3\u30d7 3: \u6728\u3092\u526a\u5b9a\u3057\u307e\u3059\u3002<\/strong><\/span><\/p>\n
\u6b21\u306b\u3001\u56de\u5e30\u30c4\u30ea\u30fc\u3092\u679d\u5208\u308a\u3057\u3066\u3001\u30c6\u30b9\u30c8\u8aa4\u5dee\u3092\u6700\u5c0f\u306b\u3059\u308b cp (\u8907\u96d1\u3055\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc) \u306b\u4f7f\u7528\u3059\u308b\u6700\u9069\u306a\u5024\u3092\u898b\u3064\u3051\u307e\u3059\u3002<\/span><\/p>\n
 cp \u306e\u6700\u9069\u5024\u306f\u3001\u524d\u306e\u51fa\u529b\u3067\u6700\u5c0f\u306ex \u8aa4\u5dee<\/strong>\u3092\u3082\u305f\u3089\u3059\u5024\u3067\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u3053\u308c\u306f\u3001\u76f8\u4e92\u691c\u8a3c\u30c7\u30fc\u30bf\u304b\u3089\u306e\u89b3\u6e2c\u5024\u306e\u8aa4\u5dee\u3092\u8868\u3057\u307e\u3059\u3002<\/span> <\/p>\n
 #identify best cp value to use\nbest <- tree$cptable[which. min<\/span> (tree$cptable[,\" xerror<\/span> \"]),\" CP<\/span> \"]\n\n#produce a pruned tree based on the best cp value\n<\/span>pruned_tree <- prune<\/span> (tree, cp=best)\n\n#plot the pruned tree\n<\/span>prp(pruned_tree,\n    faclen= 0<\/span> , #use full names for factor labels\n    extra= 1<\/span> , #display number of obs. for each terminal node\n    roundint= F<\/span> , #don't round to integers in output\n    digits= 5<\/span> ) #display 5 decimal places in output\n<\/span><\/span><\/span><\/strong><\/pre>\n
\"R<\/p>\n
\u6700\u7d42\u7684\u306b\u679d\u5208\u308a\u3055\u308c\u305f\u30c4\u30ea\u30fc\u306b\u306f 10 \u500b\u306e\u7d42\u7aef\u30ce\u30fc\u30c9\u304c\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u5404\u30bf\u30fc\u30df\u30ca\u30eb \u30ce\u30fc\u30c9\u306f\u3001\u6b7b\u4ea1\u3057\u305f\u4e57\u5ba2\u306e\u6570\u3068\u751f\u5b58\u8005\u306e\u6570\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u305f\u3068\u3048\u3070\u3001\u4e00\u756a\u5de6\u306e\u30ce\u30fc\u30c9\u3067\u306f\u3001664 \u4eba\u306e\u4e57\u5ba2\u304c\u6b7b\u4ea1\u3057\u3001136 \u4eba\u304c\u751f\u304d\u6b8b\u3063\u305f\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span> <\/p>\n
\"R<\/p>\n
\u30b9\u30c6\u30c3\u30d7 4: \u30c4\u30ea\u30fc\u3092\u4f7f\u7528\u3057\u3066\u4e88\u6e2c\u3092\u884c\u3044\u307e\u3059\u3002<\/strong><\/span><\/p>\n
\u6700\u7d42\u7684\u306b\u526a\u5b9a\u3055\u308c\u305f\u6728\u3092\u4f7f\u7528\u3057\u3066\u3001\u30af\u30e9\u30b9\u3001\u5e74\u9f62\u3001\u6027\u5225\u306b\u57fa\u3065\u3044\u3066\u7279\u5b9a\u306e\u4e57\u5ba2\u304c\u751f\u304d\u6b8b\u308b\u78ba\u7387\u3092\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u305f\u3068\u3048\u3070\u30011 \u30af\u30e9\u30b9\u306e 8 \u6b73\u306e\u7537\u6027\u4e57\u5ba2\u306e\u751f\u5b58\u78ba\u7387\u306f 11\/29 = 37.9% \u3067\u3059\u3002<\/span> <\/p>\n
\"R<\/p>\n
\u3053\u308c\u3089\u306e\u4f8b\u3067\u4f7f\u7528\u3055\u308c\u3066\u3044\u308b\u5b8c\u5168\u306a R \u30b3\u30fc\u30c9\u306f\u3001\u3053\u3053\u3067<\/a>\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\u4e00\u9023\u306e\u4e88\u6e2c\u5909\u6570\u3068\u5fdc\u7b54\u5909\u6570\u306e\u9593\u306e\u95a2\u4fc2\u304c\u7dda\u5f62\u306e\u5834\u5408\u3001\u91cd\u7dda\u5f62\u56de\u5e30\u306a\u3069\u306e\u65b9\u6cd5\u3067\u6b63\u78ba\u306a\u4e88\u6e2c\u30e2\u30c7\u30eb\u3092\u751f\u6210\u3067\u304d\u307e\u3059\u3002 \u305f\u3060\u3057\u3001\u4e00\u9023\u306e\u4e88\u6e2c\u5909\u6570\u3068\u5fdc\u7b54\u306e\u9593\u306e\u95a2\u4fc2\u304c\u3088\u308a\u8907\u96d1\u306a\u5834\u5408\u3001\u591a\u304f\u306e\u5834\u5408\u3001\u975e\u7dda\u5f62\u624b\u6cd5\u306e\u65b9\u304c\u3088\u308a\u6b63\u78ba\u306a\u30e2\u30c7\u30eb\u3092\u751f\u6210\u3067\u304d\u307e\u3059\u3002 […]<\/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-1218","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nR 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