{"id":4613,"date":"2023-07-09T14:43:59","date_gmt":"2023-07-09T14:43:59","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%ae%e6%a8%99%e6%ba%96%e5%8c%96%e5%9b%9e%e5%b8%b0%e4%bf%82%e6%95%b0\/"},"modified":"2023-07-09T14:43:59","modified_gmt":"2023-07-09T14:43:59","slug":"r-%e3%81%ae%e6%a8%99%e6%ba%96%e5%8c%96%e5%9b%9e%e5%b8%b0%e4%bf%82%e6%95%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%ae%e6%a8%99%e6%ba%96%e5%8c%96%e5%9b%9e%e5%b8%b0%e4%bf%82%e6%95%b0\/","title":{"rendered":"R \u3067\u6a19\u6e96\u5316\u56de\u5e30\u4fc2\u6570\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u901a\u5e38\u3001<a href=\"https:\/\/statorials.org\/ja\/\u91cd\u7dda\u5f62\u56de\u5e30r\/\" target=\"_blank\" rel=\"noopener\">\u91cd\u7dda\u5f62\u56de\u5e30<\/a>\u3092\u5b9f\u884c\u3059\u308b\u5834\u5408\u3001\u30e2\u30c7\u30eb\u51fa\u529b\u3067\u5f97\u3089\u308c\u308b\u56de\u5e30\u4fc2\u6570\u306f<strong>\u6a19\u6e96\u5316\u3055\u308c\u3066\u3044\u307e\u305b\u3093<\/strong>\u3002\u3064\u307e\u308a\u3001\u751f\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u6700\u9069\u306a\u76f4\u7dda\u3092\u898b\u3064\u3051\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>model &lt;- lm(price ~ age + sqfeet, data=df)\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u305f\u3060\u3057\u3001\u5404\u4e88\u6e2c\u5909\u6570\u3068\u5fdc\u7b54\u5909\u6570\u3092<strong>\u6a19\u6e96\u5316\u3057\u3066<\/strong>(\u5143\u306e\u5024\u304b\u3089\u5404\u5909\u6570\u306e\u5e73\u5747\u5024\u3092\u5f15\u304d\u3001\u5909\u6570\u306e\u6a19\u6e96\u504f\u5dee\u3067\u5272\u308b\u3053\u3068\u306b\u3088\u308a)\u3001\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u3053\u3068\u306f\u53ef\u80fd\u3067\u3059\u3002<strong>\u6a19\u6e96\u5316\u3055\u308c\u305f\u56de\u5e30\u4fc2\u6570<\/strong>\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">R \u3067\u6a19\u6e96\u5316\u3055\u308c\u305f\u56de\u5e30\u4fc2\u6570\u3092\u8a08\u7b97\u3059\u308b\u6700\u3082\u7c21\u5358\u306a\u65b9\u6cd5\u306f\u3001 <a href=\"https:\/\/statorials.org\/ja\/r\u306e\u30b9\u30b1\u30fc\u30eb\u95a2\u6570\/\" target=\"_blank\" rel=\"noopener\">scale()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u5185\u306e\u5404\u5909\u6570\u3092\u6a19\u6e96\u5316\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>model &lt;- lm(scale(price) ~ scale(age) + scale(sqfeet), data=df)<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u5b9f\u969b\u306b\u6a19\u6e96\u5316\u56de\u5e30\u4fc2\u6570\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><strong>\u4f8b: R \u3067\u6a19\u6e96\u5316\u3055\u308c\u305f\u56de\u5e30\u4fc2\u6570\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/strong><\/h2>\n<p><span style=\"color: #000000;\">12 \u6238\u306e\u4f4f\u5b85\u306e\u7bc9\u5e74\u6570\u3001\u9762\u7a4d\u3001\u8ca9\u58f2\u4fa1\u683c\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080\u6b21\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data frame\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (age=c(4, 7, 10, 15, 16, 18, 24, 28, 30, 35, 40, 44),\n                 sqfeet=c(2600, 2800, 1700, 1300, 1500, 1800,\n                          1200, 2200, 1800, 1900, 2100, 1300),\n                 price=c(280000, 340000, 195000, 180000, 150000, 200000,\n                         180000, 240000, 200000, 180000, 260000, 140000))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\n   age square feet price\n1 4 2600 280000\n2 7 2800 340000\n3 10 1700 195000\n4 15 1300 180000\n5 16 1500 150000\n6 18 1800 200000\n7 24 1200 180000\n8 28 2200 240000\n9 30 1800 200000\n10 35 1900 180000\n11 40 2100 260000\n12 44 1300 140000\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001<strong>\u7bc9\u5e74\u6570<\/strong>\u3068<strong>\u5e73\u65b9\u30d5\u30a3\u30fc\u30c8\u3092<\/strong>\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u3001<strong>\u4fa1\u683c\u3092<\/strong>\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u91cd\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit regression model\n<\/span>model &lt;- lm(price ~ age + sqfeet, data=df)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = price ~ age + sqfeet, data = df)\n\nResiduals:\n   Min 1Q Median 3Q Max \n-32038 -10526 -6139 21641 34060 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 34736.54 37184.32 0.934 0.374599    \nage -409.83 612.46 -0.669 0.520187    \nsqfeet 100.87 15.75 6.405 0.000125 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 24690 on 9 degrees of freedom\nMultiple R-squared: 0.8508, Adjusted R-squared: 0.8176 \nF-statistic: 25.65 on 2 and 9 DF, p-value: 0.0001916\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u304b\u3089\u3001<strong>\u6a19\u6e96\u5316\u3055\u308c\u3066\u3044\u306a\u3044\u56de\u5e30\u4fc2\u6570\u304c<\/strong>\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30a4\u30f3\u30bf\u30fc\u30bb\u30d7\u30c8: <strong>34736.54<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\">\u5e74\u9f62: <strong>-409.83<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\">\u5e73\u65b9\u30d5\u30a3\u30fc\u30c8: <strong>100.87<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4e00\u898b\u3059\u308b\u3068\u3001\u56de\u5e30\u8868\u306e\u4fc2\u6570\u304c<strong>-409.833 \u3067<\/strong>\u3042\u308b\u306e\u306b\u5bfe\u3057\u3001\u5e73\u65b9\u30d5\u30a3\u30fc\u30c8\u4e88\u6e2c\u5909\u6570\u306e\u4fc2\u6570\u306f<strong>100.866<\/strong>\u306b\u3059\u304e\u306a\u3044\u305f\u3081\u3001\u7bc9\u5e74\u6570\u304c\u4e0d\u52d5\u7523\u4fa1\u683c\u306b\u5927\u304d\u304f\u5f71\u97ff\u3057\u3066\u3044\u308b\u3088\u3046\u306b\u898b\u3048\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3060\u3057\u3001\u6a19\u6e96\u8aa4\u5dee\u306f\u5e73\u65b9\u30d5\u30a3\u30fc\u30c8\u3088\u308a\u3082\u5e74\u9f62\u306e\u65b9\u304c\u306f\u308b\u304b\u306b\u5927\u304d\u3044\u305f\u3081\u3001\u5bfe\u5fdc\u3059\u308b p \u5024\u306f\u5b9f\u969b\u306b\u306f\u5e74\u9f62\u3067\u306f\u5927\u304d\u304f (p = 0.520)\u3001\u5e73\u65b9\u30d5\u30a3\u30fc\u30c8\u3067\u306f\u5c0f\u3055\u304f\u306a\u308a\u307e\u3059 (p = 0.000)\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u56de\u5e30\u4fc2\u6570\u304c\u6975\u7aef\u306b\u7570\u306a\u308b\u7406\u7531\u306f\u30012 \u3064\u306e\u5909\u6570\u306e\u30b9\u30b1\u30fc\u30eb\u304c\u6975\u7aef\u306b\u7570\u306a\u308b\u305f\u3081\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u5e74\u9f62<\/strong>\u306e\u5024\u306e\u7bc4\u56f2\u306f 4 \uff5e 44 \u6b73\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u5e73\u65b9\u30d5\u30a3\u30fc\u30c8<\/strong>\u5024\u306e\u7bc4\u56f2\u306f 1,200 \uff5e 2,800 \u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4ee3\u308f\u308a\u306b\u751f\u30c7\u30fc\u30bf\u3092<strong>\u6b63\u898f\u5316\u3057<\/strong>\u3001\u65b0\u3057\u3044\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#standardize each variable and fit regression model\n<\/span>model_std &lt;- lm(scale(price) ~ scale(age) + scale(sqfeet), data=df)\n\n<span style=\"color: #008080;\">#turn off scientific notation<\/span>\noptions(scipen= <span style=\"color: #008000;\">999<\/span> )\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model_std)\n\nCall:\nlm(formula = scale(price) ~ scale(age) + scale(sqfeet), data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-0.5541 -0.1820 -0.1062 0.3743 0.5891 \n\nCoefficients:\n                            Estimate Std. Error t value Pr(&gt;|t|)\n(Intercept) -0.0000000000000002253 0.1232881457926768426 0.000 1.000000\nscale(age) -0.0924421263946849786 0.1381464029075653854 -0.669 0.520187\nscale(sqfeet) 0.8848591938302141635 0.1381464029075653577 6.405 0.000125\n                 \n(Intercept)      \nscale(age)       \nscale(sqfeet)***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.4271 on 9 degrees of freedom\nMultiple R-squared: 0.8508, Adjusted R-squared: 0.8176 \nF-statistic: 25.65 on 2 and 9 DF, p-value: 0.0001916\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u8868\u306e\u56de\u5e30\u4fc2\u6570\u306f<strong>\u6a19\u6e96\u5316\u3055\u308c\u3066<\/strong>\u304a\u308a\u3001\u3053\u306e\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u9069\u5408\u3055\u305b\u308b\u305f\u3081\u306b\u6a19\u6e96\u5316\u3055\u308c\u305f\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u305f\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8868\u5185\u306e\u4fc2\u6570\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5e73\u65b9\u30d5\u30a3\u30fc\u30c8\u304c\u4e00\u5b9a\u3067\u3042\u308b\u3068\u4eee\u5b9a\u3059\u308b\u3068\u3001<strong>\u7bc9\u5e74\u6570<\/strong>\u304c 1 \u6a19\u6e96\u504f\u5dee\u3067\u5897\u52a0\u3059\u308b\u3068\u3001\u4f4f\u5b85\u4fa1\u683c\u306f<strong>0.092<\/strong>\u6a19\u6e96\u504f\u5dee\u3067\u6e1b\u5c11\u3057\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7bc9\u5e74\u6570\u304c\u4e00\u5b9a\u3067\u3042\u308b\u3068\u4eee\u5b9a\u3059\u308b\u3068<strong>\u3001\u5e73\u65b9\u30d5\u30a3\u30fc\u30c8\u306e<\/strong>1 \u6a19\u6e96\u504f\u5dee\u306e\u5897\u52a0\u306f\u3001\u4f4f\u5b85\u4fa1\u683c\u306e<strong>0.885<\/strong>\u6a19\u6e96\u504f\u5dee\u306e\u5897\u52a0\u306b\u95a2\u9023\u3057\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7bc9\u5e74\u6570\u3088\u308a\u3082\u9762\u7a4d\u306e\u65b9\u304c\u4f4f\u5b85\u4fa1\u683c\u306b\u5927\u304d\u304f\u5f71\u97ff\u3059\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3057\u305f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8<\/strong>: \u5404\u4e88\u6e2c\u5909\u6570\u306e p \u5024\u306f\u3001\u524d\u306e\u56de\u5e30\u30e2\u30c7\u30eb\u306e p \u5024\u3068\u307e\u3063\u305f\u304f\u540c\u3058\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3069\u306e\u6700\u7d42\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3059\u308b\u304b\u3092\u6c7a\u5b9a\u3059\u308b\u969b\u3001\u4f4f\u5b85\u306e\u4fa1\u683c\u3092\u4e88\u6e2c\u3059\u308b\u306b\u306f<strong>\u7bc9\u5e74\u6570<\/strong>\u3088\u308a\u3082<strong>\u5e73\u65b9\u30d5\u30a3\u30fc\u30c8\u306e\u65b9<\/strong>\u304c\u306f\u308b\u304b\u306b\u91cd\u8981\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u3063\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u8ffd\u52a0\u60c5\u5831\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/\u56de\u5e30\u89e3\u91c8\u8868\u3092\u8aad\u3080\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u56de\u5e30\u8868\u306e\u8aad\u307f\u65b9\u3068\u89e3\u91c8\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u56de\u5e30\u4fc2\u6570\u3092\u3068\u3099\u3046\u89e3\u91c8\u3059\u308b\u304b\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u56de\u5e30\u4fc2\u6570\u306e\u89e3\u91c8\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u7dda\u5f62\u56de\u5e30-p-\u5024\/\" target=\"_blank\" rel=\"noopener\">\u7dda\u5f62\u56de\u5e30\u3067\u306e P \u5024\u306e\u89e3\u91c8\u65b9\u6cd5<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u901a\u5e38\u3001\u91cd\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u5834\u5408\u3001\u30e2\u30c7\u30eb\u51fa\u529b\u3067\u5f97\u3089\u308c\u308b\u56de\u5e30\u4fc2\u6570\u306f\u6a19\u6e96\u5316\u3055\u308c\u3066\u3044\u307e\u305b\u3093\u3002\u3064\u307e\u308a\u3001\u751f\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u6700\u9069\u306a\u76f4\u7dda\u3092\u898b\u3064\u3051\u307e\u3059\u3002 model &lt;- lm(price ~ age + sqfeet, data= [&hellip;]<\/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-4613","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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