{"id":1272,"date":"2023-07-27T01:33:14","date_gmt":"2023-07-27T01:33:14","guid":{"rendered":"https:\/\/statorials.org\/ja\/r%e3%81%ae%e3%83%ac%e3%83%8f%e3%82%99%e3%83%ac%e3%83%83%e3%82%b7%e3%82%99\/"},"modified":"2023-07-27T01:33:14","modified_gmt":"2023-07-27T01:33:14","slug":"r%e3%81%ae%e3%83%ac%e3%83%8f%e3%82%99%e3%83%ac%e3%83%83%e3%82%b7%e3%82%99","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r%e3%81%ae%e3%83%ac%e3%83%8f%e3%82%99%e3%83%ac%e3%83%83%e3%82%b7%e3%82%99\/","title":{"rendered":"R \u3067\u30ec\u30d0\u30ec\u30c3\u30b8\u7d71\u8a08\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u7d71\u8a08<\/a>\u3067\u306f\u3001\u5fdc\u7b54\u5909\u6570\u306e\u5024\u304c\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u6b8b\u308a\u306e\u89b3\u6e2c\u5024\u3088\u308a\u3082\u306f\u308b\u304b\u306b\u5927\u304d\u3044\u89b3\u6e2c\u5024\u306f\u5916\u308c\u5024<\/strong>\u3068\u307f\u306a\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n

\u540c\u69d8\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u6b8b\u308a\u306e\u89b3\u6e2c\u5024\u3068\u6bd4\u8f03\u3057\u3066\u975e\u5e38\u306b\u6975\u7aef\u306a\u4e88\u6e2c\u5909\u6570\u306e\u5024\u304c 1 \u3064\u4ee5\u4e0a\u3042\u308b\u89b3\u6e2c<\/strong>\u5024\u306f\u3001\u3066\u3053\u6bd4\u304c\u9ad8\u3044\u3068\u898b\u306a\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n

\u3042\u3089\u3086\u308b\u30bf\u30a4\u30d7\u306e\u5206\u6790\u306b\u304a\u3051\u308b\u6700\u521d\u306e\u30b9\u30c6\u30c3\u30d7\u306e 1 \u3064\u306f\u3001\u7279\u5b9a\u306e\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u306b\u5927\u304d\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u305f\u3081\u3001\u9ad8\u3044\u5f71\u97ff\u529b\u3092\u6301\u3064\u89b3\u6e2c\u5024\u3092\u8a73\u3057\u304f\u8abf\u3079\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e\u30e2\u30c7\u30eb\u5185\u306e\u5404\u89b3\u6e2c\u5024\u306e\u3066\u3053\u6bd4\u3092\u8a08\u7b97\u3057\u3066\u8996\u899a\u5316\u3059\u308b\u65b9\u6cd5\u3092\u6bb5\u968e\u7684\u306b\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u30b9\u30c6\u30c3\u30d7 1: \u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h3>\n

\u307e\u305a\u3001R \u306b\u7d44\u307f\u8fbc\u307e\u308c\u3066\u3044\u308bmtcars<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb<\/a>\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n

 #load the dataset<\/span>\ndata(mtcars)\n\n#fit a regression model<\/span>\nmodel <- lm(mpg~disp+hp, data=mtcars)\n\n#view model summary\n<\/span>summary(model)\n\nCoefficients:\n             Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 30.735904 1.331566 23.083 < 2nd-16 ***\navailable -0.030346 0.007405 -4.098 0.000306 ***\nhp -0.024840 0.013385 -1.856 0.073679 .  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.127 on 29 degrees of freedom\nMultiple R-squared: 0.7482, Adjusted R-squared: 0.7309 \nF-statistic: 43.09 on 2 and 29 DF, p-value: 2.062e-09\n<\/strong><\/pre>\n
\u30b9\u30c6\u30c3\u30d7 2: \u5404\u89b3\u6e2c\u5024\u306e\u3066\u3053\u6bd4\u3092\u8a08\u7b97\u3059\u308b<\/strong><\/span><\/h3>\n
\u6b21\u306b\u3001 hatvalues()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u5185\u306e\u5404\u89b3\u6e2c\u5024\u306e\u3066\u3053\u6bd4\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #calculate leverage for each observation in the model\n<\/span>hats <- as<\/span> . data<\/span> . frame<\/span> (hatvalues(model))\n\n#display leverage stats for each observation\n<\/span>hats\n\n                    hatvalues(model)\nMazda RX4 0.04235795\nMazda RX4 Wag 0.04235795\nDatsun 710 0.06287776\nHornet 4 Drive 0.07614472\nHornet Sportabout 0.08097817\nValiant 0.05945972\nDuster 360 0.09828955\nMerc 240D 0.08816960\nMerc 230 0.05102253\nMerc 280 0.03990060\nMerc 280C 0.03990060\nMerc 450SE 0.03890159\nMerc 450SL 0.03890159\nMerc 450SLC 0.03890159\nCadillac Fleetwood 0.19443875\nLincoln Continental 0.16042361\nChrysler Imperial 0.12447530\nFiat 128 0.08346304\nHonda Civic 0.09493784\nToyota Corolla 0.08732818\nToyota Corona 0.05697867\nDodge Challenger 0.06954069\nAMC Javelin 0.05767659\nCamaro Z28 0.10011654\nPontiac Firebird 0.12979822\nFiat X1-9 0.08334018\nPorsche 914-2 0.05785170\nLotus Europa 0.08193899\nFord Pantera L 0.13831817\nFerrari Dino 0.12608583\nMaserati Bora 0.49663919\nVolvo 142E 0.05848459\n<\/strong><\/pre>\n
\u901a\u5e38\u3001\u30ec\u30d0\u30ec\u30c3\u30b8\u5024\u304c 2 \u3088\u308a\u5927\u304d\u3044\u89b3\u6e2c\u5024\u3092\u8a73\u3057\u304f\u8abf\u3079\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u3092\u884c\u3046\u7c21\u5358\u306a\u65b9\u6cd5\u306f\u3001\u89b3\u6e2c\u5024\u3092\u30ec\u30d0\u30ec\u30c3\u30b8\u5024\u306b\u57fa\u3065\u3044\u3066\u964d\u9806\u306b\u4e26\u3079\u66ff\u3048\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n
 #sort observations by leverage, descending<\/span>\nhats[ order<\/span> (-hats[' hatvalues(model)<\/span> ']), ]\n\n [1] 0.49663919 0.19443875 0.16042361 0.13831817 0.12979822 0.12608583\n [7] 0.12447530 0.10011654 0.09828955 0.09493784 0.08816960 0.08732818\n[13] 0.08346304 0.08334018 0.08193899 0.08097817 0.07614472 0.06954069\n[19] 0.06287776 0.05945972 0.05848459 0.05785170 0.05767659 0.05697867\n[25] 0.05102253 0.04235795 0.04235795 0.03990060 0.03990060 0.03890159\n[31] 0.03890159 0.03890159\n<\/strong><\/pre>\n
\u6700\u9ad8\u306e\u30ec\u30d0\u30ec\u30c3\u30b8\u5024\u306f0.4966<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3053\u306e\u6570\u5024\u306f 2 \u3092\u8d85\u3048\u306a\u3044\u305f\u3081\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u3069\u306e\u89b3\u6e2c\u5024\u3082\u9ad8\u3044\u5f71\u97ff\u529b\u3092\u6301\u3063\u3066\u3044\u306a\u3044\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u30b9\u30c6\u30c3\u30d7 3: \u5404\u89b3\u6e2c\u5024\u306e\u5f71\u97ff\u3092\u8996\u899a\u5316\u3059\u308b<\/strong><\/span><\/h3>\n
\u6700\u5f8c\u306b\u3001\u5404\u89b3\u6e2c\u5024\u306e\u5f71\u97ff\u3092\u8996\u899a\u5316\u3059\u308b\u305f\u3081\u306e\u7c21\u5358\u306a\u30c1\u30e3\u30fc\u30c8\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n
 #plot leverage values for each observation<\/span>\nplot(hatvalues(model), type = ' h<\/span> ')\n<\/strong><\/pre>\n
\"R\u306e\u6d3b\u7528\"<\/p>\n
 X \u8ef8\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u5404\u89b3\u6e2c\u5024\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u8868\u793a\u3057\u3001Y \u5024\u306f\u5404\u89b3\u6e2c\u5024\u306b\u5bfe\u5fdc\u3059\u308b\u3066\u3053\u6bd4\u7d71\u8a08\u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
R \u3067\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a>
R \u3067\u91cd\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a>
R \u3067\u6b8b\u5dee\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u7d71\u8a08\u3067\u306f\u3001\u5fdc\u7b54\u5909\u6570\u306e\u5024\u304c\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u6b8b\u308a\u306e\u89b3\u6e2c\u5024\u3088\u308a\u3082\u306f\u308b\u304b\u306b\u5927\u304d\u3044\u89b3\u6e2c\u5024\u306f\u5916\u308c\u5024\u3068\u307f\u306a\u3055\u308c\u307e\u3059\u3002 \u540c\u69d8\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u6b8b\u308a\u306e\u89b3\u6e2c\u5024\u3068\u6bd4\u8f03\u3057\u3066\u975e\u5e38\u306b\u6975\u7aef\u306a\u4e88\u6e2c\u5909\u6570\u306e\u5024\u304c 1 \u3064\u4ee5\u4e0a\u3042\u308b\u89b3\u6e2c\u5024\u306f\u3001\u3066\u3053\u6bd4\u304c\u9ad8\u3044\u3068\u898b\u306a […]<\/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-1272","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nR 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