{"id":526,"date":"2023-07-29T14:54:06","date_gmt":"2023-07-29T14:54:06","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e4%ba%88%e6%b8%ac%e5%8c%ba%e9%96%93-r\/"},"modified":"2023-07-29T14:54:06","modified_gmt":"2023-07-29T14:54:06","slug":"%e4%ba%88%e6%b8%ac%e5%8c%ba%e9%96%93-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e4%ba%88%e6%b8%ac%e5%8c%ba%e9%96%93-r\/","title":{"rendered":"R \u3067\u4e88\u6e2c\u533a\u9593\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u306f\u3001<\/a>\u6b21\u306e 2 \u3064\u306e\u3053\u3068\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/span><\/p>\n

(1)<\/strong> 1 \u3064\u4ee5\u4e0a\u306e\u4e88\u6e2c\u5909\u6570\u3068\u5fdc\u7b54\u5909\u6570\u306e\u9593\u306e\u95a2\u4fc2\u3092\u5b9a\u91cf\u5316\u3057\u307e\u3059\u3002<\/span><\/p>\n

(2)<\/b>\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u5c06\u6765\u306e\u5024\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n

(2)<\/strong>\u306b\u95a2\u3057\u3066\u306f\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u5c06\u6765\u306e\u5024\u3092\u4e88\u6e2c\u3059\u308b\u5834\u5408\u3001\u591a\u304f\u306e\u5834\u5408\u3001\u6b63\u78ba\u306a\u5024<\/em>\u3068\u3001\u53ef\u80fd\u6027\u306e\u9ad8\u3044\u5024\u306e\u7bc4\u56f2\u3092\u542b\u3080\u533a\u9593<\/em>\u306e\u4e21\u65b9\u3092\u4e88\u6e2c\u3057\u305f\u3044\u3068\u8003\u3048\u307e\u3059\u3002\u3053\u306e\u9593\u9694\u306f\u3001\u4e88\u6e2c\u9593\u9694<\/strong>\u3068\u547c\u3070\u308c\u307e\u3059\u3002<\/span><\/p>\n

\u305f\u3068\u3048\u3070\u3001\u5b66\u7fd2\u6642\u9593\u3092<\/em>\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u3001\u8a66\u9a13\u306e\u30b9\u30b3\u30a2\u3092<\/em>\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b\u3068\u3057\u307e\u3059\u3002\u3053\u306e\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3059\u308b\u3068\u30016 \u6642\u9593\u52c9\u5f37\u3057\u305f\u751f\u5f92\u306f\u8a66\u9a13\u306791 \u70b9<\/strong>\u3092\u7372\u5f97\u3059\u308b\u3068\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u305f\u3060\u3057\u3001\u3053\u306e\u4e88\u6e2c\u306b\u306f\u4e0d\u78ba\u5b9f\u6027\u304c\u3042\u308b\u305f\u3081\u30016 \u6642\u9593\u52c9\u5f37\u3057\u305f\u751f\u5f92\u304c85<\/strong> \uff5e 97<\/strong>\u70b9\u306e\u8a66\u9a13\u30b9\u30b3\u30a2\u3092\u7372\u5f97\u3059\u308b\u78ba\u7387\u304c 95% \u3067\u3042\u308b\u3053\u3068\u3092\u793a\u3059\u4e88\u6e2c\u533a\u9593\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u5024\u306e\u7bc4\u56f2\u306f 95% \u4e88\u6e2c\u9593\u9694\u3068\u3057\u3066\u77e5\u3089\u308c\u3066\u304a\u308a\u3001\u591a\u304f\u306e\u5834\u5408\u3001\u6b63\u78ba\u306a\u4e88\u6e2c\u5024\u3092\u77e5\u308b\u3053\u3068\u3088\u308a\u3082\u5f79\u7acb\u3061\u307e\u3059\u3002<\/span><\/p>\n

R \u3067\u4e88\u6e2c\u533a\u9593\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5<\/strong><\/span><\/h2>\n

R \u3067\u4e88\u6e2c\u533a\u9593\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3059\u308b\u305f\u3081\u306b\u3001\u3044\u304f\u3064\u304b\u306e\u7570\u306a\u308b\u8eca\u306e\u7279\u6027\u306b\u95a2\u3059\u308b\u60c5\u5831\u304c\u542b\u307e\u308c\u308b\u7d44\u307f\u8fbc\u307f\u306emtcars<\/em>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n

 #view first six rows of<\/span> mtcars<\/em><\/span>\nhead(mtcars)\n\n# mpg cyl disp hp drat wt qsec vs am gear carb\n#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\n#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\n#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\n#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\n#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\n#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1\n<\/strong><\/pre>\n
\u307e\u305a\u3001\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066disp<\/em>\u3092\u4f7f\u7528\u3057\u3001\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066mpg \u3092<\/em>\u4f7f\u7528\u3057\u3066\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #fit simple linear regression model<\/span>\nmodel <- lm(mpg ~ disp, data = mtcars)\n\n#view summary of fitted model<\/span>\nsummary(model)\n\n#Call:\n#lm(formula = mpg ~ availability, data = mtcars)\n#\n#Residuals:\n# Min 1Q Median 3Q Max \n#-4.8922 -2.2022 -0.9631 1.6272 7.2305 \n#\n#Coefficients:\n#Estimate Std. Error t value Pr(>|t|)    \n#(Intercept) 29.599855 1.229720 24.070 < 2e-16 ***\n#disp -0.041215 0.004712 -8.747 9.38e-10 ***\n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n#\n#Residual standard error: 3.251 on 30 degrees of freedom\n#Multiple R-squared: 0.7183, Adjusted R-squared: 0.709 \n#F-statistic: 76.51 on 1 and 30 DF, p-value: 9.38e-10\n<\/strong><\/pre>\n
\u6b21\u306b\u3001\u9069\u5408\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001 disp<\/em>\u306e 3 \u3064\u306e\u65b0\u3057\u3044\u5024\u306b\u57fa\u3065\u3044\u3066mpg<\/em>\u306e\u5024\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #create data frame with three new values for<\/span> avail\n<\/span><\/em>new_disp <- data.frame(disp= c(150, 200, 250))\n\n#use the fitted model to predict the value for mpg<\/em><\/span> based on the three new values<\/span>\n#for<\/span> avail<\/span>\n<\/em>predict(model, newdata = new_disp)\n\n#1 2 3 \n#23.41759 21.35683 19.29607 \n<\/strong><\/pre>\n
\u3053\u308c\u3089\u306e\u5024\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n
    \n
  •  EPA<\/em>\u304c 150 \u306e\u65b0\u8eca\u306e\u5834\u5408\u3001 mpg<\/em>\u306f23.41759<\/strong>\u306b\u306a\u308b\u3068\u4e88\u60f3\u3055\u308c\u307e\u3059\u3002<\/span><\/li>\n
  •  EPA<\/em>\u304c 200 \u306e\u65b0\u8eca\u306e\u5834\u5408\u3001 mpg<\/em>\u306f21.35683<\/strong>\u306b\u306a\u308b\u3068\u4e88\u60f3\u3055\u308c\u307e\u3059\u3002<\/span><\/li>\n
  •  EPA<\/em>\u304c 250 \u306e\u65b0\u8eca\u306e\u5834\u5408\u3001 mpg<\/em>\u306f19.29607<\/strong>\u306b\u306a\u308b\u3068\u4e88\u60f3\u3055\u308c\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n
    \u6b21\u306b\u3001\u8fd1\u4f3c\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u308c\u3089\u306e\u4e88\u6e2c\u5024\u306e\u5468\u56f2\u306e\u4e88\u6e2c\u533a\u9593\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n
     #create prediction intervals around the predicted values<\/span>\npredict(model, newdata = new_disp, interval = \" predict<\/span> \")<\/span>\n\n# fit lwr upr\n#1 23.41759 16.62968 30.20549\n#2 21.35683 14.60704 28.10662\n#3 19.29607 12.55021 26.04194\n<\/strong><\/pre>\n
    \u3053\u308c\u3089\u306e\u5024\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n
      \n
    •  EPA<\/em>\u304c 150 \u306e\u8eca\u306e 95% mpg<\/em>\u4e88\u6e2c\u9593\u9694\u306f\u3001 16.62968<\/strong> \uff5e 30.20549<\/strong>\u3067\u3059\u3002<\/span><\/li>\n
    •  EPA<\/em>\u304c 200 \u306e\u81ea\u52d5\u8eca\u306e 95% mpg<\/em>\u4e88\u6e2c\u9593\u9694\u306f\u3001 14.60704<\/b> \uff5e 28.10662<\/strong>\u3067\u3059\u3002<\/span><\/li>\n
    •  EPA<\/em>\u304c 250 \u306e\u8eca\u306e 95% mpg<\/em>\u4e88\u6e2c\u9593\u9694\u306f\u3001 12.55021<\/b> \uff5e 26.04194<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n
      \u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u306f\u3001R \u306f 95% \u306e\u4e88\u6e2c\u9593\u9694\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u305f\u3060\u3057\u3001 level<\/strong>\u30b3\u30de\u30f3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u308c\u3092\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u5909\u66f4\u3067\u304d\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u300199% \u306e\u4e88\u6e2c\u533a\u9593\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
       #create 99% prediction intervals around the predicted values\n<\/span>predict(model, newdata = new_disp, interval = \" predict<\/span> \", level = 0.99<\/span><\/span><\/span> )<\/span>\n\n# fit lwr upr\n#1 23.41759 14.27742 32.55775\n#2 21.35683 12.26799 30.44567\n#3 19.29607 10.21252 28.37963\n<\/strong><\/pre>\n
       99% \u4e88\u6e2c\u9593\u9694\u306f 95% \u4e88\u6e2c\u9593\u9694\u3088\u308a\u3082\u5e83\u3044\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u9593\u9694\u304c\u5e83\u3044\u307b\u3069\u3001\u4e88\u6e2c\u5024\u304c\u542b\u307e\u308c\u308b\u53ef\u80fd\u6027\u304c\u9ad8\u304f\u306a\u308b\u305f\u3081\u3001\u3053\u308c\u306f\u7406\u306b\u304b\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
       R \u3067\u4e88\u6e2c\u533a\u9593\u3092\u8996\u899a\u5316\u3059\u308b\u65b9\u6cd5<\/strong><\/span><\/h2>\n
      \u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u6b21\u306e\u6a5f\u80fd\u3092\u5099\u3048\u305f\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
        \n
      • \u53ef\u7528\u6027<\/em>\u3068mpg<\/em>\u306e\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u306e\u6563\u5e03\u56f3<\/span><\/li>\n
      • \u8fd1\u4f3c\u56de\u5e30\u76f4\u7dda\u306e\u9752\u3044\u7dda<\/span><\/li>\n
      • \u7070\u8272\u306e\u4fe1\u983c\u5e2f<\/span><\/li>\n
      • \u8d64\u3044\u4e88\u6e2c\u30d0\u30f3\u30c9<\/span><\/li>\n<\/ul>\n
         #define dataset<\/span>\ndata <- mtcars[, c(\"mpg\", \"disp\")]\n\n#create simple linear regression model\n<\/span>model <- lm(mpg ~ disp, data = mtcars)\n\n#use model to create prediction intervals\n<\/span>predictions <- predict(model, interval = \" predict<\/span> \")\n\n#create dataset that contains original data along with prediction intervals\n<\/span>all_data <- cbind(data, predictions)\n\n#load ggplot2<\/em> library\n<\/span>library(ggplot2)\n\n#createplot\n<\/span>ggplot(all_data, aes(x = disp, y = mpg)) + #define x and y axis variables<\/span>\n  geom_point() + #add scatterplot points<\/span>\n  stat_smooth(method = lm) + #confidence bands<\/span>\n  geom_line(aes(y = lwr), col = \"coral2\", linetype = \"dashed\") + #lwr pred interval<\/span>\n  geom_line(aes(y = upr), col = \"coral2\", linetype = \"dashed\") #upr pred interval<\/span><\/strong><\/pre>\n
        \u4fe1\u983c\u533a\u9593\u3068\u4e88\u6e2c\u533a\u9593\u3092\u3044\u3064\u4f7f\u7528\u3059\u308b\u304b<\/span><\/strong><\/h2>\n
        \u4e88\u6e2c\u9593\u9694\u306f\u3001<\/strong>\u5358\u4e00\u306e\u5024\u306b\u95a2\u3059\u308b\u4e0d\u78ba\u5b9f\u6027\u3092\u6349\u3048\u307e\u3059\u3002\u4fe1\u983c\u533a\u9593\u306f\u3001<\/strong>\u4e88\u6e2c\u5e73\u5747\u5024\u5468\u8fba\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u6349\u3048\u307e\u3059\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u540c\u3058\u5024\u306b\u5bfe\u3059\u308b\u4e88\u6e2c\u533a\u9593\u306f\u5e38\u306b\u4fe1\u983c\u533a\u9593\u3088\u308a\u3082\u5e83\u304f\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n
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