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法學如何在基本 R 中將變數新增到模型？
如果我們想在基本 R 中將變數新增到模型，那麼可以使用 update 函式。update 函式將透過新增新變數來更新之前的模型，並且該變數可以是單個變數，也可以是兩個或更多變數的互動，甚至是現有變數的任何可能變換。
示例
考慮以下資料框架 -
x1<-rnorm(20) x2<-rnorm(20,5,1.14) x3<-rnorm(20,5,0.58) y1<-rnorm(20,20,2.25) df1<-data.frame(x1,x2,x3,y1) df1
輸出
x1 x2 x3 y1 1 0.23523969 7.577512 5.443941 19.76642 2 0.11106994 7.504542 3.897426 19.65692 3 -0.09726361 7.277049 5.335444 19.27655 4 0.26056059 3.933092 4.203294 22.50656 5 -0.78472270 5.375368 5.480062 19.56555 6 -0.14489152 4.310053 5.704146 17.52129 7 -0.96409135 5.145660 4.753728 22.70288 8 -1.04832947 3.954133 4.820469 21.58309 9 -0.65659070 3.994727 4.791794 19.09328 10 0.88016095 6.480780 4.364470 18.50680 11 0.93215306 4.410714 4.664997 14.50948 12 1.49864968 5.172408 5.121840 21.58837 13 1.63126398 4.313327 4.389091 16.06222 14 0.33486400 4.756670 5.012716 16.63648 15 1.20832732 5.942533 6.097934 24.82682 16 1.27126998 6.753667 3.977962 22.59800 17 -0.42438014 4.766934 4.684150 19.70354 18 0.18121480 6.760182 5.444401 25.38505 19 -2.73192870 5.247787 5.305925 20.75227 20 -0.44498078 5.203272 5.877478 19.10085
建立一個線性迴歸模型，使用 x1 和 x2 預測 y1 -
示例
Model_1<-lm(y1~x1+x2,data=df1) summary(Model_1)
輸出
Call: lm(formula = y1 ~ x1 + x2, data = df1) Residuals: Min 1 Q Median 3Q Max -4.4836 -1.8695 -0.5435 2.1606 4.8678 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 16.2664 2.9395 5.534 3.64e-05 *** x1 -0.4001 0.6179 -0.647 0.526 x2 0.7027 0.5289 1.329 0.202 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.776 on 17 degrees of freedom Multiple R-squared: 0.1029, Adjusted R-squared: -0.002624 F-statistic: 0.9751 on 2 and 17 DF, p-value: 0.3973
透過新增 x3 建立模型 -
示例
Model_1<-lm(update(y1~x1+x2,~.+x3,data=df1)) summary(Model_1)
輸出
Call: lm(formula = update(y1 ~ x1 + x2, ~. + x3, data = df1)) Residuals: Min 1Q Median 3Q Max -4.4014 -2.0418 -0.6401 2.3419 4.1880 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 13.5651 5.9847 2.267 0.0376 * x1 -0.3204 0.6498 -0.493 0.6287 x2 0.6838 0.5418 1.262 0.2251 x3 0.5635 1.0796 0.522 0.6089 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.838 on 16 degrees of freedom Multiple R-squared: 0.1179, Adjusted R-squared: -0.04746 F-statistic: 0.7131 on 3 and 16 DF, p-value: 0.5584
透過新增 x3 和 x1 與 x2 之間的互動建立模型 -
示例
Model_2<-lm(update(y1~x1+x2,~.+x1*x2+x3,data=df1)) summary(Model_2)
輸出
Call: lm(formula = update(y1 ~ x1 + x2, ~. + x1 * x2 + x3, data = df1)) Residuals: Min 1Q Median 3Q Max -3.1970 -1.5739 -0.1827 0.9408 4.5058 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 13.9974 5.5099 2.540 0.0226 * x1 -8.9403 4.4024 -2.031 0.0604 . x2 0.3321 0.5293 0.627 0.5398 x3 0.7861 0.9996 0.786 0.4439 x1:x2 1.6809 0.8505 1.976 0.0668 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.611 on 15 degrees of freedom Multiple R-squared: 0.3002, Adjusted R-squared: 0.1135 F-statistic: 1.608 on 4 and 15 DF, p-value: 0.2236
更新於:07-Dec-2020
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