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法学如何在基本 R 中将变量添加到模型？
如果我们想在基本 R 中将变量添加到模型，那么可以使用 update 函数。update 函数将通过添加新变量来更新之前的模型，并且该变量可以是单个变量，也可以是两个或更多变量的交互，甚至是现有变量的任何可能变换。
示例
考虑以下数据框架 -
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
输出
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
创建一个线性回归模型，使用 x1 和 x2 预测 y1 -
示例
Model_1<-lm(y1~x1+x2,data=df1) summary(Model_1)
输出
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
通过添加 x3 创建模型 -
示例
Model_1<-lm(update(y1~x1+x2,~.+x3,data=df1)) summary(Model_1)
输出
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
通过添加 x3 和 x1 与 x2 之间的交互创建模型 -
示例
Model_2<-lm(update(y1~x1+x2,~.+x1*x2+x3,data=df1)) summary(Model_2)
输出
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
更新于:07-Dec-2020
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