如何在基础R中向模型添加变量？
如果我们想在基础R中向模型添加变量，则可以使用更新函数。更新函数将通过添加新变量来更新先前的模型，并且该变量可以是单个变量，也可以是两个或多个相互作用以及现有变量的任何可能转换。
示例
请看以下数据帧-
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
输出结果
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
使用x1和x2创建线性回归模型以预测y1-
示例
Model_1<-lm(y1~x1+x2,data=df1) summary(Model_1)
输出结果
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)
输出结果
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以及x1和x2之间的相互作用来创建模型-
示例
Model_2<-lm(update(y1~x1+x2,~.+x1*x2+x3,data=df1)) summary(Model_2)
输出结果
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