如何从R中的线性模型中提取残差和预测值？
残差是实际值和预测值之间的差值，预测值是线性模型为实际值预测的值。为了从线性模型中提取残差和预测值，我们需要对模型对象使用resid和predict函数。
考虑以下数据框-
示例
x1<-rnorm(20,14,3.25) y1<-rnorm(20,6,0.35) df1<-data.frame(x1,y1) df1输出结果
x1 y1 1 14.565652 6.506233 2 13.350634 6.481486 3 8.636661 5.806754 4 11.495087 6.164963 5 12.159347 6.749101 6 16.642371 6.061237 7 9.137345 6.121711 8 12.616223 5.911341 9 10.109950 5.819494 10 15.953629 6.067601 11 13.579602 6.438686 12 14.708544 5.175576 13 19.234206 6.926994 14 13.539790 5.669169 15 15.101462 6.253202 16 13.812982 6.042699 17 12.680245 6.019822 18 15.292250 6.174533 19 12.759720 5.648624 20 11.371360 5.879896
在x1和y1之间创建线性模型-
Model1<-lm(y1~x1,data=df1)
从Model1中查找残差和预测值-
残留物(Model1)
1 2 3 4 5 6
0.34576809 0.38483276 -0.04232628 0.16576122 0.71501218 -0.20829562
7 8 9 10 11 12
0.24633525 -0.14674241 -0.10696233 -0.16575896 0.33000726 -0.99239332
13 14 15 16 17 18
0.52134132 -0.43741908 0.06459654 -0.07823690 -0.04162379 -0.02409178
19 20
-0.41699579 -0.11280836
> predict(Model1)
1 2 3 4 5 6 7 8
6.160465 6.096654 5.849080 5.999202 6.034088 6.269532 5.875376 6.058083
9 10 11 12 13 14 15 16
5.926456 6.233360 6.108679 6.167970 6.405653 6.106588 6.188605 6.120936
17 18 19 20
6.061445 6.198625 6.065619 5.992704示例
x2<-rpois(20,5) y2<-rpois(20,2) df2<-data.frame(x2,y2) df2输出结果
x2 y2 1 7 0 2 5 1 3 3 1 4 3 2 5 6 3 6 2 8 7 4 2 8 6 0 9 9 1 10 3 0 11 4 3 12 7 1 13 6 2 14 6 0 15 5 2 16 9 1 17 5 3 18 5 2 19 6 5 20 7 0
在x2和y2之间创建线性模型-
Model2<-lm(y2~x2,data=df2)
从Model2中查找残差和预测值-
残留物（模型2）
1 2 3 4 5 6
-1.15697674 -1.02325581 -1.88953488 -0.88953488 1.40988372 4.67732558
7 8 9 10 11 12
-0.45639535 -1.59011628 0.70930233 -2.88953488 0.54360465 -0.15697674
13 14 15 16 17 18
0.40988372 -1.59011628 -0.02325581 0.70930233 0.97674419 -0.02325581
19 20
3.40988372 -1.15697674预测（模型2）
1 2 3 4 5 6 7 8
1.1569767 2.0232558 2.8895349 2.8895349 1.5901163 3.3226744 2.4563953 1.5901163
9 10 11 12 13 14 15 16
0.2906977 2.8895349 2.4563953 1.1569767 1.5901163 1.5901163 2.0232558 0.2906977
17 18 19 20
2.0232558 2.0232558 1.5901163 1.1569767