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How to extract the residuals and predicted values from linear model in R?
The residuals are the difference between actual values and the predicted values and the predicted values are the values predicted for the actual values by the linear model. To extract the residuals and predicted values from linear model, we need to use resid and predict function with the model object.
Consider the below data frame −
Example
x1<-rnorm(20,14,3.25) y1<-rnorm(20,6,0.35) df1<-data.frame(x1,y1) df1
Output
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
Creating linear model between x1 and y1 −
Model1<-lm(y1~x1,data=df1)
Finding the residuals and predicted values from Model1 −
resid(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
Example
x2<-rpois(20,5) y2<-rpois(20,2) df2<-data.frame(x2,y2) df2
Output
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
Creating linear model between x2 and y2 −
Model2<-lm(y2~x2,data=df2)
Finding the residuals and predicted values from Model2 −
resid(Model2)
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
predict(Model2)
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
Updated on: 06-Feb-2021
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