How to extract the residuals and predicted values from linear model in R?

R ProgrammingServer Side ProgrammingProgramming

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

 Live Demo

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

 Live Demo

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
raja
Published on 06-Feb-2021 11:07:09
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