How to extract residuals from a linear regression model

linear regression

rstats

Published

October 2, 2022

In this tutorial, we will learn how to extract residual values from a linear regression model in R. Residuals are values that is remaining after adjusting or subtracting effects of variable in the model. We will see two approaches to pull residuals from linear regression model result we get after using lm() function. First we will learn how. to extract residuals directly from the linear regression fit object using residuals method. And then we will use the R package broom’s augment() function to extract residuals from the regression model.

First let us load the packages needed.

library(tidyverse)
library(broom)

We will using the. classic iris data that is built in with R for building a simple linear regression model.

iris %>% head()
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

With lm() function, here we build a simple linear regression model between two numerical variables from iris dataset. Note that since we provide iris data set as data argument, we can refer the variables in the linear model without any quotes.

lm_fit |t|)    
## (Intercept)   4.30660    0.07839   54.94                                
##  1          5.1          1.4    4.88  0.221  0.0186  0.408 0.00285       0.548 
##  2          4.9          1.4    4.88  0.0209 0.0186  0.408 0.0000255     0.0518
##  3          4.7          1.3    4.84 -0.138  0.0197  0.408 0.00118      -0.343 
##  4          4.6          1.5    4.92 -0.320  0.0176  0.408 0.00565      -0.793 
##  5          5            1.4    4.88  0.121  0.0186  0.408 0.000854      0.300 
##  6          5.4          1.7    5.00  0.398  0.0158  0.407 0.00780       0.986 
##  7          4.6          1.4    4.88 -0.279  0.0186  0.408 0.00455      -0.692 
##  8          5            1.5    4.92  0.0800 0.0176  0.408 0.000353      0.198 
##  9          4.4          1.4    4.88 -0.479  0.0186  0.407 0.0134       -1.19  
## 10          4.9          1.5    4.92 -0.0200 0.0176  0.408 0.0000220    -0.0495
## # … with 140 more rows

And we can extract the residuals using pull() function as shown below.

broom::augment(lm_fit) 
 pull(.resid)
##   [1]  0.22090540  0.02090540 -0.13820238 -0.31998683  0.12090540  0.39822871
##   [7] -0.27909460  0.08001317 -0.47909460 -0.01998683  0.48001317 -0.16087906
##  [13] -0.07909460 -0.45641792  1.00268985  0.78001317  0.56179762  0.22090540
##  [19]  0.69822871  0.18001317  0.39822871  0.18001317 -0.11552569  0.09822871
##  [25] -0.28355574  0.03912094  0.03912094  0.28001317  0.32090540 -0.26087906
##  [31] -0.16087906  0.48001317  0.28001317  0.62090540 -0.01998683  0.20268985
##  [37]  0.66179762  0.02090540 -0.43820238  0.18001317  0.16179762 -0.33820238
##  [43] -0.43820238  0.03912094  0.01644426 -0.07909460  0.13912094 -0.27909460
##  [49]  0.38001317  0.12090540  0.77146188  0.25324634  0.58967743 -0.44229252
##  [55]  0.31235411 -0.44675366  0.07146188 -0.75604693  0.41235411 -0.70140030
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--- title: "How to extract residuals from a linear regression model" date: 2022-10-02 categories: ['linear regression', 'rstats'] format: html: code-fold: false code-tools: true --- In this tutorial, we will learn how to extract residual values from a linear regression model in R. Residuals are values that is remaining after adjusting or subtracting effects of variable in the model. We will see two approaches to pull residuals from linear regression model result we get after using lm() function. First we will learn how. to extract residuals directly from the linear regression fit object using residuals method. And then we will use the R package broom's augment() function to extract residuals from the regression model. First let us load the packages needed. ```r library(tidyverse) library(broom) ``` We will using the. classic iris data that is built in with R for building a simple linear regression model. ```r iris %>% head() ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5.0 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ``` With lm() function, here we build a simple linear regression model between two numerical variables from iris dataset. Note that since we provide iris data set as data argument, we can refer the variables in the linear model without any quotes. ```r lm_fit |t|) ## (Intercept) 4.30660 0.07839 54.94 ## 1 5.1 1.4 4.88 0.221 0.0186 0.408 0.00285 0.548 ## 2 4.9 1.4 4.88 0.0209 0.0186 0.408 0.0000255 0.0518 ## 3 4.7 1.3 4.84 -0.138 0.0197 0.408 0.00118 -0.343 ## 4 4.6 1.5 4.92 -0.320 0.0176 0.408 0.00565 -0.793 ## 5 5 1.4 4.88 0.121 0.0186 0.408 0.000854 0.300 ## 6 5.4 1.7 5.00 0.398 0.0158 0.407 0.00780 0.986 ## 7 4.6 1.4 4.88 -0.279 0.0186 0.408 0.00455 -0.692 ## 8 5 1.5 4.92 0.0800 0.0176 0.408 0.000353 0.198 ## 9 4.4 1.4 4.88 -0.479 0.0186 0.407 0.0134 -1.19 ## 10 4.9 1.5 4.92 -0.0200 0.0176 0.408 0.0000220 -0.0495 ## # … with 140 more rows ``` And we can extract the residuals using pull() function as shown below. ```r broom::augment(lm_fit) pull(.resid) ## [1] 0.22090540 0.02090540 -0.13820238 -0.31998683 0.12090540 0.39822871 ## [7] -0.27909460 0.08001317 -0.47909460 -0.01998683 0.48001317 -0.16087906 ## [13] -0.07909460 -0.45641792 1.00268985 0.78001317 0.56179762 0.22090540 ## [19] 0.69822871 0.18001317 0.39822871 0.18001317 -0.11552569 0.09822871 ## [25] -0.28355574 0.03912094 0.03912094 0.28001317 0.32090540 -0.26087906 ## [31] -0.16087906 0.48001317 0.28001317 0.62090540 -0.01998683 0.20268985 ## [37] 0.66179762 0.02090540 -0.43820238 0.18001317 0.16179762 -0.33820238 ## [43] -0.43820238 0.03912094 0.01644426 -0.07909460 0.13912094 -0.27909460 ## [49] 0.38001317 0.12090540 0.77146188 0.25324634 0.58967743 -0.44229252 ## [55] 0.31235411 -0.44675366 0.07146188 -0.75604693 0.41235411 -0.70140030 ## ## ## ```