How to compute the residuals of a linear model (in R)
Task
If a model has been fit to a dataset, the residuals are the differences between the actual data points and the results the model would predict. Given a linear model and a dataset, how can we compute those residuals?
Solution
Let’s assume that you’ve already built a linear model similar to the one below. This one uses a small amount of fake data, but it’s just an example. See also how to fit a linear model to two columns of data.
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xs <- c( 393, 453, 553, 679, 729, 748, 817 )
ys <- c( 24, 25, 27, 36, 55, 68, 84 )
model <- lm(ys ~ xs)
We can extract the residuals of the model in either of two ways.
R has a built-in residuals() function for this purpose.
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residuals(model)
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9.162630 2.199457 -9.072500 -16.795165 -4.431143 6.047185 12.889535
The model itself has a $residuals attribute.
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model$residuals
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9.162630 2.199457 -9.072500 -16.795165 -4.431143 6.047185 12.889535
Content last modified on 24 July 2023.
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Contributed by Elizabeth Czarniak (CZARNIA_ELIZ@bentley.edu)