How to Calculate Mean Absolute Error in R

Mean Absolute Error in R, when we do modeling always need to measure the accuracy of the model fit. The mean absolute error (MAE) allows us to measure the accuracy of a given model.

The formula for mean absolute error is

MAE = (1/n) * Σ|yi – xi|

where:

Σ symbol Indicate that “sum”

Yi indicates that i^th observed value.

Xi indicates that i^th predicted value

N indicates the total number of observations

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Mean Absolute Error in R

How to Calculate Mean Absolute Error in R, here we are exploring two possible ways.

Approach 1: Calculate Mean Absolute Error Between Two Vectors

For approach 1 we can make use of the Metrics package. Let’s load the library first.

library(Metrics)

Now we need observed and predicted observations. Let’s create some random values.

observed <- c(13, 15, 15, 15, 14, 22, 25, 25, 23, 20, 22)
predicted <- c(12, 13, 13, 14, 15, 24, 24, 26, 22, 26, 24)

Calculate the mean absolute error between observed and predicted values.

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mae(observed, predicted)

[1] 1.818182

The mean absolute error (MAE) turns out to be 1.818182.

This MAE value indicates that the average absolute difference between the observed values and the predicted values is 1.818182.

Approach 2: Calculate Mean Absolute Error for a Regression Model

One of the common model fit methods is regression modeling, Let’s see how to calculate MAE for a given regression model.

Let’s load the library

library(Metrics)

We need a data frame for regression model fitting. Let’s create the data frame.

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df <- data.frame(x1=c(4, 3, 5, 4, 4, 8, 6, 10, 5, 6),
                 x2=c(8, 7, 8, 9, 10, 15, 11, 15, 18, 24),
                 y=c(15, 16, 17, 20, 22, 27, 25, 28, 29, 22))
head(df)

   x1 x2  y
1  4  8 15
2  3  7 16
3  5  8 17
4  4  9 20
5  4 10 22
6  8 15 27

Fit the regression model

model <- lm(y~x1+x2, data=df)
model

Call:
lm(formula = y ~ x1 + x2, data = df)
Coefficients:
(Intercept)           x1           x2 
    10.9240       1.2587       0.3403

Now we can calculate MAE value between predicted and observed values

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mae(df$y, predict(model))
[1] 2.591844

The mean absolute error (MAE) is 2.591844.

This indicates that the average absolute difference between the observed values and the predicted values is 2.591844.

Conclusion

Lower the MAE value indicates a better model fit. This will be more useful when we compare two different models.

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