mean absolute error

mae.numeric

R Documentation

Description

The mae()-function computes the mean absolute error between the observed and predicted <numeric> vectors. The weighted.mae() function computes the weighted mean absolute error.

Usage

## S3 method for class 'numeric'
mae(actual, predicted, ...)

## S3 method for class 'numeric'
weighted.mae(actual, predicted, w, ...)

mae(...)

weighted.mae(...)

Arguments

actual	A <numeric>-vector of length \(n\). The observed (continuous) response variable.
predicted	A <numeric>-vector of length \(n\). The estimated (continuous) response variable.
…	Arguments passed into other methods.
w	A <numeric>-vector of length \(n\). The weight assigned to each observation in the data.

Value

A <numeric> vector of length 1.

Calculation

The metric is calulated as follows,

\[ \frac{\sum_i^n |y_i - \upsilon_i|}{n} \]

Examples

# 1) fit a linear
# regression
model <- lm(
  mpg ~ .,
  data = mtcars
)

# 1.1) define actual
# and predicted values
# to measure performance
actual    <- mtcars$mpg
predicted <- fitted(model)

# 2) evaluate in-sample model
# performance using Mean Absolute Error (MAE)
cat(
  "Mean Absolute Error", mae(
    actual    = actual,
    predicted = predicted,
  ),
  "Mean Absolute Error (weighted)", weighted.mae(
    actual    = actual,
    predicted = predicted,
    w         = mtcars$mpg/mean(mtcars$mpg)
  ),
  sep = "\n"
)