mean squared error

mse.numeric

R Documentation

Description

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

Usage

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

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

mse(...)

weighted.mse(...)

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 calculated as,

\[ \frac{1}{n} \sum_i^n (y_i - \upsilon_i)^2 \]

Where \(y_i\) and \(\upsilon_i\) are the actual and predicted values respectively.

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 Squared Error (MSE)
cat(
  "Mean Squared Error", mse(
    actual    = actual,
    predicted = predicted,
  ),
  "Mean Squared Error (weighted)", weighted.mse(
    actual    = actual,
    predicted = predicted,
    w         = mtcars$mpg/mean(mtcars$mpg)
  ),
  sep = "\n"
)