Mean Squared Error

Computes the mean squared error (MSE) between predicted and reference values.

Usage

mse(x, ...)

# S3 method for class 'lm'
mse(x, ...)

# Default S3 method
mse(x, ref, na.rm = FALSE, ...)

Arguments

x

an object. Methods are available for numeric vectors and model objects (e.g. lm).

...

additional arguments passed to methods

ref

numeric vector of reference (true) values

na.rm

logical; whether to remove missing values

Value

a numeric scalar containing the MSE

Details

The mean squared error is defined as: $$ \frac{1}{n} \sum (ref - x)^2 $$

See also

mean()

Other model.metrics: auc(), averagePrecision(), brierScore(), logLoss(), mae(), mape(), nmae(), nmse(), rmse(), smape()

Examples

x <- c(2.5, 3.0, 2.8)
ref <- c(3.0, 2.5, 3.0)

mse(x, ref)
#> [1] 0.18

# with linear model
fit <- lm(mpg ~ hp, data = mtcars)
mse(fit)
#> [1] 13.98982