Error Calculation

Calculate the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and for matrices also the Frobenius norm (identical to RMSE).

Usage

MSE(true, predicted, na.rm = TRUE)
RMSE(true, predicted, na.rm = TRUE)
MAE(true, predicted, na.rm = TRUE)
frobenius(true, predicted, na.rm = TRUE)

Arguments

true

true values.

predicted

predicted values

na.rm

ignore missing values.

Value

The error value.

Details

Frobenius norm requires matrices.

See also

Other evaluation: calcPredictionAccuracy(), evaluate(), evaluationResultList-class, evaluationResults-class, evaluationScheme(), evaluationScheme-class, plot()

Examples

true <- rnorm(10)
predicted <- rnorm(10)

MAE(true, predicted)
#> [1] 1.284605
MSE(true, predicted)
#> [1] 3.156311
RMSE(true, predicted)
#> [1] 1.776601

true <- matrix(rnorm(9), nrow = 3)
predicted <- matrix(rnorm(9), nrow = 3)

frobenius(true, predicted)
#> [1] 1.124603