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Root Mean Square Error (RMSE) fit function

Source: R/metrics.R
rmse.Rd

Calculates the root mean square error between observed and modelled values:

Usage

rmse(obs, sim, na.rm = TRUE)

Arguments

obs

Numeric vector of observed values.

sim

Numeric vector of simulated (modelled) values.

na.rm

Logical; should missing values (NA) in either vector be removed pairwise before computation? Default is TRUE.

Value

numeric; root mean square error, in the same units as obs/sim.

Details

$$\text{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^n (\text{sim}_i - \text{obs}_i)^2}$$

Already 0 (perfect fit) at its best and increasing as fit worsens, so it is minimise-oriented as-is and needs no _loss companion

  • pass it straight into FUN_list, which calib_aeme and run_and_fit minimise.

Like mae, RMSE stays in the variable's native units and so is not directly comparable across variables with different units or magnitudes when combined in a multi-variable FUN_list - see mae for details. RMSE additionally squares errors before averaging, so - like NSE - it weights large deviations more heavily than MAE does.

Examples

obs <- c(1, 2, 3, 4)
sim <- c(1.1, 2.1, 2.9, 4.2)
rmse(obs, sim)
#> [1] 0.1322876