root_mean_squared_error: RMSE and normalized RMSE

In spatialRF: Easy Spatial Modeling with Random Forest

RMSE and normalized RMSE

Description

Computes the rmse or normalized rmse (nrmse) between two numeric vectors of the same length representing observations and model predictions.

Usage

root_mean_squared_error(
  o,
  p,
  normalization = c("rmse", "all", "mean", "sd", "maxmin", "iq")
)

Arguments

o	Numeric vector with observations, must have the same length as p.
p	Numeric vector with predictions, must have the same length as o.
normalization	character, normalization method, Default: "rmse" (see Details).

Details

The normalization methods go as follows:

• "rmse": RMSE with no normalization.

• "mean": RMSE dividied by the mean of the observations (rmse/mean(o)).

• "sd": RMSE dividied by the standard deviation of the observations (rmse/sd(o)).

• "maxmin": RMSE divided by the range of the observations (rmse/(max(o) - min(o))).

• "⁠iq"⁠: RMSE divided by the interquartile range of the observations (rmse/(quantile(o, 0.75) - quantile(o, 0.25)))

Value

Named numeric vector with either one or 5 values, as selected by the user.

See Also

Other utilities: .vif_to_df(), auc(), beowulf_cluster(), objects_size(), optimization_function(), prepare_importance_spatial(), rescale_vector(), setup_parallel_execution(), standard_error(), statistical_mode(), thinning(), thinning_til_n()

Examples

root_mean_squared_error(
  o = runif(10),
  p = runif(10)
)

spatialRF documentation built on Dec. 20, 2025, 1:07 a.m.