intFun.rmse: Root mean square error (RMSE)

In amber: Automated Model Benchmarking R Package

Description

This function computes the root mean square error (RMSE), which is defined as:

$rmse(λ, φ)=√{\frac{1}{t_{f}-t_{0}}\int_{t_{0}}^{t_{f}}(v_{mod}(t,λ, φ)-v_{ref}(t,λ, φ))^{2}dt}$

where λ is the longitude, φ is the latitude, t is the time, t_0 is the initial time step, t_f is the final time time step, v_{mod} is a modelled variable and v_{ref} is the corresponding reference variable.

Usage

intFun.rmse(mod, ref)

Arguments

mod	An R object (model output data)
ref	An R object (reference data)

Value

An R object that gives the root mean square error when comparing mod against ref.

Examples

library(raster)
# create two raster stacks
for(i in 1:100)
{
 mod <- raster::raster(matrix(runif(100,-1,1), ncol=10))
 ref <- raster::raster(matrix(runif(100,-2,2), ncol=10))
 assign(paste('mod', i , sep='_'), mod)
 assign(paste('ref', i , sep='_'), ref)
}
my.list.mod <- lapply(ls(pattern='mod_'), get)
my.list.ref <- lapply(ls(pattern='ref_'), get)
mod <- do.call(stack, my.list.mod)
ref <- do.call(stack, my.list.ref)
# compute RMSE
rmse <- intFun.rmse(mod,ref)
plot(rmse); text(rmse, digits=2)

amber documentation built on Aug. 28, 2020, 5:08 p.m.