ObjFctRaster: Compute objective functions for raster data

In ModelDataComp: Model-Data Comparison

Description

Calculates several model performance metrics for raster layers per grid cell.

Usage

ObjFctRaster(sim.r, obs.r, ...)

Arguments

sim.r	rasters with simulated/predicted/modeled values
obs.r	raster with observed/reference values. It is also possible to provide a list of rasters, i.e. if there are multiple reference datasets. In this case, the simulated values will be concatenated and the performance metrics will be computed against all reference datasets at once.
...	further arguments (not used)

Details

The function is based on ObjFct

Value

The functions returns a raster brick with different model performance measures. The first layer (N) contains the number of sim/obs pairs in each grid cell. The other layers are model performance measures as computed by ObjFct.

Author(s)

Matthias Forkel <[e-mail]> [aut, cre]

See Also

ObjFct

Examples

# create some example data for observations and simulations 
obs.r <- brick(system.file("external/rlogo.grd", package="raster"))
obs.r[obs.r < 20] <- NA
sim.r <- obs.r 
sim.r[] <- values(sim.r) * rnorm(length(obs.r), 0.98, 0.2)  
plot(obs.r)
plot(sim.r)

# compute objective functions
obj.r <- ObjFctRaster(sim.r, obs.r)
plot(obj.r, 1:9)
plot(obj.r, 10:18)
plot(obj.r, 19:27)
plot(obj.r, 28:34)

# example with two reference datasets
obs2.r <- obs.r
obs2.r[] <- values(obs.r) * rnorm(length(obs.r), 1, 0.01)  
obj.r <- ObjFctRaster(sim.r, list(obs.r, obs2.r))
plot(obj.r, 1:9)

# example with different number of layers, e.g. different temporal coverage
sim.r <- brick(stack(sim.r, sim.r))
names(sim.r) <- seq(as.Date("2000-01-01"), as.Date("2005-01-01"), by="year")
names(obs.r) <- seq(as.Date("2002-01-01"), as.Date("2004-01-01"), by="year")
obj.r <- ObjFctRaster(sim.r, obs.r)
plot(obj.r, 1:9)

ModelDataComp documentation built on Nov. 22, 2020, 3 a.m.