hydroGOF (version 0.2-2)

br2: br2

Description

Coefficient of determination (r2) multiplied by the slope of the regression line between sim and obs, with treatment of missing values.

Usage

br2(sim, obs, ...)
## S3 method for class 'default':
br2(sim, obs, na.rm=TRUE, ...)
## S3 method for class 'data.frame':
br2(sim, obs, na.rm=TRUE, ...)
## S3 method for class 'matrix':
br2(sim, obs, na.rm=TRUE, ...)

Arguments

sim

numeric, zoo, matrix or data.frame with simulated values

obs

numeric, zoo, matrix or data.frame with observed values

na.rm

a logical value indicating whether 'NA' should be stripped before the computation proceeds. When an 'NA' value is found at the i-th position in obs OR sim, the i-th value of obs AND sim ar

...

further arguments passed to or from other methods.

Value

• br2 between sim and obs. If sim and obs are matrixes, the returned value is a vector, with the br2 between each column of sim and obs.

Details

$$br2 = \mid b \mid R2, \mid b \mid \leq 1; br2= \mid b \mid R2, \mid b \mid > 1$$

A model that systematically over or under-predicts all the time will still result in "good" r2 (close to 1), even if all predictions were wrong (Krause et al., 2005). The br2 coefficient allows accounting for the discrepancy in the magnitude of two signals (depicted by 'b') as well as their dynamics (depicted by 'r2')

References

Krause, P., Boyle, D. P., and Base, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89-97, 2005

See Also

gof, cor, lm

Examples

obs <- 1:10
sim <- 1:10
br2(sim, obs)

obs <- 1:10
sim <- 2:11
br2(sim, obs)

##################
# Loading daily streamflows of the Ega River (Spain), from 1961 to 1970
require(zoo)
data(EgaEnEstellaQts)
obs <- EgaEnEstellaQts

# Generating a simulated daily time series, initially equal to the observed series
sim <- obs 

# Computing 'br2' for the "best" (unattainable) case
br2(sim=sim, obs=obs)

# Randomly changing the first 2000 elements of 'sim', by using a normal distribution 
# with mean 10 and standard deviation equal to 1 (default of 'rnorm').
sim[1:2000] <- obs[1:2000] + rnorm(2000, mean=10)

# Computing the new  'br2'
br2(sim=sim, obs=obs)