br2
Coefficient of determination (r2) multiplied by the slope of the regression line between sim
and obs
, with treatment of missing values.
br2(sim, obs, ...) ## Default S3 method: br2(sim, obs, na.rm=TRUE, use.abs=FALSE, ...) ## S3 method for class 'data.frame' br2(sim, obs, na.rm=TRUE, use.abs=FALSE, ...) ## S3 method for class 'matrix' br2(sim, obs, na.rm=TRUE, use.abs=FALSE, ...) ## S3 method for class 'zoo' br2(sim, obs, na.rm=TRUE, use.abs=FALSE, ...)
sim |
numeric, zoo, matrix or data.frame with simulated values |
obs |
numeric, zoo, matrix or data.frame with observed values |
na.rm |
logical value indicating whether 'NA' should be stripped before the computation proceeds. |
use.abs |
logical value indicating whether the condition to select the formula used to compute |
... |
further arguments passed to or from other methods. |
br2 = abs(b)*R2, b <= 1; br2= R2/abs(b), b > 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
)
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
.
obs
and sim
has to have the same length/dimension
The missing values in obs
and sim
are removed before the computation proceeds, and only those positions with non-missing values in obs
and sim
are considered in the computation
The slope b
is computed as the coefficient of the linear regression between sim
and obs
, forcing the intercept be equal to zero.
Mauricio Zambrano Bigiarini <mzb.devel@gmail.com>
Krause, P., Boyle, D. P., and Base, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89-97, 2005
# Looking at the difference between r2 and br2 for a case with systematic # over-prediction of observed values obs <- 1:10 sim1 <- 2*obs + 5 sim2 <- 2*obs + 25 # The coefficient of determination is equal to 1 even if there is no one single # simulated value equal to its corresponding observed counterpart r2 <- (cor(sim1, obs, method="pearson"))^2 # r2=1 # 'br2' effectively penalises the systematic over-estimation br2(sim1, obs) # br2 = 0.3684211 br2(sim2, obs) # br2 = 0.1794872 ggof(sim1, obs) ggof(sim2, obs) # Computing 'br2' without forcing the intercept be equal to zero br2.2 <- r2/2 # br2 = 0.5 ################## # Loading daily streamflows of the Ega River (Spain), from 1961 to 1970 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)
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