Relative Nash-Sutcliffe efficiency
Relative Nash-Sutcliffe efficiency between sim and obs, with treatment of missing values.
rNSE(sim, obs, ...) ## Default S3 method: rNSE(sim, obs, na.rm=TRUE, ...) ## S3 method for class 'data.frame' rNSE(sim, obs, na.rm=TRUE, ...) ## S3 method for class 'matrix' rNSE(sim, obs, na.rm=TRUE, ...) ## S3 method for class 'zoo' rNSE(sim, obs, na.rm=TRUE, ...)
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. |
... |
further arguments passed to or from other methods. |
rNSE = 1 - ( sum( ( (obs - sim)/ obs )^2 ) / sum( abs( (obs - mean(obs)) / mean(obs) )^2 )
Relative Nash-Sutcliffe efficiency between sim and obs.
If sim and obs are matrixes, the returned value is a vector, with the relative Nash-Sutcliffe efficiency 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
If some of the observed values are equal to zero (at least one of them), this index can not be computed.
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
Legates, D. R., and G. J. McCabe Jr. (1999), Evaluating the Use of "Goodness-of-Fit" Measures in Hydrologic and Hydroclimatic Model Validation, Water Resour. Res., 35(1), 233-241.
sim <- 1:10 obs <- 1:10 rNSE(sim, obs) sim <- 2:11 obs <- 1:10 rNSE(sim, obs) ################## # 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 the 'rNSE' for the "best" (unattainable) case rNSE(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 'rNSE' rNSE(sim=sim, obs=obs)
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