# NOT RUN {
obs <- 1:10
sim <- 1:10
NSE(sim, obs)
obs <- 1:10
sim <- 2:11
NSE(sim, obs)
#################
# Computing NSE on the (natural) logarithm of simulated and observed values
obs <- 1:10/10
sim <- 2:11/10
NSE(sim=sim, obs=obs, FUN=log)
##################
# 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 'NSE' for the "best" (unattainable) case
NSE(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 'NSE'
NSE(sim=sim, obs=obs)
# }
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