# NOT RUN {
obs <- 1:10
sim <- 2:11
# }
# NOT RUN {
ggof(sim, obs)
# }
# NOT RUN {
##################
# 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
# Getting the numeric goodness of fit for the "best" (unattainable) case
gof(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)
# Getting the new numeric goodness-of-fit measures
gof(sim=sim, obs=obs)
# Getting the graphical representation of 'obs' and 'sim' along with the numeric
# goodness-of-fit measures for the daily and monthly time series
# }
# NOT RUN {
ggof(sim=sim, obs=obs, ftype="dm", FUN=mean)
# }
# NOT RUN {
# Getting the graphical representation of 'obs' and 'sim' along with some numeric
# goodness-of-fit measures for the seasonal time series
# }
# NOT RUN {
ggof(sim=sim, obs=obs, ftype="seasonal", FUN=mean)
# }
# NOT RUN {
# Computing the daily residuals
# even if this is a dummy example, it is enough for illustrating the capability
r <- sim-obs
# Summarizing and plotting the residuals
# }
# NOT RUN {
library(hydroTSM)
# summary
smry(r)
# daily, monthly and annual plots, boxplots and histograms
hydroplot(r, FUN=mean)
# seasonal plots and boxplots
hydroplot(r, FUN=mean, pfreq="seasonal")
# }
# NOT RUN {
# }
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