Goodness-of-fit (GoF) functions for numerical and graphical comparison of simulated and observed time series, mainly focused on hydrological modelling.
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, to be used during the calibration, validation, and application of hydrological models.
Missing values in observed and/or simulated values can be removed before computations.
Quantitative statistics included are: Mean Error (me), Mean Absolute Error (mae), Root Mean Square Error (rms), Normalized Root Mean Square Error (nrms), Pearson product-moment correlation coefficient (r), Spearman Correlation coefficient (r.Spearman), Coefficient of Determination (R2), Ratio of Standard Deviations (rSD), Nash-Sutcliffe efficiency (NSE), Modified Nash-Sutcliffe efficiency (mNSE), Relative Nash-Sutcliffe efficiency (rNSE), Index of Agreement (d), Modified Index of Agreement (md), Relative Index of Agreement (rd), Coefficient of Persistence (cp), Percent Bias (pbias), Kling-Gupta efficiency (KGE), the coef. of determination multiplied by the slope of the linear regression between 'sim' and 'obs' (bR2), and volumetric efficiency (VE).
Package: | hydroGOF |
Type: | Package |
Version: | 0.4-0 |
Date: | 2020-03-11 |
License: | GPL >= 2 |
LazyLoad: | yes |
Packaged: | Wed Mar 11 22:23:51 -03 2020; MZB |
BuiltUnder: | R version 3.6.3 (2020-02-29) -- "Holding the Windsock"; x86_64-pc-linux-gnu (64-bit)) |
Mauricio Zambrano Bigiarini <mauricio.zambrano@ing.unitn.it>
Maintainer: Mauricio Zambrano Bigiarini <mauricio.zambrano@ing.unitn.it>
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obs <- 1:100 sim <- obs # Numerical goodness of fit gof(sim,obs) # Reverting the order of simulated values sim <- 100:1 gof(sim,obs) ## Not run: ggof(sim, obs) ## End(Not run) ################## # 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 observations sim <- obs # Getting the numeric goodness-of-fit measures 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 gof(sim=sim, obs=obs) # Graphical representation of 'obs' vs 'sim', along with the numeric # goodness-of-fit measures ## Not run: ggof(sim=sim, obs=obs) ## End(Not run)
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