Description Usage Arguments Details Value Note See Also Examples
Provide several metrics to assess the quality of the predictions of a model (see note) against observations.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  R2(sim, obs, na.action = stats::na.omit)
RMSE(sim, obs, na.rm = T)
nRMSE(sim, obs, na.rm = T)
MAE(sim, obs, na.rm = T)
ABS(sim, obs, na.rm = T)
MSE(sim, obs, na.rm = T)
EF(sim, obs, na.rm = T)
NSE(sim, obs, na.rm = T)
Bias(sim, obs, na.rm = T)
MAPE(sim, obs, na.rm = T)
FVU(sim, obs, na.rm = T)
RME(sim, obs, na.rm = T)

sim 
Simulated values 
obs 
Observed values 
na.action 
A function which indicates what should happen when the data contain NAs. 
na.rm 
Boolean. Remove 
The statistics for model quality can differ between sources. Here is a
short description of each statistic and its equation (see html version
for LATEX
):
R2()
: coefficient of determination, computed using lm
on obs~sim.
RMSE()
: Root Mean Squared Error, computed as
RMSE = sqrt(mean((simobs)^2)
NSE()
: NashSutcliffe Efficiency, alias of EF, provided for user convenience.
nRMSE()
: Normalized Root Mean Squared Error, also denoted as CV(RMSE), and computed as:
nRMSE = (RMSE/mean(obs))*100
MAE()
: Mean Absolute Error, computed as:
MAE = mean(abs(simobs))
ABS()
: Mean Absolute Bias, which is an alias of MAE()
FVU()
: Fraction of variance unexplained, computed as:
FVU = SS_res/SS_tot
MSE()
: Mean squared Error, computed as:
MSE = mean((simobs)^2)
EF()
: Model efficiency, also called NashSutcliffe efficiency (NSE). This statistic is
related to the FVU as EF= 1FVU. It is also related to the R2
because they share the same equation, except SStot is applied relative to the
identity function (i.e. 1:1 line) instead of the regression line. It is computed
as:
EF = 1SS_res/SS_tot
Bias()
: Modelling bias, simply computed as:
Bias = mean(simobs)
MAPE()
: Mean Absolute Percent Error, computed as:
MAPE = mean(abs(obssim)/obs)
RME()
: Relative mean error (%), computed as:
RME = mean((simobs)/obs)
A statistic depending on the function used.
SS_res is the residual sum of squares and SS_tot the total sum of squares. They are computed as:
SS_res= sum((obssim)^2)
SS_tot= sum((obsmean(obs))^2
Also, it should be noted that y_i refers to the observed values and \hat{y_i} to the predicted values, and \bar{y} to the mean value of observations.
This function was inspired from the evaluate()
function
from the SticsEvalR
package. This function is used by stics_eval
1 2 3 4 
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