ErrorCrit | R Documentation |
Function which computes an error criterion with the provided function.
ErrorCrit(InputsCrit, OutputsModel, warnings = TRUE, verbose = TRUE)
InputsCrit |
[object of class InputsCrit] see |
OutputsModel |
[object of class OutputsModel] see |
warnings |
(optional) [boolean] boolean indicating if the warning messages are shown, default = |
verbose |
(optional) [boolean] boolean indicating if the function is run in verbose mode or not, default = |
If InputsCrit
is of class Single:
[list] containing the ErrorCrit_* functions outputs, see ErrorCrit_RMSE or ErrorCrit_NSE for details |
If InputsCrit
is of class Multi:
[list] of list containing the ErrorCrit_* functions outputs, see ErrorCrit_RMSE or ErrorCrit_NSE for details |
If InputsCrit
is of class Compo:
$CritValue | [numeric] value of the composite criterion |
$CritName | [character] name of the composite criterion |
$CritBestValue | [numeric] theoretical best criterion value |
$Multiplier | [numeric] integer indicating whether the criterion is indeed an error (+1) or an efficiency (-1) |
$CritCompo$MultiCritValues | [numeric] values of the sub-criteria |
$CritCompo$MultiCritNames | [numeric] names of the sub-criteria |
$CritCompo$MultiCritWeights | [character] weighted values of the sub-criteria |
$MultiCrit | [list] of list containing the ErrorCrit_* functions outputs, see ErrorCrit_NSE or ErrorCrit_KGE for details |
Olivier Delaigue
CreateInputsCrit
, ErrorCrit_RMSE
, ErrorCrit_NSE
,
ErrorCrit_KGE
, ErrorCrit_KGE2
library(airGR) ## loading catchment data data(L0123001) ## preparation of the InputsModel object InputsModel <- CreateInputsModel(FUN_MOD = RunModel_GR4J, DatesR = BasinObs$DatesR, Precip = BasinObs$P, PotEvap = BasinObs$E) ## calibration period selection Ind_Run <- seq(which(format(BasinObs$DatesR, format = "%Y-%m-%d")=="1990-01-01"), which(format(BasinObs$DatesR, format = "%Y-%m-%d")=="1999-12-31")) ## preparation of RunOptions object RunOptions <- CreateRunOptions(FUN_MOD = RunModel_GR4J, InputsModel = InputsModel, IndPeriod_Run = Ind_Run) ## simulation Param <- c(X1 = 257.238, X2 = 1.012, X3 = 88.235, X4 = 2.208) OutputsModel <- RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions, Param = Param) ## single efficiency criterion: Nash-Sutcliffe Efficiency InputsCritSingle <- CreateInputsCrit(FUN_CRIT = ErrorCrit_NSE, InputsModel = InputsModel, RunOptions = RunOptions, Obs = list(BasinObs$Qmm[Ind_Run]), VarObs = "Q", transfo = "", Weights = NULL) str(ErrorCrit(InputsCrit = InputsCritSingle, OutputsModel = OutputsModel)) ## 2 efficiency critera: RMSE and the Nash-Sutcliffe Efficiency InputsCritMulti <- CreateInputsCrit(FUN_CRIT = list(ErrorCrit_RMSE, ErrorCrit_NSE), InputsModel = InputsModel, RunOptions = RunOptions, Obs = list(BasinObs$Qmm[Ind_Run], BasinObs$Qmm[Ind_Run]), VarObs = list("Q", "Q"), transfo = list("", "sqrt"), Weights = NULL) str(ErrorCrit(InputsCrit = InputsCritMulti, OutputsModel = OutputsModel)) ## efficiency composite criterion: Nash-Sutcliffe Efficiency mixing ## both raw and log-transformed flows InputsCritCompo <- CreateInputsCrit(FUN_CRIT = list(ErrorCrit_NSE, ErrorCrit_NSE), InputsModel = InputsModel, RunOptions = RunOptions, Obs = list(BasinObs$Qmm[Ind_Run], BasinObs$Qmm[Ind_Run]), VarObs = list("Q", "Q"), transfo = list("", "log"), Weights = list(0.4, 0.6)) str(ErrorCrit(InputsCrit = InputsCritCompo, OutputsModel = OutputsModel))
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