Commit 27233dc5 authored by Thirel Guillaume's avatar Thirel Guillaume
Browse files

Update CreateInputsCrit_Lavenne.Rd

parent 5f8a5b86
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......@@ -9,7 +9,7 @@
\description{
Creation of the \code{InputsCrit} object required to the \code{\link{ErrorCrit}} function. This function defines a composed criterion based on the formula proposed by Lavenne et al. (2019).
Creation of the \code{InputsCrit} object required to the \code{\link{ErrorCrit}} function. This function defines a composite criterion based on the formula proposed by Lavenne et al. (2019).
}
......@@ -29,7 +29,7 @@ CreateInputsCrit_Lavenne(FUN_CRIT = ErrorCrit_KGE,
\arguments{
\item{FUN_CRIT}{[function] error criterion function (e.g. \code{\link{ErrorCrit_KGE}}, \code{\link{ErrorCrit_NSE}})}. Default \code{\link{ErrorCrit_KGE}}
\item{FUN_CRIT}{[function] error criterion function (e.g. \code{\link{ErrorCrit_KGE}}, \code{\link{ErrorCrit_NSE}}). Default \code{\link{ErrorCrit_KGE}}}
\item{InputsModel}{[object of class \emph{InputsModel}] see \code{\link{CreateInputsModel}} for details}
......@@ -37,13 +37,13 @@ CreateInputsCrit_Lavenne(FUN_CRIT = ErrorCrit_KGE,
\item{Obs}{[numeric (atomic or list)] series of observed variable ([mm/time step] for discharge or SWE, [-] for SCA)}
\item{VarObs}{(optional) [character (atomic or list)] names of the observed variable (\code{"Q"} by default, or one of \code{"SCA"}, \code{"SWE"}])}
\item{VarObs}{(optional) [character (atomic or list)] names of the observed variable (\code{"Q"} by default, or one of \code{"SCA"}, \code{"SWE"})}
\item{AprParamR}{[numeric] a priori parameter set from a donor catchment}
\item{AprCrit}{(optional) [numeric] performance criterion of the donor catchment (1 by default)}
\item{k}{(optional) [numeric] coefficient used for the weighted average between \code{FUN_CRIT} and the gap between optimised parameter set and a priori parameter set calculated with the funciton produced by \code{\link{CreateErrorCrit_GAPX}}}
\item{k}{(optional) [numeric] coefficient used for the weighted average between \code{FUN_CRIT} and the gap between the optimised parameter set and an a priori parameter set calculated with the function produced by \code{\link{CreateErrorCrit_GAPX}}}
\item{BoolCrit}{(optional) [boolean] boolean (the same length as \code{Obs}) giving the time steps to consider in the computation (all time steps are considered by default. See details)}
......@@ -55,22 +55,24 @@ CreateInputsCrit_Lavenne(FUN_CRIT = ErrorCrit_KGE,
\value{
\code{CreateInputsCrit_Lavenne} returns an object of class \emph{Compo} that is a list of lists such as the one described in \code{\link{CreateInputsCrit}}.
[list] object of class InputsCrit containing the data required to evaluate the model outputs (see \code{\link{CreateInputsCrit}} for more details).
\code{CreateInputsCrit_Lavenne} returns an object of class \emph{Compo}.
Items \code{Weights} of the criteria are respectively equal to \code{k} and \code{k * max(0, AprCrit)}.
To calculate the Lavenne criterion, it is necessary to use the \code{ErrorCrit} function as for any other composed criterion.
To calculate the de Lavenne criterion, it is necessary to use the \code{ErrorCrit} function as for any other composite criterion.
}
\details{
The parameters \code{FUN_CRIT}, \code{Obs}, \code{VarObs}, \code{BoolCrit}, \code{transfo}, and \code{epsilon} must be use as they would be used for \code{\link{CreateInputsCrit}} in the case of a single criterion.
The parameters \code{FUN_CRIT}, \code{Obs}, \code{VarObs}, \code{BoolCrit}, \code{transfo}, and \code{epsilon} must be used as they would be used for \code{\link{CreateInputsCrit}} in the case of a single criterion.
\code{\link{ErrorCrit_RMSE}} cannot be used in a composite criterion since it is not a unitless value.
\code{CreateInputsCrit_Lavenne} creates a composed criterion in respect with Equations 1 and 2 of Lavenne et al. (2019).
\code{CreateInputsCrit_Lavenne} creates a composite criterion in respect with Equations 1 and 2 of de Lavenne et al. (2019).
}
......@@ -97,7 +99,7 @@ Param <- c(X1 = 257.238, X2 = 1.012, X3 = 88.235, X4 = 2.208)
OutputsModel <- RunModel_GR4J(InputsModel = InputsModel,
RunOptions = RunOptions, Param = Param)
## The "a priori" parameters for Lavenne formula
## The "a priori" parameters for the de Lavenne formula
AprParamR <- c(X1 = 157, X2 = 0.8, X3 = 100, X4 = 1.5)
## Single efficiency criterion: GAPX with a priori parameters
......
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