hydromodInR: hydromodInR

In hzambran/hydroPSO: Particle Swarm Optimisation, with Focus on Environmental Models

hydromodInR

Description

This function runs an R-based model using a single parameter set taken from a matrix/data.frame with many parameter sets.

This function is for internal use within the package, and it is only documented in case it be useful to other packages. See Details section for more information.

Usage

hydromodInR(part, Particles, model.FUN, model.FUN.args )

Arguments

part	numeric, a single integer value indicating the row number in Particles which is used to select the parameter set used to run the model specified in model.FUN.
Particles	matrix/data.frame with all the parameter sets that can be used to run the model defined in model.FUN. It has as many columns as parameters used by the model, and as many rows as parameter sets provided by the user in Particles.
model.FUN	character, valid R function representing the model code to be calibrated/optimised.
model.FUN.args	list with the arguments to be passed to model.FUN.

Details

This function takes an R-based user-defined model, a matrix/data.frame with many parameter sets (e.g., randomly generated or the output of a previous optimisation/calibration), and index indicating which specific parameter set must be used from the matrix/data.frame, and then runs the model and returns a goodness-of-fit value as measure of model performance, by comparing observations against simulated equivalents.

This function it was designed to run a model implemented as an R function, not an executable file that runs in the system console.

Value

This function MUST return a list with two elements:

sim	numeric, with the simulated values obtained by running the model
GoF	numeric, goodness-of-fit value representing how close each one of the simulated values in sim are to their observed counterparts, by using the USER-DEFINED gof.FUN function

Author(s)

Mauricio Zambrano-Bigiarini, [e-mail]

See Also

hydroPSO, hydromod

hzambran/hydroPSO documentation built on Dec. 1, 2024, 3:28 p.m.