calibrar (version 0.9.0)

calibrar-package: Automated Calibration for Complex Models

Description

Automated Calibration for Complex Models

Author

Ricardo Oliveros-Ramos Maintainer: Ricardo Oliveros-Ramos <[e-mail]>

Details

calibrar package: Automated Calibration for Complex Models

This package allows the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as base::optim. It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients. Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided. See <https://roliveros-ramos.github.io/calibrar/> for more details.

References

calibrar: an R package for the calibration of ecological models (Oliveros-Ramos and Shin 2014)

Examples

if (FALSE) {
require(calibrar)
set.seed(880820)
path = NULL # NULL to use the current directory
# create the demonstration files
demo = calibrar_demo(model="PoissonMixedModel", L=5, T=100) 
# get calibration information
calibrationInfo = calibration_setup(file=demo$path)
# get observed data
observed = calibration_data(setup=calibrationInfo, path=demo$path)
# read forcings for the model
forcing = read.csv(file.path(demo$path, "master", "environment.csv"), row.names=1)
# Defining 'runModel' function
runModel = function(par, forcing) {
output = calibrar:::.PoissonMixedModel(par=par, forcing=forcing)
# adding gamma parameters for penalties
output = c(output, list(gammas=par$gamma)) 
return(output)
}
# real parameters
cat("Real parameters used to simulate data\n")
print(demo$par)
# objective functions
obj  = calibration_objFn(model=runModel, setup=calibrationInfo, 
                               observed=observed, forcing=forcing)
cat("Starting calibration...\n")
control = list(weights=calibrationInfo$weights, maxit=3.6e5) # control parameters
cat("Running optimization algorithms\n", "\t", date(), "\n")
cat("Running optim AHR-ES\n")
ahr = calibrate(par=demo$guess, fn=obj, lower=demo$lower, upper=demo$upper, control=control)
summary(ahr)
}