This is the main function that handles the calibration of SOFUN model parameters.
calib_sofun(drivers, obs, settings, optim_out = TRUE, ...)
A data frame with driver data. See p_model_drivers
for a description of the data structure.
A data frame containing observational data used for model
calibration. See p_model_validation
for a description of the data
structure.
A list containing model calibration settings. See the 'P-model usage' vignette for more information and examples.
method
A string indicating the optimization method, either 'GenSA'
or 'BayesianTools'
.
par
A list of model parameters. For each parameter, an initial value and lower and upper bounds should be provided. The calibratable parameters include model parameters 'kphio', 'kphio_par_a', 'kphio_par_b', 'soilm_thetastar', 'soilm_betao', 'beta_costunitratio', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax' and 'rootzone_whc' , and (if doing Bayesian calibration) error parameters for each target variable, named for example 'err_gpp'. This list must match the input parameters of the calibration metric and the parameters should be given in the order above.
metric
A cost function. See the 'Cost functions for parameter calibration' vignette for examples.
control
A list of arguments passed on to the optimization function.
If method = 'GenSA'
, see GenSA. If method = 'BayesianTools'
the list should include at least settings
and sampler
, see
BayesianTools::runMCMC.
A logical indicating whether the function returns the raw output of the optimization functions (defaults to TRUE).
Optional arguments passed on to the cost function specified as
settings$metric
.
.
A named list containing the calibrated parameter vector `par` and the output object from the optimization `mod`. For more details on this output and how to evaluate it, see runMCMC (also this post) and GenSA.
# Fix model parameters that won't be calibrated
params_fix <- list(
kphio_par_a = 0,
kphio_par_b = 1.0,
soilm_thetastar = 0.6*240,
soilm_betao = 0.01,
beta_unitcostratio = 146,
rd_to_vcmax = 0.014,
tau_acclim = 30,
kc_jmax = 0.41
)
# Define calibration settings
settings <- list(
method = "BayesianTools",
par = list(
kphio = list(lower=0.04, upper=0.09, init=0.05),
err_gpp = list(lower = 0.01, upper = 4, init = 2)
),
metric = rsofun::cost_likelihood_pmodel,
control = list(
sampler = "DEzs",
settings = list(
nrChains = 1,
burnin = 0,
iterations = 50 # kept artificially low
)
)
)
# Run the calibration for GPP data
calib_output <- rsofun::calib_sofun(
drivers = rsofun::p_model_drivers,
obs = rsofun::p_model_validation,
settings = settings,
# extra arguments for the cost function
par_fixed = params_fix,
targets = c("gpp")
)
#>
Running DEzs-MCMC, chain 1 iteration 51 of 51 . Current logp -3172.814 -4389.228 -3900.472 . Please wait!
#> runMCMC terminated after 0.783000000000001seconds