View source: R/pr_fit_parameters.R
pr_fit_parameters | R Documentation |
Calculates the estimated phenophases for a given set of parameters and a specified model (be sure to match parameter and requirements with the model.
pr_fit_parameters(
par = NULL,
data,
cost = rmse,
model = "TT",
method = "GenSA",
lower,
upper,
control,
...
)
par |
a vector of starting parameter values (function specific) |
data |
nested data structure with validation data as returned by format_phenocam() or format_pep725(), or your own dataset adhering to the same data structure. |
cost |
the cost function to use in the optimization, it should return a RMSE or other value which needs to be minimized |
model |
the model name to be used in optimizing the model |
method |
optimization method to use (default = GenSA) - GenSA : Generalized Simulated Annealing algorithm - genoud : GENetic Optimization Using Derivatives - BayesianTools: various bayesian based optimization tools |
lower |
lower limit of parameter values (function specific) |
upper |
upper limit of parameter values (function specific) |
control |
optimization control parameters (default = NULL) |
... |
extra arguments to pass to the function, mostly BayesianTools |
## Not run:
estimate <- pr_fit_parameters(par,data,model)
# estimate will return the best estimated parameter set given the
# validation data
## End(Not run)
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