calibrate function

Sequential parameter estimation for the calibration of complex models

This function performs the optimization of a function, possibly in sequential phases of increasing complexity, and it is designed for the calibration of a model, by minimizing the error function fn associated to it.

calibrate(
  par,
  fn,
  gr,
  ...,
  method,
  lower,
  upper,
  phases,
  control,
  hessian,
  replicates,
  parallel
)

## Default S3 method:
calibrate(
  par,
  fn,
  gr = NULL,
  ...,
  method = NULL,
  lower = NULL,
  upper = NULL,
  phases = NULL,
  control = list(),
  hessian = FALSE,
  replicates = 1,
  parallel = FALSE
)

Arguments

• par: A numeric vector or list. The length of the par argument defines the number of parameters to be estimated (i.e. the dimension of the problem).
• fn: The function to be minimized.
• gr: A function computing the gradient of fn. If NULL, a numerical approximation of the gradient is used. It can be also a character specifying the method for the computation of the numerical gradient: 'central', 'forward' (the default), 'backward' or 'richardson'.
• ...: Additional parameters to be passed to fn.
• method: The optimization method to be used. The default method is the AHR-ES (Adaptative Hierarchical Recombination Evolutionary Strategy, Oliveros-Ramos & Shin, 2016). See details for the methods available.
• lower: Lower threshold value(s) for parameters. One value or a vector of the same length as par. If one value is provided, it is used for all parameters. NA means -Inf. By default -Inf is used (unconstrained).
• upper: Upper threshold value(s) for parameters. One value or a vector of the same length as par. If one value is provided, it is used for all parameters. NA means Inf. By default Inf is used (unconstrained).
• phases: An optional vector of the same length as par, indicating the phase at which each parameter becomes active. If omitted, default value is 1 for all parameters, performing a single optimization.
• control: Parameter for the control of the algorithm itself, see details.
• hessian: Logical. Should a numerically differentiated Hessian matrix be returned? Currently not implemented.
• replicates: The number of replicates for the evaluation of fn. The default value is 1. A value greater than 1 is only useful for stochastic functions.
• parallel: Logical. Use parallel computation numerical of gradient?

Details

In the control list, aggFn is a function to aggregate fn to a scalar value if the returned value is a vector. Some optimization algorithm can exploite the additional information provided by a vectorial output from fn.

Examples

calibrate(par=rep(NA, 5), fn=sphereN)
## Not run:

calibrate(par=rep(NA, 5), fn=sphereN, replicates=3)
calibrate(par=rep(0.5, 5), fn=sphereN, replicates=3, lower=-5, upper=5)
calibrate(par=rep(0.5, 5), fn=sphereN, replicates=3, lower=-5, upper=5, phases=c(1,1,1,2,3))
calibrate(par=rep(0.5, 5), fn=sphereN, replicates=c(1,1,4), lower=-5, upper=5, phases=c(1,1,1,2,3))
## End(Not run)

See Also

Other optimisers: ahres(), optim2(), optimh()

Author(s)

Ricardo Oliveros-Ramos