lineqGPOptim: Gaussian Process Model Optimizations
In lineqGPR: Gaussian Process Regression Models with Linear Inequality Constraints

Description Usage Arguments Value Comments Author(s) See Also

Function for optimizations of "lineqGP" S3 class objects.

lineqGPOptim(
  model,
  x0 = model$kernParam$par,
  eval_f = "logLik",
  lb = rep(0.01, length(x0)),
  ub = rep(Inf, length(x0)),
  opts = list(algorithm = "NLOPT_LD_MMA", print_level = 0, ftol_abs = 0.001, maxeval =
    50, check_derivatives = FALSE, parfixed = rep(FALSE, length(x0))),
  seed = 1,
  estim.varnoise = FALSE,
  bounds.varnoise = c(0, Inf),
  add.constr = FALSE,
  additive = FALSE,
  mcmc.opts = list(probe = "Genz", nb.mcmc = 1000),
  max.trials = 10,
  ...
)

`model`	a list with the structure of the constrained Kriging model.
`x0`	the initial values for the parameters to be optimized over.
`eval_f`	a function to be minimized, with first argument the vector of parameters over which minimization is to take place. It should return a scalar result.
`lb`	a vector with lower bounds of the params. The params are forced to be positive. See `nloptr`.
`ub`	a vector with upper bounds of the params. See `nloptr`.
`opts`	see `nl.opts`. Parameter `parfixed` indices of fixed parameters to do not be optimised. If `estim.varnoise` is true, the noise variance is estimated.
`seed`	an optional number. Set a seed to replicate results.
`estim.varnoise`	an optional logical. If `TRUE`, a noise variance is estimated.
`bounds.varnoise`	a vector with bounds of noise variance.
`add.constr`	an optional logical. If `TRUE`, the inequality constraints are taken into account in the optimisation.
`additive`	an optional logical. If `TRUE`, the likelihood of an additive GP model is computed in the optimisation.
`mcmc.opts`	if `add.constr`, mcmc options passed to methods.
`max.trials`	the value of the maximum number of trials when errors are produced by instabilities.
`...`	further arguments passed to or from other methods.