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Continue training a DGP emulator

Source: R/dgp.R
continue.Rd

This function implements additional training iterations for a DGP emulator.

Usage

continue(
  object,
  N = NULL,
  cores = 1,
  ess_burn = 10,
  verb = TRUE,
  burnin = NULL,
  B = NULL
)

Arguments

object

an instance of the dgp class.

N

additional number of iterations to train the DGP emulator. If set to NULL, the number of iterations is set to 500 if the DGP emulator was constructed without the Vecchia approximation, and is set to 200 if Vecchia approximation was used. Defaults to NULL.

cores

the number of processes to be used to optimize GP components (in the same layer) at each M-step of the training. If set to NULL, the number of processes is set to (max physical cores available - 1) if the DGP emulator was constructed without the Vecchia approximation. Otherwise, the number of processes is set to max physical cores available %/% 2. Only use multiple processes when there is a large number of GP components in different layers and optimization of GP components is computationally expensive. Defaults to 1.

ess_burn

number of burnin steps for ESS-within-Gibbs at each I-step of the training. Defaults to 10.

verb

a bool indicating if a progress bar will be printed during training. Defaults to TRUE.

burnin

the number of training iterations to be discarded for point estimates calculation. Must be smaller than the overall training iterations so-far implemented. If this is not specified, only the last 25% of iterations are used. This overrides the value of burnin set in dgp(). Defaults to NULL.

B

the number of imputations to produce predictions. Increase the value to account for more imputation uncertainty. This overrides the value of B set in dgp() if B is not NULL. Defaults to NULL.

Value

An updated object.

Details

See further examples and tutorials at https://mingdeyu.github.io/dgpsi-R/dev/.

Note

  • One can also use this function to fit an untrained DGP emulator constructed by dgp() with training = FALSE.

  • The following slots:

    • loo and oos created by validate(); and

    • results created by predict() in object will be removed and not contained in the returned object.

Examples

if (FALSE) { # \dontrun{

# See dgp() for an example.
} # }