predictobj.rcalibration-class | R Documentation |

S4 class for prediction after Robust rcalibration with or without the specification of the discrepancy model.

Objects of this class are created and initialized with the function `predict`

that computes the prediction and the uncertainty quantification.

`mean`

:object of class

`vector`

. The predictive mean at testing inputs combing the mathematical model and discrepancy function.`math_model_mean`

:object of class

`vector`

. The predictive mean at testing inputs using only the mathematical model (and the trend if specified).`math_model_mean_no_trend`

:object of class

`vector`

. The predictive mean at testing inputs using only the mathematical model without the trend.`delta`

:object of class

`vector`

. The predictive discrepancy function.`interval`

:object of class

`matrix`

. The upper and lower predictive credible interval. If interval_data is TRUE in the`predict.rcalibration`

, the experimental noise is included for computing the predictive credible interval.

Mengyang Gu [aut, cre]

Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>

A. O'Hagan and M. C. Kennedy (2001), *Bayesian calibration of computer models*, *Journal of the Royal Statistical Society: Series B (Statistical Methodology*, **63**, 425-464.

M. Gu (2016), *Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output*, Ph.D. thesis., Duke University.

M. Gu and L. Wang (2017) *Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction*. arXiv preprint arXiv:1707.08215.

`predict.rcalibration`

for more details about how to do prediction for a `rcalibration`

object.

RobustCalibration documentation built on Sept. 8, 2023, 5:23 p.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.