predict_separable_2dim_MS: Fast prediction when the test points lie on a 2D lattice for...
In RobustCalibration: Robust Calibration of Imperfect Mathematical Models
predict_separable_2dim_MS R Documentation
Fast prediction when the test points lie on a 2D lattice for multiple sources of observations.
Description
This function computes fast computation when the test points lie on a 2D lattice for multiple sources of observations.
Usage
predict_separable_2dim_MS(object, testing_input_separable,
X_testing=NULL, math_model=NULL,...)
Arguments
object
an object of class rcalibration.
testing_input_separable
a list of two. In the first (outer) list, each list is a source of test input. Then in the second (interior) list, The first element is a vector of the coordinate of the latitue
and the second element is a vector of the coordinate of the longitude.
X_testing
mean/trend for prediction where the defaul value is NULL.
math_model
a list of functions of math models to be calibrated.
Value
The returned value is a S4 CLass predictobj.rcalibration_MS.
Author(s)
Mengyang Gu [aut, cre]
Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>
References
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.
Mengyang Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.
M. Gu and L. Wang (2018) Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction. SIAM/ASA Journal on Uncertainty Quantification, 6, 1555-1583.
M. Gu (2019) Jointly Robust Prior for Gaussian Stochastic Process in Emulation, Calibration and Variable Selection
. Bayesian Analysis, 14, 857-885.
RobustCalibration documentation built on June 22, 2024, 10:37 a.m.
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| predict_separable_2dim_MS | R Documentation |
Fast prediction when the test points lie on a 2D lattice for multiple sources of observations.
Description
This function computes fast computation when the test points lie on a 2D lattice for multiple sources of observations.
Usage
predict_separable_2dim_MS(object, testing_input_separable,
X_testing=NULL, math_model=NULL,...)
Arguments
object |
an object of class |
testing_input_separable |
a list of two. In the first (outer) list, each list is a source of test input. Then in the second (interior) list, The first element is a vector of the coordinate of the latitue and the second element is a vector of the coordinate of the longitude. |
X_testing |
mean/trend for prediction where the defaul value is NULL. |
math_model |
a list of functions of math models to be calibrated. |
Value
The returned value is a S4 CLass predictobj.rcalibration_MS.
Author(s)
Mengyang Gu [aut, cre]
Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>
References
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.
Mengyang Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.
M. Gu and L. Wang (2018) Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction. SIAM/ASA Journal on Uncertainty Quantification, 6, 1555-1583.
M. Gu (2019) Jointly Robust Prior for Gaussian Stochastic Process in Emulation, Calibration and Variable Selection . Bayesian Analysis, 14, 857-885.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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