rgasp-class: Robust GaSP class

In RobustGaSP: Robust Gaussian Stochastic Process Emulation

Robust GaSP class

Description

S4 class for Robust GaSP if the range and noise-variance ratio parameters are given and/or have been estimated.

Objects from the Class

Objects of this class are created and initialized with the function rgasp that computes the calculations needed for setting up the analysis.

Slots

p:

Object of class integer. The dimensions of the inputs.

num_obs:

Object of class integer. The number of observations.

input:

Object of class matrix with dimension n x p. The design of experiments.

output:

Object of class matrix with dimension n x 1. The Observations or output vector.

X:

Object of class matrix of with dimension n x q. The mean basis function, i.e. the trend function.

zero_mean:

A character to specify whether the mean is zero or not. "Yes" means it has zero mean and "No"" means the mean is not zero.

q:

Object of class integer. The number of mean basis.

LB:

Object of class vector with dimension p x 1. The lower bound for inverse range parameters beta.

beta_initial:

Object of class vector with the initial values of inverse range parameters p x 1.

beta_hat:

Object of class vector with dimension p x 1. The inverse-range parameters.

log_post:

Object of class numeric with the logarithm of marginal posterior.

R0:

Object of class list of matrices where the j-th matrix is an absolute difference matrix of the j-th input vector.

theta_hat:

Object of class vector with dimension q x 1. The the mean (trend) parameter.

L:

Object of class matrix with dimension n x n. The Cholesky decomposition of the correlation matrix R, i.e.

L%*%t(L)=R

sigma2_hat:

Object of the class numeric. The estimated variance parameter.

LX:

Object of the class matrix with dimension q x q. The Cholesky decomposition of the correlation matrix

t(X)%*%R^{-1}%*%X

CL:

Object of the class vector used for the lower bound and the prior.

nugget:

A numeric object used for the noise-variance ratio parameter.

nugget.est:

A logical object of whether the nugget is estimated (T) or fixed (F).

kernel_type:

A vector of character to specify the type of kernel to use.

alpha:

Object of class vector with dimension p x 1 for the roughness parameters in the kernel.

method:

Object of class character to specify the method of parameter estimation. There are three values: post_mode, mle and mmle.

isotropic:

Object of class logical to specify whether the kernel is isotropic.

call:

The call to rgasp function to create the object.

Methods

show

Prints the main slots of the object.

predict

See predict.

Note

The response output must have one dimension. The number of observations in input must be equal to the number of experiments output.

Author(s)

Mengyang Gu [aut, cre], Jesus Palomo [aut], James Berger [aut]

Maintainer: Mengyang Gu <[e-mail]>

See Also

RobustGaSP for more details about how to create a RobustGaSP object.

RobustGaSP documentation built on June 1, 2022, 9:08 a.m.