mvcokm-class {ARCokrig}

mvcokm Class

Description

This is an S4 class definition for mvcokm in the ARCokrig package

Slots

output

a list of s elements, each of which contains a matrix of computer model outputs.

input

a list of s elements, each of which contains a matrix of inputs.

param

a list of s elements, each of which contains a vector of initial values for correlation parameters (and nugget variance parameters if nugget terms are included in AR-cokriging models).

cov.model

a string indicating the type of covariance function in AR-cokriging models. Current covariance functions include

exp

product form of exponential covariance functions.

matern_3_2

product form of Matern covariance functions with smoothness parameter 3/2.

matern_5_2

product form of Matern covariance functions with smoothness parameter 5/2.

Gaussian

product form of Gaussian covariance functions.

powexp

product form of power-exponential covariance functions with roughness parameter fixed at 1.9.

aniso_exp

anisotropic form of exponential covariance function.

aniso_matern_3_2

anisotropic form of Matern covariance functions with smoothness parameter 3/2.

aniso_matern_5_2

anisotropic form of Matern covariance functions with smoothness parameter 5/2.

nugget.est

a logical value indicating whether the nugget is included or not. Default value is FALSE.

prior

a list of arguments to setup the prior distributions with the jointly robust prior as default

name

the name of the prior. Current implementation includes JR, Reference, Jeffreys, Ind_Jeffreys

hyperparam

hyperparameters in the priors. For jointly robust (JR) prior, three parameters are included: a refers to the polynomial penalty to avoid singular correlation matrix with a default value 0.2; b refers to the exponenetial penalty to avoid diagonal correlation matrix with a default value 1; nugget.UB is the upper bound of the nugget variance with default value 1, which indicates that the nugget variance has support (0, 1).

opt

a list of arguments to setup the optim routine.

NestDesign

a logical value indicating whether the experimental design is hierarchically nested within each level of the code.

tuning

a list of arguments to control the MCEM algorithm for non-nested design. It includes the arguments

maxit

the maximum number of MCEM iterations.

tol

a tolerance to stop the MCEM algorithm. If the parameter difference between any two consecutive MCEM algorithm is less than this tolerance, the MCEM algorithm is stopped.

n.sample

the number of Monte Carlo samples in the MCEM algorithm.

info

a list that contains

iter

number of iterations used in the MCEM algorithm

eps

parameter difference after the MCEM algorithm stops

Author(s)

Pulong Ma <[e-mail]>