This is an S4 class definition for cokm in the ARCokrig package
outputa list of \(s\) elements, each of which contains a matrix of computer model outputs.
inputa list of \(s\) elements, each of which contains a matrix of inputs.
parama 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.modela string indicating the type of covariance function in AR-cokriging models. Current covariance functions include
product form of exponential covariance functions.
product form of Matern covariance functions with smoothness parameter 3/2.
product form of Matern covariance functions with smoothness parameter 5/2.
product form of Gaussian covariance functions.
product form of power-exponential covariance functions with roughness parameter fixed at 1.9.
nugget.esta logical value indicating whether nugget parameter is included or not. Default value is FALSE.
priora list of arguments to setup the prior distributions with the reference prior as default
the name of the prior. Current implementation includes
JR, Reference, Jeffreys, Ind_Jeffreys
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).
opta list of arguments to setup the optim routine.
NestDesigna logical value indicating whether the experimental design is hierarchically nested within each level of the code.
tuninga list of arguments to control the MCEM algorithm for non-nested design. It includes the arguments
the maximum number of MCEM iterations.
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.
the number of Monte Carlo samples in the MCEM algorithm.
a logical value to show the MCEM iterations if it is true.
infoa list that contains
number of iterations used in the MCEM algorithm
parameter difference after the MCEM algorithm stops.