mvcokm {ARCokrig}  R Documentation 
Construct the mvcokm object
Description
This function constructs the mvcokm object in
autogressive cokriging models for multivariate outputs. The model is known as the parallel partial (PP) cokriging emulator.
Usage
mvcokm(
formula = list(~1, ~1),
output,
input,
cov.model = "matern_5_2",
nugget.est = FALSE,
prior = list(),
opt = list(),
NestDesign = TRUE,
tuning = list(),
info = list()
)
Arguments
formula 
a list of s elements, each of which contains the formula to specify fixed basis functions or regressors.

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.

cov.model 
a string indicating the type of covariance
function in the PP 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 powerexponential covariance functions with roughness parameter fixed at 1.9.

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 nonnested
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.
 verbose
a logical value to show the MCEM iterations if it is true.

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 <mpulong@gmail.com>
See Also
ARCokrig
, mvcokm.fit
, mvcokm.predict
, mvcokm.condsim
[Package
ARCokrig version 0.1.2
Index]