R: Construct the mvcokm object

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 power-exponential 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 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. 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>

Construct the mvcokm object

Description

Usage

Arguments

Author(s)

See Also