Description Objects from the Class Slots Methods Author(s) See Also

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

Objects of this class are created and initialized with the function `ppgasp`

that computes the calculations needed for setting up the analysis.

`p`

:Object of class

`integer`

. The dimensions of the inputs.`num_obs`

:Object of class

`integer`

. The number of observations.`k`

:Object of class

`integer`

. The number of outputs in each computer model run.`input`

:Object of class

`matrix`

with dimension n x p. The design of experiments.`output`

:Object of class

`matrix`

with dimension n x k. Each row denotes a output vector in each run of the computer model.`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

`matrix`

. The estimated variance parameter of each output.`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.`call`

:The

`call`

to`ppgasp`

function to create the object.

- show
Prints the main slots of the object.

- predict
See

`predict`

.

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

Maintainer: Mengyang Gu <[email protected]>

`RobustGaSP`

for more details about how to create a `RobustGaSP`

object.

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