predict.homGP: Gaussian process predictions using a homoskedastic noise GP...

In hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication

Gaussian process predictions using a homoskedastic noise GP object (of class homGP)

Description

Gaussian process predictions using a homoskedastic noise GP object (of class homGP)

Usage

## S3 method for class 'homGP'
predict(object, x, xprime = NULL, ...)

Arguments

object	an object of class homGP; e.g., as returned by mleHomGP
x	matrix of designs locations to predict at (one point per row)
xprime	optional second matrix of predictive locations to obtain the predictive covariance matrix between x and xprime
...	no other argument for this method

Details

The full predictive variance corresponds to the sum of sd2 and nugs. See mleHomGP for examples.

Value

list with elements

• mean: kriging mean;

• sd2: kriging variance (filtered, e.g. without the nugget value)

• cov: predictive covariance matrix between x and xprime

• nugs: nugget value at each prediction location, for consistency with mleHomGP.

hetGP documentation built on Sept. 11, 2024, 6:56 p.m.