predict.hetGP: Gaussian process predictions using a heterogeneous noise GP...

In hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication

Gaussian process predictions using a heterogeneous noise GP object (of class hetGP)

Description

Gaussian process predictions using a heterogeneous noise GP object (of class hetGP)

Usage

## S3 method for class 'hetGP'
predict(object, x, noise.var = FALSE, xprime = NULL, nugs.only = FALSE, ...)

Arguments

object	an object of class hetGP; e.g., as returned by mleHetGP
x	matrix of designs locations to predict at (one point per row)
noise.var	should the variance of the latent variance process be returned?
xprime	optional second matrix of predictive locations to obtain the predictive covariance matrix between x and xprime
nugs.only	if TRUE, only return noise variance prediction
...	no other argument for this method.

Details

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

Value

list with elements

• mean: kriging mean;

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

• nugs: noise variance prediction

• sd2_var: (returned if noise.var = TRUE) kriging variance of the noise process (i.e., on log-variances if logN = TRUE)

• cov: (returned if xprime is given) predictive covariance matrix between x and xprime

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