CRNGP)Gaussian process predictions using a GP object for correlated noise (of class CRNGP)
# S3 method for CRNGP
predict(object, x, xprime = NULL, t0 = NULL, ...)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.
an object of class CRNGP; e.g., as returned by mleCRNGP
matrix of designs locations to predict at (one point per row). Last column is for the integer valued seed.
If trajectories are considered, i.e., with time, the prediction will occur at the same times as the training data unless t0 is provided.
optional second matrix of predictive locations to obtain the predictive covariance matrix between x and xprime
single column matrix of times to predict at, if trajectories are considered. By default the prediction is at the same times as the training data.
no other argument for this method
The full predictive variance corresponds to the sum of sd2 and nugs. See mleHomGP for examples.