Produces the process values of a spectral GP object on the defined grid or predicts process values for a new set of inputs (domain points).

1 2 |

`object` |
A GP object, created by |

`newdata` |
An optional two-column matrix-like object (vector for one-dimensional data)
of locations of interest, for which the first column is the first
coordinate and the second column the second coordinate. Locations
should lie in |

`mapping` |
Optional output of |

`...` |
Other arguments. |

Does prediction for a spectral GP, either at the gridpoints or
for locations by associating locations with the nearest gridpoint,
depending on the arguments supplied. If `newdata`

and
`mapping`

are both NULL, then prediction is done on the grid. If only
`newdata`

is supplied, the mapping is done using
`new.mapping`

and then the prediction is done. If `mapping`

is supplied (this should be done for computational efficiency if
prediction at the same locations will be done repeatedly) then the
mapping is used directly to calculate the predictions.

A vector of process values (matrix for two-dimensional processes in which prediction on the grid is requested).

Christopher Paciorek paciorek@alumni.cmu.edu

Type 'citation("spectralGP")' for references.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
library(spectralGP)
gp1=gp(128,matern.specdens,c(1,4))
gp2=gp(c(64,64),matern.specdens,c(1,4))
simulate(gp1)
simulate(gp2)
gridvals=predict(gp1)
gridvals2=predict(gp2)
loc1=runif(100)
loc2=cbind(runif(100),runif(100,0,1))
map1=new.mapping(gp1,loc1)
map2=new.mapping(gp2,loc2)
vals1=predict(gp1,mapping=map1)
vals2=predict(gp2,mapping=map2)
#equivalently:
vals1=predict(gp1,loc1)
vals2=predict(gp2,loc2)
plot(gp1)
points(loc1,vals1)
``` |

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