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 twocolumn matrixlike object (vector for onedimensional 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 (0,1)^d, as the process representation is on a grid on (0,1)^d. 
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 twodimensional processes in which prediction on the grid is requested).
Christopher Paciorek paciorek@alumni.cmu.edu
Type 'citation("spectralGP")' for references.
gp
, new.mapping
, plot.gp
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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