xy2unit {spectralGP} | R Documentation |
Scales locations to the unit hypercube for use in spectral GP
Description
Scales locations to so that they can be related to the
gridpoints in a spectral GP representation. The
locations.scale
argument allows one to scale the
locations
to a separate set of locations. E.g., if one wants
to predict over a certain set of locations, but has a separate
training set of locations that lie within the prediction set, one
would use the prediction locations as the locations.scale
argument.
Usage
xy2unit(locations, locations.scale = NULL)
Arguments
locations |
A two-column matrix-like object (vector for one-dimensional data) of locations to be scaled. |
locations.scale |
A two-column matrix-like object (vector for one-dimensional data) of locations that provides the function with the min and max coordinates in each direction. |
Details
One may want to use both training and prediction locations as the
locations.scale
argument to ensure that all locations of
interest will lie in and be able to be related to the gridpoints.
Value
A matrix (vector for one-dimensional data) of scaled locations lying
in .
Author(s)
Christopher Paciorek paciorek@alumni.cmu.edu
References
Type 'citation("spectralGP")' for references.
See Also
Examples
library(spectralGP)
gp1=gp(c(128,128),matern.specdens,c(1,4))
n=100
locs=cbind(runif(n,0.2,1.2),runif(n,-0.2,1.4))
locs.predict=cbind(runif(n,-0.4,0.8),runif(n,-0.1,1.7))
scaled.locs=xy2unit(locs,rbind(locs,locs.predict))
scaled.locs.predict=xy2unit(locs.predict,rbind(locs,locs.predict))
train.map=new.mapping(gp1,scaled.locs)
predict.map=new.mapping(gp1,scaled.locs.predict)
plot(locs,xlim=c(min(locs[,1],locs.predict[,1]),max(locs[,1],
locs.predict[,1])),ylim=c(min(locs[,2],locs.predict[,2]),
max(locs[,2],locs.predict[,2])))
points(locs.predict,col=2)
plot(scaled.locs,xlim=c(0,1),ylim=c(0,1))
points(scaled.locs.predict,col=2)