Description Usage Arguments Details Value Author(s) References See Also Examples
Perform Gaussian processes prediction (under isotropic or separable formulation)
at new XX
locations using a GP object stored on the Cside
1 2 
gpi 
a Cside GP object identifier (positive integer);
e.g., as returned by 
gpsepi 
similar to 
XX 
a 
lite 
a scalar logical indicating whether ( 
nonug 
a scalar logical indicating if a (nonzero) nugget should be used in the predictive
equations; this allows the user to toggle between visualizations of uncertainty due just
to the mean, and a full quantification of predictive uncertainty. The latter (default 
Returns the parameters of Studentt predictive equations. By
default, these include a full predictive covariance matrix between all
XX
locations. However, this can be slow when nrow(XX)
is large, so a lite
options is provided, which only returns the
diagonal of that matrix.
GP prediction is sometimes called “kriging”, especially in the spatial statistics literature. So this function could also be described as returning evaluations of the “kriging equations”
The output is a list
with the following components.
mean 
a vector of predictive means of length 
Sigma 
covariance matrix of
for a multivariate Studentt distribution; alternately
if 
df 
a Studentt degrees of freedom scalar (applies to all

Robert B. Gramacy [email protected]
For standard GP prediction, refer to any graduate text, e.g., Rasmussen & Williams Gaussian Processes for Machine Learning
vignette("laGP")
,
newGP
, mleGP
, jmleGP
,
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46  ## a "computer experiment"  a much smaller version than the one shown
## in the aGP docs
## Simple 2d test function used in Gramacy & Apley (2015);
## thanks to Lee, Gramacy, Taddy, and others who have used it before
f2d < function(x, y=NULL)
{
if(is.null(y)) {
if(!is.matrix(x) && !is.data.frame(x)) x < matrix(x, ncol=2)
y < x[,2]; x < x[,1]
}
g < function(z)
return(exp((z1)^2) + exp(0.8*(z+1)^2)  0.05*sin(8*(z+0.1)))
z < g(x)*g(y)
}
## design with N=441
x < seq(2, 2, length=11)
X < expand.grid(x, x)
Z < f2d(X)
## fit a GP
gpi < newGP(X, Z, d=0.35, g=1/1000)
## predictive grid with NN=400
xx < seq(1.9, 1.9, length=20)
XX < expand.grid(xx, xx)
ZZ < f2d(XX)
## predict
p < predGP(gpi, XX, lite=TRUE)
## RMSE: compare to similar experiment in aGP docs
sqrt(mean((p$mean  ZZ)^2))
## visualize the result
par(mfrow=c(1,2))
image(xx, xx, matrix(p$mean, nrow=length(xx)), col=heat.colors(128),
xlab="x1", ylab="x2", main="predictive mean")
image(xx, xx, matrix(p$meanZZ, nrow=length(xx)), col=heat.colors(128),
xlab="x1", ylab="x2", main="bas")
## clean up
deleteGP(gpi)
## see the newGP and mleGP docs for examples using lite = FALSE for
## sampling from the joint predictive distribution

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