```
## a "computer experiment" -- a much smaller version than the one shown
## in the aGP docs
## Simple 2-d 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(-(z-1)^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$mean-ZZ, 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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