exp2d.Z {tgp} | R Documentation |
Evaluate the functional (mean) response for the 2-d
exponential data (truth) at the X
inputs, and randomly
sample noisy Z
–values having normal error with standard
deviation provided.
exp2d.Z(X, sd=0.001)
X |
Must be a matrix or a data.frame with two columns
describing input locations |
sd |
Standard deviation of iid normal noise added to the responses |
The response is evaluated as
Z(X) = X1 * exp(-X1^2 -X2^2),
thus creating the outputs Z
and Ztrue
.
Zero-mean normal noise with sd=0.001
is added to the
responses Z
and ZZ
Output is a data.frame
with columns:
Z |
Numeric vector describing the responses (with noise) at the
X input locations |
Ztrue |
Numeric vector describing the true responses (without
noise) at the X input locations |
Robert B. Gramacy rbgramacy@ams.ucsc.edu
Gramacy, R. B., Lee, H. K. H. (2006). Bayesian treed Gaussian process models. Available as UCSC Technical Report ams2006-01.
http://www.ams.ucsc.edu/~rbgramacy/tgp.html
N <- 20 x <- seq(-2,6,length=N) X <- expand.grid(x, x) Zdata <- exp2d.Z(X) persp(x,x,matrix(Zdata$Ztrue, nrow=N), theta=-30, phi=20, main="Z true", xlab="x1", ylab="x2", zlab="Ztrue")