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 |
Gramacy, R. B. (2007). tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models. Journal of Statistical Software, 19(9). http://www.jstatsoft.org/v19/i09
Gramacy, R. B., Lee, H. K. H. (2007). Bayesian treed Gaussian process models with an application to computer modeling Journal of the American Statistical Association, to appear. Also available as as ArXiv article 0710.4536 http://arxiv.org/abs/0710.4536
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")