Description Usage Arguments Details Value Author(s) References See Also Examples
Evaluate the functional (mean) response for the 2d
exponential data (truth) at the X
inputs, and randomly
sample noisy Z
–values having normal error with standard
deviation provided.
1 
X 
Must be a 
sd 
Standard deviation of iid normal noise added to the responses 
The response is evaluated as
Z(X) = X1 * exp(X1^2X2^2),
thus creating the outputs Z
and Ztrue
.
Zeromean 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

Ztrue 
Numeric vector describing the true responses (without
noise) at the 
Robert B. Gramacy, rbg@vt.edu, and Matt Taddy, mataddy@amazon.com
Gramacy, R. B. (2020) Surrogates: Gaussian Process Modeling, Design and Optimization for the Applied Sciences. Boca Raton, Florida: Chapman Hall/CRC. https://bobby.gramacy.com/surrogates/
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). https://www.jstatsoft.org/v19/i09
Robert B. Gramacy, Matthew Taddy (2010). Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models. Journal of Statistical Software, 33(6), 1–48. https://www.jstatsoft.org/v33/i06/.
Gramacy, R. B., Lee, H. K. H. (2008). Bayesian treed Gaussian process models with an application to computer modeling. Journal of the American Statistical Association, 103(483), pp. 11191130. Also available as ArXiv article 0710.4536 https://arxiv.org/abs/0710.4536
https://bobby.gramacy.com/r_packages/tgp/
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