exp2d {tgp} | R Documentation |
2-d Exponential Data
Description
A 2-dimensional data set that can be used to validate non-stationary models.
Usage
data(exp2d)
Format
A data frame
with 441 observations on the following 4 variables.
X1
Numeric vector describing the first dimension of
X
inputsX2
Numeric vector describing the second dimension of
X
inputsZ
Numeric vector describing the response
Z(X)+N(0,sd=0.001)
Ztrue
Numeric vector describing the true response
Z(X)
, without noise
Details
The true response is evaluated as
Z(X)=x_1 * \exp(x_1^2-x_2^2).
Zero-mean normal noise
with sd=0.001
has been added to the true response
Note
This data is used in the examples of the functions listed below in
the “See Also” section via the exp2d.rand
function
Author(s)
Robert B. Gramacy, rbg@vt.edu, and Matt Taddy, mataddy@amazon.com
References
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 doi:10.18637/jss.v019.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/. doi:10.18637/jss.v033.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. 1119-1130. Also available as ArXiv article 0710.4536 https://arxiv.org/abs/0710.4536
https://bobby.gramacy.com/r_packages/tgp/
See Also
exp2d.rand
, exp2d.Z
,
btgp
, and other b*
functions