exp2d: 2-d Exponential Data

In tgp: Bayesian Treed Gaussian Process Models

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 inputs

X2

Numeric vector describing the second dimension of X inputs

Z

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) = X1 * exp(-X1^2 -X2^2).

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, [e-mail], and Matt Taddy, [e-mail]

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

tgp documentation built on Jan. 7, 2023, 1:17 a.m.