friedman.1.data {tgp}R Documentation

First Friedman Dataset

Description

Function to generate X and Y values from the 10-dim “first” Friedman data set used to validate the Multivariate Adaptive Regression Splines (MARS) model. This function is stationary, with three non-linear and interacting variables, along with two linear, and five irrelevant effects.

Usage

friedman.1.data(n = 100)

Arguments

n Number of samples

Details

10-dim inputs X are drawn from N(0,1), and responses are N(m(X),1) where m(X) = E[X] and

E[X] = 10*sin(pi*X[,1]*X[,2]) + 20*(X[,3]-0.5)^2 + 10*X[,4] + 5*X[,5]

Value

Output is a data.frame with columns

X1...X10 describing the 10-d sampled inputs
Y sample responses (with N(0,1) noise)
Ytruth true responses (without noise)

Note

An example using this data is contained in the package vignette: vignette("tgp").

Author(s)

Robert B. Gramacy rbgramacy@ams.ucsc.edu

References

Friedman, J. H. (1991). Multivariate adaptive regression splines. “Annals of Statistics”, 19, No. 1, 1–67.

Gramacy, R. B. & Lee, H. K. H. (2005). Gaussian Processes and Limiting Linear Models. available as UCSC Technical Report ams2005-17

Chipman, H., George, E., & McCulloch, R. (2002). Bayesian treed models. Machine Learning, 48, 303–324.

http://www.ams.ucsc.edu/~rbgramacy/tgp.php

See Also

tgp, bgpllm, btlm, blm, bgp, btgpllm bgp


[Package tgp version 1.1-5 Index]