tgp (version 1.1-11)

friedman.1.data: 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

Value

• Output is a data.frame with columns
• X1{describing the 10-d sampled inputs} Y{sample responses (with N(0,1) noise)} Ytruth{true responses (without noise)}
• Friedman, J. H. (1991). Multivariate adaptive regression splines. Annals of Statistics, 19, No. 1, 1--67.

  Gramacy, R. B., Lee, H. K. H. (2006). Bayesian treed Gaussian process models. Available as UCSC Technical Report ams2006-01.

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

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

  [object Object]

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

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

  datasets

Details

10-dim inputs X are drawn from N(0,1), and responses are N(m(X),1) where m(X) = E[X] and $$E[X] = 10\sin(\pi x_1 x_2) + 20(x_3-0.5)^2 + 10x_4 + 5x_5$$