friedman.1.data {tgp} | R Documentation |
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.
friedman.1.data(n = 100)
n |
Number of samples |
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]
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) |
An example using this data is contained in the package vignette:
vignette("tgp")
.
Robert B. Gramacy rbgramacy@ams.ucsc.edu
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
tgp
, bgpllm
, btlm
,
blm
, bgp
, btgpllm
bgp