friedman.1.data {tgp} | R Documentation |
Generate X and Y values from the 10-dim “first” Friedman data set used to validate the Multivariate Adaptive Regression Splines (MARS) model. This test function has three non-linear and interacting variables, along with two linear, and five which are irrelevant
friedman.1.data(n = 100)
n |
Number of samples desired |
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... 10 |
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. (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
bgpllm
, btlm
,
blm
, bgp
, btgpllm
, bgp