exp2d.rand {tgp} | R Documentation |
A Random subsample of data(exp2d)
exp2d.rand(n1 = 50, n2 = 30)
n1 |
Number of samples from the first, interesting, quadrant |
n2 |
Number of samples from the other three, uninteresting, quadrants |
Data is subsampled without replacement from data(exp2d)
.
Of the n1 + n2 >= 441
input/response pairs X,Z
, n1
are taken from the first quadrant, i.e., where the response is interesting,
and the remaining n1
are taken from the other three quadrant. The
remaining 441 - (n1 + n2)
are treated as predictive locations
Output is a list
with entries:
X |
2-d data.frame with n1 + n2 input locations |
Z |
Numeric vector describing the responses (with noise) at the X input locations |
Ztrue |
Numeric vector describing the true responses (without noise) at the X input locations |
XX |
2-d data.frame containing the remaining 441 - (n1 + n2)
input locations |
ZZ |
Numeric vector describing the responses (with noise) at the XX predictive locations |
ZZtrue |
Numeric vector describing the responses (without noise) at the XX predictive locations |
This data is used in the examples of the functions listed above in the “See Also” section, below
Robert B. Gramacy rbgramacy@ams.ucsc.edu
Gramacy, R. B., Lee, H. K. H., & Macready, W. (2004). Parameter space exploration with Gaussian process trees. ICML (pp. 353–360). Omnipress & ACM Digital Library.
Gramacy, R. B. & Lee, H. K. H. (2005). Gaussian Processes and Limiting Linear Models. available as UCSC Technical Report ams2005-17
http://www.ams.ucsc.edu/~rbgramacy/tgp.php
exp2d
, tgp
, bgpllm
, btlm
,
blm
, bgp
, btgpllm
, bgp
# random data ed <- exp2d.rand() # higher span = 0.5 required because the data is sparse # and was generated randomly ed.g <- interp.loess(ed$X[,1], ed$X[,2], ed$Z, span=0.5) # perspective plot, and plot of the input (X) locations par(mfrow=c(1,2), bty="n") persp(ed.g, main="loess surface", theta=-30, phi=20, xlab="X[,1]", ylab="X[,2]", zlab="Z") plot(ed$X, main="Randomly Subsampled Inputs")