tgp.default.params | R Documentation |
Construct a default list of parameters to the b*
functions– the interfaces to treed Gaussian process
modeling
tgp.default.params(d, meanfn = c("linear", "constant"), corr = c("expsep", "exp", "mrexpsep", "matern", "sim", "twovar"), splitmin = 1, basemax = d, ...)
d |
number of input dimensions |
meanfn |
A choice of mean function for the process. When
Z = cbind(rep(1,nrow(X), X)) %*% beta + W(X), where W(X) represents the Gaussian process
part of the model (if present). Otherwise, when
Z = beta0 + W(X) |
corr |
Gaussian process correlation model. Choose between the isotropic
power exponential family ( |
splitmin |
Indicates which column of the inputs |
basemax |
Indicates which column of the inputs |
... |
These ellipses arguments are interpreted as augmentations to the prior specification. You may use these to specify a custom setting of any of default parameters in the output list detailed below |
The output is the following list of params
...
col |
dimension of regression coefficients beta: 1 for input |
meanfn |
copied from the inputs |
corr |
copied from the inputs |
bprior |
Linear (beta) prior, default is |
beta |
|
tree |
p(split leaf eta) = alpha*(1+depth(eta))^(-beta) with zero probability given to trees
with partitions containing less than |
s2.p |
|
tau2.p |
|
d.p |
c(1.0,20.0,10.0,10.0) Mixture of gamma prior parameter (initial values)
for the range parameter(s) |
nug.p |
|
gamma |
p(b|d)= t1 + exp(-g*(t2-t1)/(d-0.5)) |
d.lam |
|
nug.lam |
|
s2.lam |
|
tau2.lam |
|
delta.p |
|
nugf.p |
|
dp.sim |
|
Please refer to the examples for the functions in
"See Also" below, vignette("tgp")
and vignette(tgp2)
Robert B. Gramacy, rbg@vt.edu, and Matt Taddy, mataddy@amazon.com
Gramacy, R. B. (2007). tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models. Journal of Statistical Software, 19(9). https://www.jstatsoft.org/v19/i09 doi: 10.18637/jss.v019.i09
Robert B. Gramacy, Matthew Taddy (2010). Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models. Journal of Statistical Software, 33(6), 1–48. https://www.jstatsoft.org/v33/i06/ doi: 10.18637/jss.v033.i06
Gramacy, R. B., Lee, H. K. H. (2008). Bayesian treed Gaussian process models with an application to computer modeling. Journal of the American Statistical Association, 103(483), pp. 1119-1130. Also available as ArXiv article 0710.4536 https://arxiv.org/abs/0710.4536
Robert B. Gramacy, Heng Lian (2011). Gaussian process single-index models as emulators for computer experiments. Available as ArXiv article 1009.4241 https://arxiv.org/abs/1009.4241
https://bobby.gramacy.com/r_packages/tgp/
blm
, btlm
, bgp
,
btgp
, bgpllm
, btgpllm
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