tgp.default.params {tgp}R Documentation

Default Treed Gaussian Process Model Parameters

Description

Construct a default list of parameters to the b* functions– the interfaces to treed Gaussian process modeling

Usage

tgp.default.params(col, base = "gp", ...)

Arguments

col number of input dimensions ncol(X)+1
base Base model to be used. Right now, the only supported option is the default, base = "gp". Future versions of this package will support other base models.
... These elipses 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

Value

The output is the following list of params...

base input argument base
tree c(0.5,2,max(c(10,col+1)),1) indicating the tree prior parameters alpha, beta, minpart and splitmin
corr "expsep" separable power exponential family correlation model; alternate is "exp" isotropic power family, or "matern"
bprior Linear (beta) prior, default is "bflat" which gives an “improper” prior which can perform badly when the signal-to-noise ratio is low. In these cases the “proper” hierarchical specification "b0" or "bmzt" prior may perform better
start c(0.5,0.1,1.0,1.0) starting values for range d, nugget g, s2, and tau2
beta rep(0,col) starting values for beta linear parameters
tree c(0.25,2,min(c(10,col+1))) tree prior process parameters c(alpha, beta, nmin) specifying

p(split leaf eta) = alpha*(1+depth(eta))^(-beta)

with zero probability given to trees with partitions containing less than nmin data points

s2.p c(5,10) s2 inverse-gamma prior parameters c(a0, g0) where g0 is scale (1/rate) parameter
tau2.p c(5,10) tau2 inverse-gamma prior parameters c(a0, g0) where g0 is scale (1/rate) parameter
d.p c(1.0,20.0,10.0,10.0) Mixture of gamma prior parameter (initial values) for the range parameter c(a1,g1,a2,g2) where g1 and g2 are scale (1/rate) parameters
nug.p c(1,1,1,1) Mixture of gamma prior parameter (initial values) for the range parameter c(a1,g1,a2,g2) where g1 and g2 are scale (1/rate) parameters; default reduces to simple exponential prior
gamma c(10,0.2,10) LLM parameters c(g, t1, t2), with growth parameter g > 0 minimum parameter t1 >= 0 and maximum parameter t1 >= 0, where t1 + t2 <= 1 specifies

p(b|d)= t1 + exp(-g*(t2-t1)/(d-0.5))

d.lam "fixed" Hierarchical exponential distribution parameters to a1, g1, a2, and g2 of the prior distribution for the range parameter d.p; "fixed" indicates that the hierarchical prior is “turned off”
nug.lam "fixed" Hierarchical exponential distribution parameters to a1, g1, a2, and g2 of the prior distribution for the nug parameter nug.p; "fixed" indicates that the hierarchical prior is “turned off”
s2.lam c(0.2,10) Hierarchical exponential distribution prior for a0 and g0 of the prior distribution for the s2 parameter s2.p; "fixed" indicates that the hierarchical prior is “turned off”
tau2.lam c(0.2,0.1) Hierarchical exponential distribution prior for a0 and g0 of the prior distribution for the s2 parameter tau2.p; "fixed" indicates that the hierarchical prior is “turned off”

Note

Please refer to the examples for the functions in the "See Also" list below and vignette("tgp")

Author(s)

Robert B. Gramacy rbgramacy@ams.ucsc.edu

References

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

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

See Also

blm, btlm, bgp, btgp, bgpllm, btgpllm


[Package tgp version 1.2-6 Index]