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tgp (version 1.1-11)

tgp.default.params: Default Treed Gaussian Process Model Parameters

Description

Construct a default list of parameters to the tgp function-- the generic interface to treed Gaussian process modeling

Usage

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

Arguments

col
number of input dimensions dim(X)[2] plus 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.

Value

  • The output is the following list of params...
  • corr"expsep" separable power exponential family correlation model; alternate is "exp" isotropic power family
  • bpriorLinear (beta) prior, default is "bflat"; alternates include "b0" hierarchical Normal prior, "bmle" empirical Bayes Normal prior, "bcart" Bayesian linear CART style prior from Chipman et al, "b0tau" a independent Normal prior with inverse-gamma variance.
  • startc(0.5,0.1,1.0,1.0) starting values for range $d$, nugget $g$, $\sigma^2$, and $\tau^2$
  • betarep(0,d) starting values for beta linear parameters
  • treec(0.25,2,10) tree prior process parameters c(alpha, beta, nmin) specifying $$p_{\mbox{\tiny split}}(\eta, \mathcal{T}) = \alpha*(1+\eta)^\beta$$ with zero probability to trees with partitions containing less than nmin data points
  • s2.pc(5,10) $\sigma^2$ inverse-gamma prior parameters c(a0, g0) where g0 is scale (1/rate) parameter
  • tau2.pc(5,10) $\tau^2$ inverse-gamma prior parameters c(a0, g0) where g0 is scale (1/rate) parameter
  • d.pc(1.0,20.0,10.0,10.0) Mixture of gamma prior parameter (initial values) for for the range parameter c(a1,g1,a2,g2) where g1 and g2 are scale (1/rate) parameters
  • d.pcode{c(1,1,1,1)} Mixture of gamma prior parameter (initial values) for for the range parameter c(a1,g1,a2,g2) where g1 and g2 are scale (1/rate) parameters; default reduces to simple exponential prior
  • gammac(10,0.2,10) Limiting Linear model parameters c(g, t1, t2), with growth parameter g > 0 minimum parameter t1 >= 0 and maximum parameter t1 >= 0, where t1 + t2 <= 1<="" code=""> specifies $$p(b|d)=t_1 + \exp\left{\frac{-g(t_2-t_1)}{d-0.5}\right}$$
  • 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.lamc(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.lamc(0.2,10) 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 dQuote{turned off}

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

tgp