tgp.default.params {tgp} | R Documentation |
Construct a default list of parameters to the b*
functions– the interfaces to treed Gaussian process
modeling
tgp.default.params(col, base = "gp", ...)
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 |
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 |
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” |
Please refer to the examples for the functions in the
"See Also" list below and vignette("tgp")
Robert B. Gramacy rbgramacy@ams.ucsc.edu
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
blm
, btlm
, bgp
,
btgp
, bgpllm
, btgpllm