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(d, meanfn = c("linear", "constant"), corr = "expsep", ...)
d |
number of input dimensions ncol(X) |
meanfn |
A choice of mean function for the process. When
meanfn = "linear" (default), then we have the process
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 ("exp" ) or the separable power exponential
family ("expsep" , default); the current version also supports
the isotropic Matern ("matern" ) as “beta”
functionality. The option "mrexpsep" uses a
multi-resolution GP model. |
... |
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
...
tree |
c(0.5,2,max(c(10,col+1)),1) indicating the tree prior
parameters alpha, beta, minpart
and splitmin |
col |
dimension of regression coefficients beta: 1 for input meanfn = "constant" , or
ncol(X)+1 for meanfn = "linear" |
meanfn |
copied from the inputs |
corr |
copied from the inputs |
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(s) c(a1,g1,a2,g2) where g1 and
g2 are scale (1/rate) parameters. If
corr="mrexpsep" , then this is a vector of length 8: The
first four parameters remain the same and correspond to the
"coarse" process, and the
second set of four values, which default to c(1,10,1,10) ,
are the equivalent prior parameters for the range parameter(s) in the residual "fine" process. |
nug.p |
c(1,1,1,1) Mixture of gamma prior parameter (initial values)
for the nugget 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” |
delta.p |
c(1,1,1,1) Parameters in the mixture of gammas prior
on the delta scaling parameter for corr="mrexpsep" :
c(a1,g1,a2,g2) where g1 and
g2 are scale (1/rate) parameters; default reduces to simple
exponential prior. Delta scales the variance of the residual "fine" process with respect to
the variance of the underlying "coarse" process. |
nugf.p |
c(1,1,1,1) Parameters in the mixture of gammas prior
on the residual "fine" process nugget parameter for
corr="mrexpsep" : c(a1,g1,a2,g2) where g1 and
g2 are scale (1/rate) parameters; default reduces to simple exponential prior. |
Please refer to the examples for the functions in
"See Also" below and vignette("tgp")
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).
http://www.jstatsoft.org/v19/i09 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