- object
"tgp"-class object that is the output of one of
the b* functions: blm, btlm
bgp, bgpllm, btgp, or
btgpllm
- XX
Optional data.frame, matrix,
or vector of predictive input locations
with ncol(XX) == ncol(object$X)
- BTE
3-vector of Monte-carlo parameters (B)urn in, (T)otal, and
(E)very. Predictive samples are saved every E MCMC rounds starting
at round B, stopping at T. The default BTE=c(0,1,1) is
specified to give the kriging means and variances as outputs, plus
one sample from the posterior predictive distribution
- R
Number of repeats or restarts of BTE MCMC rounds,
default R=1 is no restarts
- MAP
When TRUE (default) predictive data (i.e.,
kriging mean and variance estimates, and samples from the
posterior predictive distribution) are obtained for the
fixed MAP model encoded in object. Otherwise,
when MAP=FALSE sampling from the joint posterior
of the model parameters (i.e., tree and GPs) and the posterior
predictive distribution are obtained starting from the MAP model and
proceeding just as the b* functions
- pred.n
TRUE (default) value results in prediction at
the inputs X; FALSE
skips prediction at X resulting in a faster
implementation
- krige
TRUE (default) value results in collection of
kriging means and variances at predictive (and/or data)
locations; FALSE skips the gathering of kriging statistics
giving a savings in storage
- zcov
If TRUE then the predictive covariance matrix is
calculated-- can be computationally (and memory) intensive if
X or XX is large. Otherwise only the variances
(diagonal of covariance matrices) are calculated (default). See
outputs Zp.s2, ZZ.s2, etc., below
- Ds2x
TRUE results in ALC (Active Learning--Cohn)
computation of expected reduction in uncertainty calculations at the
X locations, which can be used for adaptive sampling;
FALSE (default) skips this computation, resulting in
a faster implementation
- improv
TRUE results in samples from the
improvement at locations XX with respect to the observed
data minimum. These samples are used to calculate the expected
improvement over XX, as well as to rank all of the points in
XX in the order that they should be sampled to minimize the
expected multivariate improvement (refer to Schonlau et al, 1998).
Alternatively, improv can be set to any positive integer 'g',
in which case the ranking is performed with respect to the expectation
for improvement raised to the power 'g'. Increasing 'g' leads to
rankings that are more oriented towards a global optimization.
The option FALSE (default) skips these computations,
resulting in a faster implementation. Optionally, a two-vector
can be supplied where improv[2] is interpreted as the
(maximum) number of points to rank by improvement.
See the note in btgp documentation.
If not specified, then the larger of 10% of nn = nrow(XX)
and min(10, nn) is taken by default
- sens.p
Either NULL or a vector of parameters for
sensitivity analysis, built by the function sens.
Refer there for details
- trace
TRUE results in a saving of samples from the
posterior distribution for most of the parameters in the model. The
default is FALSE for speed/storage reasons. See note below
- verb
Level of verbosity of R-console print statements: from 0
(default: none); 1 which shows the “progress meter”; 2
includes an echo of initialization parameters; up to 3 and 4 (max)
with more info about successful tree operations
- ...
Ellipses are not used in the current version
of predict.tgp. They are are only included in order to
maintain S3 generic/method consistency