selectQdims {anMC} | R Documentation |
Iteratively select active dimensions
Description
The function selectQdims
iteratively selects the number of active dimensions and the dimensions themselves for the computation of .
The number of dimensions is increased until
is smaller than the error of the procedure.
Usage
selectQdims(
E,
threshold,
mu,
Sigma,
pn = NULL,
method = 1,
reducedReturn = T,
verb = 0,
limits = NULL,
pmvnorm_usr = pmvnorm
)
Arguments
E |
discretization design for the field. |
threshold |
threshold. |
mu |
mean vector. |
Sigma |
covariance matrix. |
pn |
coverage probability function based on |
method |
integer chosen between
|
reducedReturn |
boolean to select the type of return. See Value for further details. |
verb |
level of verbosity: 0 returns nothing, 1 returns minimal info. |
limits |
numeric vector of length 2 with q_min and q_max. If |
pmvnorm_usr |
function to compute core probability on active dimensions. Inputs:
returns a the probability value with attribute "error", the absolute error. Default is the function |
Value
If reducedReturn=F
returns a list containing
indQ
: the indices of the active dimensions chosen for;
pq
: the biased estimatorwith attribute
error
, the estimated absolute error;Eq
: the points of the designselected for
;
muEq
: the subvector ofmu
selected for;
KEq
: the submatrix ofSigma
composed by the indexes selected for.
Otherwise it returns only indQ
.
References
Azzimonti, D. and Ginsbourger, D. (2018). Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation. Journal of Computational and Graphical Statistics, 27(2), 255-267. Preprint at hal-01289126
Chevalier, C. (2013). Fast uncertainty reduction strategies relying on Gaussian process models. PhD thesis, University of Bern.
Genz, A. (1992). Numerical computation of multivariate normal probabilities. Journal of Computational and Graphical Statistics, 1(2):141–149.