discrepancyCriteria_cplus {sensitivity} | R Documentation |
Discrepancy measure
Description
Compute discrepancy criteria. This function uses a C++ implementation of the function discrepancyCriteria
from package DiceDesign.
Usage
discrepancyCriteria_cplus(design,type='all')
Arguments
design |
a matrix corresponding to the design of experiments.
The discrepancy criteria are computed for a design in the unit cube [0,1] | |||||||||||||||
type |
type of discrepancies (single value or vector) to be computed:
|
Details
The discrepancy measures how far a given distribution of points deviates
from a perfectly uniform one. Different discrepancies are available.
For example, if we denote by the volume of a subset
of
and
the number of points of
falling in
, the
discrepancy is:
where ,
and
. The other L2-discrepancies are defined according to the same principle with different form from the subset
.
Among all the possibilities, discrepancyCriteria_cplus implements only the L2 discrepancies because it can be expressed analytically even for high dimension.
Centered L2-discrepancy is computed using the analytical expression done by Hickernell (1998). The user will refer to Pleming and Manteufel (2005) to have more details about the wrap around discrepancy.
Value
A list containing the L2-discrepancies of the design
.
Author(s)
Laurent Gilquin
References
Fang K.T, Li R. and Sudjianto A. (2006) Design and Modeling for Computer Experiments, Chapman & Hall.
Hickernell F.J. (1998) A generalized discrepancy and quadrature error bound. Mathematics of Computation, 67, 299-322.
Pleming J.B. and Manteufel R.D. (2005) Replicated Latin Hypercube Sampling, 46th Structures, Structural Dynamics & Materials Conference, 16-21 April 2005, Austin (Texas) – AIAA 2005-1819.
See Also
The distance criterion provided by maximin_cplus
Examples
dimension <- 2
n <- 40
X <- matrix(runif(n*dimension),n,dimension)
discrepancyCriteria_cplus(X)