Coverage
Compute the coverage measure
coverage(design)
design |
a matrix (or a data.frame) representing the design of experiments representing the design of experiments in the unit cube [0,1]^d. If this last condition is not fulfilled, a transformation into [0,1]^{d} is applied before the computation of the criteria. |
The coverage criterion is defined by
coverage) =1/gMean *[ 1/n * [( g_1 - gMean )^2 + ... + (g_n - gMean)^2] ]^(1/2)
where g_i is the minimal distance between the point x_i
and the other points of the design and gMean is
the mean of the g_i.
Note that for a regular mesh, cov=0. Then, a small value of cov means that the design is close to a regular grid.
A real number equal to the value of the coverage criterion for the design.
J. Franco
Gunzburer M., Burkdart J. (2004) Uniformity measures for point samples in hypercubes, https://people.sc.fsu.edu/~jburkardt/.
discrepancy measures provided by discrepancyCriteria.
dimension <- 2 n <- 40 X <- matrix(runif(n*dimension), n, dimension) coverage(X)
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