XiaoXuMDLE function

Implementation of the Xiao Xu TA algorithm (experimental, for comparison with MDLEs only)

Implementation of the Xiao Xu TA algorithm (experimental, for comparison with MDLEs only)

XiaoXuMDLE( oa, ell, noptim.oa = 1, nseq = 2000, nrounds = 50, nsteps = 3000, dmethod = "manhattan", p = 50 ) createF(Dc, Dp, s, ell, nseq = 2000) optimize( Dc, s, ell, Fhat, nrounds = 50, nsteps = 3000, dmethod = "manhattan", p = 50 )

Arguments

  • oa: matrix or data.frame that contains an ingoing symmetric OA. Levels must be denoted as 0 to s-1 or as 1 to s.
  • ell: the multiplier for each number of levels
  • noptim.oa: integer: number of optimization rounds applied to initial oa itself before starting expansion
  • nseq: tuning parameters for TA algorithm
  • nrounds: tuning parameters for TA algorithm
  • nsteps: tuning parameters for TA algorithm
  • dmethod: distance method for phi_p, "manhattan" (default) or "euclidean"
  • p: p for phi_p (the larger, the closer to maximin distance)
  • Dc: matrix
  • Dp: matrix
  • s: original number of levels
  • Fhat: distribution function (created with createF)

Returns

XiaoXuMDLE returns a matrix with attribute phi_p.

createF returns a distribution function.

optimize returns a matrix with attribute phi_p.

Details

The ingoing oa is optimized by function phi_optimize, using noptim.rounds=noptim.oa; this yields the matrix Dp for use in the internal functions DcFromDp and createF.

Function XiaoXuMDLE returns the value that is produced by applying the internal function optimize

to the resulting Dc and F.

Examples

## create 8-level columns from 4-level columns XiaoXuMDLE(DoE.base::L16.4.5, 2, nrounds = 5, nsteps=50)

References

For full detail, see SOAs-package.

Xiao and Xu (2018)

  • Maintainer: Ulrike Groemping
  • License: GPL (>= 2)
  • Last published: 2023-08-10