DWFMult: Cocktail Algorithm implementation for D-Optimality

In optedr: Calculating Optimal and D-Augmented Designs for Single- and Multi-Factor Models

Cocktail Algorithm implementation for D-Optimality

Description

Cocktail Algorithm implementation for D-Optimality

Usage

DWFMult(
  init_design,
  grad,
  design_space,
  grid.length,
  join_thresh,
  delete_thresh,
  k,
  delta_weights,
  tol,
  tol2,
  max_iter
)

Arguments

init_design	optional dataframe with the initial design for the algorithm.
grad	function of partial derivatives of the model.
design_space	named list with bounds for each design variable.
grid.length	numeric value for the sensitivity search grid / LHS size.
join_thresh	numeric value for the merge heuristic.
delete_thresh	numeric value for the weight deletion threshold.
k	number of unknown parameters of the model.
delta_weights	numeric value in (0, 1), parameter of the algorithm.
tol	numeric value for convergence of the weight loop.
tol2	numeric value for the outer stop condition.
max_iter	maximum number of outer iterations.

Value

An object of class optdes.

See Also

Other cocktail algorithms: CWFMult(), DsWFMult(), IWFMult(), KLWFMult(), WFMult()

optedr documentation built on June 23, 2026, 5:07 p.m.