IWFMult: Cocktail Algorithm implementation for L-, I- and A-Optimality

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

Cocktail Algorithm implementation for L-, I- and A-Optimality

Description

Cocktail Algorithm implementation for L-, I- and A-Optimality

Usage

IWFMult(
  init_design,
  grad,
  matB,
  design_space,
  grid.length,
  join_thresh,
  delete_thresh,
  delta_weights,
  tol,
  tol2,
  criterion,
  max_iter
)

Arguments

init_design	optional dataframe with the initial design for the algorithm.
grad	function of partial derivatives of the model.
matB	optional matrix for L-optimality.
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.
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.
criterion	character variable with the chosen optimality criterion.
max_iter	maximum number of outer iterations.

Value

An object of class optdes.

See Also

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

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