DWFMult: Cocktail Algorithm implementation for D-Optimality
In Kezrael/optedr: Calculating Optimal and D-Augmented Designs

DWFMult

R Documentation

Cocktail Algorithm implementation for D-Optimality

Function that calculates the DsOptimal design. The rest of the parameters can help the convergence of the algorithm.

DWFMult(
  init_design,
  grad,
  min,
  max,
  grid.length,
  join_thresh,
  delete_thresh,
  k,
  delta_weights,
  tol,
  tol2
)

`init_design`	optional dataframe with the initial design for the algorithm. A dataframe with two columns: `Point` contains the support points of the design. `Weight` contains the corresponding weights of the `Point`s.
`grad`	function of partial derivatives of the model.
`min`	numeric value with the inferior bound of the space of the design.
`max`	numeric value with the upper bound of the space of the design.
`grid.length`	numeric value that gives the grid to evaluate the sensitivity function when looking for a maximum.
`join_thresh`	numeric value that states how close, in real units, two points must be in order to be joined together by the join heuristic.
`delete_thresh`	numeric value with the minimum weight, over 1 total, that a point needs to have in order to not be deleted from the design.
`k`	number of unknown parameters of the model.
`delta_weights`	numeric value in (0, 1), parameter of the algorithm.
`tol`	numeric value for the convergence of the weight optimizing algorithm.
`tol2`	numeric value for the stop condition of the algorithm.

list correspondent to the output of the correspondent algorithm called, dependent on the criterion. A list of two objects:

optdes: a dataframe with the optimal design in two columns, Point and Weight.
sens: a plot with the sensitivity function to check for optimality of the design.