DsWFMult: Cocktail Algorithm implementation for Ds-Optimality

In optedr: Calculating Optimal and D-Augmented Designs

Cocktail Algorithm implementation for Ds-Optimality

Description

Function that calculates the Ds-Optimal designs for the interest parameters given by intPar. The rest of the parameters can help the convergence of the algorithm.

Usage

DsWFMult(
  init_design,
  grad,
  par_int,
  min,
  max,
  grid.length,
  join_thresh,
  delete_thresh,
  delta_weights,
  tol,
  tol2
)

Arguments

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 Points.
grad	function of partial derivatives of the model.
par_int	numeric vector with the index of the parameters of interest. Only necessary when the Criterion chosen is 'Ds-Optimality'.
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.
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.

Value

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.

See Also

Other cocktail algorithms: DWFMult(), IWFMult(), WFMult()

optedr documentation built on Nov. 18, 2022, 5:12 p.m.