WFMult {optedr}

Master function for the cocktail algorithm, that calls the appropriate one given the criterion.

Description

Depending on the Criterion the cocktail algorithm for the chosen criterion is called, and the necessary parameters for the functions are given from the user input.

Usage

WFMult(
  init_design,
  grad,
  Criterion,
  par_int = NA,
  matB = NA,
  min,
  max,
  grid.length,
  join_thresh,
  delete_thresh,
  k,
  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.

Criterion

character variable with the chosen optimality criterion. Can be one of the following:

• 'D-Optimality'

• 'Ds-Optimality'

• 'A-Optimality'

• 'I-Optimality'

par_int

numeric vector with the index of the parameters of interest. Only necessary when the Criterion chosen is 'Ds-Optimality'.

matB

optional matrix of dimensions k x k, integral of the information matrix of the model over the interest region for I-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.

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.

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(), DsWFMult(), IWFMult()