IWFMult: Cocktail Algorithm implementation for I-Optimality and...

Description Usage Arguments Value See Also

View source: R/wf_mult.R

Description

Function that calculates the I-Optimal designs given the matrix B (should be integral of the information matrix over the interest region), or A-Optimal if given diag(k). The rest of the parameters can help the convergence of the algorithm.

Usage

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IWFMult(
  init_design,
  grad,
  matB,
  min,
  max,
  grid.length,
  join_thresh,
  delete_thresh,
  delta_weights,
  tol,
  tol2
)

Arguments

init_design

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.

matB

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

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:

See Also

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


optedr documentation built on Jan. 20, 2022, 5:08 p.m.