WFMult | R Documentation |
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.
WFMult(
init_design,
grad,
criterion,
par_int = NA,
matB = NA,
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:
|
grad |
function of partial derivatives of the model. |
criterion |
character variable with the chosen optimality criterion. Can be one of the following:
|
par_int |
numeric vector with the index of the |
matB |
optional matrix of dimensions k x k, for L-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. |
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.
Other cocktail algorithms:
DWFMult()
,
DsWFMult()
,
IWFMult()
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