IWFMult | R Documentation |
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.
IWFMult(
init_design,
grad,
matB,
min,
max,
grid.length,
join_thresh,
delete_thresh,
delta_weights,
tol,
tol2,
crit_name
)
init_design |
optional dataframe with the initial design for the algorithm. A dataframe with two columns:
|
grad |
function of partial derivatives of the model. |
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. |
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()
,
WFMult()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.