View source: R/StepI_chooseints.R
StepI_chooseints | R Documentation |
Runs the Gauss Dantzig Selector (GDS) multiple times, each time
with a different set of randomly selected two-factor interactions.
All m
main effects are included in each GDS run. For each set of
randomly selected interactions, the best GDS output is chosen among
delta.n
values of delta
. We use kmeans with 2
clusters and BIC to select such best model.
StepI_chooseints( delta.n = 10, nint, nrep, Xmain, Xint, Y, opt.heredity = c("none") )
delta.n |
a positive integer suggesting the number of delta values
to be tried. |
nint |
a positive integer representing the number of randomly
chosen interactions. The suggested value to use is the ceiling of 20%
of the total number of interactions, that is, for |
nrep |
a positive integer representing the number of times GDS should
be run. The suggested value is |
Xmain |
a n x m matrix of |
Xint |
a matrix of \code{m choose 2}) two-factor interactions. |
Y |
a vector of |
opt.heredity |
a string with either |
A list containing the (a) matrix of the output of each GDS run with each row representing the selected effects from the corresponding GDS run, (b) a vector with the corresponding BIC values of each model.
Singh, R. and Stufken, J. (2022). Factor selection in screening experiments by aggregation over random models, 1–31. doi: 10.48550/arXiv.2205.13497
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