StepI_chooseints {GDSARM} | R Documentation |
Step I: Multiple GDS runs with random interactions
Description
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.
Usage
StepI_chooseints(
delta.n = 10,
nint,
nrep,
Xmain,
Xint,
Y,
opt.heredity = c("none")
)
Arguments
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 |
Xint |
a matrix of |
Y |
a vector of |
opt.heredity |
a string with either |
Value
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.
Source
Singh, R. and Stufken, J. (2022). Factor selection in screening experiments by aggregation over random models, 1–31. doi: 10.48550/arXiv.2205.13497