Runs the stepwise regression on the output received from
top models of the consolidated output of different GDS runs. With
n being the number of runs, the stepwise regression starts
with at most (n-3) selected effects from the previous step. The
remaining effects from the previous step as well as all main effects are
given a chance to enter into the model using the forward-backward stepwise
regression.
StepIII_stepwise(
xstart,
xremain,
Xmain,
Xint,
Y,
cri.penter = 0.01,
cri.premove = 0.05,
opt.heredity = "none"
)A list returning the selected effects as well as the corresponding important factors.
a vector with effects' names corresponding to the starting model.
a vector with effects' names corresponding to the remaining main effects and other effects that needs to be explored with stepwise regression.
a \(n \times m\) matrix of m main effects.
a matrix of m choose 2 two-factor
interactions.
a vector of n responses.
the p-value cutoff for the most significant effect to enter into the stepwise regression model
the p-value cutoff for the least significant effect to exit from the stepwise regression model
a string with either none, or weak, or strong. Denotes
whether the effect-heredity (weak or strong) should be embedded in GDS-ARM.
The default value is none as suggested in Singh and Stufken (2022).