StepIII_stepwise: Step III: Stepwise on the consolidated output from different...

In GDSARM: Gauss - Dantzig Selector: Aggregation over Random Models

Step III: Stepwise on the consolidated output from different GDS runs

Description

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.

Usage

StepIII_stepwise(
  xstart,
  xremain,
  Xmain,
  Xint,
  Y,
  cri.penter = 0.01,
  cri.premove = 0.05,
  opt.heredity = "none"
)

Arguments

xstart	a vector with effects' names corresponding to the starting model.
xremain	a vector with effects' names corresponding to the remaining main effects and other effects that needs to be explored with stepwise regression.
Xmain	a n x m matrix of m main effects.
Xint	a matrix of m choose 2 two-factor interactions.
Y	a vector of n responses.
cri.penter	the p-value cutoff for the most significant effect to enter into the stepwise regression model
cri.premove	the p-value cutoff for the least significant effect to exit from the stepwise regression model
opt.heredity	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).

Value

A list returning the selected effects as well as the corresponding important factors.

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

GDSARM documentation built on July 14, 2022, 1:05 a.m.