Title: | Run Orders with Assignment-Expansion Method |
Version: | 0.1.0 |
Maintainer: | Romario Conto <racontol@unal.edu.co> |
Description: | It enables the identification of sequentialexperimentation orders for factorial designs that jointly reduce bias and the number of level changes. The method used is that presented by Conto et al. (2025), known as the Assignment-Expansion method, which consists of adapting the linear programming assignment problem to generate balanced experimentation orders. The properties identified are then generalized to designs with a larger number of factors and levels using the expansion method proposed by Correa et al. (2009) and later generalized by Bhowmik et al. (2017). For more details see Conto et al. (2025) <doi:10.1016/j.cie.2024.110844>, Correa et al. (2009) <doi:10.1080/02664760802499337> and Bhowmik et al. (2017) <doi:10.1080/03610926.2016.1152490>. |
License: | MIT + file LICENSE |
Encoding: | UTF-8 |
RoxygenNote: | 7.3.2 |
URL: | https://github.com/RomarioContoL/rob |
BugReports: | https://github.com/RomarioContoL/rob/issues |
Imports: | FMC, minimalRSD |
NeedsCompilation: | no |
Packaged: | 2025-04-18 04:16:48 UTC; pc |
Author: | Romario Conto |
Repository: | CRAN |
Date/Publication: | 2025-04-22 13:50:02 UTC |
Function to add a new column to the matrix
Description
Function to add a new column to the matrix
Usage
adcol(x, y, z, run)
Arguments
x |
levels vector of the new factor |
y |
number of levels of the new factor |
z |
level vector of the initial matrix |
run |
initial run matrix |
Value
matrix with the new run order
Examples
x = matrix(c(-1, 1), ncol = 1)
y = length(x)
z = c(2,2,2)
run=matrix(c(1,-1,1,-1,1,1,-1,-1), ncol=2)
adcol(x,y,z,run)
Assignment-Expansion method
Description
Assignment-Expansion method
Usage
runorder(z)
Arguments
z |
vector with the levels of the factor |
Value
order of experimentation with bias and number of level changes in balance
Examples
z<-c(2,2,2,2,2,2)
runorder(z)
z<-c(4,3,2,3,2)
runorder(z)
z<-c(3,3,2,4)
runorder(z)