GenAUD: Generating Augmented Uniform Design of Experiments
In UniDOE: Uniform Design of Experiments

Description Usage Arguments Value Author(s) References Examples

This function takes n,s,q; a unchanged initial design and other arguments to output a list (described below).

1	GenAUD(X0,n,crit,maxiter,hits_ratio,vis)

`X0`	an integer matrix R object
`n`	an integer R object. Run of Experiment
`crit`	an character R object. Type of criterion to use. "maximin" – maximin Discrepancy; "CD2" –Centered L2 Discrepancy; "WD2" – Wrap-around L2 Discrepancy; "MD2" –Mixture L2 Discrepancy;
`maxiter`	a positive integer R object. Maximum iteration number in outer while loop of SOAT algorithm.
`hits_ratio`	an float R object. Default value is 0.1, which is the ratio to accept changes of design in inner for loop. Details can be checked in (Zhang, A. and Li, H. (2017). UniDOE: An R package for constructing uniform design of experiments via stochastic and adaptive threshold accepting algorithm. Technical Report.)
`vis`	an boolean variable. If true, plot the trace of criterion values.

A list that contains Initial design matrix(initial_design),optimal design matrix(final_design), initial criterion value(initial_criterion), final criterion value(criterion_value) and criterion list(criterion_lists) in update process.

Aijun Zhang, Haoyu Li, Shijie Quan

Zhang, A. and Li, H. (2017). UniDOE: An R package for constructing uniform design of experiments via stochastic and adaptive threshold accepting algorithm. Technical Report.

#Example 1.
#Set a fixed initial matrix:
n=12 #(must be multiples of q)
mat0 = matrix(c(1,1,1,2,2,2,3,3,3),ncol=3,byrow=TRUE)# nb. of columns=s
crit = "MD2" #(Mixture L2 criteria)
res = GenAUD(X0=mat0,n,crit=crit,maxiter=100)

# Example 2.
# Set a fixed initial matrix with visualization:
n=9 #(must be multiples of q)
mat0 = matrix(c(1,1,1,2,2,2,3,3,3), ncol = 3, byrow = TRUE)
crit = "MD2" #(Mixture L2 criteria)
list1=GenAUD(X0=mat0,n,crit=crit,vis=TRUE,maxiter=100)