UniDOE (version 1.0.2)

GenAUD: Generating Augmented Uniform Design of Experiments

Description

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

Usage

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

Arguments

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.

Value

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.

References

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.

Examples

# NOT RUN {
#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)
# }