GenUD {UniDOE} | R Documentation |
This function takes n,s,q and other arguments to output a list(described below).
GenUD(n,s,q,init,initX,crit,maxiter,hits_ratio,vis)
n |
an integer R object. Run of Experiment |
s |
an integer R object. Factor of Experiment. |
q |
an integer R object. Level of Experiment. |
init |
an string vector R object: "rand"(default), "orth" or "input". Criterion used |
initX |
an user-defined integer matrix R object, which is fixed in search. Needed when init="orth" or init="input" |
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 SATA 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 R object. If true, plot the criterion value sequence. |
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.
##e.g.1 n=12 #(must be multiples of q) s=3 q=4 crit = "MD2"#(Mixture L2 criteria) res = GenUD(n,s,q,crit=crit,maxiter=100) ##e.g.2 n=10 #(must be multiples of q) s=3 q=5 init = "rand" crit = "MD2" #(Mixture L2 criteria) vis=TRUE res=GenUD(n,s,q,init=init,crit=crit,maxiter=100,vis=vis) ##e.g.3 #If init = "orth", #algorithm will fix columns of initX and expand total number of columns to s for final design. s=5 init = "orth" # initX should be orthogonal design with nrow=n&level=q initX = matrix(c(1,1,2,2,3,3,3,3,1,1,2,2), ncol=2) res = GenUD(s=s,init=init,initX = initX,maxiter=100) ##e.g.4 #If init="input", algorithm will search for better a better design with same size as initX. init = "input" # initX should be orthogonal design with nrow=n&level=q initX = matrix(c(1,1,2,2,3,3,3,3,1,1,2,2),ncol=2) res = GenUD(init=init,initX = initX,maxiter=100)