%mktex(4 2 ** 4, n=8, seed=306)
proc sort data=randomized out=randes(drop=x1);
by x2 x1;
run;
proc print noobs data=randes;
run;
%mktbibd(b=20, nattrs=16, setsize=4, seed=104)
%mktppro(design=randes, IBD=bibd, print=f p)
%choiceff(data=chdes, /* candidate set of choice sets */
init=chdes, /* initial design */
initvars=x1-x16, /* factors in the initial design */
model=class(x1-x16 / sta), /* model with stdz orthogonal coding */
nsets=80, /* number of choice sets */
nalts=2, /* number of alternatives */
rscale= /* relative D-efficiency scale factor */
%sysevalf(80 * 4 / 16), /* 4 of 16 attrs in 80 sets vary */
beta=zero) /* assumed beta vector, Ho: b=0 */
%mktorth(options=parent, maxlev=144)
data x(keep=n design);
set mktdeslev;
array x[144];
c = 0;
one = 0;
k = 0;
do i = 1 to 144;
c + (x[i] > 0);
if x[i] > 1 then do;
p = i;
k = x[i];
end;
if x[i] = 1 then do;
one + 1;
s = i;
end;
end;
if c = 1 then do;
c = 2;
one = 1;
s = p;
k = p - 1;
end;
if c = 2 and one = 1 and k > 2 and s * p = n;
design = compbl(left(design));
run;
proc print;
run;
%mktex(6 3 ** 6, n=18, seed=424)
proc sort data=randomized out=randes(drop=x1);
by x2 x1;
run;
proc print data=randes noobs;
run;
%mktbsize(nattrs=20, setsize=6, options=ubd)
%mktbibd(b=10, nattrs=20, setsize=6, seed=104)
%mktppro(design=randes, IBD=bibd, print=f p)
%choiceff(data=chdes, /* candidate set of choice sets */
init=chdes, /* initial design */
initvars=x1-x20, /* factors in the initial design */
model=class(x1-x20 / sta), /* model with stdz orthogonal coding */
nsets=60, /* number of choice sets */
nalts=3, /* number of alternatives */
rscale= /* relative D-efficiency scale factor */
%sysevalf(60 * 6 / 20), /* 6 of 20 attrs in 60 sets vary */
beta=zero) /* assumed beta vector, Ho: b=0 */