Hello @SukumarBalusamy,
Good news! You will be able to recover your lost seed!
Let me first note that, with a small probability, your DATA _NULL_ step will produce an invalid seed: The BEST9. format will use scientific notation (e.g. 4.657E-10) if ranuni(0) happens to be very small. PROC PLAN would then error out with a log message like
ERROR: The value SEED = 4.657E-10 is not an integer.
But apparently this unlikely case did not happen when you created your RandList dataset.
Further, note that leading zeros in macro variable Seedval will be ignored by PROC PLAN. This means that if we recover a seed of, say, 1234, the true "historical" value of Seedval might have been 1234 or 01234 or 001234 or 0001234, but this is unimportant for PROC PLAN. The probability that two significantly different seeds produce the same PROC PLAN output (with 200 observations) is extremely small, if not zero, and the DATA step below can find all of them, if needed (see comment about the STOP statement).
It turned out that the DATA step that I outlined in item 1 of my previous post is so fast that we don't have to limit it to "7 to 9 blocks." Instead, we let it generate treatments as long as they match the treatments in dataset RandList. Thus we can omit items 2 and 3 of the strategy and just write the recovered seed to the dataset or to the log. On my workstation it took only a few seconds to recover a seed, the longest time (about 15 seconds) if the seed was 9999999, i.e., the last seed checked. Nevertheless, the DATA step also allows for partial matches with RandList (just use a smaller value for macro variable nt and comment out the STOP statement immediately following the OUTPUT statement), which may produce more than one "candidate" seed value.
/* Create treatment format */
proc format;
value trtf
1, 2 = 'A'
3, 4 = 'B';
run;
%let c=397204094; /* "multiplier" found in RANUNI documentation */
%let d=%sysevalf(2**31-1); /* =2147483647 */
%let nt=200; /* number of treatments to be generated: 50 blocks with 4 treatments each */
/* Find "candidate" seed value(s) for PROC PLAN to reproduce (parts of) dataset RandList */
data candseeds(keep=seed);
length t $10;
/* Preparation: compute 10**k * 397204094 modulo 2**31-1, k=1, ..., 9 */
array n[9] _temporary_;
n[1]=mod(10*&c, &d);
do k=2 to 9;
n[k]=mod(10*n[k-1], &d);
end;
/* Prepare assignment of 2nd and 3rd treatment in a block */
array t2[4,3] _temporary_ (2 3 4 1 3 4 2 1 4 2 3 1);
array t3[4,4,2] _temporary_ (. . 3 4 2 4 3 2
3 4 . . 1 4 3 1
2 4 1 4 . . 1 2
3 2 3 1 2 1 . .);
array ppt[&nt] $1 _temporary_; /* for treatments A, A, B, B generated by PROC PLAN */
array trt[&nt] _temporary_; /* for treatments 1, 2, 3, 4 generated by the DATA step */
/* Read the first &nt treatments from RandList into array ppt */
do p=1 to &nt;
set RandList(keep=treatment) point=p;
ppt[p]=char(treatment,5); /* shorten "Arm A" to "A", etc. */
end;
/* Run through all possible seeds>0 */
do seed=1 to 9999999;
m=seed;
/* Generate the first &nt treatments like PROC PLAN would do it */
do i=1 to &nt;
/* Emulate RANUNI(seed) */
t=put(m,10.);
s=input(char(t,10), 1.)*&c;
do k=1 to length(left(t))-1;
s+input(char(t,10-k), 1.)*n[k];
end;
m=mod(s,&d);
r=m/&d; /* random number between 0 and 1, computed with the RANUNI algorithm */
/* Assign treatments based on the random number and the previous treatments in the block */
select(mod(i,4));
when(1) trt[i]=ceil(4*r);
when(2) trt[i]=t2[trt[i-1],ceil(3*r)];
when(3) trt[i]=t3[trt[i-2],trt[i-1],ceil(2*r)];
otherwise trt[i]=10-trt[i-3]-trt[i-2]-trt[i-1]; /* = the only remaining treatment */
end;
if put(trt[i],trtf.) ne ppt[i] then leave; /* move on to next seed if a discrepancy was found */
end;
if i>&nt then do; /* i.e., no discrepancies were found */
output; /* write "candidate" seed to dataset CANDSEEDS */
stop; /* Remove this STOP statement if you expect more than one "candidate" seed, */
end; /* e.g., if &nt is less than the number of observations in RandList. */
end;
stop; /* necessary because of SET statement with POINT= option */
run;
Please run the code above using your RandList dataset. The expected outcome is a single observation in dataset CANDSEEDS containing the seed value with which PROC PLAN should reproduce dataset RandList.
Click image to register for webinar
