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SAS Language Reference: Dictionary |

Seed Values |

Random-number
functions and CALL routines generate streams of
random numbers from an initial starting point, called a *seed*, that either the user or the computer clock supplies. A seed must be a nonnegative integer with a
value less than 2^{31}-1 (or 2,147,483,647). If you use a positive seed, you
can always replicate the stream of random numbers by using the same DATA step.
If you use zero as the seed, the computer clock initializes the stream, and
the stream of random numbers is not replicable.

Each random-number function and CALL routine generates pseudo-random numbers from a specific statistical distribution. Every random-number function requires a seed value expressed as an integer constant or a variable that contains the integer constant. Every CALL routine calls a variable that contains the seed value. Additionally, every CALL routine requires a variable that contains the generated random numbers.

The seed variable must be initialized prior to the first execution of the function or CALL statement. After each execution of a function, the current seed is updated internally, but the value of the seed argument remains unchanged. After each iteration of the CALL statement, however, the seed variable contains the current seed in the stream that generates the next random number. With a function, it is not possible to control the seed values, and, therefore, the random numbers after the initialization.

Comparison of Random-Number Functions and CALL Routines |

Except for the NORMAL and UNIFORM functions, which are equivalent to the RANNOR and RANUNI functions, respectively, SAS provides a CALL routine that has the same name as each random-number function. Using CALL routines gives you greater control over the seed values. As an illustration of this control over seed values, all the random-number CALL routines show an example of interrupting the stream of random numbers to change the seed value.

With a CALL routine, you can generate multiple streams of random numbers within a single DATA step. If you supply a different seed value to initialize each of the seed variables, the streams of the generated random numbers are computationally independent. With a function, however, you cannot generate more than one stream by supplying multiple seeds within a DATA step. The following two examples illustrate the difference.

Examples |

This example uses the CALL RANUNI routine to generate three streams of random numbers from the uniform distribution with ten numbers each. See the results in The CALL Routine Example.

options nodate pageno=1 linesize=80 pagesize=60; data multiple(drop=i); retain Seed_1 1298573062 Seed_2 447801538 Seed_3 631280; do i=1 to 10; call ranuni (Seed_1,X1); call ranuni (Seed_2,X2); call ranuni (Seed_3,X3); output; end; run; proc print data=multiple; title 'Multiple Streams from a CALL Routine'; run;

Multiple Streams from a CALL Routine 1 Obs Seed_1 Seed_2 Seed_3 X1 X2 X3 1 1394231558 512727191 367385659 0.64924 0.23876 0.17108 2 1921384255 1857602268 1297973981 0.89471 0.86501 0.60442 3 902955627 422181009 188867073 0.42047 0.19659 0.08795 4 440711467 761747298 379789529 0.20522 0.35472 0.17685 5 1044485023 1703172173 591320717 0.48638 0.79310 0.27536 6 2136205611 2077746915 870485645 0.99475 0.96753 0.40535 7 1028417321 1800207034 1916469763 0.47889 0.83829 0.89243 8 1163276804 473335603 753297438 0.54169 0.22041 0.35078 9 176629027 1114889939 2089210809 0.08225 0.51916 0.97286 10 1587189112 399894790 284959446 0.73909 0.18622 0.13269 |

Using the same three seeds that were used in Example 1, this example uses a function to create three variables. The results that are produced are different from those in Example 1 because the values of all three variables are generated by the first seed. When you use an individual function more than once in a DATA step, the function accepts only the first seed value that you supply and ignores the rest.

options nodate pageno=1 linesize=80 pagesize=60; data single(drop=i); do i=1 to 3; Y1=ranuni(1298573062); Y2=ranuni(447801538); Y3=ranuni(631280); output; end; run; proc print data=single; title 'A Single Stream across Multiple Variables'; run;

The Function Example shows the results. The values of Y1, Y2, and Y3 in this example come from the same random-number stream generated from the first seed. You can see this by comparing the values by observation across these three variables with the values of X1 in The CALL Routine Example.

A Single Stream across Multiple Variables 1 Obs Y1 Y2 Y3 1 0.64924 0.89471 0.42047 2 0.20522 0.48638 0.99475 3 0.47889 0.54169 0.08225 |

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