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The RANUNI function in SAS is used to generate random numbers from a uniform distribution. It returns a random number between 0 and 1. This function can be used for randomizing data, creating random samples, and for simulation studies. Examples of how to use the RANUNI function include generating a random number for each observation in a dataset and creating random samples from a population.
You can use the RANUNI function in SAS to generate values from the .
This function uses the following syntax:
RANUNI(seed)
where:
- seed: A non-negative integer to use as initial starting point for generating random values.
The following example shows how to use this function in practice.
Example 1: Use RANUNI Function to Generate One Random Value
We can use the following syntax with the RANUNI function to create a dataset that contains one random value between 0 and 1:
/*create dataset with one random value between 0 and 1*/
data my_data;
my_value=ranuni(0);
run;
/*view dataset*/
proc print data=my_data;
The RANUNI function generated the value 0.49370.
By default, the RANUNI function generates a random value between 0 and 1.
However, you can multiply the result of the RANUNI function by n to instead generate a random value between 1 and n.
For example, we can use the following syntax to generate a random value between 0 and 10:
/*create dataset with one random value between 0 and 10*/
data my_data;
my_value=ranuni(0)*10;
run;
/*view dataset*/
proc print data=my_data;
This time the RANUNI function generated the value 4.17403.
Example 2: Use RANUNI Function to Generate Several Random Values
We can use the following syntax with the RANUNI function to create a dataset that contains ten random values between 0 and 100:
/*create dataset with 10 random values between 0 and 100*/
data my_data;
do i=1 to 10 by 1;
my_value=ranuni(0)*100;
output;
end;
run;
/*view dataset*/
proc print data=my_data;
Notice that each of the values in the my_value column are between 0 and 100.
The following tutorials explain how to perform other common tasks in SAS: