Last week I showed how to generate random points uniformly inside a 2-d circular region. That article showed that the distance of a point to the circle's center cannot be distributed uniformly. Instead, you should use the square root of a uniform variate to generate 2-D distances to the origin. This result generalizes to higher dimensions. If you want to simulate points uniformly in the d-dimensional ball, generate the radius to be proportional to the dth-root of a uniform random variate.

Although it is possible to generalize the polar mapping and obtain a parameterization of the d-dimensional ball, there is an easier approach to simulate points in a ball. The basic idea is to simulate d independent standardized normal variables, project them radially onto the unit sphere, and then adjust their distance to the origin appropriately. You can find this algorithm in many textbooks (Devroye, 1986; Fishman, 1996), but Harman and Lacko (2010) summarize the process nicely. To paraphrase:

If Y is drawn from the uncorrelated multivariate normal distribution, then S = Y / ||Y|| has the uniform distribution on the unit d-sphere. Multiplying S by U^1/d, where U has the uniform distribution on the unit interval (0,1), creates the uniform distribution in the unit d-dimensional ball.

It is easy to carry out this operation in SAS. In Base SAS, you can write a DATA step that uses arrays. The following program provides a SAS/IML solution. I used the programming features of SAS/IML Studio to create a 3-D rotating scatter plot of the simulated cloud of points. You can download the SAS program that contains the DATA step and the IMLPlus program that creates the animation.

proc iml;
call randseed(12345);
d = 3;                               /* dimension = number of variables */
N = 1000;                            /* sample size = number of obs     */
radius = 2;                          /* radius of circle */
Y = randfun(N // d, "Normal");       /* Y ~ MVN(0, I(d)) */
u = randfun(N, "Uniform");           /* U ~ U(0,1)       */
r = radius * u##(1/d);               /* r proportional to d_th root of U */
X = r # Y / sqrt(Y[,##]);            /* Y[,##] is sum of squares for each row */
/* X contains N random uniformly distributed points in the d-ball */