Input id $ x y;   Datalines; 00-01 5 3 00-02 4 6 00-03 6 4 00-04 7 5 00-05 9 2 00-06 4 6 00-07 5 8 00-08 1 8 00-09 7 3 00-10  7 4 ; run; Please, I want to generate Z random subset of data set CO chosen observation 4 at a time (10 chose 4). Let’s take Z in this case=7; I like to add additional column to identify each of the random subset—let call that variable REPS. I will appreciate a code that can run fast because the actual code might end up having up to 10,000 REPS. And number of variables can be larger.   I read Rick's blog and saw some examples, but I did not see an example where the combination involve actual data set. If it's not possible for more than a variable, I can do only the id (character variable) and merge back to the original data set.   Any help will be appreciated!   Thanks in Advance Jack ... View more

Hello:   Expert does any one now what is wrong with the following code:   I am simulating a  truncated distribution  that is multivariate in nature with the follow code from Rick's blog, but it not working well:   data TruncNormal(keep=x fa fb); Fa = cdf('Normal', 30); /* for a = 30 */ Fb = cdf('Normal', 50); /* for b = 50 */ call streaminit(1234); do i = 1 to 1000; /* sample size = 1000 */ v = Fa + (Fb-Fa)*rand('Uniform'); /* V ~ U(F(a), F(b)) */ x = quantile('Normal', v); /* truncated normal on [a,b] */ output; end; run; ods select histogram; proc univariate data=TruncNormal; histogram x / endpoints=30 to 50 by 5; run;   The aim is to simulate a truncated  [a=30,b=50] distribution that is normally distribution. Minimum value is 30 maximum value is 50. The orginal normal distribution has a mean of 38 and standard of 5. Following generation,I like to check to see that the generated distribution is in fact a PDF.   The above case is aimed to generate on one instance of such a truncated distribution, but I want to genereate three of such variable so that I have a multivariate distrunction  with column=3 and a with specified covariance. The plan is to generate the truncated distribution thrice and then merge but that can't build the covariance.   I see that one can still use acceptance-rejection, but building covariance/correlation may be challenging.   Thanks for your help. ... View more

Hello All:   Please, I am trying to simulate a GLM model with two classication variables and an interaction term between them. That is,    y=b1*s+b2*t+b3*t*s +e; s and t have 2 levels. I see an example in Wilkin book Simulation with SAS, but for some reasons when I tried obtaining the estimate after simulation I obtain a parameter estimate that is way off. Number of observation in each level of s are equal.   I appreciate your thought in advance.     J ... View more