The following example illustrates the use of PROC SIMNORMAL to generate two normal random variates that have specified means and covariance.
In this example, the means and covariances are given; these might have come from previous experiments, observational studies, or other considerations.
First you create a _TYPE_=COV data set as the input data set, and then you run PROC SIMNORM with NUMREAL=5000, creating a sample that contains 5,000 observations. The simple statistics of this sample are checked using PROC CORR. The results are shown in Figure 90.1.
data scov(type=COV) ; input _TYPE_ $ 1-4 _NAME_ $ 9-10 S1 S2 ; datalines ; COV S1 1.915 0.3873 COV S2 0.3873 4.321 MEAN 1.305 2.003 run;
proc simnorm data=scov outsim=ssim
numreal = 5000
seed = 54321 ;
var s1 s2 ;
run;
proc corr data=ssim cov ; var s1 s2 ; title "Statistics for PROC SIMNORM Sample Using NUMREAL=5000" ; run;
Figure 90.1: Statistics for PROC SIMNORM Sample Using NUMREAL=5000
| Statistics for PROC SIMNORM Sample Using NUMREAL=5000 |
| 2 Variables: | S1 S2 |
|---|
| Covariance Matrix, DF = 4999 | ||
|---|---|---|
| S1 | S2 | |
| S1 | 1.895805499 | 0.424837163 |
| S2 | 0.424837163 | 4.132974275 |
| Simple Statistics | ||||||
|---|---|---|---|---|---|---|
| Variable | N | Mean | Std Dev | Sum | Minimum | Maximum |
| S1 | 5000 | 1.30254 | 1.37688 | 6513 | -3.90682 | 6.49864 |
| S2 | 5000 | 1.98790 | 2.03297 | 9940 | -5.69812 | 9.42833 |