Replies to This Discussion

Permalink Reply by Jozo Kovac on March 24, 2010 at 4:49am

create crosstab (proc freq) and consider CHISQ.
dont forget to discretize numeric variables (proc rank groups=5; ...) .
here's example from sas doc:
proc freq data=Color order=data;
tables Eyes*(Hair Sex Country) / expected cellchi2 norow nocol chisq;
output out=ChiSqData n nmiss pchi lrchi;
weight Count;
title 'Chi-Square Tests for 3 by 5 Table of Eye and Hair Color and...';
run;

Permalink Reply by Randa Al-Teraifi on February 26, 2011 at 7:21am

Sorry my answer is a bit late but hope other people find it helpful.

 

/*  This program generates 300 observations of the variables y₁, y₂, y₃, y₄ .  Beginning with the correlation matrix R and a vector of means m = (m₁,m₂,m₃,m₄)' and standard deviations s = (s₁,s₂,s₃,s₄)' read in as variables using a CARDS statement. 

The means were arbitrarily selected as those in Liu and Gould 2002. The standard deviation vector and the correlation matrix were obtained from the data used by Allison et al (2003). 

*/

 

data   MVN_par;                        /* data for the parameter for the multivariate normal data*/

input  r₁ r₂ r₃ r₄ means vars ;   /* these can be adjusted to cater for the other two scenarios*/  

cards;                                    

1     0.986   0.967   0.949   92  14.3            /* These can be extended to an  n by n correlation

0.986  1      0.992   0.980   88  14                  matrix and n-dimensional means and standard

0.967  0.992   1      0.995   85  14.2              deviations vectors

run;

 

proc iml;

use MVN_par;

read all var {r₁ r₂ r₃ r₄} into R;

read all var {means}       into mu;

read all var {vars}        into sigma;

p = ncol(R);                                            /* p is the number of variables generated*/

diag_sig = diag(sigma);

DRD = diag_sig * R * diag_sig` ;         /* D is the a diagonal matrix whose element are

                                                                   the standard deviations of each y_i */

U = half(DRD);

do i = 1 to 300;                                      /*  this can be replaced with k to generate the

                                                                  dataset with k patients. */

 

z = rannor(j(p,1,1234));                        /* Generating random numbers. Z_i i = 1,2,...,p

                                                                  independent and have N(0,1)distribution.

                                                                  The var-cov matrix for Z' is the identity matrix.*/

yprime = y`;

yall = yall // yprime;

end;

varnames = {y₁ y₂ y₃ y₄};                      /* naming the variables*/

create my_MVN from yall (|colname = varnames|);

append from yall;

proc print  data = my_MVN;