Error at end of Little's MCAR Macro

[Arbormazoo]

I am not a proficient macro user and have not used PROC IML prior to now, so forgive me if the answer is obvious.  I'm working with SAS 9.4.

When I run the code, the only error I receive is:

"ERROR: (execution) Matrix has not been set to a value."

I am familiar with the fact that this error generally means that something has not been defined (ex. y=x, but x doesn't exist).  I have scoured the previous boards, and read through SAS documents, and have not been able to identify why the final %mcartest (line 4329) would result in this error.  Previous problems, using the same macro, were related to data input and perhaps errors within the code, but I have not come across any examples where the source of error is the %mcartest.

If anyone can provide some feedback/suggestions, I would greatly appreciate it.  I have attached the original code as a word document for convenience, and the log as a PDF.

Thank you, in advance.

**********************************************************************************************************************************************

%macro mcartest
(indata= ems.recodeptsd /* Input DATASET name */
,testvars= sc1 ptsd1_1 ptsd1_2 ptsd1_3 ptsd1_4 ptsd1_5 ptsd1_6 ptsd1_7 ptsd1_8 ptsd1_9 ptsd1_10 ptsd1_11 ptsd1_12 ptsd1_13
ptsd1_14 ptsd1_15 ptsd1_16 ptsd1_17 /* SPECIFY VARIABLE SET FOR THE MCAR TEST */
,misscode= . /* SPECIFY THE MISSING VALUE CODE */
);
/******************************************************************************************************************
* This SAS macro implements the chi-square test for a missing completely at random (MCAR) mechanism, as *
* outlined in Little's (1998) JASA article. Note that the macro requires SAS version 8.2 (or higher) because *
* PROC MI is used to obtain ML estimates of the covariance matrix and mean vector. * *
******************************************************************************************************************/

%local numvars ;
%let numvars = %sysfunc(countw(&testvars));

data one;
set &indata (keep=&testvars);
array m[&numvars] &testvars ;
array r[&numvars] r1 - r&numvars ;

do i = 1 to &numvars;
if m[i] = &misscode then m[i] = .;
r[i] = not missing(m[i]);
end;
drop i;
run;

proc sort;
by r1-r&numvars;
run;

proc mi data = one nimpute = 0 noprint;
var &testvars;
em outem = emcov;
run;

proc iml;

use one;
read all var {&testvars} into y;
read all var {%do i = 1 %to &numvars; r&i %end;} into r;
use emcov;
read all var {&testvars} into em;

mu = em[1,];
sigma = em[2:nrow(em),];

/* ASSIGN AN INDEX VARIABLE DENOTING EACH CASE'S PATTERN */

jcol = j(nrow(y), 1 , 1);

do i = 2 to nrow(y);
rdiff = r[i,] - r[i - 1,];
if max(rdiff) = 0 & min(rdiff) = 0 then jcol[i,] = jcol[i - 1,];
else jcol[i,] = jcol[i - 1,] + 1;
end;

/* NUMBER OF DISTINCT MISSING DATA PATTERNS */

j = max(jcol);

/* PUT THE NUMBER OF CASES IN EACH PATTERN IN A COL VECTOR M */
/* PUT THE MISSING DATA INDICATORS FOR EACH PATTERN IN A MATRIX RJ */

m = j(j, 1, 0);
rj = j(j, ncol(r), 0);

do i = 1 to j;
count = 0;
do k = 1 to nrow(y);
if jcol[k,] = i then do;
count = count + 1;
end;
if jcol[k,] = i & count = 1 then rj[i,] = r[k,];
m[i,] = count;
end;
end;

/* COMPUTE D^2 STATISTIC FOR EACH J PATTERN */

d2j = j(j, 1, 0);

do i = 1 to j;

/* OBSERVED VALUES FOR PATTERN J */
yj = y[loc(jcol = i),loc(rj[i,] = 1)];

/* VARIABLE MEANS FOR PATTERN J */
ybarobsj = yj[+,]/nrow(yj);

/* D = P X Pj MATRIX OF INDICATORS (SEE P. 1199) */
Dj = j(ncol(y), rj[i,+], 0);

count = 1;
do k = 1 to ncol(rj);
if rj[i,k] = 1 then do;
Dj[k, count] = 1;
count = count + 1;
end;
end;

/* REDUCE EM ESTIMATES TO CONTAIN OBSERVED ELEMENTS */
muobsj = mu * Dj;
sigmaobsj = t(Dj) * sigma * Dj;

/* THE CONTRIBUTION TO THE D^2 STATISTIC FOR EACH OF THE J PATTERNS */
d2j[i,] = m[i,] * (ybarobsj - muobsj) * inv(sigmaobsj) * t(ybarobsj - muobsj);

end;

/* THE D^2 STATISTIC */
d2 = d2j[+,];

/* DF FOR D^2 */
df = rj[+,+] - ncol(rj);
p = 1 - probchi(d2,df);

/* PRINT ANALYSIS RESULTS */
file print;
put "Number of Observed Variables = " (ncol(rj)) 3.0;
put "Number of Missing Data Patterns = " (j) 3.0; put;
put "Summary of Missing Data Patterns (0 = Missing, 1 = Observed)"; put;
put "Frequency | Pattern | d2j"; put;
do i = 1 to nrow(rj);
put (m[i,]) 6.0 " | " @;
do j = 1 to ncol(rj);
put (rj[i,j]) 2.0 @;
end;
put " | " (d2j[i,]) 8.6;
end;
put;
put "Sum of the Number of Observed Variables Across Patterns (Sigma psubj) = " (rj[+,+]) 5.0; put;
put "Little's (1988) Chi-Square Test of MCAR"; put;
put "Chi-Square (d2) = " (d2) 10.3;
put "df (Sigma psubj - p) = " (df) 7.0;
put "p-value = " (p) 10.3;

%mend mcartest;

%mcartest
quit;
run;

[Reeza]

Please post the code you ran directly into the forum. Copying from PDF can inadvertently add other characters which will mess up the code, sometimes extra spaces, page numbers or invisible characters.

[Arbormazoo]

I have revised my original post.  Thank you for taking the time to help.

[Ksharp]

Post it at IML forum. and @Rick_SAS  is there .