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 and the log (with error statement) as a PDF for convenience, as well. 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;
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