Hi,
So I wrote a simulation program in proc IML. It works fine when the number of reps are low. However, when I run the full program, it crashes on me and I get this error message eventually:
ERROR: Permanent copy of file WORK.DAT.DATA was deleted.
ERROR: Cannot create data set WORK.DAT. [Permanent copy of file WORK.DAT.DATA was deleted.]
I am guessing I am reading and writing the work.dat file too many times? Any help would be appreciated. I pasted the program below (sorry a lot of the beginning is a really makeshift way of changing the seed for each condition - if anyone knows a more elegant way to change the seed for each condition and have it be a known value - I would appreciate info on that too! ).
PROC IML;
path = 'C:\';
*Specify the number of observations and simulate the data;
probmiss = .2;
rando = 352930;
nreps = 2000;
do lpath = 1 to 3;
do apath = 1 to 4;
do bpath = 1 to 4;
do samp = 1 to 4;
if lpath = 1 then lambda = .4;
if lpath = 2 then lambda = .6;
if lpath = 3 then lambda = .8;
if apath = 1 then alpha = 0;
if apath = 2 then alpha = .1;
if apath = 3 then alpha = .3;
if apath = 4 then alpha = .5;
if bpath = 1 then beta = 0;
if bpath = 2 then beta = .1;
if bpath = 3 then beta = .3;
if bpath = 4 then beta = .5;
if samp = 1 then nobs = 100;
if samp = 2 then nobs = 200;
if samp = 3 then nobs = 500;
if samp = 4 then nobs = 1000;
if lpath = 1 & apath = 1 & bpath = 1 & samp = 1 then seedcalc = rando+510;
if lpath = 1 & apath = 1 & bpath = 2 & samp = 1 then seedcalc = rando+610;
if lpath = 1 & apath = 1 & bpath = 3 & samp = 1 then seedcalc = rando+701;
if lpath = 1 & apath = 1 & bpath = 4 & samp = 1 then seedcalc = rando+801;
if lpath = 1 & apath = 2 & bpath = 1 & samp = 1 then seedcalc = rando+901;
*** HERE I TRUNCATED ALL THE COMBOS FOR ILLUSTRATION PURPOSES - the actual program has every permutation written out ;
PSIM = 1 - (alpha*alpha);
PSIY = 1 - (beta*beta);
LambdaE = 1 - (lambda*lambda);
*LambdaX;
LX = {1.00};
*LambdaY Matrix;
LY = {1 0, 0 0, 0 0, 0 1.00};
LY[2,1] = lambda;
LY[3,1] = lambda;
*Gamma Matrix, First row is a path, Second row is c' path;
GA = {0, 0};
GA [1,1] = alpha;
*Phi Covariance Matrix;
PH = {1};
*Psi Covariance Matrix;
PS = {0 0.00, 0.00 0};
PS [1,1] = PSIM;
PS [2,2] = PSIY;
*Theta Delta Matrix;
TD = {0};
*Theta Epsilon Matrix;
TE = {0.00 0.00 0 0.00,
0.00 0 0.00 0,
0 0.00 0 0.00,
0.00 0 0.00 0};
TE [2,2] = LambdaE;
TE [3,3] = LambdaE;
*Beta Matrix, Second Row First Column is b path;
B = {0.00 0.00, 0 0.00};
B[2,1] = Beta;
*Identity Matrix;
I = {1 0, 0 1};
COVY = LY*(INV(I-B))*(GA*PH*GA`+PS)*(INV(I-B`))*LY`+TE;
COVX = LX*PH*LX` + TD;
COVYX = LY*(INV(I-B))*GA*PH*LX`;
COVXY = LX*PH*GA`*(INV(I-B`))*LY`;
UPPER = COVX || COVXY;
LOWER = COVYX || COVY;
cov = UPPER // LOWER;
*We set the mean vector to zero;
means = j(5,1,0);
*Specify the random number seed;
call randseed(seedcalc);
do iteration = 1 to nreps;
*Draw sample from multivariate normal distribution;
sampledata = randnormal(nobs,means,cov);
*Make some values missing;
missingindicator = j(nobs,1,.);
call randgen(missingindicator,'Bernoulli',probmiss);
sampledata[loc(missingindicator = 1),2] = -999;
*Output data to SAS data set;
create dat from sampledata[colname={X M1 M2 M3 Y}];
append from sampledata;
close dat;
*Create filename;
file = path + "mplus_NOBS" + strip(char(nobs)) + "_MISS" + strip(char(probmiss))+ "_alpha" + strip(char(alpha))+ "_beta" +
strip(char(beta))+ "_lambda" + strip(char(lambda)) + "_iter" + strip(char(iteration)) + ".dat";
*Output SAS data set to .dat file;
submit file;
data _null_;
set dat;
file "&file";
put x m1 m2 m3 y;
run;
endsubmit;
end;
end; end; end; end;
quit;
