reads the macro but doesn't give the output

deleted_user — Thu, 04 Jun 2009 07:06:57 GMT

My problem is that I don't get output while working with macro an also there are not errors in the program. below is the program:

%macro ctanest1(N=134, vars=5, nesttest=0, pchor=0, lisrel=0, mplus=0, SAMPMAT1=
0.993 0.564 0.568 0.415 0.456
0.564 0.993 0.491 0.442 0.434
0.568 0.491 0.993 0.449 0.649
0.415 0.442 0.449 0.993 0.601
0.456 0.434 0.649 0.601 0.993,
IMPMAT1B=
0.993 0.564 0.531 0.466 0.46
0.564 0.993 0.496 0.436 0.429
0.531 0.496 0.993 0.449 0.662
0.466 0.436 0.449 0.993 0.582
0.46 0.429 0.662 0.582 0.993,
IMPMAT2B=, lowdiag=0, reps=1);

%if &lowdiag = 1 %then %do;
%let fullmat = %eval((&vars + 1)*(&vars)/2);
%end;
%else %if &lowdiag = 0 %then %do;
%let fullmat = %eval(&vars*&vars);
%end;

%if &pchor = 0 %then %do;
data t;
array t(&fullmat) (&SAMPMAT1);
run;
%end;

data imp1;
array imp1(&fullmat) (&IMPMAT1b);
run;

/* If performing nested tests, read in the second model imp cov, otherwise just use the same one */
%if &nesttest = 1 %then %do;
data imp2;
array imp2(&fullmat) (&IMPMAT2b);
run;
%end;
%else %do;
data imp2;
array imp2(&fullmat) (&IMPMAT1b);
run;
%end;

proc iml;

START TETRAD;

IF &nesttest = 0 THEN DO;
tetlabel = initlabel_nonnest;
tetres = initres_nonnest;
END;
ELSE DO;
tetlabel = initlabel;
tetres = initres;
END;

/* indexing tetrads; */
id = 1;
DO i = 2 to NROW(tetres);
id = id//i;
END;

**Column 9 is id;
tetlabel = tetlabel||id;

IF &reps > 1 THEN DO;
*RANDOMIZE the tetrads;
ran = J(NROW(tetlabel),1,0);
DO i = 1 to NROW(tetlabel);
ran[i,1]=uniform(0);
END;
tetlabel = tetlabel||ran;
tetlabel2 = tetlabel;
tetlabel[rank(tetlabel[,10]),]=tetlabel2;
tetres = tetres||ran;
tetres2 = tetres;
tetres[rank(tetres[,2]),]=tetres2;
/* Now drop the random column from tetlabel */
tetlabel = tetlabel[,1:9];
END;

/*
***********************************************************************;
*Now loop through and find the vanishing tetrads for the model with fewer implied;
***********************************************************************;
*Loop here only if there are tetrads; */
IF &nesttest = 1 & zerotet = 0 THEN DO;

/* The tenth column contains the residuals */
tetlabel = tetlabel||tetres[,1];

/* find unique covariances among implied tetrads */
/* Note that the order of the tetrads gets flipped here to match with the order that
LISREL and M-Plus give in their ACM's
(do this by placing the j+1 element in front of the j element); */
tetlabel2 = J(NROW(tetlabel),4,'aaaa');
DO i = 1 to NROW(tetlabel);
k = 0;
DO j = 1 to 7 BY 2;
k = k+1;
IF tetlabel[i,j] < 10 & tetlabel[i,j+1] < 10 THEN
tetlabel2[i,k] = ROWCATC('0'||CHAR(tetlabel[i,j+1])||'0'||CHAR(tetlabel[i,j]));
ELSE DO;
IF tetlabel[i,j] >= 10 & tetlabel[i,j+1]>=10 THEN
tetlabel2[i,k] = ROWCATC(CHAR(tetlabel[i,j+1])||CHAR(tetlabel[i,j]));
ELSE IF tetlabel[i,j] >= 10 THEN
tetlabel2[i,k] = ROWCATC('0'||CHAR(tetlabel[i,j+1])||CHAR(tetlabel[i,j]));
ELSE tetlabel2[i,k] = ROWCATC(CHAR(tetlabel[i,j+1])||'0'||CHAR(tetlabel[i,j]));
END;
END;
END;
tetcov = UNIQUE(SHAPE(tetlabel2,1)); /* row vector of unique covariances; */

/*
**************************************************************************;
construct derivative matrix: d
**************************************************************************;
*/
D = J(NCOL(tetcov),NROW(tetlabel2),0);
DO i = 1 to NCOL(tetcov);
DO j = 1 to NROW(tetlabel2);
IF tetcov[,i] = tetlabel2[j,1]
THEN D[i,j] = IMPMAT2[tetlabel[j,3],tetlabel[j,4]];
IF tetcov[,i] = tetlabel2[j,2]
THEN D[i,j] = IMPMAT2[tetlabel[j,1],tetlabel[j,2]];
IF tetcov[,i] = tetlabel2[j,3]
THEN D[i,j] = -1*IMPMAT2[tetlabel[j,7],tetlabel[j,8]];
IF tetcov[,i] = tetlabel2[j,4]
THEN D[i,j] = -1*IMPMAT2[tetlabel[j,5],tetlabel[j,6]];
END;
END;

/* compute asymptotic covariance matrix of the IMPLIED covariances */
SigmaS = J(NCOL(tetcov),NCOL(tetcov),0);
DO i = 1 to NCOL(tetcov);
DO j = 1 to NCOL(tetcov);
e = NUM(SUBSTR(tetcov[,i],1,2));
f = NUM(SUBSTR(tetcov[,i],3,2));
g = NUM(SUBSTR(tetcov[,j],1,2));
h = NUM(SUBSTR(tetcov[,j],3,2));
/* This calculates it for a correlation matrix; */
IF corr = 1 THEN DO;
SigmaS[i,j] = (1/2)
* (IMPMAT2[e,f]*IMPMAT2[g,h])
* (IMPMAT2[e,g]**2 + IMPMAT2[e,h]**2 + IMPMAT2[f,g]**2 + IMPMAT2[f,h]**2)
+ IMPMAT2[e,g]*IMPMAT2[f,h] + IMPMAT2[e,h]*IMPMAT2[f,g]
- IMPMAT2[e,f]*(IMPMAT2[f,g]*IMPMAT2[f,h]+IMPMAT2[e,g]*IMPMAT2[e,h])
- IMPMAT2[g,h]*(IMPMAT2[f,g]*IMPMAT2[e,g]+IMPMAT2[f,h]*IMPMAT2[e,h]);
END;
/* This calculates it for a covariance matrix; */
ELSE DO;
SigmaS[i,j] = IMPMAT2[e,g]*IMPMAT2[f,h] + IMPMAT2[e,h]*IMPMAT2[f,g];
END;
END;
END;

SigmaTemp = D`*SigmaS*D;

/* Add an 11th column for marking tetrads to be dropped; */
tetlabel = tetlabel||J(NROW(tetlabel),1,0);

/*
*********************************************************************;
*Using the SWEEP operator;
*********************************************************************;
*/
/* ***Don't go into this loop if only one vanishing tetrad; */
IF nrow(tetlabel) > 1 THEN DO;

temp33 = sweep(SigmaTemp);
temp33 = temp33||J(NROW(temp33),1,0);

DO i = 1 to NROW(temp33);
IF sum(abs(temp33[i,])) <> 0 THEN tetlabel[i,11]=1;
END;

/* Now drop the zero rows and columns (singular); */
temp5 = J(1,NCOL(temp33),0);

DO i = 1 to NROW(temp33);
IF sum(abs(temp33[i,])) <> 0 THEN DO;
temp5 = temp5 // temp33[i,1:NCOL(temp33)];
END;
END;
temp2 = J(NROW(temp5),1,0);
DO i = 1 to NCOL(temp5);
IF sum(abs(temp5[,i])) <> 0 THEN DO;
temp2 = temp2 || temp5[1:NROW(temp5),i];
END;
END;

SigmaTemp2 = temp2[2:NROW(temp2),2:NCOL(temp2)];

temp7 = J(1,11,0);
temp8 = J(1,11,0);

/* Now find the redundant tetrads; */
DO i = 1 to NROW(tetlabel);
IF tetlabel[i,11]=1 then DO;
temptet= tetlabel[i,];
temp7 = temp7//temptet;
END;
IF tetlabel[i,11]=0 then DO;
temptet2= tetlabel[i,];
temp8 = temp8//temptet2;
END;
END;
IF NROW(temp7) > 1 THEN DO;
temp7a = temp7[2:NROW(temp7),1:8];
temp7b = temp7[2:NROW(temp7),10];
END;
IF NROW(temp8) > 1 THEN DO;
temp8a = temp8[2:NROW(temp8),1:8];
temp8b = temp8[2:NROW(temp8),10];
END;

/* These are the tetrads in the smaller model, and their residuals; */
tetlabel_small = temp7a;
tetres_small = temp7b;

/*
*******************************************************************************;
*END of loop--now reorder the tetrads so that we're pulling the same ones in the larger model;
*******************************************************************************;
*/
tetlabel = J(NROW(tetlabel),8,0);

/* Order is nonredundant---not in model---redundant; */
tetlabel = temp7a//temp3//temp8a;
tetres = temp7b//temp4//temp8b;
END;
ELSE DO;
tetlabel_small = tetlabel[1,1:8];
tetres_small = tetlabel[1,10];
tetlabel = tetlabel_small;
tetres = tetres_small;
END;
END;
FREE Temp33 Temp5 Temp2 Temp7 Temp8 Sigmatemp2 Temptet Temptet2 Temp7a Temp7b Temp8a Temp8b;
/*
***********************************************************************;
*Now loop through a SECOND time and find the vanishing tetrads for the model with more implied;
***********************************************************************;
*/
/* This used to select on some instances---doesn't appear necessary anymore; */

IF &nesttest = 1 THEN DO;
/* indexing tetrads; */
id = 1;
DO i = 2 to NROW(tetres);
id = id//i;
END;
/* Column 9 is id; */
tetlabel = tetlabel||id;
END;

/* find unique covariances among implied tetrads */
tetlabel2 = J(NROW(tetlabel),4,'aaaa');
DO i = 1 to NROW(tetlabel);
k = 0;
DO j = 1 to 7 BY 2;
k = k+1;
IF tetlabel[i,j] < 10 & tetlabel[i,j+1] < 10 THEN
tetlabel2[i,k] = ROWCATC('0'||CHAR(tetlabel[i,j+1])||'0'||CHAR(tetlabel[i,j]));
ELSE DO;
IF tetlabel[i,j] >= 10 & tetlabel[i,j+1]>=10 THEN
tetlabel2[i,k] = ROWCATC(CHAR(tetlabel[i,j+1])||CHAR(tetlabel[i,j]));
ELSE IF tetlabel[i,j] >= 10 THEN
tetlabel2[i,k] = ROWCATC('0'||CHAR(tetlabel[i,j+1])||CHAR(tetlabel[i,j]));
ELSE tetlabel2[i,k] = ROWCATC(CHAR(tetlabel[i,j+1])||'0'||CHAR(tetlabel[i,j]));
END;
END;
END;

tetcov = UNIQUE(SHAPE(tetlabel2,1)); /* row vector of unique covariances; */

/*
**************************************************************************;
construct derivative matrix: d
**************************************************************************;
*/
D = J(NCOL(tetcov),NROW(tetlabel2),0);

DO i = 1 to NCOL(tetcov);
DO j = 1 to NROW(tetlabel2);
IF tetcov[,i] = tetlabel2[j,1]
THEN D[i,j] = IMPMAT[tetlabel[j,3],tetlabel[j,4]];
IF tetcov[,i] = tetlabel2[j,2]
THEN D[i,j] = IMPMAT[tetlabel[j,1],tetlabel[j,2]];
IF tetcov[,i] = tetlabel2[j,3]
THEN D[i,j] = -1*IMPMAT[tetlabel[j,7],tetlabel[j,8]];
IF tetcov[,i] = tetlabel2[j,4]
THEN D[i,j] = -1*IMPMAT[tetlabel[j,5],tetlabel[j,6]];
END;
END;

/* compute asymptotic covariance matrix of the IMPLIED covariances */
SigmaS = J(NCOL(tetcov),NCOL(tetcov),0);
DO i = 1 to NCOL(tetcov);
DO j = 1 to NCOL(tetcov);
e = NUM(SUBSTR(tetcov[,i],1,2));
f = NUM(SUBSTR(tetcov[,i],3,2));
g = NUM(SUBSTR(tetcov[,j],1,2));
h = NUM(SUBSTR(tetcov[,j],3,2));
/* This calculates it for a correlation matrix; */
IF corr = 1 THEN DO;
SigmaS[i,j] = (1/2)
* (IMPMAT[e,f]*IMPMAT[g,h])
* (IMPMAT[e,g]**2 + IMPMAT[e,h]**2 + IMPMAT[f,g]**2 + IMPMAT[f,h]**2)
+ IMPMAT[e,g]*IMPMAT[f,h] + IMPMAT[e,h]*IMPMAT[f,g]
- IMPMAT[e,f]*(IMPMAT[f,g]*IMPMAT[f,h]+IMPMAT[e,g]*IMPMAT[e,h])
- IMPMAT[g,h]*(IMPMAT[f,g]*IMPMAT[e,g]+IMPMAT[f,h]*IMPMAT[e,h]);
END;
/* This calculates it for a covariance matrix; */
ELSE DO;
SigmaS[i,j] = IMPMAT[e,g]*IMPMAT[f,h] + IMPMAT[e,h]*IMPMAT[f,g];
END;
END;
END;

tetlabel = tetlabel||J(NROW(tetlabel),1,0);
SigmaTemp = D`*SigmaS*D;
FREE SigmaS D;
/*
*********************************************************************;
*Using the SWEEP operator;
*********************************************************************;
*/
/* ***Don't go into this loop if only one vanishing tetrad; */
IF nrow(tetlabel) > 1 THEN DO;

temp33 = sweep(SigmaTemp);
temp33 = temp33||J(NROW(temp33),1,0);

DO i = 1 to NROW(temp33);
IF sum(abs(temp33[i,])) <> 0 THEN tetlabel[i,10]=1;
END;

/* Now drop the zero rows and columns (singular); */
temp5 = J(1,NCOL(temp33),0);

DO i = 1 to NROW(temp33);
IF sum(abs(temp33[i,])) <> 0 THEN DO;
temp5 = temp5 // temp33[i,1:NCOL(temp33)];
END;
END;
temp2 = J(NROW(temp5),1,0);
DO i = 1 to NCOL(temp5);
IF sum(abs(temp5[,i])) <> 0 THEN DO;
temp2 = temp2 || temp5[1:NROW(temp5),i];
END;
END;

SigmaTemp2 = temp2[2:NROW(temp2),2:NCOL(temp2)];

/* Keep the nonredundant tetrads; */
DO i = 1 to NROW(tetlabel);
IF tetlabel[i,10]=1 then DO;
temptet= tetlabel[i,];
tetlab4 = tetlab4//temptet;
tetlab5 = tetlab5//tetres[i,1];
END;
END;
tetlabel = tetlab4[,1:9];
tetres = tetlab5;
END;
ELSE DO;
tetlabel = tetlabel[,1:9];
END;
FREE Sigmatemp Temp33 Temp5 Temp2 SigmaTemp2 Temptet Tetlab4 Tetlab5;

/*
*********************************************************************;
*Now use the SWEEP results on the original covariance matrix of tetrads
*to get the model fit for the larger model;
*********************************************************************;
*/
/* find unique covariances among non-redundant tetrads */
tetlabel2 = J(NROW(tetlabel),.5*(NCOL(tetlabel)-1),'aaaa');
DO i = 1 to NROW(tetlabel);
k = 0;
DO j = 1 to 7 BY 2;
k = k+1;
IF tetlabel[i,j] < 10 & tetlabel[i,j+1] < 10 THEN
tetlabel2[i,k] = ROWCATC('0'||CHAR(tetlabel[i,j+1])||'0'||CHAR(tetlabel[i,j]));
ELSE DO;
IF tetlabel[i,j] >= 10 & tetlabel[i,j+1]>=10 THEN
tetlabel2[i,k] = ROWCATC(CHAR(tetlabel[i,j+1])||CHAR(tetlabel[i,j]));
ELSE IF tetlabel[i,j] >= 10 THEN
tetlabel2[i,k] = ROWCATC('0'||CHAR(tetlabel[i,j+1])||CHAR(tetlabel[i,j]));
ELSE tetlabel2[i,k] = ROWCATC(CHAR(tetlabel[i,j+1])||'0'||CHAR(tetlabel[i,j]));
END;
END;
END;

tetcov = UNIQUE(SHAPE(tetlabel2,1)); /* row vector of unique covariances; */

/*
**************************************************************************;
construct derivative matrix: d
**************************************************************************;
*/
D = J(NCOL(tetcov),NROW(tetlabel2),0);
DO i = 1 to NCOL(tetcov);
DO j = 1 to NROW(tetlabel2);
IF tetcov[,i] = tetlabel2[j,1]
THEN D[i,j] = SAMPMAT[tetlabel[j,3],tetlabel[j,4]];
IF tetcov[,i] = tetlabel2[j,2]
THEN D[i,j] = SAMPMAT[tetlabel[j,1],tetlabel[j,2]];
IF tetcov[,i] = tetlabel2[j,3]
THEN D[i,j] = -1*SAMPMAT[tetlabel[j,7],tetlabel[j,8]];
IF tetcov[,i] = tetlabel2[j,4]
THEN D[i,j] = -1*SAMPMAT[tetlabel[j,5],tetlabel[j,6]];
END;
END;

/* compute asymptotic covariance matrix of the covariances -- normal theory estimator;
Skip this if bringing in an outside polychoric matrix; */

IF &lisrel = 0 & &mplus = 0 THEN DO;
SigmaS = J(NCOL(tetcov),NCOL(tetcov),0);
DO i = 1 to NCOL(tetcov);
DO j = 1 to NCOL(tetcov);
e = NUM(SUBSTR(tetcov[,i],1,2));
f = NUM(SUBSTR(tetcov[,i],3,2));
g = NUM(SUBSTR(tetcov[,j],1,2));
h = NUM(SUBSTR(tetcov[,j],3,2));
/* This calculates it for a correlation matrix; */
IF corr = 1 THEN DO;
SigmaS[i,j] = (1/2)
* (SAMPMAT[e,f]*SAMPMAT[g,h])
* (SAMPMAT[e,g]**2 + SAMPMAT[e,h]**2 + SAMPMAT[f,g]**2 + SAMPMAT[f,h]**2)
+ SAMPMAT[e,g]*SAMPMAT[f,h] + SAMPMAT[e,h]*SAMPMAT[f,g]
- SAMPMAT[e,f]*(SAMPMAT[f,g]*SAMPMAT[f,h]+SAMPMAT[e,g]*SAMPMAT[e,h])
- SAMPMAT[g,h]*(SAMPMAT[f,g]*SAMPMAT[e,g]+SAMPMAT[f,h]*SAMPMAT[e,h]);
END;
/* This calculates it for a covariance matrix; */
ELSE DO;
SigmaS[i,j] = SAMPMAT[e,g]*SAMPMAT[f,h] + SAMPMAT[e,h]*SAMPMAT[f,g];
END;
END;
END;
END;
ELSE DO;
/* Read in the ACM from LISREL or M-Plus; */
temp1 = J(1,NCOL(acmcov),0);
DO i = 1 TO NCOL(acmcov);
DO j = 1 TO NCOL(tetcov);
IF acmcov[1,i] = tetcov[1,j] THEN temp1[1,i]=i;
END;
END;
acmdrop = J(1,1,0);
DO i = 1 TO NCOL(acmcov);
IF temp1[1,i] > 0 THEN acmdrop = acmdrop||temp1[1,i];
END;
acmdrop2 = acmdrop[,2:NCOL(acmdrop)];

/* Use the ACM brought in at the top; */
SigmaS = symmat[acmdrop2,acmdrop2];
FREE temp1 acmdrop acmdrop2;
END;

/* Perform Test */
SigmaT = D`*SigmaS*D;
df = NROW(SigmaT);
Chi = &N*(tetres`*INV(SigmaT)*tetres);
p = 1-PROBCHI(Chi,df);
IF &nesttest = 1 THEN PRINT "Tetrad Test for Model with more vanishing tetrads";
ELSE PRINT "Tetrad Test for the Model";
PRINT Chi[label='Chi-Square'] df[label='df'] p[label='p-value'];

Chibig = chi;
dfbig = df;
pbig = p;

/* This collects the randomization results by replication if testing just one model */
IF &reps > 1 & &nesttest = 0 THEN DO;
IF r = 1 THEN DO;
results = r||chibig||dfbig||pbig;
clabel = {'replication' ' chi' ' df' ' p'};
END;
ELSE DO;
represult = r||chibig||dfbig||pbig;
results = results//represult;
END;
END;

FREE SigmaT SigmaS D;

/*
*****************************************************************;
*Now run the smaller model;
*****************************************************************;
*/
IF &nesttest = 1 & zerotet = 0 THEN DO;

tetlabel = tetlabel_small;
tetres = tetres_small;

/* find unique covariances among non-redundant tetrads */
tetlabel2 = J(NROW(tetlabel),.5*(NCOL(tetlabel)),'aaaa');
DO i = 1 to NROW(tetlabel);
k = 0;
DO j = 1 to 7 BY 2;
k = k+1;
IF tetlabel[i,j] < 10 & tetlabel[i,j+1] < 10 THEN
tetlabel2[i,k] = ROWCATC('0'||CHAR(tetlabel[i,j+1])||'0'||CHAR(tetlabel[i,j]));
ELSE DO;
IF tetlabel[i,j] >= 10 & tetlabel[i,j+1]>=10 THEN
tetlabel2[i,k] = ROWCATC(CHAR(tetlabel[i,j+1])||CHAR(tetlabel[i,j]));
ELSE IF tetlabel[i,j] >= 10 THEN
tetlabel2[i,k] = ROWCATC('0'||CHAR(tetlabel[i,j+1])||CHAR(tetlabel[i,j]));
ELSE tetlabel2[i,k] = ROWCATC(CHAR(tetlabel[i,j+1])||'0'||CHAR(tetlabel[i,j]));
END;
END;
END;
tetcov = UNIQUE(SHAPE(tetlabel2,1)); /* row vector of unique covariances; */

/*
**************************************************************************;
construct derivative matrix: d
**************************************************************************;
*/
D = J(NCOL(tetcov),NROW(tetlabel2),0);
DO i = 1 to NCOL(tetcov);
DO j = 1 to NROW(tetlabel2);
IF tetcov[,i] = tetlabel2[j,1]
THEN D[i,j] = SAMPMAT[tetlabel[j,3],tetlabel[j,4]];
IF tetcov[,i] = tetlabel2[j,2]
THEN D[i,j] = SAMPMAT[tetlabel[j,1],tetlabel[j,2]];
IF tetcov[,i] = tetlabel2[j,3]
THEN D[i,j] = -1*SAMPMAT[tetlabel[j,7],tetlabel[j,8]];
IF tetcov[,i] = tetlabel2[j,4]
THEN D[i,j] = -1*SAMPMAT[tetlabel[j,5],tetlabel[j,6]];
END;
END;

/* compute asymptotic covariance matrix of the covariances -- normal theory estimator;
Skip this if bringing in an outside polychoric matrix; */

IF &lisrel = 0 & &mplus = 0 THEN DO;
SigmaS = J(NCOL(tetcov),NCOL(tetcov),0);
DO i = 1 to NCOL(tetcov);
DO j = 1 to NCOL(tetcov);
e = NUM(SUBSTR(tetcov[,i],1,2));
f = NUM(SUBSTR(tetcov[,i],3,2));
g = NUM(SUBSTR(tetcov[,j],1,2));
h = NUM(SUBSTR(tetcov[,j],3,2));
/* This calculates it for a correlation matrix; */
IF corr = 1 THEN DO;
SigmaS[i,j] = (1/2)
* (SAMPMAT[e,f]*SAMPMAT[g,h])
* (SAMPMAT[e,g]**2 + SAMPMAT[e,h]**2 + SAMPMAT[f,g]**2 + SAMPMAT[f,h]**2)
+ SAMPMAT[e,g]*SAMPMAT[f,h] + SAMPMAT[e,h]*SAMPMAT[f,g]
- SAMPMAT[e,f]*(SAMPMAT[f,g]*SAMPMAT[f,h]+SAMPMAT[e,g]*SAMPMAT[e,h])
- SAMPMAT[g,h]*(SAMPMAT[f,g]*SAMPMAT[e,g]+SAMPMAT[f,h]*SAMPMAT[e,h]);
END;
/* This calculates it for a covariance matrix; */
ELSE DO;
SigmaS[i,j] = SAMPMAT[e,g]*SAMPMAT[f,h] + SAMPMAT[e,h]*SAMPMAT[f,g];
END;
END;
END;
END;
ELSE DO;
/* Read in the ACM from LISREL or M-Plus; */
temp1 = J(1,NCOL(acmcov),0);
DO i = 1 TO NCOL(acmcov);
DO j = 1 TO NCOL(tetcov);
IF acmcov[1,i] = tetcov[1,j] THEN temp1[1,i]=i;
END;
END;
acmdrop = J(1,1,0);
DO i = 1 TO NCOL(acmcov);
IF temp1[1,i] > 0 THEN acmdrop = acmdrop||temp1[1,i];
END;
acmdrop2 = acmdrop[,2:NCOL(acmdrop)];

/* Use the ACM brought in at the top; */
SigmaS = symmat[acmdrop2,acmdrop2];
END;

/* Perform Test */
SigmaT = D`*SigmaS*D;
df = NROW(SigmaT);
Chi = &N*(tetres`*INV(SigmaT)*tetres);
p = 1-PROBCHI(Chi,df);
PRINT "Tetrad Test for Model with fewer Vanishing tetrads";
PRINT Chi[label='Chi-Square'] df[label='df'] p[label='p-value'];
Chitest = Chibig - Chi;
dftest = dfbig - df;
ptest = 1-PROBCHI(Chitest,dftest);
PRINT "Nested Tetrad Test for two models";
PRINT Chitest[label='Chi-Square'] dftest[label='df'] ptest[label='p-value'];

/* This collects the randomization results by replication */
IF &reps > 1 THEN DO;
IF r = 1 THEN DO;
results = r||chibig||dfbig||pbig||chi||df||p||chitest||dftest||ptest;
clabel = {' reps' ' big-chi' 'big-df' ' big-p' 'small-chi' 'small-df' ' small-p' 'test-chi' 'test-df' ' test-p'};
END;
ELSE DO;
represult = r||chibig||dfbig||pbig||chi||df||p||chitest||dftest||ptest;
results = results//represult;
END;
END;

END;

FINISH;

/********************************************************************************/
/* MODULE: MAIN */
/* LAST MODIFIED: 6/5/2003 */
/* */
/* Gives requisite matrices, sample size, loops through Tetrad module */
/********************************************************************************/

START;

/* Setting a dummy to check for models implying NO vanishing tetrads; */
zerotet = 0;
N = &N;
notnest = 99;
matswitch = 99;

IF &lisrel = 1 | &mplus = 1 THEN DO;
use acm1;
read all into c1;

s=J(1,1,0); /*Set up dummy row for concatenation */
DO i=1 to ncol(c1);
IF c1 =0 then
DO;

END; /*Do nothing on these zero-ones, but don't advance counter */
ELSE IF c1 =1 then
DO;

END;
ELSE DO;
s = s//c1;
END;
END;
FREE c1;
s = s[2:NROW(s),]; /*Removing dummy row; */

/* Multiply this by N for the M-Plus ACM; */
IF &mplus = 1 THEN DO;
s2 = s*&N;
s = s2;
END;

/*Now convert this vector into a square ACM matrix to use in CTA; */
ssize = (&vars*(&vars-1)/2);
acm=J(ssize,ssize,0);
ct = 1;
DO i=1 to nrow(acm);
DO j=1 to nrow(acm);
IF i < j then DO;
acm[i,j] = 0;
END;
ELSE DO;
acm[i,j] = s[ct];
ct = ct + 1;
END;
END;
END;
/*Symmetrize this matrix; */
symmat=acm+acm`;
DO i=1 to nrow(symmat);
DO j=1 to nrow(symmat);
IF i=j then symmat[i,j]= symmat[i,j]/ 2;
END;
END;
END;
FREE ssize acm;

IF &pchor = 1 THEN DO;
/*Bringing in the file of the Polychoric Matrix; */
use pchor1;
read all into d1a;

/*Now convert this vector into a square matrix to use in CTA; */
pcm=J(&vars,&vars,0);
ct = 1;
DO i=1 to nrow(pcm);
DO j=1 to nrow(pcm);
IF i < j then DO;
/*Do nothing, since already a 0 value; */
END;
ELSE DO;
pcm[i,j] = d1a[ct];
ct = ct + 1;
END;
END;
END;

/*Symmetrize this matrix; */
SAMPMAT=pcm+pcm`;
DO i=1 to nrow(SAMPMAT);
DO j=1 to nrow(SAMPMAT);
IF i=j then SAMPMAT[i,j]= SAMPMAT[i,j]/ 2;
END;
END;
FREE d1a pcm;
END;
ELSE DO;

/* Convert the SAMPMAT1 vector into a square matrix to use in CTA; */
/* Symmetrize this matrix, if not already symmetric; */

use t;
read all into SAMPMAT1;

IF NCOL(SAMPMAT1) = (&vars*(&vars+1)/2) THEN DO;
/* IF lowdiag = 1 THEN DO; */
SAMPMAT=J(&vars,&vars,0);
ct = 1;
DO i=1 to nrow(SAMPMAT);
DO j=1 to nrow(SAMPMAT);
IF i < j then DO;
SAMPMAT[i,j] = 0;
END;
ELSE DO;
SAMPMAT[i,j] = SAMPMAT1[ct];
ct = ct + 1;
END;
END;
END;
tempmat=sampmat+sampmat`;
DO i=1 to nrow(tempmat);
DO j=1 to nrow(tempmat);
IF i=j then tempmat[i,j]= tempmat[i,j]/ 2;
END;
END;
SAMPMAT = tempmat;
FREE tempmat;
END;
ELSE IF NCOL(SAMPMAT1) = &vars * &vars THEN DO;
/* Convert this vector into a square matrix to use in CTA; */
SAMPMAT=J(&vars,&vars,0);
ct = 1;
DO i=1 to nrow(SAMPMAT);
DO j=1 to nrow(SAMPMAT);
SAMPMAT[i,j] = SAMPMAT1[ct];
ct = ct + 1;
END;
END;
END;
ELSE DO;
PRINT "The sample matrix entered does not conform to the # of variables specified";
END;
END;

/*Model implied cov matrix for the model with MORE vanishing tetrads;
Convert the IMPMAT1B vector into a square matrix to use in CTA;
Symmetrize this matrix, if not already symmetric; */

use imp1;
read all into IMPMAT1B;

IF NCOL(IMPMAT1B) = (&vars*(&vars+1)/2) THEN DO;
/*IF lowdiag = 1 THEN DO; */
IMPMAT1A=J(&vars,&vars,0);
ct = 1;
DO i=1 to nrow(IMPMAT1A);
DO j=1 to nrow(IMPMAT1A);
IF i < j then DO;
IMPMAT1A[i,j] = 0;
END;
ELSE DO;
IMPMAT1A[i,j] = IMPMAT1B[ct];
ct = ct + 1;
END;
END;
END;
tempmat=IMPMAT1A+IMPMAT1A`;
DO i=1 to nrow(tempmat);
DO j=1 to nrow(tempmat);
IF i=j then tempmat[i,j]= tempmat[i,j]/ 2;
END;
END;
IMPMAT1A = tempmat;
END;
ELSE IF NCOL(IMPMAT1B) = &vars * &vars THEN DO;
/*Convert this vector into a square matrix to use in CTA; */
IMPMAT1A=J(&vars,&vars,0);
ct = 1;
DO i=1 to nrow(IMPMAT1A);
DO j=1 to nrow(IMPMAT1A);
IMPMAT1A[i,j] = IMPMAT1B[ct];
ct = ct + 1;
END;
END;
END;
ELSE DO;
PRINT "The first model implied covariance matrix entered does not conform to the # of variables specified";
END;

/*Make the implied matrix a correlation matrix; */
tempdiag = J(NROW(IMPMAT1A),NCOL(IMPMAT1A),0);
DO i = 1 to NROW(IMPMAT1A);
DO j = 1 to NCOL(IMPMAT1A);
IF i = j THEN tempdiag[i,j]=1/sqrt(IMPMAT1A[i,j]);
END;
END;
IMPMAT = tempdiag*IMPMAT1A*tempdiag;

/* Now adjust the magnitude of this matrix; */
DO i = 1 to NROW(IMPMAT);
DO j = 1 to NCOL(IMPMAT);
IMPMAT[i,j]=IMPMAT[i,j]*.005;
END;
END;
FREE tempmat tempdiag IMPMAT1A IMPMAT1B;

/* Model implied cov matrix for the model with FEWER vanishing tetrads;
Convert this vector into a square matrix to use in CTA;
Symmetrize this matrix, if not already symmetric; */

use imp2;
read all into IMPMAT2B;

IF NCOL(IMPMAT2B) = (&vars*(&vars+1)/2) THEN DO;
/*IF lowdiag = 1 THEN DO; */
IMPMAT2A=J(&vars,&vars,0);
ct = 1;
DO i=1 to nrow(IMPMAT2A);
DO j=1 to nrow(IMPMAT2A);
IF i < j then DO;
IMPMAT2A[i,j] = 0;
END;
ELSE DO;
IMPMAT2A[i,j] = IMPMAT2B[ct];
ct = ct + 1;
END;
END;
END;
tempmat=IMPMAT2A+IMPMAT2A`;
DO i=1 to nrow(tempmat);
DO j=1 to nrow(tempmat);
IF i=j then tempmat[i,j]= tempmat[i,j]/ 2;
END;
END;
IMPMAT2A = tempmat;
END;
ELSE IF NCOL(IMPMAT2B) = &vars * &vars THEN DO;
/*Convert this vector into a square matrix to use in CTA; */
IMPMAT2A=J(&vars,&vars,0);
ct = 1;
DO i=1 to nrow(IMPMAT2A);
DO j=1 to nrow(IMPMAT2A);
IMPMAT2A[i,j] = IMPMAT2B[ct];
ct = ct + 1;
END;
END;
END;
ELSE DO;
PRINT "The second model implied covariance matrix entered does not conform to the # of variables specified";
END;

/*Make the implied matrix a correlation matrix; */
tempdiag = J(NROW(IMPMAT2A),NCOL(IMPMAT2A),0);
DO i = 1 to NROW(IMPMAT2A);
DO j = 1 to NCOL(IMPMAT2A);
IF i = j THEN tempdiag[i,j]=1/sqrt(IMPMAT2A[i,j]);
END;
END;
IMPMAT2 = tempdiag*IMPMAT2A*tempdiag;

/* Now adjust the magnitude of this matrix; */
DO i = 1 to NROW(IMPMAT2);
DO j = 1 to NCOL(IMPMAT2);
IMPMAT2[i,j]=IMPMAT2[i,j]*.005;
END;
END;
FREE IMPMAT2B IMPMAT2a tempmat tempdiag;

CORR = 0;
IF SUM(DIAG(SAMPMAT))=NCOL(SAMPMAT) THEN CORR = 1;

k = NCOL(SAMPMAT);

/* computing sample tetrad residuals */
tetres = {0}; tetlabel = {0 0 0 0 0 0 0 0};
DO a = 1 to k;
DO b = a+1 to k;
DO c = b+1 to k;
DO d = c+1 to k;
/* NOTE: the 3rd and 4th elements of the 2nd and 3rd tetrads should be reversed to
allow calculating the SE's correctly down below--but then would need to sort
the elements within the tetrads at a later point; */
tetres = tetres//(SAMPMAT[a,b]*SAMPMAT[c,d] - SAMPMAT[a,c]*SAMPMAT[b,d]);
tetlabel = tetlabel//(a||b||c||d||a||c||b||d);
tetres = tetres//(SAMPMAT[a,c]*SAMPMAT[b,d] - SAMPMAT[a,d]*SAMPMAT[b,c]);
tetlabel = tetlabel//(a||c||b||d||a||d||b||c);
tetres = tetres//(SAMPMAT[a,b]*SAMPMAT[c,d] - SAMPMAT[a,d]*SAMPMAT[b,c]);
tetlabel = tetlabel//(a||b||c||d||a||d||b||c);
END;
END;
END;
END;
tetres = tetres[2:NROW(tetres),];
tetlabel = tetlabel[2:NROW(tetlabel),];

/* computing implied tetrad residuals */
tetresI = {0};
DO a = 1 to k;
DO b = a+1 to k;
DO c = b+1 to k;
DO d = c+1 to k;
tetresI = tetresI//(IMPMAT[a,b]*IMPMAT[c,d] - IMPMAT[a,c]*IMPMAT[b,d]);
tetresI = tetresI//(IMPMAT[a,c]*IMPMAT[b,d] - IMPMAT[a,d]*IMPMAT[b,c]);
tetresI = tetresI//(IMPMAT[a,b]*IMPMAT[c,d] - IMPMAT[a,d]*IMPMAT[b,c]);
END;
END;
END;
END;
tetresI = tetresI[2:NROW(tetresI),];

/* computing implied tetrad residuals for RESTRICTED model*/
tetresI2 = {0};
DO a = 1 to k;
DO b = a+1 to k;
DO c = b+1 to k;
DO d = c+1 to k;
tetresI2 = tetresI2//(IMPMAT2[a,b]*IMPMAT2[c,d] - IMPMAT2[a,c]*IMPMAT2[b,d]);
tetresI2 = tetresI2//(IMPMAT2[a,c]*IMPMAT2[b,d] - IMPMAT2[a,d]*IMPMAT2[b,c]);
tetresI2 = tetresI2//(IMPMAT2[a,b]*IMPMAT2[c,d] - IMPMAT2[a,d]*IMPMAT2[b,c]);
END;
END;
END;
END;
tetresI2 = tetresI2[2:NROW(tetresI2),];

/* This is used when bringing in an external asymptotic cov matrix; */
acmlabel = J(NROW(tetlabel),.5*(NCOL(tetlabel)),'aaaa');
i = 1;
DO i = 1 to NROW(tetlabel);
k = 0;
DO j = 1 to 7 BY 2;
k = k+1;
IF tetlabel[i,j] < 10 & tetlabel[i,j+1] < 10 THEN DO;
acmlabel[i,k] = ROWCATC('0'||CHAR(tetlabel[i,j+1])||'0'||CHAR(tetlabel[i,j]));
END;
ELSE DO;
IF tetlabel[i,j] >= 10 & tetlabel[i,j+1]>=10 THEN
acmlabel[i,k] = ROWCATC(CHAR(tetlabel[i,j+1])||CHAR(tetlabel[i,j]));
ELSE DO;
IF tetlabel[i,j] >= 10 THEN
acmlabel[i,k] = ROWCATC('0'||CHAR(tetlabel[i,j+1])||CHAR(tetlabel[i,j]));
ELSE acmlabel[i,k] = ROWCATC(CHAR(tetlabel[i,j+1])||'0'||CHAR(tetlabel[i,j]));
END;
END;
END;
END;
acmcov = UNIQUE(SHAPE(acmlabel,1)); *row vector of unique covariances;
FREE acmlabel;

/* find model-implied vanishing tetrads & calculate t-values */
i = 1;
DO i = 1 to NROW(tetlabel);
a = tetlabel[i,1]; b = tetlabel[i,2]; c = tetlabel[i,3]; d = tetlabel[i,4];
IF MOD(i,3) = 2 THEN DO;
temp = c; c = d; d = temp;
END;
IF MOD(i,3) = 0 THEN DO;
temp = c; c = d; d = temp;
END;
AVari = 1/&N*(Impmat[b,d]**2 * Impmat[a,a] * Impmat[c,c] +
Impmat[a,c]**2 * Impmat[d,d] * Impmat[b,b] +
Impmat[c,d]**2 * Impmat[a,a] * Impmat[b,b] +
Impmat[a,b]**2 * Impmat[d,d] * Impmat[c,c] +
2*(Impmat[b,d]*Impmat[a,c]*Impmat[a,d]*Impmat[b,c] -
Impmat[b,d]*Impmat[c,d]*Impmat[a,a]*Impmat[b,c] -
Impmat[b,d]*Impmat[a,b]*Impmat[a,d]*Impmat[c,c] -
Impmat[a,c]*Impmat[c,d]*Impmat[a,d]*Impmat[b,b] -
Impmat[a,c]*Impmat[a,b]*Impmat[d,d]*Impmat[b,c] +
Impmat[c,d]*Impmat[a,b]*Impmat[a,d]*Impmat[b,c]) +
2*((Impmat[b,d]*Impmat[a,c]-Impmat[a,b]*Impmat[c,d])**2));
IF i = 1 THEN AVAR = Avari;
ELSE AVar = Avar//Avari;
END;
FREE Avari;

/* find model-implied vanishing tetrads for model with FEWER vanishing tetrads
& calculate t-values */
i = 1;
DO i = 1 to NROW(tetlabel);
a = tetlabel[i,1]; b = tetlabel[i,2]; c = tetlabel[i,3]; d = tetlabel[i,4];
IF MOD(i,3) = 2 THEN DO;
temp = c; c = d; d = temp;
END;
IF MOD(i,3) = 0 THEN DO;
temp = c; c = d; d = temp;
END;
AVari2 = 1/&N*(Impmat2[b,d]**2 * Impmat2[a,a] * Impmat2[c,c] +
Impmat2[a,c]**2 * Impmat2[d,d] * Impmat2[b,b] +
Impmat2[c,d]**2 * Impmat2[a,a] * Impmat2[b,b] +
Impmat2[a,b]**2 * Impmat2[d,d] * Impmat2[c,c] +
2*(Impmat2[b,d]*Impmat2[a,c]*Impmat2[a,d]*Impmat2[b,c] -
Impmat2[b,d]*Impmat2[c,d]*Impmat2[a,a]*Impmat2[b,c] -
Impmat2[b,d]*Impmat2[a,b]*Impmat2[a,d]*Impmat2[c,c] -
Impmat2[a,c]*Impmat2[c,d]*Impmat2[a,d]*Impmat2[b,b] -
Impmat2[a,c]*Impmat2[a,b]*Impmat2[d,d]*Impmat2[b,c] +
Impmat2[c,d]*Impmat2[a,b]*Impmat2[a,d]*Impmat2[b,c]) +
2*((Impmat2[b,d]*Impmat2[a,c]-Impmat2[a,b]*Impmat2[c,d])**2));
IF i = 1 THEN AVAR2 = Avari2;
ELSE AVar2 = Avar2//Avari2;
END;
FREE AVari2;

/* This section is used for nested tetrad tests; */
IF &nesttest = 1 THEN DO;
/* Check whether the models are tetrad nested, and which is bigger; */
temp1 = J(1,1,0);
temp2 = J(1,1,0);
temp3 = J(1,1,0);
difftet = J(1,NCOL(tetlabel)+2,0);
badtet = J(1,NCOL(tetlabel)+2,0);

tvalueI = J(NROW(tetresI),3,0);
DO i = 1 to NROW(tvalueI);
tvalueI[i,1]= tetresI[i,1];
tvalueI[i,2]= AVar[i,1];
tvalueI[i,3]=tvalueI[i,1]/sqrt(tvalueI[i,2]);
END;

tvalueI2 = J(NROW(tetresI2),3,0);
DO i = 1 to NROW(tvalueI2);
tvalueI2[i,1]= tetresI2[i,1];
tvalueI2[i,2]= AVar2[i,1];
tvalueI2[i,3]=tvalueI2[i,1]/sqrt(tvalueI2[i,2]);
END;
DO i = 1 to NROW(tetlabel);
/* This is the t-value criterion for defining as vanishing (keep non-vanishing);
This loop finds vanishing tetrads in the first model but not in the second one; */

IF ABS(tvalueI[i,3]) < 0.001 & ABS(tvalueI2[i,3]) >= 0.001 THEN DO;
temp1 = temp1 || i;
temptet = tetlabel[i,]||tvalueI[i,3]||tvalueI2[i,3];
difftet = difftet // temptet;
END;
/* This loop finds vanishing tetrads in the second model but not in the first one; */
IF ABS(tvalueI[i,3]) >= 0.001 & ABS(tvalueI2[i,3]) < 0.001 THEN DO;
temp2 = temp2 || i;
temptet2 = tetlabel[i,]||tvalueI[i,3]||tvalueI2[i,3];
badtet = badtet // temptet2;
END;
/* This loop finds vanishing tetrads implied by both models; */
IF ABS(tvalueI[i,3]) < 0.001 & ABS(tvalueI2[i,3]) < 0.001 THEN DO;
temp3 = temp3 || i;
END;
END;

/* **Find ERROR in specification (not nested); */
notnest = 0;
if NCOL(temp1) >= 2 & NCOL(temp2) >=2 then do;
notnest = 1;
END;

if NCOL(temp1) = 1 & NCOL(temp2) = 1 & NCOL(temp3) >=2 then do;
notnest = 2;
END;

if NCOL(temp1) = 1 & NCOL(temp3) = 1 then do;
notnest = 3;
zerotet = 1;
END;

if NCOL(temp2) = 1 & NCOL(temp3) = 1 then do;
notnest = 4;
zerotet = 1;
END;

/* These are models in which the order of the model implied cov's was reversed between smaller
and larger models, so need to flip them here to correct operator error; */

matswitch = 0;
if NCOL(temp1) = 1 & NCOL(temp2) >=2 then do;
matswitch = 1;
temp21 = AVar2;
temp22 = tetresI2;
temp23 = IMPMAT2;
AVar2 = AVar;
tetresI2 = tetresI;
IMPMAT2 = IMPMAT;
AVar = temp21;
tetresI = temp22;
IMPMAT = temp23;
END;
END;
FREE temp1 temp2 temp3 difftet badtet temp21 temp22 temp23;

/* Combining the residuals with the implied tetrads and computing t-values; */
tvalueI = J(NROW(tetresI),6,0);
DO i = 1 to NROW(tvalueI);
tvalueI[i,1]= tetresI[i,1];
tvalueI[i,2]= AVar[i,1];
tvalueI[i,3]=tvalueI[i,1]/sqrt(tvalueI[i,2]);
tvalueI[i,4]= tetresI2[i,1];
tvalueI[i,5]= AVar2[i,1];
tvalueI[i,6]=tvalueI[i,4]/sqrt(tvalueI[i,5]);
END;

/* Computing the variances for the sample tetrads; */
DO i = 1 to NROW(tetlabel);
a = tetlabel[i,1]; b = tetlabel[i,2]; c = tetlabel[i,3]; d = tetlabel[i,4];
IF MOD(i,3) = 2 THEN DO;
temp = c; c = d; d = temp;
END;
IF MOD(i,3) = 0 THEN DO;
temp = c; c = d; d = temp;
END;

AVars = (1/N) *
(Sampmat[d,b]*Sampmat[d,b]*Sampmat[a,a]*Sampmat[c,c] +
Sampmat[a,c]*Sampmat[a,c]*Sampmat[d,d]*Sampmat[b,b] +
Sampmat[d,c]*Sampmat[d,c]*Sampmat[a,a]*Sampmat[b,b] +
Sampmat[a,b]*Sampmat[a,b]*Sampmat[d,d]*Sampmat[c,c] +
2 *(Sampmat[d,b]*Sampmat[a,c]*Sampmat[a,d]*Sampmat[b,c] -
Sampmat[d,b]*Sampmat[d,c]*Sampmat[a,a]*Sampmat[b,c] -
Sampmat[d,b]*Sampmat[a,b]*Sampmat[a,d]*Sampmat[c,c] -
Sampmat[a,c]*Sampmat[d,c]*Sampmat[d,a]*Sampmat[b,b] -
Sampmat[a,c]*Sampmat[a,b]*Sampmat[d,d]*Sampmat[b,c] +
Sampmat[d,c]*Sampmat[a,b]*Sampmat[a,d]*Sampmat[b,c])+
2 *((Sampmat[d,b]*Sampmat[a,c]-Sampmat[a,b]*Sampmat[d,c])**2));

IF i = 1 THEN AVart = Avars;
ELSE AVart = Avart//Avars;
END;
FREE AVars;
/* Combining the residuals with the tetrads and computing t-values; */
tvalue = J(NROW(tetres),3,0);
do i = 1 to NROW(tvalue);
tvalue[i,1]= tetres[i,1];
tvalue[i,2]= AVart[i,1];
tvalue[i,3]=tvalue[i,1]/sqrt(tvalue[i,2]);
END;

/* Now keep the tetrads that are implied vanishing by the model; */
tetlabel = tetlabel||tvalueI;
tetres = tetres||tvalueI;
temp1 = J(1,8,0);
temp2 = J(1,1,0);
temp3 = J(1,8,0);
temp4 = J(1,1,0);
temp5 = J(1,8,0);
temp6 = J(1,1,0);

DO i = 1 to NROW(tetlabel);
/* This is the t-value criterion for defining as vanishing (keep non-vanishing);
This loop finds all vanishing tetrads; */
IF ABS(tetlabel[i,11]) < 0.001 THEN DO;
temp1 = temp1 // tetlabel[i,1:8];
temp2 = temp2 // tetres[i,1];
END;
/* This loop finds vanishing tetrads in the larger model but not in the smaller one; */
IF ABS(tetlabel[i,11]) < 0.001 & ABS(tetlabel[i,14]) >= 0.001 THEN DO;
temp3 = temp3 // tetlabel[i,1:8];
temp4 = temp4 // tetres[i,1];
END;
/* This loop finds vanishing tetrads implied by both models; */
IF ABS(tetlabel[i,11]) < 0.001 & ABS(tetlabel[i,14]) < 0.001 THEN DO;
temp5 = temp5 // tetlabel[i,1:8];
temp6 = temp6 // tetres[i,1];
END;
END;

/*Comparing nested models--- Check that they are nested; */
notnest2 = 0;
IF &nesttest = 1 & NROW(temp3) = 1 THEN DO;
notnest2 = 1;
END;
IF NROW(temp1) = 1 THEN DO;
print "There are no vanishing tetrads in either model";
/* FIX THIS */
/* NEED TO JUMP OUT OF THE PROGRAM AT THIS POINT */
END;
ELSE IF &nesttest = 1 THEN DO;
temp3 = temp3[2:NROW(temp3),];
temp4 = temp4[2:NROW(temp4),];
END;
ELSE DO;
/* This is the non-nested test: so keep all of its tetrads (temp1 and temp2); */
temp3 = temp1[2:NROW(temp1),];
temp4 = temp2[2:NROW(temp2),];
END;
IF &nesttest = 1 & zerotet = 0 THEN DO;
tetlabel = temp5[2:NROW(temp5),];
tetres = temp6[2:NROW(temp6),];
END;
/* This is a nested test where one model has NO vanishing tetrads; */
ELSE IF &nesttest = 1 & zerotet = 1 THEN DO;
tetlabel = temp3;
tetres = temp4;
END;
ELSE DO;
/* This is a non-nested test; */
tetlabel_nonnest = temp3;
tetres_nonnest = temp4;
initlabel_nonnest = tetlabel_nonnest;
initres_nonnest = tetres_nonnest;
END;
/* Don't free up temp3 temp4 yet, as they are needed below; */
FREE temp1 temp2 temp5 temp6;
if notnest2 = 1 then print "These models are NOT nested---same # of df";
if matswitch = 0 then print "Model implied matrices are in correct order";
if matswitch = 1 then print "NOTE: Model implied matrices were incorrectly entered for large and small models---they have been reversed";
if notnest = 0 then print "Models are tetrad nested";
if notnest = 1 then print "ERROR: These models are NOT tetrad nested. Nested test is INAPPROPRIATE";
if notnest = 2 then print "ERROR: These models contain the same tetrads. Nested test is INAPPROPRIATE";
if notnest = 3 then print "NOTE: The first model contains no tetrads; tetrad chi-square for first model also represents nested test";
if notnest = 4 then print "NOTE: The second model contains no tetrads; tetrad chi-square for first model also represents nested test";

initlabel = tetlabel;
initres = tetres;

DO r = 1 to &reps;
RUN TETRAD;
END;

/* Fixing the formatting of the output */
IF &reps > 1 THEN DO;
resultsch = J(NROW(results),NCOL(results),'aaaaaaaaa');
IF &nesttest = 1 THEN DO;
DO q = 1 to NROW(results);
resultsch[q,] = CHAR(results[q,1],4,0)||CHAR(results[q,2],8,2)||CHAR(results[q,3],4,0)||
CHAR(results[q,4],8,4)||CHAR(results[q,5],8,2)||CHAR(results[q,6],4,0)||CHAR(results[q,7],8,4)
||CHAR(results[q,8],8,2)||CHAR(results[q,9],4,0)||CHAR(results[q,10],8,4);
END;
END;
ELSE DO;
DO q = 1 to NROW(results);
resultsch[q,] = CHAR(results[q,1],4,0)||CHAR(results[q,2],8,2)||CHAR(results[q,3],4,0)||
CHAR(results[q,4],8,4);
END;
END;
END;

IF &reps > 1 THEN DO;
PRINT Resultsch[label='results' colname=clabel];
CREATE Outfile FROM RESULTS[colname=clabel];
APPEND FROM RESULTS;
END;

FINISH;
RUN;
QUIT;

%mend ctanest1;

topic Re: reads the macro but doesn't give the output in ODS and Base Reporting

reads the macro but doesn't give the output

Re: reads the macro but doesn't give the output

Re: reads the macro but doesn't give the output