I ran the sample code for this particular macro and was able to make it work.    Data snippet:   Obs actor_id abuse bereavement bully depressed isolated relationship self_harm stress_anxiety substance suicide Other nonConfor AI Asian Hispanic GenOther White age14to24 COVID irflag 1 169 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 0 1 1 2 499 1 2 1 2 1 2 1 2 1 2 1 1 1 1 1 1 0 0 1 1 3 547 1 1 1 2 1 1 2 2 1 1 1 1 1 1 0 1 1 0 1 1 4 615 2 2 1 2 2 2 2 2 2 2 1 1 1 1 1 1 0 0 0 1 5 924 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 0 0 1 1 6 930 1 1 1 2 2 2 2 2 1 2 1 1 1 1 1 1 0 0 1 1 7 1075 2 1 1 2 1 2 1 1 1 1 1 1 1 0 1 1 1 0 1 1 8 1129 1 1 1 2 2 2 1 1 1 2 1 1 1 1 1 1 0 0 1 1 9 1193 1 1 1 2 2 1 1 2 1 1 1 1 1 1 1 1 0 0 1 1 10 1197 1 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 0 0 1 1 11 1310 2 2 1 2 2 2 2 2 1 2 1 1 0 1 1 1 1 0 1 0 12 1367 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 13 1651 1 1 1 2 1 2 1 2 1 1 1 1 1 1 1 1 0 0 1 1 14 1695 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 0 0 0 1 15 1706 1 1 1 2 2 1 1 2 1 1 1 1 1 0 1 1 1 0 0 1 16 1723 1 1 1 2 2 2 2 1 1 2 1 1 1 1 1 1 0 0 1 1 17 1861 2 2 2 2 2 2 2 2 1 2 1 1 1 1 1 1 0 0 0 1 18 2011 2 2 1 2 2 2 2 2 1 2 1 1 1 1 1 1 0 0 0 0 19 2022 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 0 0 1 1 20 2050 1 1 1 2 2 1 2 2 1 2 1 1 1 1 1 1 0 0 1 1 21 2060 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 22 2167 1 1 1 2 2 1 2 2 1 2 1 1 1 1 1 1 0 0 1 1 23 2206 2 1 1 2 2 2 2 2 2 2 1 1 1 1 1 1 0 0 1 1 24 2366 1 2 1 2 2 1 2 2 1 2 1 0 1 1 1 1 0 0 1 1   I ran this macro code: %LCA_Covariates_3Step(postprobs = my_post4, id = actor_id, covariates =  Other nonConfor AI Asian Hispanic genOther White age14to24 covid irflag); *Covariates = Male Other nonConfor AI Asian Hispanic Other White GenOther age14to24 covid irflag **code calling the 2nd macro w And keep encountering an error stating: ERROR: OTHER is not in the scope of variables for the data set.   SAS log: MLOGIC(LCA_COVARIATES_3STEP):  Parameter COVARIATES has value Other nonConfor AI Asian Hispanic       genOther White age14to24 covid irflag MLOGIC(LCA_COVARIATES_3STEP):  Parameter GROUPS has value MLOGIC(LCA_COVARIATES_3STEP):  Parameter ASSIGNMENT has value Modal MLOGIC(LCA_COVARIATES_3STEP):  Parameter ADJUSTMENT has value BCH MLOGIC(LCA_COVARIATES_3STEP):  Parameter REF_CLASS has value 1 MLOGIC(LCA_COVARIATES_3STEP):  Parameter SAMPLING_WEIGHT has value MLOGIC(LCA_COVARIATES_3STEP):  Parameter AUTOMATICALLY_ADD_INTERCEPT has value 1 MPRINT(LCA_COVARIATES_3STEP):   TITLE "Using LCA_Covariates_3Step Macro"; MLOGIC(TRIM):  Beginning execution. MLOGIC(TRIM):  This macro was compiled from the autocall file C:\Program       Files\SASHome\SASFoundation\9.4\core\sasmacro\trim.sas MLOGIC(TRIM):  Parameter VALUE has value "" MLOGIC(TRIM):  %LOCAL  I MLOGIC(TRIM):  %DO loop beginning; index variable I; start value is 2; stop value is 1; by value is       -1. MLOGIC(TRIM):  %IF condition %qsubstr(&value,&i,1) ne is TRUE MLOGIC(TRIM):  %GOTO trimmed (label resolves to TRIMMED). MLOGIC(TRIM):  %IF condition &i>0 is TRUE MLOGIC(TRIM):  Ending execution. MLOGIC(LCA_COVARIATES_3STEP):  %IF condition %TRIM("&groups")="" is TRUE MLOGIC(LCA_COVARIATES_3STEP):  %LET (variable name is THERE_ARE_GROUPS) MLOGIC(LCA_COVARIATES_3STEP):  %IF condition %EVAL(&there_are_groups=1) is FALSE MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  Beginning execution. MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  Parameter POSTPROBS has value my_post4 MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  Parameter ID has value actor_id MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  Parameter COVARIATES has value Other nonConfor AI Asian       Hispanic genOther White age14to24 covid irflag MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  Parameter ASSIGNMENT has value Modal MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  Parameter ADJUSTMENT has value BCH MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  Parameter REF_CLASS has value 1 MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  Parameter SAMPLING_WEIGHT has value MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  Parameter AUTOMATICALLY_ADD_INTERCEPT has value 1 MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   DATA bchtemp1; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   SET my_post4; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   RUN;   NOTE: There were 157274 observations read from the data set WORK.MY_POST4. NOTE: The data set WORK.BCHTEMP1 has 157274 observations and 16 variables. NOTE: DATA statement used (Total process time):       real time           0.16 seconds       cpu time            0.10 seconds     MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   PROC TRANSPOSE DATA=my_post4 OUT=TemporaryDatasetForBCHmacro; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   RUN;   NOTE: There were 157274 observations read from the data set WORK.MY_POST4. NOTE: The data set WORK.TEMPORARYDATASETFORBCHMACRO has 16 observations and 157276 variables. NOTE: PROCEDURE TRANSPOSE used (Total process time):       real time           0.78 seconds       cpu time            0.71 seconds     MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   PROC IML; NOTE: IML Ready MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   USE TemporaryDatasetForBCHmacro; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   READ ALL VAR {_NAME_} INTO name; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   CLOSE TemporaryDatasetForBCHmacro; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   nClasses = (SUM(SUBSTR(UPCASE(name),1,6)="POSTLC")); MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   CALL SYMPUT("nClasses",CHAR(nClasses)); MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   QUIT; NOTE: Exiting IML. NOTE: PROCEDURE IML used (Total process time):       real time           0.51 seconds       cpu time            0.51 seconds     MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   PROC DATASETS NOLIST NOWARN NOPRINT; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   DELETE TemporaryDatasetForBCHmacro; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   RUN;   NOTE: Deleting WORK.TEMPORARYDATASETFORBCHMACRO (memtype=DATA). MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %PUT _all_ LCA_COVARIATES_3STEP_NO_GROUPS ADJUSTMENT BCH LCA_COVARIATES_3STEP_NO_GROUPS ASSIGNMENT Modal LCA_COVARIATES_3STEP_NO_GROUPS AUTOMATICALLY_ADD_INTERCEPT 1 LCA_COVARIATES_3STEP_NO_GROUPS COVARIATES Other nonConfor AI Asian Hispanic genOther White age14to24 covid irflag LCA_COVARIATES_3STEP_NO_GROUPS ID actor_id LCA_COVARIATES_3STEP_NO_GROUPS NCLASSES            4 LCA_COVARIATES_3STEP_NO_GROUPS POSTPROBS my_post4 LCA_COVARIATES_3STEP_NO_GROUPS REF_CLASS 1 LCA_COVARIATES_3STEP_NO_GROUPS SAMPLING_WEIGHT LCA_COVARIATES_3STEP ADJUSTMENT BCH LCA_COVARIATES_3STEP ASSIGNMENT Modal LCA_COVARIATES_3STEP AUTOMATICALLY_ADD_INTERCEPT 1 LCA_COVARIATES_3STEP COVARIATES Other nonConfor AI Asian Hispanic genOther White age14to24 covid irflag LCA_COVARIATES_3STEP GROUPS LCA_COVARIATES_3STEP ID actor_id LCA_COVARIATES_3STEP POSTPROBS my_post4 LCA_COVARIATES_3STEP REF_CLASS 1 LCA_COVARIATES_3STEP SAMPLING_WEIGHT LCA_COVARIATES_3STEP THERE_ARE_GROUPS 0 AUTOMATIC AFDSID 0 AUTOMATIC AFDSNAME AUTOMATIC AFLIB AUTOMATIC AFSTR1 AUTOMATIC AFSTR2 AUTOMATIC FSPBDV AUTOMATIC SYSADDRBITS 64 AUTOMATIC SYSBUFFR AUTOMATIC SYSCC 3000 AUTOMATIC SYSCHARWIDTH 1 AUTOMATIC SYSCMD AUTOMATIC SYSDATASTEPPHASE AUTOMATIC SYSDATE 03DEC20 AUTOMATIC SYSDATE9 03DEC2020 AUTOMATIC SYSDAY Thursday AUTOMATIC SYSDEVIC AUTOMATIC SYSDMG 0 AUTOMATIC SYSDSN WORK    TEMPORARYDATASETFORBCHMACRO AUTOMATIC SYSENCODING utf-8 AUTOMATIC SYSENDIAN LITTLE AUTOMATIC SYSENV FORE AUTOMATIC SYSERR 0 AUTOMATIC SYSERRORTEXT File WORK.WEIGHTED_GLOGIT_SANDWICH_COV.DATA does not exist. AUTOMATIC SYSFILRC 0 AUTOMATIC SYSHOSTINFOLONG X64_SRV16 WIN 10.0.14393  Server AUTOMATIC SYSHOSTNAME Aperture AUTOMATIC SYSINCLUDEFILEDEVICE AUTOMATIC SYSINCLUDEFILEDIR AUTOMATIC SYSINCLUDEFILEFILEREF AUTOMATIC SYSINCLUDEFILENAME AUTOMATIC SYSINDEX 20 AUTOMATIC SYSINFO 0 AUTOMATIC SYSJOBID 544 AUTOMATIC SYSLAST WORK.TEMPORARYDATASETFORBCHMACRO AUTOMATIC SYSLCKRC 0 AUTOMATIC SYSLIBRC 0 AUTOMATIC SYSLOGAPPLNAME AUTOMATIC SYSMACRONAME LCA_COVARIATES_3STEP_NO_GROUPS AUTOMATIC SYSMAXLONG 2147483647 AUTOMATIC SYSMENV AUTOMATIC SYSMSG AUTOMATIC SYSNCPU 4 AUTOMATIC SYSNOBS 16 AUTOMATIC SYSODSESCAPECHAR 03 AUTOMATIC SYSODSGRAPHICS 1 AUTOMATIC SYSODSPATH  SASUSER.TEMPLAT(UPDATE) SASHELP.TMPLMST(READ) AUTOMATIC SYSPARM AUTOMATIC SYSPRINTTOLOG AUTOMATIC SYSPRINTTOLIST AUTOMATIC SYSPROCESSID 41DCA65620DAD0E54018000000000000 AUTOMATIC SYSPROCESSMODE SAS DMS Session AUTOMATIC SYSPROCESSNAME DMS Process AUTOMATIC SYSPROCNAME DATASETS AUTOMATIC SYSRC 0 AUTOMATIC SYSSCP WIN AUTOMATIC SYSSCPL X64_SRV16 AUTOMATIC SYSSITE 70080458 AUTOMATIC SYSSIZEOFLONG 4 AUTOMATIC SYSSIZEOFPTR 8 AUTOMATIC SYSSIZEOFUNICODE 2 AUTOMATIC SYSSTARTID AUTOMATIC SYSSTARTNAME AUTOMATIC SYSTCPIPHOSTNAME Aperture AUTOMATIC SYSTIME 22:04 AUTOMATIC SYSTIMEZONE AUTOMATIC SYSTIMEZONEIDENT AUTOMATIC SYSTIMEZONEOFFSET -18000 AUTOMATIC SYSUSERID jrunkle AUTOMATIC SYSVER 9.4 AUTOMATIC SYSVLONG 9.04.01M6P110718 AUTOMATIC SYSVLONG4 9.04.01M6P11072018 AUTOMATIC SYSWARNINGTEXT Data set WORK.SANDWICH_COVARIANCE was not replaced because this step was stopped. MLOGIC(CALCULATECLASS_WEIGHTS):  Beginning execution. MLOGIC(CALCULATECLASS_WEIGHTS):  Parameter POSTPROBSDATASET has value my_post4 MLOGIC(CALCULATECLASS_WEIGHTS):  Parameter SUBJECTID has value actor_id MLOGIC(CALCULATECLASS_WEIGHTS):  Parameter NCLASSES has value 4 MLOGIC(CALCULATECLASS_WEIGHTS):  Parameter SAMPLING_WEIGHT has value MLOGIC(CALCULATECLASS_WEIGHTS):  Parameter ASSIGNMENT has value Modal MLOGIC(CALCULATECLASS_WEIGHTS):  Parameter ADJUSTMENT has value BCH   NOTE: PROCEDURE DATASETS used (Total process time):       real time           0.07 seconds       cpu time            0.06 seconds     MPRINT(CALCULATECLASS_WEIGHTS):   PROC IML; NOTE: IML Ready MPRINT(CALCULATECLASS_WEIGHTS):   Assignment = LOWCASE(TRIM("Modal")); MPRINT(CALCULATECLASS_WEIGHTS):   Adjustment = LOWCASE(TRIM("BCH")); MPRINT(CALCULATECLASS_WEIGHTS):   IF (Assignment^="modal" & Assignment^="proportional") THEN DO; MPRINT(CALCULATECLASS_WEIGHTS):   PRINT ("Assignment option not recognized:"); MPRINT(CALCULATECLASS_WEIGHTS):   PRINT(Assignment); MPRINT(CALCULATECLASS_WEIGHTS):   END; MPRINT(CALCULATECLASS_WEIGHTS):   IF (Adjustment^="bch" & Adjustment^="none") THEN DO; MPRINT(CALCULATECLASS_WEIGHTS):   PRINT ("Adjustment option not recognized:"); MPRINT(CALCULATECLASS_WEIGHTS):   PRINT(Adjustment); MPRINT(CALCULATECLASS_WEIGHTS):   END; MPRINT(CALCULATECLASS_WEIGHTS):   USE my_post4; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop beginning; index variable CLASS; start value is 1; stop       value is 4; by value is 1. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 2; loop will iterate again. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 3; loop will iterate again. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 4; loop will iterate again. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 5; loop will not iterate again. MPRINT(CALCULATECLASS_WEIGHTS):   READ ALL VAR {actor_id PostLC1 PostLC2 PostLC3 PostLC4 Best}; MPRINT(CALCULATECLASS_WEIGHTS):   CLOSE my_post4; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop beginning; index variable CLASS; start value is 2; stop       value is 4; by value is 1. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 3; loop will iterate again. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 4; loop will iterate again. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 5; loop will not iterate again. MPRINT(CALCULATECLASS_WEIGHTS):   PostLC = PostLC1 || PostLC2 || PostLC3 || PostLC4 ; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop beginning; index variable CLASS; start value is 2; stop       value is 4; by value is 1. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 3; loop will iterate again. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 4; loop will iterate again. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 5; loop will not iterate again. MPRINT(CALCULATECLASS_WEIGHTS):   IsBestLC = (Best=1) || (Best=2) || (Best=3) || (Best=4) ; MPRINT(CALCULATECLASS_WEIGHTS):   UnadjustedWeights = J(NROW(IsBestLC),4,.); MPRINT(CALCULATECLASS_WEIGHTS):   IF (Assignment="modal") THEN DO; MPRINT(CALCULATECLASS_WEIGHTS):   UnadjustedWeights = IsBestLC; MPRINT(CALCULATECLASS_WEIGHTS):   END; MPRINT(CALCULATECLASS_WEIGHTS):   ELSE DO; MPRINT(CALCULATECLASS_WEIGHTS):   UnadjustedWeights = PostLC; MPRINT(CALCULATECLASS_WEIGHTS):   END; MPRINT(CALCULATECLASS_WEIGHTS):   IF (LENGTH(LOWCASE(TRIM("_")))>1) THEN DO; MPRINT(CALCULATECLASS_WEIGHTS):   DO class = 1 TO 4; MPRINT(CALCULATECLASS_WEIGHTS):   UnadjustedWeights[,class] = ( + 0)#UnadjustedWeights[,class]; MPRINT(CALCULATECLASS_WEIGHTS):   END; MPRINT(CALCULATECLASS_WEIGHTS):   END; MPRINT(CALCULATECLASS_WEIGHTS):   ELSE DO; MPRINT(CALCULATECLASS_WEIGHTS):   END; MPRINT(CALCULATECLASS_WEIGHTS):   IF (Adjustment="bch") THEN DO; MPRINT(CALCULATECLASS_WEIGHTS):   D = J(4,4,.); MPRINT(CALCULATECLASS_WEIGHTS):   DO t = 1 TO 4; MPRINT(CALCULATECLASS_WEIGHTS):   DO s = 1 TO 4; MPRINT(CALCULATECLASS_WEIGHTS):   D[t,s] = ((PostLC[,t] # UnadjustedWeights[,s])[+]) / (PostLC[+,t]); MPRINT(CALCULATECLASS_WEIGHTS):   END; MPRINT(CALCULATECLASS_WEIGHTS):   END; MPRINT(CALCULATECLASS_WEIGHTS):   Class_Weights = UnadjustedWeights * INV(D); MPRINT(CALCULATECLASS_WEIGHTS):   END; MPRINT(CALCULATECLASS_WEIGHTS):   ELSE DO; MPRINT(CALCULATECLASS_WEIGHTS):   Class_Weights = UnadjustedWeights; MPRINT(CALCULATECLASS_WEIGHTS):   END; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop beginning; index variable CLASS; start value is 1; stop       value is 4; by value is 1. MPRINT(CALCULATECLASS_WEIGHTS):   ClassWeightLC1 = Class_Weights[,1]; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 2; loop will iterate again. MPRINT(CALCULATECLASS_WEIGHTS):   ClassWeightLC2 = Class_Weights[,2]; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 3; loop will iterate again. MPRINT(CALCULATECLASS_WEIGHTS):   ClassWeightLC3 = Class_Weights[,3]; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 4; loop will iterate again. MPRINT(CALCULATECLASS_WEIGHTS):   ClassWeightLC4 = Class_Weights[,4]; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 5; loop will not iterate again. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop beginning; index variable CLASS; start value is 1; stop       value is 4; by value is 1. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 2; loop will iterate again. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 3; loop will iterate again. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 4; loop will iterate again. MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 5; loop will not iterate again. MPRINT(CALCULATECLASS_WEIGHTS):   CREATE Class_Weights VAR {actor_id ClassWeightLC1 ClassWeightLC2 ClassWeightLC3 ClassWeightLC4}; MPRINT(CALCULATECLASS_WEIGHTS):   APPEND ; MPRINT(CALCULATECLASS_WEIGHTS):   CLOSE Class_Weights; NOTE: The data set WORK.CLASS_WEIGHTS has 157274 observations and 5 variables. MPRINT(CALCULATECLASS_WEIGHTS):   QUIT; NOTE: Exiting IML. NOTE: PROCEDURE IML used (Total process time):       real time           0.31 seconds       cpu time            0.28 seconds     MPRINT(CALCULATECLASS_WEIGHTS):   DATA Class_Weights_Long; MPRINT(CALCULATECLASS_WEIGHTS):   SET Class_Weights; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop beginning; index variable CLASS; start value is 1; stop       value is 4; by value is 1. MPRINT(CALCULATECLASS_WEIGHTS):   Class = 1; MPRINT(CALCULATECLASS_WEIGHTS):   ClassWeight = ClassWeightLC1; MPRINT(CALCULATECLASS_WEIGHTS):   OUTPUT; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 2; loop will iterate again. MPRINT(CALCULATECLASS_WEIGHTS):   Class = 2; MPRINT(CALCULATECLASS_WEIGHTS):   ClassWeight = ClassWeightLC2; MPRINT(CALCULATECLASS_WEIGHTS):   OUTPUT; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 3; loop will iterate again. MPRINT(CALCULATECLASS_WEIGHTS):   Class = 3; MPRINT(CALCULATECLASS_WEIGHTS):   ClassWeight = ClassWeightLC3; MPRINT(CALCULATECLASS_WEIGHTS):   OUTPUT; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 4; loop will iterate again. MPRINT(CALCULATECLASS_WEIGHTS):   Class = 4; MPRINT(CALCULATECLASS_WEIGHTS):   ClassWeight = ClassWeightLC4; MPRINT(CALCULATECLASS_WEIGHTS):   OUTPUT; MLOGIC(CALCULATECLASS_WEIGHTS):  %DO loop index variable CLASS is now 5; loop will not iterate again. MPRINT(CALCULATECLASS_WEIGHTS):   KEEP actor_id Class ClassWeight; MPRINT(CALCULATECLASS_WEIGHTS):   RUN;   NOTE: There were 157274 observations read from the data set WORK.CLASS_WEIGHTS. NOTE: The data set WORK.CLASS_WEIGHTS_LONG has 629096 observations and 3 variables. NOTE: DATA statement used (Total process time):       real time           0.11 seconds       cpu time            0.04 seconds     MLOGIC(CALCULATECLASS_WEIGHTS):  Ending execution. MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):  ; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   DATA ForAnalysis; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   MERGE my_post4 Class_Weights; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   BY actor_id; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   RUN;   NOTE: There were 157274 observations read from the data set WORK.MY_POST4. NOTE: There were 157274 observations read from the data set WORK.CLASS_WEIGHTS. NOTE: The data set WORK.FORANALYSIS has 157274 observations and 20 variables. NOTE: DATA statement used (Total process time):       real time           0.16 seconds       cpu time            0.12 seconds     MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %IF condition %EVAL(&automatically_add_intercept>0) is TRUE MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   DATA ForAnalysis; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   SET ForAnalysis; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   _Intercept_ = 1; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   RUN;   NOTE: There were 157274 observations read from the data set WORK.FORANALYSIS. NOTE: The data set WORK.FORANALYSIS has 157274 observations and 21 variables. NOTE: DATA statement used (Total process time):       real time           0.14 seconds       cpu time            0.11 seconds     MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %LET (variable name is COVARIATES) MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   DATA ForAnalysisLong; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   SET ForAnalysis; MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %DO loop beginning; index variable CLASS; start value is 1;       stop value is 4; by value is 1. MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   LC = 1; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   ClassWeight = ClassWeightLC1; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   OUTPUT; MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %DO loop index variable CLASS is now 2; loop will iterate       again. MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   LC = 2; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   ClassWeight = ClassWeightLC2; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   OUTPUT; MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %DO loop index variable CLASS is now 3; loop will iterate       again. MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   LC = 3; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   ClassWeight = ClassWeightLC3; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   OUTPUT; MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %DO loop index variable CLASS is now 4; loop will iterate       again. MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   LC = 4; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   ClassWeight = ClassWeightLC4; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   OUTPUT; MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %DO loop index variable CLASS is now 5; loop will not       iterate again. MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %DO loop beginning; index variable CLASS; start value is 1;       stop value is 4; by value is 1. MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %DO loop index variable CLASS is now 2; loop will iterate       again. MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %DO loop index variable CLASS is now 3; loop will iterate       again. MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %DO loop index variable CLASS is now 4; loop will iterate       again. MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  %DO loop index variable CLASS is now 5; loop will not       iterate again. MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   DROP ClassWeightLC1 ClassWeightLC2 ClassWeightLC3 ClassWeightLC4 ; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   RUN;   NOTE: There were 157274 observations read from the data set WORK.FORANALYSIS. NOTE: The data set WORK.FORANALYSISLONG has 629096 observations and 19 variables. NOTE: DATA statement used (Total process time):       real time           0.37 seconds       cpu time            0.28 seconds     MLOGIC(DOWEIGHTEDGLOGIT):  Beginning execution. MLOGIC(DOWEIGHTEDGLOGIT):  Parameter INPUTDATASET has value ForAnalysisLong MLOGIC(DOWEIGHTEDGLOGIT):  Parameter OUTCOMECLASS has value LC MLOGIC(DOWEIGHTEDGLOGIT):  Parameter REF_CLASS has value 1 MLOGIC(DOWEIGHTEDGLOGIT):  Parameter WEIGHT has value ClassWeight MLOGIC(DOWEIGHTEDGLOGIT):  Parameter COVARIATES has value _Intercept_ Other nonConfor AI Asian       Hispanic genOther White age14to24 covid irflag MLOGIC(DOWEIGHTEDGLOGIT):  Parameter SUBJECTID has value actor_id MLOGIC(DOWEIGHTEDGLOGIT):  Parameter OBSERVATIONID has value LC MLOGIC(DOWEIGHTEDGLOGIT):  Parameter REGULARIZATIONCONSTANTEXP has value 0 MLOGIC(DOWEIGHTEDGLOGIT):  Parameter REGULARIZATIONCONSTANTFORCOV has value 0 MLOGIC(DOWEIGHTEDGLOGIT):  Parameter REGULARIZATIONCONSTANTFORSE has value 0 MPRINT(DOWEIGHTEDGLOGIT):   PROC IML; NOTE: IML Ready MPRINT(DOWEIGHTEDGLOGIT):   USE ForAnalysisLong; MPRINT(DOWEIGHTEDGLOGIT):   READ ALL VAR {LC} INTO Y; MPRINT(DOWEIGHTEDGLOGIT):   READ ALL VAR {ClassWeight} INTO W; MPRINT(DOWEIGHTEDGLOGIT):   READ ALL VAR {_Intercept_ Other nonConfor AI Asian Hispanic genOther White age14to24 covid irflag} INTO XMatrix; ERROR: OTHER is not in the scope of variables for the data set.   statement : READ at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   READ ALL VAR {actor_id} INTO id; MPRINT(DOWEIGHTEDGLOGIT):   CLOSE ForAnalysisLong; MPRINT(DOWEIGHTEDGLOGIT):   numCovariates = NCOL(XMatrix); MPRINT(DOWEIGHTEDGLOGIT):   numClasses = Y[<>]; MPRINT(DOWEIGHTEDGLOGIT):   CALL SYMPUT("numCovariates",(CHAR(numCovariates))); MPRINT(DOWEIGHTEDGLOGIT):   CALL SYMPUT("numClasses",(CHAR(numClasses))); MPRINT(DOWEIGHTEDGLOGIT):   DataForNlmixedAsMatrix = id || W || Y || XMatrix; MPRINT(DOWEIGHTEDGLOGIT):   VariableNames = "id" || "W" || "Y" || ("X1":"X           0"); MPRINT(DOWEIGHTEDGLOGIT):   CREATE DataForNlmixed FROM DataForNlmixedAsMatrix [COLNAME=VariableNames]; NOTE: The colname matrix size does not match the number of columns in the FROM matrix. MPRINT(DOWEIGHTEDGLOGIT):   APPEND FROM DataForNlmixedAsMatrix ; MPRINT(DOWEIGHTEDGLOGIT):   CLOSE DataForNlmixed; NOTE: The data set WORK.DATAFORNLMIXED has 629096 observations and 3 variables. MPRINT(DOWEIGHTEDGLOGIT):   QUIT; NOTE: Exiting IML. NOTE: The SAS System stopped processing this step because of errors. NOTE: PROCEDURE IML used (Total process time):       real time           0.62 seconds       cpu time            0.57 seconds   MPRINT(DOWEIGHTEDGLOGIT):   ODS EXCLUDE ALL; MPRINT(DOWEIGHTEDGLOGIT):   RUN; MPRINT(DOWEIGHTEDGLOGIT):   ODS NORESULTS; MPRINT(DOWEIGHTEDGLOGIT):   RUN;   MPRINT(DOWEIGHTEDGLOGIT):   PROC NLMIXED DATA=DataForNlmixed COV START HESS METHOD=GAUSS TECH=NRRIDG MAXITER=500 MAXFUNC=1000; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop beginning; index variable K; start value is 1; stop value is 0;       by value is 1.  Loop will not be executed. MPRINT(DOWEIGHTEDGLOGIT):   PARMS ; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop beginning; index variable C; start value is 1; stop value is 4;       by value is 1. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is FALSE MPRINT(DOWEIGHTEDGLOGIT):   eta1 = 0; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 2; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&numCovariates>1) is FALSE MPRINT(DOWEIGHTEDGLOGIT):   eta2 = b12*X1 ; MPRINT(DOWEIGHTEDGLOGIT):   expEta2 = EXP(eta2 + 0); MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 3; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&numCovariates>1) is FALSE MPRINT(DOWEIGHTEDGLOGIT):   eta3 = b13*X1 ; MPRINT(DOWEIGHTEDGLOGIT):   expEta3 = EXP(eta3 + 0); MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 4; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&numCovariates>1) is FALSE MPRINT(DOWEIGHTEDGLOGIT):   eta4 = b14*X1 ; MPRINT(DOWEIGHTEDGLOGIT):   expEta4 = EXP(eta4 + 0); MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 5; loop will not iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop beginning; index variable C; start value is 1; stop value is 4;       by value is 1. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is FALSE MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 2; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 3; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 4; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 5; loop will not iterate again. MPRINT(DOWEIGHTEDGLOGIT):   denominator = 1 + expEta2 + expEta3 + expEta4 ; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop beginning; index variable C; start value is 1; stop value is 4;       by value is 1. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is FALSE MPRINT(DOWEIGHTEDGLOGIT):   IF Y=1 THEN likelihood = 1/denominator; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 2; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MPRINT(DOWEIGHTEDGLOGIT):   IF Y=2 THEN likelihood = expEta2/denominator; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 3; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MPRINT(DOWEIGHTEDGLOGIT):   IF Y=3 THEN likelihood = expEta3/denominator; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 4; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MPRINT(DOWEIGHTEDGLOGIT):   IF Y=4 THEN likelihood = expEta4/denominator; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 5; loop will not iterate again. MPRINT(DOWEIGHTEDGLOGIT):   SumSquaredBetas = 0; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop beginning; index variable C; start value is 1; stop value is 4;       by value is 1. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is FALSE MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 2; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&numCovariates>1) is FALSE MPRINT(DOWEIGHTEDGLOGIT):   SumSquaredBetas = SumSquaredBetas + (b12)**2; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 3; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&numCovariates>1) is FALSE MPRINT(DOWEIGHTEDGLOGIT):   SumSquaredBetas = SumSquaredBetas + (b13)**2; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 4; loop will iterate again. MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&c ^= &Ref_Class) is TRUE MLOGIC(DOWEIGHTEDGLOGIT):  %IF condition %EVAL(&numCovariates>1) is FALSE MPRINT(DOWEIGHTEDGLOGIT):   SumSquaredBetas = SumSquaredBetas + (b14)**2; MLOGIC(DOWEIGHTEDGLOGIT):  %DO loop index variable C is now 5; loop will not iterate again. MPRINT(DOWEIGHTEDGLOGIT):   loglikelihood = W *LOG(likelihood) ; MPRINT(DOWEIGHTEDGLOGIT):   MODEL Y ~ GENERAL(loglikelihood); MPRINT(DOWEIGHTEDGLOGIT):   if Y = . then _ll = .; MPRINT(DOWEIGHTEDGLOGIT):   else if _est0 = . then _ll = .; MPRINT(DOWEIGHTEDGLOGIT):   else do; MPRINT(DOWEIGHTEDGLOGIT):   _ll = _est0; MPRINT(DOWEIGHTEDGLOGIT):   end; MPRINT(DOWEIGHTEDGLOGIT):   ODS OUTPUT PARAMETERESTIMATES=Weighted_GLogit_Coef CONVERGENCESTATUS=Weighted_Glogit_Convergence; MPRINT(DOWEIGHTEDGLOGIT):   RUN; NOTE: The parameters b12, X1, b13, b14 are assigned the default starting value of 1.0, because they       are not assigned initial values with the PARMS statement. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: At least one element of the gradient is greater than 1e-3. NOTE: Moore-Penrose inverse is used in covariance matrix. WARNING: The final Hessian matrix is full rank but has at least one negative eigenvalue. Second-order          optimality condition violated. NOTE: The data set WORK.WEIGHTED_GLOGIT_CONVERGENCE has 1 observations and 2 variables. NOTE: The data set WORK.WEIGHTED_GLOGIT_COEF has 4 observations and 10 variables. NOTE: PROCEDURE NLMIXED used (Total process time):       real time           18.07 seconds       cpu time            17.96 seconds     MPRINT(DOWEIGHTEDGLOGIT):   PROC PRINT DATA=Weighted_GLogit_Coef; MPRINT(DOWEIGHTEDGLOGIT):   RUN;   NOTE: There were 4 observations read from the data set WORK.WEIGHTED_GLOGIT_COEF. NOTE: PROCEDURE PRINT used (Total process time):       real time           0.01 seconds       cpu time            0.00 seconds     MPRINT(DOWEIGHTEDGLOGIT):   DATA Weighted_GLogit_Coef; MPRINT(DOWEIGHTEDGLOGIT):   SET Weighted_GLogit_Coef; MPRINT(DOWEIGHTEDGLOGIT):   Row = _N_; MPRINT(DOWEIGHTEDGLOGIT):   Covariate = INT((Row-1)/( 4-1))+1; MPRINT(DOWEIGHTEDGLOGIT):   Class = Row-(Covariate-1)*( 4-1); MPRINT(DOWEIGHTEDGLOGIT):   IF (Class=>1) THEN Class = Class + 1; MPRINT(DOWEIGHTEDGLOGIT):   DROP Row; MPRINT(DOWEIGHTEDGLOGIT):   Std_Err = .; MPRINT(DOWEIGHTEDGLOGIT):   KEEP Covariate Class Estimate Std_Err; MPRINT(DOWEIGHTEDGLOGIT):   RUN;   NOTE: There were 4 observations read from the data set WORK.WEIGHTED_GLOGIT_COEF. NOTE: The data set WORK.WEIGHTED_GLOGIT_COEF has 4 observations and 4 variables. NOTE: DATA statement used (Total process time):       real time           0.02 seconds       cpu time            0.03 seconds     MPRINT(DOWEIGHTEDGLOGIT):   PROC PRINT DATA=Weighted_GLogit_Coef; MPRINT(DOWEIGHTEDGLOGIT):   RUN;   NOTE: There were 4 observations read from the data set WORK.WEIGHTED_GLOGIT_COEF. NOTE: PROCEDURE PRINT used (Total process time):       real time           0.01 seconds       cpu time            0.00 seconds     MPRINT(DOWEIGHTEDGLOGIT):   PROC SORT DATA=Weighted_GLogit_Coef; MPRINT(DOWEIGHTEDGLOGIT):   BY Class Covariate; MPRINT(DOWEIGHTEDGLOGIT):   RUN;   NOTE: There were 4 observations read from the data set WORK.WEIGHTED_GLOGIT_COEF. NOTE: The data set WORK.WEIGHTED_GLOGIT_COEF has 4 observations and 4 variables. NOTE: PROCEDURE SORT used (Total process time):       real time           0.02 seconds       cpu time            0.03 seconds     MPRINT(DOWEIGHTEDGLOGIT):   PROC PRINT DATA=Weighted_GLogit_Coef; MPRINT(DOWEIGHTEDGLOGIT):   RUN;   NOTE: There were 4 observations read from the data set WORK.WEIGHTED_GLOGIT_COEF. NOTE: PROCEDURE PRINT used (Total process time):       real time           0.01 seconds       cpu time            0.00 seconds     MPRINT(DOWEIGHTEDGLOGIT):   DATA TempDatasetForMacro; MPRINT(DOWEIGHTEDGLOGIT):   SET ForAnalysisLong; MPRINT(DOWEIGHTEDGLOGIT):   BY actor_id; MPRINT(DOWEIGHTEDGLOGIT):   RETAIN BCHMacroIntID 0; MPRINT(DOWEIGHTEDGLOGIT):   IF first.actor_id THEN BCHMacroIntID = BCHMacroIntID + 1; MPRINT(DOWEIGHTEDGLOGIT):   OUTPUT; MPRINT(DOWEIGHTEDGLOGIT):   RUN;   NOTE: There were 629096 observations read from the data set WORK.FORANALYSISLONG. NOTE: The data set WORK.TEMPDATASETFORMACRO has 629096 observations and 20 variables. NOTE: DATA statement used (Total process time):       real time           0.49 seconds       cpu time            0.34 seconds     MPRINT(DOWEIGHTEDGLOGIT):   PROC IML; NOTE: IML Ready MPRINT(DOWEIGHTEDGLOGIT):   USE Weighted_GLogit_Coef; MPRINT(DOWEIGHTEDGLOGIT):   READ ALL VAR {Covariate Class Estimate}; MPRINT(DOWEIGHTEDGLOGIT):   CLOSE Weighted_GLogit_Coef; MPRINT(DOWEIGHTEDGLOGIT):   USE TempDatasetForMacro; MPRINT(DOWEIGHTEDGLOGIT):   READ ALL VAR {LC} INTO Y; MPRINT(DOWEIGHTEDGLOGIT):   READ ALL VAR {ClassWeight} INTO W; MPRINT(DOWEIGHTEDGLOGIT):   READ ALL VAR {_Intercept_ Other nonConfor AI Asian Hispanic genOther White age14to24 covid irflag} INTO XMatrix; ERROR: OTHER is not in the scope of variables for the data set.   statement : READ at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   READ ALL VAR {BCHMacroIntID} INTO ClusterId; MPRINT(DOWEIGHTEDGLOGIT):   READ ALL VAR {LC} INTO MemberID; MPRINT(DOWEIGHTEDGLOGIT):   CLOSE TempDatasetForMacro; MPRINT(DOWEIGHTEDGLOGIT):   p = NCOL(XMatrix); MPRINT(DOWEIGHTEDGLOGIT):   ntotal = NROW(Y); MPRINT(DOWEIGHTEDGLOGIT):   nsub = ClusterID[<>]; MPRINT(DOWEIGHTEDGLOGIT):   PRINT ClusterID; MPRINT(DOWEIGHTEDGLOGIT):   PRINT ntotal nsub; MPRINT(DOWEIGHTEDGLOGIT):   m = ntotal / nsub; MPRINT(DOWEIGHTEDGLOGIT):   n = ntotal / m; MPRINT(DOWEIGHTEDGLOGIT):   nc = Y[<>]; MPRINT(DOWEIGHTEDGLOGIT):   YAsMatrix = J(n*m,nc,0); MPRINT(DOWEIGHTEDGLOGIT):   DO c = 1 TO nc; MPRINT(DOWEIGHTEDGLOGIT):   IF SUM(Y=c)>0 THEN DO; MPRINT(DOWEIGHTEDGLOGIT):   whichrows = LOC(Y=c)`; MPRINT(DOWEIGHTEDGLOGIT):   YAsMatrix[whichrows,c] = 1; MPRINT(DOWEIGHTEDGLOGIT):   END; MPRINT(DOWEIGHTEDGLOGIT):   END; MPRINT(DOWEIGHTEDGLOGIT):   CoefAsVector = Estimate; MPRINT(DOWEIGHTEDGLOGIT):   ModelInformationMatrix = J((nc-1)*p,(nc-1)*p,0); ERROR: (execution) Invalid operand to operation.   operation : J at line 1655 column 1 operands  : _TEM1002, _TEM1004, *LIT1013   _TEM1002      1 row       1 col     (numeric)            0   _TEM1004      1 row       1 col     (numeric)            0   *LIT1013      1 row       1 col     (numeric)            0   statement : ASSIGN at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   TotalScoreVector = J((nc-1)*p,1,0); ERROR: (execution) Invalid operand to operation.   operation : J at line 1655 column 1 operands  : _TEM1002, *LIT1015, *LIT1016   _TEM1002      1 row       1 col     (numeric)            0   *LIT1015      1 row       1 col     (numeric)            1   *LIT1016      1 row       1 col     (numeric)            0   statement : ASSIGN at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   EmpiricalCovOfScore = J(p*(nc-1),p*(nc-1),0); ERROR: (execution) Invalid operand to operation.   operation : J at line 1655 column 1 operands  : _TEM1002, _TEM1004, *LIT1019   _TEM1002      1 row       1 col     (numeric)            0   _TEM1004      1 row       1 col     (numeric)            0   *LIT1019      1 row       1 col     (numeric)            0   statement : ASSIGN at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   CoefAsMatrix = SHAPE(CoefAsVector,nc-1)`; ERROR: (execution) Invalid operand to operation.   operation : SHAPE at line 1655 column 1 operands  : CoefAsVector, _TEM1001   CoefAsVector      4 rows      1 col     (numeric)   0.7460985 0.5490168 0.8688431 1.6429111   _TEM1001      1 row       1 col     (numeric)            3   statement : ASSIGN at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   ref_class = 1; MPRINT(DOWEIGHTEDGLOGIT):   IF (ref_class = 1) THEN DO; MPRINT(DOWEIGHTEDGLOGIT):   Nonref_classes = 2:nc; MPRINT(DOWEIGHTEDGLOGIT):   END; MPRINT(DOWEIGHTEDGLOGIT):   IF (ref_class = nc) THEN DO; MPRINT(DOWEIGHTEDGLOGIT):   Nonref_classes = 1:(nc-1); MPRINT(DOWEIGHTEDGLOGIT):   END; MPRINT(DOWEIGHTEDGLOGIT):   IF ((ref_class >1) & (ref_class < nc)) THEN DO; MPRINT(DOWEIGHTEDGLOGIT):   Nonref_classes = (1:(ref_class-1)) || ((ref_class+1):nc); MPRINT(DOWEIGHTEDGLOGIT):   END; MPRINT(DOWEIGHTEDGLOGIT):   DO i = 1 TO n; MPRINT(DOWEIGHTEDGLOGIT):   ScoreVector = J((nc-1)*p,1,0); MPRINT(DOWEIGHTEDGLOGIT):   DO j = 1 TO m; MPRINT(DOWEIGHTEDGLOGIT):   wij = w[LOC(ClusterID=i & MemberID=j)]; MPRINT(DOWEIGHTEDGLOGIT):   * Assumes there is only one such entry. ; MPRINT(DOWEIGHTEDGLOGIT):   yij = YasMatrix[LOC(ClusterID=i & MemberID=j),]`; MPRINT(DOWEIGHTEDGLOGIT):   xij = XMatrix[LOC(ClusterID=i & MemberID=j),]; MPRINT(DOWEIGHTEDGLOGIT):   eta = J(nc,1,0); MPRINT(DOWEIGHTEDGLOGIT):   eta[Nonref_classes] = xij*CoefAsMatrix; MPRINT(DOWEIGHTEDGLOGIT):   eta = (eta <> -20) >< +20; MPRINT(DOWEIGHTEDGLOGIT):   eta = eta + 0; MPRINT(DOWEIGHTEDGLOGIT):   expEta = EXP(eta); MPRINT(DOWEIGHTEDGLOGIT):   piij = expEta / expEta[+]; MPRINT(DOWEIGHTEDGLOGIT):   Mij = DIAG(piij[Nonref_classes]) - piij[Nonref_classes]*piij[Nonref_classes]`; MPRINT(DOWEIGHTEDGLOGIT):   BigXij = (I(nc-1) @ xij)`; MPRINT(DOWEIGHTEDGLOGIT):   ScoreVector = ScoreVector + wij*BigXij*(yij[Nonref_classes]-piij[Nonref_classes]); MPRINT(DOWEIGHTEDGLOGIT):   ModelInformationMatrix = ModelInformationMatrix + wij*BigXij*Mij*BigXij`; MPRINT(DOWEIGHTEDGLOGIT):   END; MPRINT(DOWEIGHTEDGLOGIT):   TotalScoreVector = TotalScoreVector + ScoreVector; MPRINT(DOWEIGHTEDGLOGIT):   EmpiricalCovOfScore = EmpiricalCovOfScore + ScoreVector*ScoreVector`; MPRINT(DOWEIGHTEDGLOGIT):   END; ERROR: (execution) Invalid operand to operation.   operation : J at line 1655 column 1 operands  : _TEM1002, *LIT1032, *LIT1033   _TEM1002      1 row       1 col     (numeric)            0   *LIT1032      1 row       1 col     (numeric)            1   *LIT1033      1 row       1 col     (numeric)            0   statement : ASSIGN at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   CoefAsVectorOld = CoefAsVector; MPRINT(DOWEIGHTEDGLOGIT):   lambda = 0*(VECDIAG(ModelInformationMatrix))[+]; ERROR: (execution) Matrix has not been set to a value.   operation : VECDIAG at line 1655 column 1 operands  : ModelInformationMatrix   ModelInformationMatrix      0 row       0 col     (type ?, size 0)     statement : ASSIGN at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   NaiveCovCoef = INV(ModelInformationMatrix+lambda*I(NROW(ModelInformationMatrix))); ERROR: (execution) Invalid operand to operation.   operation : I at line 1655 column 1 operands  : _TEM1001   _TEM1001      1 row       1 col     (numeric)            0   statement : ASSIGN at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   CoefAsVector = CoefAsVector + NaiveCovCoef*TotalScoreVector; ERROR: (execution) Matrix has not been set to a value.   operation : * at line 1655 column 1 operands  : NaiveCovCoef, TotalScoreVector   NaiveCovCoef      0 row       0 col     (type ?, size 0)     TotalScoreVector      0 row       0 col     (type ?, size 0)     statement : ASSIGN at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   MaxAbsDev = (ABS(CoefAsVector - CoefAsVectorOld))[<>]; MPRINT(DOWEIGHTEDGLOGIT):   CorrectionFactor = (n/(n-1))*((n*m-1)/(n*m-(nc-1)*p)); MPRINT(DOWEIGHTEDGLOGIT):   * In SAS PROC SURVEYLOGISTIC, the option VADJUST=NONE uses n/(n-1). * The option VADJUST=DF uses (n/(n-1))*((n*m-1)/(n*m-(nc-1)*p)). ; MPRINT(DOWEIGHTEDGLOGIT):   SandwichCov = NaiveCovCoef` * (CorrectionFactor*EmpiricalCovOfScore) * NaiveCovCoef; ERROR: (execution) Matrix has not been set to a value.   operation : ` at line 1655 column 1 operands  : NaiveCovCoef   NaiveCovCoef      0 row       0 col     (type ?, size 0)     statement : ASSIGN at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   IF (VECDIAG(SandwichCov)[><]>0) THEN DO; MPRINT(DOWEIGHTEDGLOGIT):   Std_Err = SQRT(VECDIAG(SandwichCov)); MPRINT(DOWEIGHTEDGLOGIT):   END; MPRINT(DOWEIGHTEDGLOGIT):   ELSE DO; MPRINT(DOWEIGHTEDGLOGIT):   Std_Err = J(NROW(SandwichCov),1,.); MPRINT(DOWEIGHTEDGLOGIT):   END; ERROR: (execution) Matrix has not been set to a value.   operation : VECDIAG at line 1655 column 1 operands  : SandwichCov   SandwichCov      0 row       0 col     (type ?, size 0)     statement : IF at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   EDIT Weighted_GLogit_Coef; MPRINT(DOWEIGHTEDGLOGIT):   REPLACE ALL VAR {Std_Err }; WARNING: All data set variables are unvalued. No REPLACE done.   statement : REPLACE at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   CLOSE Weighted_GLogit_Coef; NOTE: The data set WORK.WEIGHTED_GLOGIT_COEF has 4 observations and 4 variables. MPRINT(DOWEIGHTEDGLOGIT):   CREATE Weighted_GLogit_Sandwich_Cov FROM SandwichCov; ERROR: Matrix SandwichCov has not been set to a value.   statement : CREATE at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   APPEND FROM SandwichCov; ERROR: No data set is currently open for output.   statement : APPEND at line 1655 column 1 MPRINT(DOWEIGHTEDGLOGIT):   CLOSE Weighted_GLogit_Sandwich_Cov; NOTE: Cannot close WORK.WEIGHTED_GLOGIT_SANDWICH_COV; it is not open. MPRINT(DOWEIGHTEDGLOGIT):   QUIT; NOTE: Exiting IML. NOTE: The SAS System stopped processing this step because of errors. NOTE: PROCEDURE IML used (Total process time):       real time           0.73 seconds       cpu time            0.67 seconds   MPRINT(DOWEIGHTEDGLOGIT):   ODS EXCLUDE NONE; MPRINT(DOWEIGHTEDGLOGIT):   RUN; MPRINT(DOWEIGHTEDGLOGIT):   ODS RESULTS; MPRINT(DOWEIGHTEDGLOGIT):   RUN; MLOGIC(DOWEIGHTEDGLOGIT):  Ending execution. MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):  ;     MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   PROC IML; NOTE: IML Ready MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   USE Weighted_GLogit_Coef; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   READ ALL; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   CLOSE Weighted_GLogit_Coef; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   CovariateIndex = Covariate; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   NamesString = "_Intercept_ Other nonConfor AI Asian Hispanic genOther White age14to24 covid irflag"; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   NumCov = 1 + COUNT(NamesString," "); MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   Name = J(NROW(CovariateIndex),1,"        "); MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   DO i = 1 TO NumCov; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   Name[LOC(CovariateIndex=i)] = SCANQ(NamesString,i); MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   END; ERROR: (execution) Matrix has not been set to a value.   operation : [ at line 1655 column 1 operands  : Name, _TEM1002, _TEM1003   Name      4 rows      1 col     (character, size 34)   _Intercept_ Other _Intercept_ _Intercept_   _TEM1002      0 row       0 col     (type ?, size 0)     _TEM1003      1 row       1 col     (character, size 9)   nonConfor   statement : ASSIGN at line 1655 column 1 MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   Beta = Estimate; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   Exp_Beta = EXP(Beta); MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   Upper_CI_Beta = Beta + 1.96 * Std_Err; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   Lower_CI_Beta = Beta - 1.96 * Std_Err; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   Upper_CI_Exp_Beta = EXP(Upper_CI_Beta); MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   Lower_CI_Exp_Beta = EXP(Lower_CI_Beta); MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   Z = Estimate / Std_Err; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   p = 1 - PROBNORM(ABS(Z)); MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   CREATE Covariates_Macro_This_Group VAR {Name Class Beta Std_Err Z p Lower_CI_Beta Upper_CI_Beta Exp_Beta Lower_CI_Exp_Beta Upper_CI_Exp_Beta}; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   APPEND; MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   CLOSE Covariates_Macro_This_Group; NOTE: The data set WORK.COVARIATES_MACRO_THIS_GROUP has 4 observations and 11 variables. MPRINT(LCA_COVARIATES_3STEP_NO_GROUPS):   QUIT; NOTE: Exiting IML. NOTE: The SAS System stopped processing this step because of errors. NOTE: PROCEDURE IML used (Total process time):       real time           0.03 seconds       cpu time            0.01 seconds   MLOGIC(LCA_COVARIATES_3STEP_NO_GROUPS):  Ending execution. MPRINT(LCA_COVARIATES_3STEP):  ;     MPRINT(LCA_COVARIATES_3STEP):   DATA Covariates_Macro_Answers; MPRINT(LCA_COVARIATES_3STEP):   SET Covariates_Macro_This_Group; MPRINT(LCA_COVARIATES_3STEP):   RUN; NOTE: There were 4 observations read from the data set WORK.COVARIATES_MACRO_THIS_GROUP. NOTE: The data set WORK.COVARIATES_MACRO_ANSWERS has 4 observations and 11 variables. NOTE: DATA statement used (Total process time):       real time           0.02 seconds       cpu time            0.00 seconds     MPRINT(LCA_COVARIATES_3STEP):   DATA Sandwich_Covariance; MPRINT(LCA_COVARIATES_3STEP):   SET Weighted_GLogit_Sandwich_Cov; ERROR: File WORK.WEIGHTED_GLOGIT_SANDWICH_COV.DATA does not exist. MPRINT(LCA_COVARIATES_3STEP):   RUN;   NOTE: The SAS System stopped processing this step because of errors. WARNING: The data set WORK.SANDWICH_COVARIANCE may be incomplete.  When this step was stopped there          were 0 observations and 0 variables. WARNING: Data set WORK.SANDWICH_COVARIANCE was not replaced because this step was stopped. NOTE: DATA statement used (Total process time):       real time           0.01 seconds       cpu time            0.01 seconds       MPRINT(LCA_COVARIATES_3STEP):   DATA Covariates_Macro_Convergence; MPRINT(LCA_COVARIATES_3STEP):   SET Weighted_Glogit_Convergence; MPRINT(LCA_COVARIATES_3STEP):   RUN;   NOTE: There were 1 observations read from the data set WORK.WEIGHTED_GLOGIT_CONVERGENCE. NOTE: The data set WORK.COVARIATES_MACRO_CONVERGENCE has 1 observations and 2 variables. NOTE: DATA statement used (Total process time):       real time           0.02 seconds       cpu time            0.00 seconds     MPRINT(LCA_COVARIATES_3STEP):   DATA Covariates_Macro_Answers; MPRINT(LCA_COVARIATES_3STEP):   SET Covariates_Macro_Answers; MPRINT(LCA_COVARIATES_3STEP):   WHERE Class IS NOT MISSING; MPRINT(LCA_COVARIATES_3STEP):   RUN;   NOTE: There were 4 observations read from the data set WORK.COVARIATES_MACRO_ANSWERS.       WHERE Class is not null; NOTE: The data set WORK.COVARIATES_MACRO_ANSWERS has 4 observations and 11 variables. NOTE: DATA statement used (Total process time):       real time           0.02 seconds       cpu time            0.03 seconds     MLOGIC(LCA_COVARIATES_3STEP):  %IF condition %EVAL(&there_are_groups=1) is FALSE MPRINT(LCA_COVARIATES_3STEP):   TITLE "Beta Estimates from LCA_Covariates_3Step Macro"; MPRINT(LCA_COVARIATES_3STEP):   PROC PRINT DATA=Covariates_Macro_Answers NOOBS; MPRINT(LCA_COVARIATES_3STEP):   VAR Class Name Beta Std_Err Z P; MPRINT(LCA_COVARIATES_3STEP):   RUN;   NOTE: There were 4 observations read from the data set WORK.COVARIATES_MACRO_ANSWERS. NOTE: PROCEDURE PRINT used (Total process time):       real time           0.02 seconds       cpu time            0.01 seconds     MPRINT(LCA_COVARIATES_3STEP):   TITLE "Approximate 95% Confidence Intervals for Beta Coefficients"; MPRINT(LCA_COVARIATES_3STEP):   PROC PRINT DATA=Covariates_Macro_Answers NOOBS; MPRINT(LCA_COVARIATES_3STEP):   VAR Class Name Beta Lower_CI_Beta Upper_CI_Beta; MPRINT(LCA_COVARIATES_3STEP):   RUN;   NOTE: There were 4 observations read from the data set WORK.COVARIATES_MACRO_ANSWERS. NOTE: PROCEDURE PRINT used (Total process time):       real time           0.01 seconds       cpu time            0.00 seconds     MPRINT(LCA_COVARIATES_3STEP):   TITLE "Exponentiated Betas (Odds and Odds Ratios) and Approximate 95% Confidence Intervals"; MPRINT(LCA_COVARIATES_3STEP):   PROC PRINT DATA=Covariates_Macro_Answers NOOBS; MPRINT(LCA_COVARIATES_3STEP):   VAR Class Name Exp_Beta Lower_CI_Exp_Beta Upper_CI_Exp_Beta; MPRINT(LCA_COVARIATES_3STEP):   RUN;   NOTE: There were 4 observations read from the data set WORK.COVARIATES_MACRO_ANSWERS. NOTE: PROCEDURE PRINT used (Total process time):       real time           0.01 seconds       cpu time            0.03 seconds     MPRINT(LCA_COVARIATES_3STEP):   TITLE; MPRINT(LCA_COVARIATES_3STEP):   PROC DATASETS NOLIST NODETAILS NOWARN; MPRINT(LCA_COVARIATES_3STEP):   DELETE BCHTemp1 Covariates_Macro_This_Group DataForNlmixed ForAnalysis ForAnalysisLong TempDatasetForMacro Weighted_GLogit_Coef Weighted_GLogit_Sandwich_Cov Weighted_Glogit_Convergence ; MPRINT(LCA_COVARIATES_3STEP):   QUIT;   NOTE: Deleting WORK.BCHTEMP1 (memtype=DATA). NOTE: Deleting WORK.COVARIATES_MACRO_THIS_GROUP (memtype=DATA). NOTE: Deleting WORK.DATAFORNLMIXED (memtype=DATA). NOTE: Deleting WORK.FORANALYSIS (memtype=DATA). NOTE: Deleting WORK.FORANALYSISLONG (memtype=DATA). NOTE: Deleting WORK.TEMPDATASETFORMACRO (memtype=DATA). NOTE: Deleting WORK.WEIGHTED_GLOGIT_COEF (memtype=DATA). NOTE: Deleting WORK.WEIGHTED_GLOGIT_CONVERGENCE (memtype=DATA). NOTE: PROCEDURE DATASETS used (Total process time):       real time           0.05 seconds       cpu time            0.04 seconds     MLOGIC(LCA_COVARIATES_3STEP):  Ending execution. 1658  *Covariates = Male Other nonConfor AI Asian Hispanic Other White GenOther age14to24 covid irflag 1659   **code calling the 2nd macro w   ... View more

I sincerely appreciate all contributions made in this thread. I really need to master running macros.    I went back to the drawing board and made the changes suggested. I ran the code and didn't receive any errors, but didn't receive output.    *******************************************************Here is the code I ran:   %INCLUDE "E:\SecureData\CTL\LCA Manuscript\3step_macro\LCA_Covariates_3Step_v10.sas"; %LCA_Covariates_3Step ( input_data = LCA.finalCO19, postprobs = LCA.my_post4, id = Actor_ID, covariates = Gender_name_Male Gender_name_Other Gender_name_nonConforming Race_name_AI Race_name_Asian Race_name_Hispanic Race_name_Other Race_name_White active_rescue age2_14to24 covid ir_flag, );   *******************************************************And I was trying to map to this macro: %MACRO LCA_Covariates_3Step(postprobs,         id,         Covariates,         Groups=,         Assignment=Modal,         Adjustment=BCH,         ref_class = 1,         sampling_weight=,         automatically_add_intercept = 1     );         TITLE "Using LCA_Covariates_3Step Macro";     %IF %TRIM("&groups")="" %THEN %DO;         %LET there_are_groups = 0;     %END; %ELSE %DO;         %LET there_are_groups = 1;     %END;           %IF %EVAL(&there_are_groups=1) %THEN %DO;         DATA Covariates_Macro_Answers; RUN;         /* Sort the data according to group, and separate into multiple datasets. */         PROC IML;             USE &postprobs;                 READ ALL VAR {&Groups} INTO Group;             CLOSE &postprobs;         nGroups = MAX(Group);         CALL SYMPUT("nGroups",CHAR(nGroups));                    /* the total number of groups; */          %DO this_group=1 %TO &nGroups;                         DATA _subset_post_group&this_group;                 SET &postprobs;                 IF &Groups=&this_group;             RUN;             %LCA_Covariates_3Step_No_Groups(postprobs=_subset_post_group&this_group,                 id=&id,                 Covariates=&Covariates,                 Assignment=&Assignment,                 Adjustment=&Adjustment,                 ref_class=&ref_class,                 sampling_weight=&sampling_weight,                 automatically_add_intercept=&automatically_add_intercept             );                DATA Covariates_Macro_This_Group;                 SET Covariates_Macro_This_Group;                 Group = &this_group;             RUN;             DATA Covariates_Macro_Answers;                 SET Covariates_Macro_Answers Covariates_Macro_This_Group;             RUN;             DATA Sandwich_Covariance_Grp&this_group;                 SET Weighted_GLogit_Sandwich_Cov;             RUN;             DATA Covariates_Macro_Converge_Grp&this_group;                 SET Weighted_Glogit_Convergence;             RUN;             PROC DATASETS NOLIST NODETAILS NOWARN;                 DELETE _subset_post_group&this_group;             QUIT;         %END;         %END; %ELSE %DO;         %LCA_Covariates_3Step_No_Groups(postprobs=&postprobs,             id=&id,             Covariates=&Covariates,             Assignment=&Assignment,             Adjustment=&Adjustment,             ref_class=&ref_class,             sampling_weight=&sampling_weight,             automatically_add_intercept=&automatically_add_intercept         );            DATA Covariates_Macro_Answers;             SET Covariates_Macro_This_Group;         RUN;                     DATA Sandwich_Covariance;             SET Weighted_GLogit_Sandwich_Cov;         RUN;         DATA Covariates_Macro_Convergence;             SET Weighted_Glogit_Convergence;         RUN;                           %END;     DATA Covariates_Macro_Answers;         SET Covariates_Macro_Answers;         WHERE Class IS NOT MISSING;     RUN;     %IF %EVAL(&there_are_groups=1) %THEN %DO;         TITLE "Beta Estimates from LCA_Covariates_3Step Macro";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Group Class Name Beta Std_Err Z P;         RUN;         TITLE "Approximate 95% Confidence Intervals for Beta Coefficients";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Group Class Name Beta Lower_CI_Beta Upper_CI_Beta;         RUN;         TITLE "Exponentiated Betas (Odds and Odds Ratios) and Approximate 95% Confidence Intervals";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Group Class Name Exp_Beta Lower_CI_Exp_Beta Upper_CI_Exp_Beta;         RUN;     %END; %ELSE %DO;                  TITLE "Beta Estimates from LCA_Covariates_3Step Macro";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Class Name Beta Std_Err Z P;         RUN;         TITLE "Approximate 95% Confidence Intervals for Beta Coefficients";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Class Name Beta Lower_CI_Beta Upper_CI_Beta;         RUN;         TITLE "Exponentiated Betas (Odds and Odds Ratios) and Approximate 95% Confidence Intervals";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Class Name Exp_Beta Lower_CI_Exp_Beta Upper_CI_Exp_Beta;         RUN;     %END;     TITLE;     PROC DATASETS NOLIST NODETAILS NOWARN;         DELETE BCHTemp1                Covariates_Macro_This_Group DataForNlmixed                ForAnalysis ForAnalysisLong TempDatasetForMacro                Weighted_GLogit_Coef                Weighted_GLogit_Sandwich_Cov                Weighted_Glogit_Convergence                ;     QUIT; %MEND LCA_Covariates_3Step;   *******************************************************Here is what the log said: 1671  OPTIONS  MPRINT MLOGIC; 1672  %INCLUDE "E:\SecureData\CTL\LCA Manuscript\3step_macro\LCA_Covariates_3Step_v10.sas"; 1673  %LCA_Covariates_3Step ( 1674      input_data = LCA.finalCO19, 1675      postprobs = LCA.my_post4, 1676      id = Actor_ID, 1677      covariates = Gender_name_Male Gender_name_Other Gender_name_nonConforming Race_name_AI 1677! Race_name_Asian Race_name_Hispanic Race_name_Other Race_name_White active_rescue age2_14to24 1677! covid ir_flag, 1678  ); ... View more

Macros are difficult for me. Even if I rename the macro to reflect the first name of the macro within the file, I do not get output???   %INCLUDE "E:\SecureData\CTL\LCA Manuscript\3step_macro\LCA_Covariates_3Step.sas"; %LCA_Covariates_3Step(input_data=LCA.finalCO19, postprobs = LCA.my_post4, id = Actor_ID, covariates = Gender_name_Male Gender_name_Other Gender_name_nonConforming Race_name_AI Race_name_Asian Race_name_Hispanic Race_name_Other Race_name_White active_rescue age2_14to24 covid ir_flag, adjustment = BCH, assignment = modal, ref_class = 1, automatically_add_intercept = 1 ) ; ... View more

Ah, got it. I was under the impression that by directing SAS to read the macro code from the path using a SAS %INCLUDE that all code within the larger macro file would automatically run. Is there not a way to do that?  ... View more

Thanks for your quick response. I bet you're correct in that I haven't yet executed the macro definition in the current session. How do I do this?    I have an LCA macro saved in  %INCLUDE "E:\SecureData\CTL\LCA Manuscript\3step_macro\LCA_Covariates_3Step_v10.sas"; that looks like the following:     %MACRO LCA_Covariates_3Step(postprobs,         id,         Covariates,         Groups=,         Assignment=Modal,         Adjustment=BCH,         ref_class = 1,         sampling_weight=,         automatically_add_intercept = 1     );         TITLE "Using LCA_Covariates_3Step Macro";     %IF %TRIM("&groups")="" %THEN %DO;         %LET there_are_groups = 0;     %END; %ELSE %DO;         %LET there_are_groups = 1;     %END;           %IF %EVAL(&there_are_groups=1) %THEN %DO;         DATA Covariates_Macro_Answers; RUN;         /* Sort the data according to group, and separate into multiple datasets. */         PROC IML;             USE &postprobs;                 READ ALL VAR {&Groups} INTO Group;             CLOSE &postprobs;         nGroups = MAX(Group);         CALL SYMPUT("nGroups",CHAR(nGroups));                    /* the total number of groups; */          %DO this_group=1 %TO &nGroups;                         DATA _subset_post_group&this_group;                 SET &postprobs;                 IF &Groups=&this_group;             RUN;             %LCA_Covariates_3Step_No_Groups(postprobs=_subset_post_group&this_group,                 id=&id,                 Covariates=&Covariates,                 Assignment=&Assignment,                 Adjustment=&Adjustment,                 ref_class=&ref_class,                 sampling_weight=&sampling_weight,                 automatically_add_intercept=&automatically_add_intercept             );                DATA Covariates_Macro_This_Group;                 SET Covariates_Macro_This_Group;                 Group = &this_group;             RUN;             DATA Covariates_Macro_Answers;                 SET Covariates_Macro_Answers Covariates_Macro_This_Group;             RUN;             DATA Sandwich_Covariance_Grp&this_group;                 SET Weighted_GLogit_Sandwich_Cov;             RUN;             DATA Covariates_Macro_Converge_Grp&this_group;                 SET Weighted_Glogit_Convergence;             RUN;             PROC DATASETS NOLIST NODETAILS NOWARN;                 DELETE _subset_post_group&this_group;             QUIT;         %END;         %END; %ELSE %DO;         %LCA_Covariates_3Step_No_Groups(postprobs=&postprobs,             id=&id,             Covariates=&Covariates,             Assignment=&Assignment,             Adjustment=&Adjustment,             ref_class=&ref_class,             sampling_weight=&sampling_weight,             automatically_add_intercept=&automatically_add_intercept         );            DATA Covariates_Macro_Answers;             SET Covariates_Macro_This_Group;         RUN;                     DATA Sandwich_Covariance;             SET Weighted_GLogit_Sandwich_Cov;         RUN;         DATA Covariates_Macro_Convergence;             SET Weighted_Glogit_Convergence;         RUN;                           %END;     DATA Covariates_Macro_Answers;         SET Covariates_Macro_Answers;         WHERE Class IS NOT MISSING;     RUN;     %IF %EVAL(&there_are_groups=1) %THEN %DO;         TITLE "Beta Estimates from LCA_Covariates_3Step Macro";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Group Class Name Beta Std_Err Z P;         RUN;         TITLE "Approximate 95% Confidence Intervals for Beta Coefficients";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Group Class Name Beta Lower_CI_Beta Upper_CI_Beta;         RUN;         TITLE "Exponentiated Betas (Odds and Odds Ratios) and Approximate 95% Confidence Intervals";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Group Class Name Exp_Beta Lower_CI_Exp_Beta Upper_CI_Exp_Beta;         RUN;     %END; %ELSE %DO;                  TITLE "Beta Estimates from LCA_Covariates_3Step Macro";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Class Name Beta Std_Err Z P;         RUN;         TITLE "Approximate 95% Confidence Intervals for Beta Coefficients";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Class Name Beta Lower_CI_Beta Upper_CI_Beta;         RUN;         TITLE "Exponentiated Betas (Odds and Odds Ratios) and Approximate 95% Confidence Intervals";         PROC PRINT DATA=Covariates_Macro_Answers NOOBS;             VAR Class Name Exp_Beta Lower_CI_Exp_Beta Upper_CI_Exp_Beta;         RUN;     %END;     TITLE;     PROC DATASETS NOLIST NODETAILS NOWARN;         DELETE BCHTemp1                Covariates_Macro_This_Group DataForNlmixed                ForAnalysis ForAnalysisLong TempDatasetForMacro                Weighted_GLogit_Coef                Weighted_GLogit_Sandwich_Cov                Weighted_Glogit_Convergence                ;     QUIT; %MEND LCA_Covariates_3Step;   %MACRO LCA_Covariates_3Step_No_Groups(postprobs,     id,     Covariates,     Assignment,     Adjustment,     ref_class,     sampling_weight,     automatically_add_intercept     );          /******************************************************************************/     /***** Count the number of classes in the model using the postprobs dataset ***/     /******************************************************************************/      DATA bchtemp1; SET &postprobs; RUN;         PROC TRANSPOSE DATA=&postprobs OUT=TemporaryDatasetForBCHmacro;     RUN;     PROC IML;         USE TemporaryDatasetForBCHmacro;             READ ALL VAR {_NAME_} INTO name;         CLOSE TemporaryDatasetForBCHmacro;         nClasses = (SUM(SUBSTR(UPCASE(name),1,6)="POSTLC")); /* number of classes */         CALL SYMPUT("nClasses",CHAR(nClasses));     QUIT;     PROC DATASETS NOLIST NOWARN NOPRINT;         DELETE TemporaryDatasetForBCHmacro;     RUN;     %put _all_;     /******************************************************************************/     /***** Construct BCH-adjusted class weights to represent class membership *****/     /******************************************************************************/         %CalculateClass_Weights( PostProbsDataset=&postprobs,                             SubjectID=&id,                             nClasses=&nClasses,                             Sampling_Weight=&sampling_weight,                             Assignment=&Assignment,                              Adjustment=&Adjustment);             /* Outputs datasets Class_Weights and Class_Weights_Long. */     /*******************************************************************************/     /***** Append class weights to the dataset for analysis. ***********************/     /*******************************************************************************/     DATA ForAnalysis;         MERGE &postprobs Class_Weights;         BY &id;     RUN;     /*******************************************************************************/     /***** Add an intercept column to the covariates if appropriate ****************/     /*******************************************************************************/        %IF %EVAL(&automatically_add_intercept>0) %THEN %DO;         DATA ForAnalysis;             SET ForAnalysis;             _Intercept_ = 1;         RUN;         %LET Covariates = _Intercept_ &Covariates;     %END;     /*******************************************************************************/     /***** Create long form of dataset with one row per class per participant. *****/     /*******************************************************************************/     DATA ForAnalysisLong;         SET ForAnalysis;         %DO class = 1 %TO &nClasses;             LC = &class;    ClassWeight = ClassWeightLC&class; OUTPUT;         %END;         DROP %DO class = 1 %TO &nClasses;                     ClassWeightLC&class             %END;         ;     RUN;     /*******************************************************************************/     /***** Estimate the parameters for the underlying parametric weighted **********/     /***** multinomial generalized logistic regression.                   **********/     /*******************************************************************************/          %DoWeightedGLogit(InputDataset=ForAnalysisLong,                         OutcomeClass=LC,                         ref_class=&ref_class,                         Weight=ClassWeight,                         Covariates=&Covariates,                         SubjectID=&id,                         ObservationID=LC );              /* Outputs datasets Weighted_GLogit_Coef and Weighted_GLogit_Sandwich_Cov. */     PROC IML;         USE Weighted_GLogit_Coef;             READ ALL;         CLOSE Weighted_GLogit_Coef;         CovariateIndex = Covariate;         NamesString = "&Covariates";         NumCov = 1 + COUNT(NamesString," ");         Name = J(NROW(CovariateIndex),1,"                                  ");         DO i = 1 TO NumCov;             Name[LOC(CovariateIndex=i)] = SCANQ(NamesString,i);         END;         Beta = Estimate;         Exp_Beta = EXP(Beta);         Upper_CI_Beta = Beta + 1.96 * Std_Err;         Lower_CI_Beta = Beta - 1.96 * Std_Err;         Upper_CI_Exp_Beta = EXP(Upper_CI_Beta);         Lower_CI_Exp_Beta = EXP(Lower_CI_Beta);         Z = Estimate / Std_Err;         p = 1 - PROBNORM(ABS(Z));         CREATE Covariates_Macro_This_Group VAR {Name Class Beta Std_Err Z p                                              Lower_CI_Beta Upper_CI_Beta                                              Exp_Beta Lower_CI_Exp_Beta Upper_CI_Exp_Beta};             APPEND;         CLOSE Covariates_Macro_This_Group;     QUIT; %MEND LCA_Covariates_3Step_No_Groups; %MACRO DoWeightedGLogit(InputDataset=,                     OutcomeClass=,                          ref_class=1,                     Weight=,                     Covariates=,                     SubjectID=,                          ObservationID=,                     RegularizationConstantExp=0,                     RegularizationConstantForCov=0,                          RegularizationConstantForSE=0);     PROC IML;         USE &InputDataset;              READ ALL VAR {&OutcomeClass} INTO Y;             READ ALL VAR {&Weight} INTO W;             READ ALL VAR {&Covariates} INTO XMatrix;             READ ALL VAR {&SubjectID} INTO id;         CLOSE &InputDataset;         numCovariates  = NCOL(XMatrix);         numClasses = Y[<>];         CALL SYMPUT("numCovariates",(CHAR(numCovariates)));         CALL SYMPUT("numClasses",(CHAR(numClasses)));         DataForNlmixedAsMatrix = id || W || Y || XMatrix;         VariableNames = "id" || "W" || "Y" || ("X1":"X&numCovariates");          CREATE DataForNlmixed FROM DataForNlmixedAsMatrix [COLNAME=VariableNames];             APPEND FROM DataForNlmixedAsMatrix ;         CLOSE DataForNlmixed;     QUIT;         ODS EXCLUDE ALL; RUN;     ODS NORESULTS; RUN;     PROC NLMIXED DATA=DataForNlmixed COV START HESS METHOD=GAUSS TECH=NRRIDG MAXITER=500 MAXFUNC=1000;         PARMS %DO k = 1 %TO %EVAL(&numCovariates);                 %DO c = 1 %TO %EVAL(&numClasses);                          %IF %EVAL(&c ^= &Ref_Class) %THEN %DO;                          b&k&c = 0                           %END;                 %END;               %END; ;         %DO c = 1 %TO %EVAL(&numClasses);                %IF %EVAL(&c ^= &Ref_Class) %THEN %DO;             eta&c = b1&c*X1                     %IF %EVAL(&numCovariates>1) %THEN %DO;                         %DO  k = 2 %TO &numCovariates;                             + b&k&c*X&k                         %END;                     %END;                   ;             expEta&c = EXP(eta&c + &RegularizationConstantExp);                %END; %ELSE %DO;              eta&c = 0;                %END;         %END;         denominator = 1                 %DO c = 1 %TO %EVAL(&numClasses);                          %IF %EVAL(&c ^= &Ref_Class) %THEN %DO;                           + expEta&c                     %END;             %END; ;         %DO c = 1 %TO %EVAL(&numClasses);                %IF %EVAL(&c ^= &Ref_Class) %THEN %DO;                  IF Y=&c THEN likelihood = expEta&c/denominator;                %END; %ELSE %DO;                   IF Y=&c THEN likelihood = 1/denominator;                %END;         %END;           SumSquaredBetas = 0;         %DO c = 1 %TO %EVAL(&numClasses);                %IF %EVAL(&c ^= &Ref_Class) %THEN %DO;                SumSquaredBetas = SumSquaredBetas + (b1&c)**2                     %IF %EVAL(&numCovariates>1) %THEN %DO;                         %DO  k = 2 %TO &numCovariates;                             + (b&k&c)**2                         %END;                     %END;;                %END;         %END;         loglikelihood = W *LOG(likelihood) ;         MODEL Y ~ GENERAL(loglikelihood);         ODS OUTPUT PARAMETERESTIMATES=Weighted_GLogit_Coef                      CONVERGENCESTATUS=Weighted_Glogit_Convergence;      RUN;      PROC PRINT DATA=Weighted_GLogit_Coef; RUN;     DATA Weighted_GLogit_Coef;         SET Weighted_GLogit_Coef;         Row = _N_;         Covariate = INT((Row-1)/(&numClasses-1))+1;         Class = Row-(Covariate-1)*(&numClasses-1);           IF (Class=>&ref_class) THEN Class = Class + 1; /*skip reference class */         DROP Row;         Std_Err = .;         KEEP Covariate Class Estimate Std_Err;     RUN;     PROC PRINT DATA=Weighted_GLogit_Coef; RUN;     PROC SORT DATA=Weighted_GLogit_Coef;         BY Class Covariate;     RUN;     PROC PRINT DATA=Weighted_GLogit_Coef; RUN;         DATA TempDatasetForMacro; SET &InputDataset;         BY &SubjectID;         RETAIN BCHMacroIntID 0;         IF first.&SubjectID THEN BCHMacroIntID = BCHMacroIntID + 1;             /* This creates a recoded version of the subject ID variable,                called "BCHMacroIntID," that is composed of consecutive                integers starting with 1.  This makes it possible for some                of the code in the macro to be simpler than if subjects could                have anything for ID's.              See http://www.ats.ucla.edu/stat/sas/faq/enumerate_id.htm; */         OUTPUT;     RUN;         PROC IML;          /* Might be able to improve memory efficiency using the FREE command            or the SYMSIZE and WORKSIZE options? */         USE  Weighted_GLogit_Coef;             READ ALL VAR {Covariate Class Estimate};         CLOSE Weighted_GLogit_Coef;           USE TempDatasetForMacro;             READ ALL VAR {&OutcomeClass} INTO Y;             READ ALL VAR {&Weight} INTO W;             READ ALL VAR {&Covariates} INTO XMatrix;             READ ALL VAR {BCHMacroIntID} INTO ClusterId;                READ ALL VAR {&ObservationID} INTO MemberID;         CLOSE TempDatasetForMacro;         p  = NCOL(XMatrix);         ntotal = NROW(Y);         nsub = ClusterID[<>];  /* assumes indexing starts at 1 for this variable */         PRINT ClusterID;         PRINT ntotal nsub;         m = ntotal / nsub;  /* assumes equal cluster sizes */         n = ntotal / m; /* assumes equal cluster sizes */         nc = Y[<>];  /* Number of classes.   */         YAsMatrix = J(n*m,nc,0);         DO c = 1 TO nc;             IF SUM(Y=c)>0 THEN DO;                 whichrows = LOC(Y=c)`;                 YAsMatrix[whichrows,c] = 1;             END;         END;         CoefAsVector = Estimate;         ModelInformationMatrix = J((nc-1)*p,(nc-1)*p,0);         TotalScoreVector = J((nc-1)*p,1,0);         EmpiricalCovOfScore = J(p*(nc-1),p*(nc-1),0);         CoefAsMatrix = SHAPE(CoefAsVector,nc-1)`;         ref_class = &ref_class;                   IF (ref_class = 1) THEN DO;              Nonref_classes = 2:nc;         END;         IF (ref_class = nc) THEN DO;              Nonref_classes = 1:(nc-1);         END;         IF ((ref_class >1) & (ref_class < nc)) THEN DO;             Nonref_classes = (1:(ref_class-1)) || ((ref_class+1):nc);         END;         DO i = 1 TO n;             ScoreVector = J((nc-1)*p,1,0);             DO j = 1 TO m;                                  wij = w[LOC(ClusterID=i & MemberID=j)]; * Assumes there is only one such entry. ;                 yij = YasMatrix[LOC(ClusterID=i & MemberID=j),]`;                 xij = XMatrix[LOC(ClusterID=i & MemberID=j),];                 eta = J(nc,1,0);                 eta[Nonref_classes] =  xij*CoefAsMatrix;                 eta = (eta <> -20) >< +20;                 eta = eta + &RegularizationConstantForSE;                 expEta = EXP(eta);                 piij = expEta / expEta[+];                 Mij = DIAG(piij[Nonref_classes]) - piij[Nonref_classes]*piij[Nonref_classes]`;                                 BigXij = (I(nc-1) @ xij)`;                 ScoreVector = ScoreVector + wij*BigXij*(yij[Nonref_classes]-piij[Nonref_classes]);                 ModelInformationMatrix = ModelInformationMatrix + wij*BigXij*Mij*BigXij`;             END;             TotalScoreVector = TotalScoreVector + ScoreVector;             EmpiricalCovOfScore = EmpiricalCovOfScore + ScoreVector*ScoreVector`;         END;                  CoefAsVectorOld = CoefAsVector;          lambda = &RegularizationConstantForcov*(VECDIAG(ModelInformationMatrix))[+];           NaiveCovCoef = INV(ModelInformationMatrix+lambda*I(NROW(ModelInformationMatrix)));         CoefAsVector = CoefAsVector + NaiveCovCoef*TotalScoreVector;         MaxAbsDev = (ABS(CoefAsVector - CoefAsVectorOld))[<>];         CorrectionFactor = (n/(n-1))*((n*m-1)/(n*m-(nc-1)*p));          * In SAS PROC SURVEYLOGISTIC, the option VADJUST=NONE uses n/(n-1).          *   The option VADJUST=DF uses (n/(n-1))*((n*m-1)/(n*m-(nc-1)*p)). ;         SandwichCov = NaiveCovCoef` * (CorrectionFactor*EmpiricalCovOfScore) * NaiveCovCoef;         IF (VECDIAG(SandwichCov)[><]>0) THEN DO;                       Std_Err  = SQRT(VECDIAG(SandwichCov));           END; ELSE DO;               Std_Err = J(NROW(SandwichCov),1,.);           END;         EDIT Weighted_GLogit_Coef;             REPLACE ALL VAR {Std_Err };         CLOSE Weighted_GLogit_Coef;         CREATE Weighted_GLogit_Sandwich_Cov FROM SandwichCov;             APPEND FROM SandwichCov;         CLOSE Weighted_GLogit_Sandwich_Cov;     QUIT;     ODS EXCLUDE NONE; RUN;     ODS RESULTS; RUN; %MEND DoWeightedGLogit; %MACRO CalculateClass_Weights(PostProbsDataset,                              SubjectID,                                 nClasses,                              Sampling_Weight,                              Assignment,  /* modal or proportional */                              Adjustment  /* BCH or none */                              );     PROC IML;               Assignment = LOWCASE(TRIM("&Assignment"));          Adjustment = LOWCASE(TRIM("&Adjustment"));           IF (Assignment^="modal" & Assignment^="proportional") THEN DO;                PRINT ("Assignment option not recognized:");                PRINT(Assignment);           END;           IF (Adjustment^="bch" & Adjustment^="none") THEN DO;                PRINT ("Adjustment option not recognized:");                PRINT(Adjustment);           END;         /* Calculate BCH weights.*/             USE &PostProbsDataset;             READ ALL VAR {&SubjectID                             %DO class = 1 %TO &nClasses;                                 PostLC&class                             %END;                           &sampling_weight Best};         CLOSE &PostProbsDataset;         PostLC = PostLC1     %DO class = 2 %TO &nClasses;                         || PostLC&class                     %END;  ;  /* concatenate the vectors into a matrix */           IsBestLC = (Best=1)      %DO class = 2 %TO &nClasses;                               || (Best=&class)                          %END; ;           UnadjustedWeights = J(NROW(IsBestLC),&nClasses,.);           IF (Assignment="modal") THEN DO;                UnadjustedWeights = IsBestLC;           END; ELSE DO;                UnadjustedWeights = PostLC;           END;                                    IF (LENGTH(LOWCASE(TRIM("_&sampling_weight")))>1) THEN DO;                 DO class = 1 TO &nClasses;                     UnadjustedWeights[,class] = (&sampling_weight + 0)#UnadjustedWeights[,class];                             /* adding zero is a weird work-around to get around a macro language limitation*/                END;           END; ELSE DO;           END;           IF (Adjustment="bch") THEN DO;                 D = J(&nClasses,&nClasses,.);                 /* Based on Bolck, Croon and Hagenaars (2004, Political                     Analysis), Vermunt (2010, Political Analysis) and Bakk and Vermunt (2016,                     Structural Equation Modeling), we will define D so that D(t,s) is the                     probability of being assigned to class s, for a subject whose                     true class is t. */                DO t = 1 TO &nClasses;                     DO s = 1 TO &nClasses;                          D[t,s] = ((PostLC[,t] # UnadjustedWeights[,s])[+]) / (PostLC[+,t]);                     END;                END;                 Class_Weights = UnadjustedWeights * INV(D);                 /* These are the BCH weights.  */           END; ELSE DO;                Class_Weights = UnadjustedWeights;           END;            %DO class = 1 %TO &nClasses;                ClassWeightLC&class = Class_Weights[,&class];           %END;         CREATE Class_Weights VAR {&SubjectID %DO class = 1 %TO &nClasses;                 ClassWeightLC&class                  %END;};             APPEND ;         CLOSE Class_Weights;     QUIT;      DATA Class_Weights_Long;           SET Class_Weights;          %DO class = 1 %TO &nClasses;                Class = &class;                ClassWeight = ClassWeightLC&class;                OUTPUT;           %END;               KEEP &SubjectID Class ClassWeight;      RUN; %MEND CalculateClass_Weights;     ... 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