The following MX script lines were read for group 1 #NGROUPS 1 Note: #NGroup set number of groups to 1 #DEFINE NVAR 4 #DEFINE NFAC 2 #DEFINE NCOV 2 LINEAR UNCONDITIONAL ANTISOCIAL MODEL DATA NINPUT=11 NOBSERVATIONS=221 RECTANGULAR FILE=E:\ANTI\ANTIREAD.DAT NOTE: Rectangular file contained 221 records with data LABELS ANTI1 ANTI2 ANTI3 ANTI4 READ1 READ2 READ3 READ4 GEN HOMECOG ID SELECT GEN HOMECOG ANTI1 ANTI2 ANTI3 ANTI4 ; DEFINITION GEN HOMECOG; NOTE: Selection yields 221 data vectors for analysis NOTE: Vectors contain a total of 1326 observations BEGIN MATRICES; NOTE: Definition yields 221 data vectors for analysis NOTE: Vectors contain a total of 884 observations D DIAG 1 1 FREE ! ERROR VARIANCE S SYMM NFAC NFAC FREE ! FACTOR VARIANCE/COVARIANCE MATRIX L FULL NVAR NFAC ! FACTOR LOADING M FULL NFAC 1 FREE ! FACTOR MEANS N FULL NCOV 1 ! OBSERVED COVARIATES FOR EACH CASE P FULL NFAC NCOV FREE ! EFFECTS OF COVARIATES ON FACTOR MEANS I IDEN NVAR NVAR END MATRICES; SPECIFY N GEN HOMECOG MATRIX L 1 0 1 1 1 2 1 3 START 1 ALL MEANS L*(M + P*N) ; COVARIANCES L&S + I@D; OPTION TH=-2 INTERVALS @95 D 1 1 S 1 1 S 2 1 S 2 2 M 1 1 M 2 1 P 1 1 P 1 2 P 2 1 P 2 2 END GROUP; Summary of VL file data for group 1 HOMECOG GEN ANTI1 ANTI2 ANTI3 ANTI4 Code -2.0000 -1.0000 1.0000 2.0000 3.0000 4.0000 Number 221.0000 221.0000 221.0000 221.0000 221.0000 221.0000 Mean 9.0995 0.5249 1.4932 1.8371 1.8778 2.0679 Variance 5.9991 0.2494 2.3586 3.1952 3.2294 4.3257 Minimum 3.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Maximum 14.0000 1.0000 7.0000 9.0000 10.0000 9.0000 PARAMETER SPECIFICATIONS GROUP NUMBER: 1 Linear unconditional antisocial model MATRIX D This is a DIAGONAL matrix of order 1 by 1 1 1 1 MATRIX I This is an IDENTITY matrix of order 4 by 4 MATRIX L This is a FULL matrix of order 4 by 2 It has no free parameters specified MATRIX M This is a FULL matrix of order 2 by 1 1 1 5 2 6 MATRIX N This is a FULL matrix of order 2 by 1 1 1 -1 2 -2 MATRIX P This is a FULL matrix of order 2 by 2 1 2 1 7 8 2 9 10 MATRIX S This is a SYMMETRIC matrix of order 2 by 2 1 2 1 2 2 3 4 *** WARNING! *** I am not sure I have found a solution that satisfies Kuhn-Tucker conditions for a minimum. NAG's IFAIL parameter is 1 We probably have a minimum here, but you might consider trying different starting values. You can randomize these with TH=n on the OU line, where n is the number of times you wish to do this. I STRONGLY recommend BOundaries to be set if you use TH MX PARAMETER ESTIMATES GROUP NUMBER: 1 Linear unconditional antisocial model MATRIX D This is a DIAGONAL matrix of order 1 by 1 1 1 1.5290 MATRIX I This is an IDENTITY matrix of order 4 by 4 MATRIX L This is a FULL matrix of order 4 by 2 1 2 1 1.0000 0.0000 2 1.0000 1.0000 3 1.0000 2.0000 4 1.0000 3.0000 MATRIX M This is a FULL matrix of order 2 by 1 1 1 1.8240 2 0.5465 MATRIX N This is a FULL matrix of order 2 by 1 1 1 0.0000 2 4.0000 MATRIX P This is a FULL matrix of order 2 by 2 1 2 1 0.5716 -0.0626 2 0.0791 -0.0452 MATRIX S This is a SYMMETRIC matrix of order 2 by 2 1 2 1 0.8586 2 0.1219 0.0824 *** WARNING! *** Minimization may not be successful. See above CODE GREEN - it probably was OK Your model has 10 estimated parameters and 884 Observed statistics -2 times log-likelihood of data >>> 3258.168 Degrees of freedom >>>>>>>>>>>>>>>> 874 Now I'm trying to improve on the current solution for you... *** WARNING! *** I am not sure I have found a solution that satisfies Kuhn-Tucker conditions for a minimum. NAG's IFAIL parameter is 1 We probably have a minimum here, but you might consider trying different starting values. You can randomize these with TH=n on the OU line, where n is the number of times you wish to do this. I STRONGLY recommend BOundaries to be set if you use TH MX PARAMETER ESTIMATES GROUP NUMBER: 1 Linear unconditional antisocial model MATRIX D This is a DIAGONAL matrix of order 1 by 1 1 1 1.5290 MATRIX I This is an IDENTITY matrix of order 4 by 4 MATRIX L This is a FULL matrix of order 4 by 2 1 2 1 1.0000 0.0000 2 1.0000 1.0000 3 1.0000 2.0000 4 1.0000 3.0000 MATRIX M This is a FULL matrix of order 2 by 1 1 1 1.8240 2 0.5465 MATRIX N This is a FULL matrix of order 2 by 1 1 1 0.0000 2 4.0000 MATRIX P This is a FULL matrix of order 2 by 2 1 2 1 0.5716 -0.0626 2 0.0791 -0.0452 MATRIX S This is a SYMMETRIC matrix of order 2 by 2 1 2 1 0.8586 2 0.1219 0.0824 *** WARNING! *** Minimization may not be successful. See above CODE GREEN - it probably was OK Your model has 10 estimated parameters and 884 Observed statistics -2 times log-likelihood of data >>> 3258.168 Degrees of freedom >>>>>>>>>>>>>>>> 874 Now I'm trying to improve on the current solution for you... *** WARNING! *** I am not sure I have found a solution that satisfies Kuhn-Tucker conditions for a minimum. NAG's IFAIL parameter is 1 We probably have a minimum here, but you might consider trying different starting values. You can randomize these with TH=n on the OU line, where n is the number of times you wish to do this. I STRONGLY recommend BOundaries to be set if you use TH MX PARAMETER ESTIMATES GROUP NUMBER: 1 Linear unconditional antisocial model MATRIX D This is a DIAGONAL matrix of order 1 by 1 1 1 1.5290 MATRIX I This is an IDENTITY matrix of order 4 by 4 MATRIX L This is a FULL matrix of order 4 by 2 1 2 1 1.0000 0.0000 2 1.0000 1.0000 3 1.0000 2.0000 4 1.0000 3.0000 MATRIX M This is a FULL matrix of order 2 by 1 1 1 1.8240 2 0.5465 MATRIX N This is a FULL matrix of order 2 by 1 1 1 0.0000 2 4.0000 MATRIX P This is a FULL matrix of order 2 by 2 1 2 1 0.5716 -0.0626 2 0.0791 -0.0452 MATRIX S This is a SYMMETRIC matrix of order 2 by 2 1 2 1 0.8586 2 0.1219 0.0824 *** WARNING! *** Minimization may not be successful. See above CODE GREEN - it probably was OK Your model has 10 estimated parameters and 884 Observed statistics -2 times log-likelihood of data >>> 3258.168 Degrees of freedom >>>>>>>>>>>>>>>> 874 10 Confidence intervals requested in group 1 Matrix Element Int. Estimate Lower Upper Lfail Ufail D 1 1 1 95.0 1.5290 1.3437 1.7501 0 1 6 1 S 1 1 1 95.0 0.8586 0.5030 1.2899 0 1 0 1 S 1 2 1 95.0 0.1219 -0.0245 0.2530 0 1 6 1 S 1 2 2 95.0 0.0824 0.0042 0.1727 0 1 0 1 M 1 1 1 95.0 1.8240 1.0894 2.5585 6 2 6 2 M 1 2 1 95.0 0.5465 0.2160 0.8769 0 1 6 1 P 1 1 1 95.0 0.5716 0.2022 0.9411 0 1 0 1 P 1 1 2 95.0 -0.0626 -0.1377 0.0125 0 1 0 1 P 1 2 1 95.0 0.0791 -0.0863 0.2445 0 1 0 1 P 1 2 2 95.0 -0.0452 -0.0789 -0.0115 0 0 0 1 This problem used 3.9% of my workspace Task Time elapsed (DD:HH:MM:SS) Reading script & data 0: 0: 0: 0.33 Execution 0: 0: 6: 2.12 TOTAL 0: 0: 6: 2.45 Total number of warnings issued: 6 ______________________________________________________________________________