Mplus DEVELOPMENT MUTHEN & MUTHEN 02/12/2006 11:12 AM INPUT INSTRUCTIONS Title: lsay_2b.inp LSAY math outcomes Growth mixture model with 2-classes DATA: file is lsay.dat; VARIABLE: Names are lsayid schcode classize urban tracking ntracks mthlvl female mthflg7-mthflg12 mothed fathed mothsei fathsei homeres race expect parapsh parcpsh parmpsh peerapsh peermpsh bas7 basse7 alg7 algse7 geo7 geose7 qlt7 qltse7 mth7 mthse7 mtha7 mthase7 bas8 basse8 alg8 algse8 geo8 geose8 qlt8 qltse8 mth8 mthse8 mtha8 mthase8 bas9 basse9 alg9 algse9 geo9 geose9 qlt9 qltse9 mth9 mthse9 mtha9 mthase9 bas10 basse10 alg10 algse10 geo10 geose10 qlt10 qltse10 mth10 mthse10 mtha10 mthase10 bas11 basse11 alg11 algse11 geo11 geose11 qlt11 qltse11 mth11 mthse11 mtha11 mthase11 bas12 basse12 alg12 algse12 geo12 geose12 qlt12 qltse12 mth12 mthse12 mtha12 mthase12 mthcrs7-mthcrs12 mtrk10-mtrk12 totstud lchfull lchpart parvis mcirr mclub strat mstrat comp mcomp african hispan asian expel arrest dropot self worth other satisf respect failure esteem problem cloctn dloctn eloctn floctn gloctn hloctn iloctn jloctn kloctn lloctn drink runawa suicid alc7 alc10 alc11 alc12 arest7 runa8 runa9 runa10 runa11 run12 suic8 suic9 suic10 suic11 suic12 drop7 drop8 drop9 drop10 drop11 drop12 fdrop8 fdrop9 fdrop10 fdrop11 fdrop12 enj7 good7 und7 useboy7 nerv7 wor7 scar7 use7 logic7 boybet7 job7 often7 enj8 good8 und8 useboy8 nerv8 wor8 scar8 use8 logic8 boybet8 job8 often8 enj9 good9 und9 useboy9 nerv9 wor9 scar9 use9 logic9 boybet9 job9 often9 enj10 good10 und10 useboy10 nerv10 wor10 scar10 use10 logic10 boybet10 job10 often10; Missing are all(9999); Usevar=mth7 mth8 mth9 mth10; classes=c(2); Analysis: Type = mixture missing; starts=30 3; Model: %overall% i s | mth7@0 mth8@1 mth9@2 mth10@3; %c#1% i; s; Output: tech11; Plot: type=plot3; series= mth7-mth10(*); *** WARNING Data set contains cases with missing on all variables. These cases were not included in the analysis. Number of cases with missing on all variables: 14 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS lsay_2.inp LSAY math outcomes Growth mixture model with 2-classes SUMMARY OF ANALYSIS Number of groups 1 Number of observations 3102 Number of dependent variables 4 Number of independent variables 0 Number of continuous latent variables 2 Number of categorical latent variables 1 Observed dependent variables Continuous MTH7 MTH8 MTH9 MTH10 Continuous latent variables I S Categorical latent variables C Estimator MLR Information matrix OBSERVED Optimization Specifications for the Quasi-Newton Algorithm for Continuous Outcomes Maximum number of iterations 1000 Convergence criterion 0.100D-05 Optimization Specifications for the EM Algorithm Maximum number of iterations 500 Convergence criteria Loglikelihood change 0.100D-06 Relative loglikelihood change 0.100D-06 Derivative 0.100D-05 Optimization Specifications for the M step of the EM Algorithm for Categorical Latent variables Number of M step iterations 1 M step convergence criterion 0.100D-05 Basis for M step termination ITERATION Optimization Specifications for the M step of the EM Algorithm for Censored, Binary or Ordered Categorical (Ordinal), Unordered Categorical (Nominal) and Count Outcomes Number of M step iterations 1 M step convergence criterion 0.100D-05 Basis for M step termination ITERATION Maximum value for logit thresholds 15 Minimum value for logit thresholds -15 Minimum expected cell size for chi-square 0.100D-01 Maximum number of iterations for H1 2000 Convergence criterion for H1 0.100D-03 Optimization algorithm EMA Random Starts Specifications Number of initial stage starts 30 Number of final stage starts 3 Number of initial stage iterations 10 Initial stage convergence criterion 0.100D+01 Random starts scale 0.500D+01 Random seed for generating random starts 0 Input data file(s) lsay.dat Input data format FREE SUMMARY OF DATA Number of patterns 15 Number of y patterns 15 Number of u patterns 0 COVARIANCE COVERAGE OF DATA Minimum covariance coverage value 0.100 PROPORTION OF DATA PRESENT FOR Y Covariance Coverage MTH7 MTH8 MTH9 MTH10 ________ ________ ________ ________ MTH7 0.988 MTH8 0.822 0.832 MTH9 0.714 0.657 0.722 MTH10 0.651 0.592 0.587 0.658 RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES Initial stage loglikelihood values, seeds, and initial stage start numbers: -33467.144 851945 18 -33468.001 432148 30 -33468.151 887676 22 -33470.091 569131 26 -33472.327 415931 10 -33473.888 573096 20 -33475.798 650371 14 -33479.425 285380 1 -33483.272 533739 11 -33491.537 76974 16 -33497.820 392418 28 -33500.084 127215 9 -33502.538 207896 25 -33507.780 462953 7 -33508.079 372176 23 -33509.304 366706 29 -33516.973 608496 4 -33520.272 364676 27 -33520.472 27071 15 -33537.514 347515 24 -33545.273 903420 5 -33548.236 93468 3 -33550.380 939021 8 -33562.422 107446 12 -33570.681 253358 2 -33589.543 902278 21 -33610.047 68985 17 -33640.506 637345 19 -33641.244 399671 13 -33641.558 unperturbed 0 -33642.078 195873 6 Loglikelihood values at local maxima, seeds, and initial stage start numbers: -33466.496 432148 30 -33466.496 851945 18 -33466.496 887676 22 THE MODEL ESTIMATION TERMINATED NORMALLY WARNING: THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IN CLASS 2 IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/ RESIDUAL VARIANCE FOR A LATENT VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO LATENT VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO LATENT VARIABLES. CHECK THE TECH4 OUTPUT FOR MORE INFORMATION. PROBLEM INVOLVING VARIABLE S. TESTS OF MODEL FIT Loglikelihood H0 Value -33466.496 H0 Scaling Correction Factor 1.131 for MLR Information Criteria Number of Free Parameters 14 Akaike (AIC) 66960.992 Bayesian (BIC) 67045.549 Sample-Size Adjusted BIC 67001.065 (n* = (n + 2) / 24) Entropy 0.517 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES BASED ON THE ESTIMATED MODEL Latent Classes 1 1622.36125 0.52300 2 1479.63875 0.47700 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS BASED ON ESTIMATED POSTERIOR PROBABILITIES Latent Classes 1 1622.36126 0.52300 2 1479.63874 0.47700 CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP Class Counts and Proportions Latent Classes 1 1640 0.52869 2 1462 0.47131 Average Latent Class Probabilities for Most Likely Latent Class Membership (Row) by Latent Class (Column) 1 2 1 0.853 0.147 2 0.152 0.848 MODEL RESULTS Estimates S.E. Est./S.E. Latent Class 1 I | MTH7 1.000 0.000 0.000 MTH8 1.000 0.000 0.000 MTH9 1.000 0.000 0.000 MTH10 1.000 0.000 0.000 S | MTH7 0.000 0.000 0.000 MTH8 1.000 0.000 0.000 MTH9 2.000 0.000 0.000 MTH10 3.000 0.000 0.000 S WITH I 4.129 0.633 6.518 Means I 43.636 0.544 80.270 S 3.444 0.124 27.788 Intercepts MTH7 0.000 0.000 0.000 MTH8 0.000 0.000 0.000 MTH9 0.000 0.000 0.000 MTH10 0.000 0.000 0.000 Variances I 31.763 3.890 8.164 S 9.745 0.667 14.609 Residual Variances MTH7 16.028 1.092 14.683 MTH8 18.933 0.894 21.171 MTH9 16.646 0.874 19.052 MTH10 15.524 1.380 11.251 Latent Class 2 I | MTH7 1.000 0.000 0.000 MTH8 1.000 0.000 0.000 MTH9 1.000 0.000 0.000 MTH10 1.000 0.000 0.000 S | MTH7 0.000 0.000 0.000 MTH8 1.000 0.000 0.000 MTH9 2.000 0.000 0.000 MTH10 3.000 0.000 0.000 S WITH I 4.129 0.633 6.518 Means I 57.416 0.496 115.861 S 4.485 0.085 52.737 Intercepts MTH7 0.000 0.000 0.000 MTH8 0.000 0.000 0.000 MTH9 0.000 0.000 0.000 MTH10 0.000 0.000 0.000 Variances I 48.077 3.789 12.687 S 0.184 0.390 0.472 Residual Variances MTH7 16.028 1.092 14.683 MTH8 18.933 0.894 21.171 MTH9 16.646 0.874 19.052 MTH10 15.524 1.380 11.251 Categorical Latent Variables Means C#1 0.092 0.136 0.676 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.503E-03 (ratio of smallest to largest eigenvalue) TECHNICAL 11 OUTPUT VUONG-LO-MENDELL-RUBIN LIKELIHOOD RATIO TEST FOR 1 (H0) VERSUS 2 CLASSES H0 Loglikelihood Value -33641.558 2 Times the Loglikelihood Difference 350.125 Difference in the Number of Parameters 5 Mean 5.407 Standard Deviation 7.485 P-Value 0.0000 LO-MENDELL-RUBIN ADJUSTED LRT TEST Value 341.627 P-Value 0.0000 PLOT INFORMATION The following plots are available: Histograms (sample values, estimated factor scores, estimated values) Scatterplots (sample values, estimated factor scores, estimated values) Sample means Estimated means Sample and estimated means Observed individual values Estimated individual values Estimated means and observed individual values Estimated means and estimated individual values Mixture distributions Beginning Time: 11:12:32 Ending Time: 11:12:47 Elapsed Time: 00:00:15 MUTHEN & MUTHEN 3463 Stoner Ave. Los Angeles, CA 90066 Tel: (310) 391-9971 Fax: (310) 391-8971 Web: www.StatModel.com Support: Support@StatModel.com Copyright (c) 1998-2005 Muthen & Muthen