Mplus VERSION 6.12 MUTHEN & MUTHEN 12/17/2011 8:02 PM INPUT INSTRUCTIONS Data: file is C:\My Documents\JiaPaper2\NewSimulation\WithCovCHISQtype1_age0500-8\data2.txt; !type is montecarlo; VARIABLE: NAMES ARE i x1 x2 x3 tf y age gender; USEVARIABLES ARE x1 x2 x3 y age gender; categorical are y; CLASSES = c(4); idvariable = i; ANALYSIS: TYPE = MIXTURE; ALGORITHM=INTEGRATION; estimator=MLr; !STSCALE is 1; starts 500 50; stseed = 1234; process = 6 (STARTS); MODEL: %OVERALL% f BY x1* x2 x3@1; [x3@0]; y on f age gender; [y$1](fix); f on age gender; c on age gender; %c#1% [f*] ; %c#2% [f*] ; %c#3% [f*] ; %c#4% [f*] ; output: tech1 tech9; *** WARNING in OUTPUT command TECH9 option is only available with Monte Carlo, multiple imputation or bootstrap. Request for TECH9 is ignored. 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS SUMMARY OF ANALYSIS Number of groups 1 Number of observations 500 Number of dependent variables 4 Number of independent variables 2 Number of continuous latent variables 1 Number of categorical latent variables 1 Observed dependent variables Continuous X1 X2 X3 Binary and ordered categorical (ordinal) Y Observed independent variables AGE GENDER Continuous latent variables F Categorical latent variables C Variables with special functions ID variable I Estimator MLR Information matrix OBSERVED Optimization Specifications for the Quasi-Newton Algorithm for Continuous Outcomes Maximum number of iterations 100 Convergence criterion 0.100D-05 Optimization Specifications for the EM Algorithm Maximum number of iterations 500 Convergence criteria Loglikelihood change 0.100D-02 Relative loglikelihood change 0.100D-05 Derivative 0.100D-02 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-02 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-02 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 Optimization algorithm EMA Integration Specifications Type STANDARD Number of integration points 15 Dimensions of numerical integration 1 Adaptive quadrature ON Random Starts Specifications Number of initial stage random starts 500 Number of final stage optimizations 50 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 1234 Link LOGIT Cholesky OFF Input data file(s) C:\My Documents\JiaPaper2\NewSimulation\WithCovCHISQtype1_age0500-8\data2.txt Input data format FREE UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES Y Category 1 0.706 353.000 Category 2 0.294 147.000 RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES Final stage loglikelihood values at local maxima, seeds, and initial stage start numbers: -2183.337 283916 133 -2197.482 392026 110 -2199.182 867074 17 -2202.078 433800 496 -2204.882 337791 194 -2204.906 184786 154 -2204.909 410307 408 -2206.269 488805 493 -2206.449 173287 252 -2206.450 515684 129 -2206.456 125768 223 -2206.456 342431 388 -2206.457 436303 414 -2206.458 605662 31 -2207.593 802813 45 -2207.595 328542 498 -2207.595 375876 376 -2207.599 601667 147 -2207.599 921481 159 -2207.606 512368 275 -2209.169 554044 417 -2210.967 867551 64 -2214.497 841256 122 -2215.752 37111 215 -2215.855 3667 221 -2216.144 518481 152 -2216.529 947926 70 -2216.581 669803 231 -2216.594 120806 307 -2257.318 543438 482 -2257.322 799623 197 -2257.323 795096 389 -2257.323 961187 135 -2258.303 372835 34 -2262.212 990731 404 -2262.213 615567 325 -2262.213 73684 416 -2262.214 220494 473 -2262.215 825744 256 -2262.215 788995 429 -2262.215 661648 290 -2262.216 906622 57 -2262.216 76784 455 -2262.361 170491 14 -2263.221 640668 254 -2263.635 100217 272 -2264.234 427470 495 -2269.436 659136 207 -2271.572 733833 239 -2273.705 830636 43 WARNING: WHEN ESTIMATING A MODEL WITH MORE THAN TWO CLASSES, IT MAY BE NECESSARY TO INCREASE THE NUMBER OF RANDOM STARTS USING THE STARTS OPTION TO AVOID LOCAL MAXIMA. WARNING: THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED. THE SOLUTION MAY NOT BE TRUSTWORTHY DUE TO LOCAL MAXIMA. INCREASE THE NUMBER OF RANDOM STARTS. WARNING: THE SAMPLE COVARIANCE OF THE INDEPENDENT VARIABLES IN CLASS 2 IS SINGULAR. THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.116D-10. PROBLEM INVOLVING PARAMETER 17. ONE OR MORE MULTINOMIAL LOGIT PARAMETERS WERE FIXED TO AVOID SINGULARITY OF THE INFORMATION MATRIX. THE SINGULARITY IS MOST LIKELY BECAUSE THE MODEL IS NOT IDENTIFIED, OR BECAUSE OF EMPTY CELLS IN THE JOINT DISTRIBUTION OF THE CATEGORICAL LATENT VARIABLES AND ANY INDEPENDENT VARIABLES. THE FOLLOWING PARAMETERS WERE FIXED: 27 26 25 21 THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 27 Loglikelihood H0 Value -2183.337 H0 Scaling Correction Factor 0.878 for MLR Information Criteria Akaike (AIC) 4420.674 Bayesian (BIC) 4534.469 Sample-Size Adjusted BIC 4448.769 (n* = (n + 2) / 24) FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES BASED ON THE ESTIMATED MODEL Latent Classes 1 416.33030 0.83266 2 16.39333 0.03279 3 66.27637 0.13255 4 1.00000 0.00200 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS BASED ON ESTIMATED POSTERIOR PROBABILITIES Latent Classes 1 416.33051 0.83266 2 16.39083 0.03278 3 66.27866 0.13256 4 1.00000 0.00200 CLASSIFICATION QUALITY Entropy 0.905 CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP Class Counts and Proportions Latent Classes 1 422 0.84400 2 17 0.03400 3 60 0.12000 4 1 0.00200 Average Latent Class Probabilities for Most Likely Latent Class Membership (Row) by Latent Class (Column) 1 2 3 4 1 0.965 0.000 0.035 0.000 2 0.000 0.920 0.080 0.000 3 0.150 0.012 0.838 0.000 4 0.000 0.000 0.000 1.000 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Latent Class 1 F BY X1 0.733 0.059 12.502 0.000 X2 0.799 0.041 19.674 0.000 X3 1.000 0.000 999.000 999.000 F ON AGE 0.019 0.013 1.457 0.145 GENDER 0.137 0.087 1.572 0.116 Y ON F 0.810 0.172 4.698 0.000 Y ON AGE -0.004 0.041 -0.098 0.922 GENDER 0.536 0.228 2.346 0.019 Intercepts X1 0.047 0.041 1.157 0.247 X2 0.051 0.042 1.214 0.225 X3 0.000 0.000 999.000 999.000 F -0.404 0.053 -7.627 0.000 Thresholds Y$1 1.187 0.162 7.333 0.000 Residual Variances X1 0.514 0.039 13.277 0.000 X2 0.555 0.040 13.745 0.000 X3 0.589 0.057 10.274 0.000 F 0.105 0.042 2.497 0.013 Latent Class 2 F BY X1 0.733 0.059 12.502 0.000 X2 0.799 0.041 19.674 0.000 X3 1.000 0.000 999.000 999.000 F ON AGE 0.019 0.013 1.457 0.145 GENDER 0.137 0.087 1.572 0.116 Y ON F 0.810 0.172 4.698 0.000 Y ON AGE -0.004 0.041 -0.098 0.922 GENDER 0.536 0.228 2.346 0.019 Intercepts X1 0.047 0.041 1.157 0.247 X2 0.051 0.042 1.214 0.225 X3 0.000 0.000 999.000 999.000 F 3.473 0.293 11.838 0.000 Thresholds Y$1 1.187 0.162 7.333 0.000 Residual Variances X1 0.514 0.039 13.277 0.000 X2 0.555 0.040 13.745 0.000 X3 0.589 0.057 10.274 0.000 F 0.105 0.042 2.497 0.013 Latent Class 3 F BY X1 0.733 0.059 12.502 0.000 X2 0.799 0.041 19.674 0.000 X3 1.000 0.000 999.000 999.000 F ON AGE 0.019 0.013 1.457 0.145 GENDER 0.137 0.087 1.572 0.116 Y ON F 0.810 0.172 4.698 0.000 Y ON AGE -0.004 0.041 -0.098 0.922 GENDER 0.536 0.228 2.346 0.019 Intercepts X1 0.047 0.041 1.157 0.247 X2 0.051 0.042 1.214 0.225 X3 0.000 0.000 999.000 999.000 F 1.122 0.170 6.596 0.000 Thresholds Y$1 1.187 0.162 7.333 0.000 Residual Variances X1 0.514 0.039 13.277 0.000 X2 0.555 0.040 13.745 0.000 X3 0.589 0.057 10.274 0.000 F 0.105 0.042 2.497 0.013 Latent Class 4 F BY X1 0.733 0.059 12.502 0.000 X2 0.799 0.041 19.674 0.000 X3 1.000 0.000 999.000 999.000 F ON AGE 0.019 0.013 1.457 0.145 GENDER 0.137 0.087 1.572 0.116 Y ON F 0.810 0.172 4.698 0.000 Y ON AGE -0.004 0.041 -0.098 0.922 GENDER 0.536 0.228 2.346 0.019 Intercepts X1 0.047 0.041 1.157 0.247 X2 0.051 0.042 1.214 0.225 X3 0.000 0.000 999.000 999.000 F 9.040 0.287 31.516 0.000 Thresholds Y$1 1.187 0.162 7.333 0.000 Residual Variances X1 0.514 0.039 13.277 0.000 X2 0.555 0.040 13.745 0.000 X3 0.589 0.057 10.274 0.000 F 0.105 0.042 2.497 0.013 Categorical Latent Variables C#1 ON AGE -8.844 0.079 -111.878 0.000 GENDER 0.912 0.439 2.078 0.038 C#2 ON AGE -8.386 0.132 -63.715 0.000 GENDER 27.419 0.000 999.000 999.000 C#3 ON AGE -8.582 0.000 999.000 999.000 GENDER 2.634 0.000 999.000 999.000 Intercepts C#1 93.638 0.454 206.442 0.000 C#2 64.314 0.516 124.584 0.000 C#3 90.657 0.000 999.000 999.000 LOGISTIC REGRESSION ODDS RATIO RESULTS Latent Class 1 Y ON F 2.249 Y ON AGE 0.996 GENDER 1.709 Latent Class 2 Y ON F 2.249 Y ON AGE 0.996 GENDER 1.709 Latent Class 3 Y ON F 2.249 Y ON AGE 0.996 GENDER 1.709 Latent Class 4 Y ON F 2.249 Y ON AGE 0.996 GENDER 1.709 Categorical Latent Variables C#1 ON AGE 0.000 GENDER 2.490 C#2 ON AGE 0.000 GENDER ********* C#3 ON AGE 0.000 GENDER 13.923 ALTERNATIVE PARAMETERIZATIONS FOR THE CATEGORICAL LATENT VARIABLE REGRESSION Parameterization using Reference Class 1 C#2 ON AGE 0.457 0.121 3.781 0.000 GENDER 26.506 0.439 60.373 0.000 C#3 ON AGE 0.261 0.079 3.306 0.001 GENDER 1.721 0.439 3.920 0.000 C#4 ON AGE 8.844 0.079 111.878 0.000 GENDER -0.912 0.439 -2.078 0.038 Intercepts C#2 -29.324 0.653 -44.879 0.000 C#3 -2.981 0.454 -6.571 0.000 C#4 -93.638 0.454 -206.442 0.000 Parameterization using Reference Class 2 C#1 ON AGE -0.457 0.121 -3.781 0.000 GENDER -26.506 0.439 -60.373 0.000 C#3 ON AGE -0.196 0.132 -1.489 0.136 GENDER -24.785 0.000 0.000 1.000 C#4 ON AGE 8.386 0.132 63.715 0.000 GENDER -27.419 0.000 0.000 1.000 Intercepts C#1 29.324 0.653 44.879 0.000 C#3 26.343 0.516 51.030 0.000 C#4 -64.314 0.516 -124.584 0.000 Parameterization using Reference Class 3 C#1 ON AGE -0.261 0.079 -3.306 0.001 GENDER -1.721 0.439 -3.920 0.000 C#2 ON AGE 0.196 0.132 1.489 0.136 GENDER 24.785 0.000 0.000 1.000 C#4 ON AGE 8.582 0.000 0.000 1.000 GENDER -2.634 0.000 0.000 1.000 Intercepts C#1 2.981 0.454 6.571 0.000 C#2 -26.343 0.516 -51.030 0.000 C#4 -90.657 0.000 0.000 1.000 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.291E-05 (ratio of smallest to largest eigenvalue) TECHNICAL 1 OUTPUT PARAMETER SPECIFICATION FOR LATENT CLASS 1 NU Y X1 X2 X3 AGE ________ ________ ________ ________ ________ 1 0 1 2 0 0 NU GENDER ________ 1 0 LAMBDA F Y AGE GENDER ________ ________ ________ ________ Y 0 0 0 0 X1 3 0 0 0 X2 4 0 0 0 X3 0 0 0 0 AGE 0 0 0 0 GENDER 0 0 0 0 THETA Y X1 X2 X3 AGE ________ ________ ________ ________ ________ Y 0 X1 0 5 X2 0 0 6 X3 0 0 0 7 AGE 0 0 0 0 0 GENDER 0 0 0 0 0 THETA GENDER ________ GENDER 0 ALPHA F Y AGE GENDER ________ ________ ________ ________ 1 8 0 0 0 BETA F Y AGE GENDER ________ ________ ________ ________ F 0 0 9 10 Y 11 0 12 13 AGE 0 0 0 0 GENDER 0 0 0 0 PSI F Y AGE GENDER ________ ________ ________ ________ F 14 Y 0 0 AGE 0 0 0 GENDER 0 0 0 0 PARAMETER SPECIFICATION FOR LATENT CLASS 2 NU Y X1 X2 X3 AGE ________ ________ ________ ________ ________ 1 0 1 2 0 0 NU GENDER ________ 1 0 LAMBDA F Y AGE GENDER ________ ________ ________ ________ Y 0 0 0 0 X1 3 0 0 0 X2 4 0 0 0 X3 0 0 0 0 AGE 0 0 0 0 GENDER 0 0 0 0 THETA Y X1 X2 X3 AGE ________ ________ ________ ________ ________ Y 0 X1 0 5 X2 0 0 6 X3 0 0 0 7 AGE 0 0 0 0 0 GENDER 0 0 0 0 0 THETA GENDER ________ GENDER 0 ALPHA F Y AGE GENDER ________ ________ ________ ________ 1 15 0 0 0 BETA F Y AGE GENDER ________ ________ ________ ________ F 0 0 9 10 Y 11 0 12 13 AGE 0 0 0 0 GENDER 0 0 0 0 PSI F Y AGE GENDER ________ ________ ________ ________ F 14 Y 0 0 AGE 0 0 0 GENDER 0 0 0 0 PARAMETER SPECIFICATION FOR LATENT CLASS 3 NU Y X1 X2 X3 AGE ________ ________ ________ ________ ________ 1 0 1 2 0 0 NU GENDER ________ 1 0 LAMBDA F Y AGE GENDER ________ ________ ________ ________ Y 0 0 0 0 X1 3 0 0 0 X2 4 0 0 0 X3 0 0 0 0 AGE 0 0 0 0 GENDER 0 0 0 0 THETA Y X1 X2 X3 AGE ________ ________ ________ ________ ________ Y 0 X1 0 5 X2 0 0 6 X3 0 0 0 7 AGE 0 0 0 0 0 GENDER 0 0 0 0 0 THETA GENDER ________ GENDER 0 ALPHA F Y AGE GENDER ________ ________ ________ ________ 1 16 0 0 0 BETA F Y AGE GENDER ________ ________ ________ ________ F 0 0 9 10 Y 11 0 12 13 AGE 0 0 0 0 GENDER 0 0 0 0 PSI F Y AGE GENDER ________ ________ ________ ________ F 14 Y 0 0 AGE 0 0 0 GENDER 0 0 0 0 PARAMETER SPECIFICATION FOR LATENT CLASS 4 NU Y X1 X2 X3 AGE ________ ________ ________ ________ ________ 1 0 1 2 0 0 NU GENDER ________ 1 0 LAMBDA F Y AGE GENDER ________ ________ ________ ________ Y 0 0 0 0 X1 3 0 0 0 X2 4 0 0 0 X3 0 0 0 0 AGE 0 0 0 0 GENDER 0 0 0 0 THETA Y X1 X2 X3 AGE ________ ________ ________ ________ ________ Y 0 X1 0 5 X2 0 0 6 X3 0 0 0 7 AGE 0 0 0 0 0 GENDER 0 0 0 0 0 THETA GENDER ________ GENDER 0 ALPHA F Y AGE GENDER ________ ________ ________ ________ 1 17 0 0 0 BETA F Y AGE GENDER ________ ________ ________ ________ F 0 0 9 10 Y 11 0 12 13 AGE 0 0 0 0 GENDER 0 0 0 0 PSI F Y AGE GENDER ________ ________ ________ ________ F 14 Y 0 0 AGE 0 0 0 GENDER 0 0 0 0 PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR MODEL PART TAU(U) FOR LATENT CLASS 1 Y$1 ________ 1 18 TAU(U) FOR LATENT CLASS 2 Y$1 ________ 1 18 TAU(U) FOR LATENT CLASS 3 Y$1 ________ 1 18 TAU(U) FOR LATENT CLASS 4 Y$1 ________ 1 18 PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART ALPHA(C) C#1 C#2 C#3 C#4 ________ ________ ________ ________ 1 19 20 21 0 GAMMA(C) F Y AGE GENDER ________ ________ ________ ________ C#1 0 0 22 23 C#2 0 0 24 25 C#3 0 0 26 27 C#4 0 0 0 0 STARTING VALUES FOR LATENT CLASS 1 NU Y X1 X2 X3 AGE ________ ________ ________ ________ ________ 1 0.000 0.051 0.056 0.000 0.000 NU GENDER ________ 1 0.000 LAMBDA F Y AGE GENDER ________ ________ ________ ________ Y 0.000 1.000 0.000 0.000 X1 1.000 0.000 0.000 0.000 X2 1.000 0.000 0.000 0.000 X3 1.000 0.000 0.000 0.000 AGE 0.000 0.000 1.000 0.000 GENDER 0.000 0.000 0.000 1.000 THETA Y X1 X2 X3 AGE ________ ________ ________ ________ ________ Y 0.000 X1 0.000 0.539 X2 0.000 0.000 0.612 X3 0.000 0.000 0.000 0.819 AGE 0.000 0.000 0.000 0.000 0.000 GENDER 0.000 0.000 0.000 0.000 0.000 THETA GENDER ________ GENDER 0.000 ALPHA F Y AGE GENDER ________ ________ ________ ________ 1 0.000 0.000 0.000 0.000 BETA F Y AGE GENDER ________ ________ ________ ________ F 0.000 0.000 0.000 0.000 Y 0.000 0.000 0.000 0.000 AGE 0.000 0.000 0.000 0.000 GENDER 0.000 0.000 0.000 0.000 PSI F Y AGE GENDER ________ ________ ________ ________ F 0.050 Y 0.000 1.000 AGE 0.000 0.000 4.274 GENDER 0.000 0.000 0.000 0.124 STARTING VALUES FOR LATENT CLASS 2 NU Y X1 X2 X3 AGE ________ ________ ________ ________ ________ 1 0.000 0.051 0.056 0.000 0.000 NU GENDER ________ 1 0.000 LAMBDA F Y AGE GENDER ________ ________ ________ ________ Y 0.000 1.000 0.000 0.000 X1 1.000 0.000 0.000 0.000 X2 1.000 0.000 0.000 0.000 X3 1.000 0.000 0.000 0.000 AGE 0.000 0.000 1.000 0.000 GENDER 0.000 0.000 0.000 1.000 THETA Y X1 X2 X3 AGE ________ ________ ________ ________ ________ Y 0.000 X1 0.000 0.539 X2 0.000 0.000 0.612 X3 0.000 0.000 0.000 0.819 AGE 0.000 0.000 0.000 0.000 0.000 GENDER 0.000 0.000 0.000 0.000 0.000 THETA GENDER ________ GENDER 0.000 ALPHA F Y AGE GENDER ________ ________ ________ ________ 1 0.000 0.000 0.000 0.000 BETA F Y AGE GENDER ________ ________ ________ ________ F 0.000 0.000 0.000 0.000 Y 0.000 0.000 0.000 0.000 AGE 0.000 0.000 0.000 0.000 GENDER 0.000 0.000 0.000 0.000 PSI F Y AGE GENDER ________ ________ ________ ________ F 0.050 Y 0.000 1.000 AGE 0.000 0.000 4.274 GENDER 0.000 0.000 0.000 0.124 STARTING VALUES FOR LATENT CLASS 3 NU Y X1 X2 X3 AGE ________ ________ ________ ________ ________ 1 0.000 0.051 0.056 0.000 0.000 NU GENDER ________ 1 0.000 LAMBDA F Y AGE GENDER ________ ________ ________ ________ Y 0.000 1.000 0.000 0.000 X1 1.000 0.000 0.000 0.000 X2 1.000 0.000 0.000 0.000 X3 1.000 0.000 0.000 0.000 AGE 0.000 0.000 1.000 0.000 GENDER 0.000 0.000 0.000 1.000 THETA Y X1 X2 X3 AGE ________ ________ ________ ________ ________ Y 0.000 X1 0.000 0.539 X2 0.000 0.000 0.612 X3 0.000 0.000 0.000 0.819 AGE 0.000 0.000 0.000 0.000 0.000 GENDER 0.000 0.000 0.000 0.000 0.000 THETA GENDER ________ GENDER 0.000 ALPHA F Y AGE GENDER ________ ________ ________ ________ 1 0.000 0.000 0.000 0.000 BETA F Y AGE GENDER ________ ________ ________ ________ F 0.000 0.000 0.000 0.000 Y 0.000 0.000 0.000 0.000 AGE 0.000 0.000 0.000 0.000 GENDER 0.000 0.000 0.000 0.000 PSI F Y AGE GENDER ________ ________ ________ ________ F 0.050 Y 0.000 1.000 AGE 0.000 0.000 4.274 GENDER 0.000 0.000 0.000 0.124 STARTING VALUES FOR LATENT CLASS 4 NU Y X1 X2 X3 AGE ________ ________ ________ ________ ________ 1 0.000 0.051 0.056 0.000 0.000 NU GENDER ________ 1 0.000 LAMBDA F Y AGE GENDER ________ ________ ________ ________ Y 0.000 1.000 0.000 0.000 X1 1.000 0.000 0.000 0.000 X2 1.000 0.000 0.000 0.000 X3 1.000 0.000 0.000 0.000 AGE 0.000 0.000 1.000 0.000 GENDER 0.000 0.000 0.000 1.000 THETA Y X1 X2 X3 AGE ________ ________ ________ ________ ________ Y 0.000 X1 0.000 0.539 X2 0.000 0.000 0.612 X3 0.000 0.000 0.000 0.819 AGE 0.000 0.000 0.000 0.000 0.000 GENDER 0.000 0.000 0.000 0.000 0.000 THETA GENDER ________ GENDER 0.000 ALPHA F Y AGE GENDER ________ ________ ________ ________ 1 0.000 0.000 0.000 0.000 BETA F Y AGE GENDER ________ ________ ________ ________ F 0.000 0.000 0.000 0.000 Y 0.000 0.000 0.000 0.000 AGE 0.000 0.000 0.000 0.000 GENDER 0.000 0.000 0.000 0.000 PSI F Y AGE GENDER ________ ________ ________ ________ F 0.050 Y 0.000 1.000 AGE 0.000 0.000 4.274 GENDER 0.000 0.000 0.000 0.124 STARTING VALUES FOR LATENT CLASS INDICATOR MODEL PART TAU(U) FOR LATENT CLASS 1 Y$1 ________ 1 0.876 TAU(U) FOR LATENT CLASS 2 Y$1 ________ 1 0.876 TAU(U) FOR LATENT CLASS 3 Y$1 ________ 1 0.876 TAU(U) FOR LATENT CLASS 4 Y$1 ________ 1 0.876 STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART ALPHA(C) C#1 C#2 C#3 C#4 ________ ________ ________ ________ 1 0.000 0.000 0.000 0.000 GAMMA(C) F Y AGE GENDER ________ ________ ________ ________ C#1 0.000 0.000 0.000 0.000 C#2 0.000 0.000 0.000 0.000 C#3 0.000 0.000 0.000 0.000 C#4 0.000 0.000 0.000 0.000 Beginning Time: 20:02:27 Ending Time: 20:03:03 Elapsed Time: 00:00:36 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) 1998-2011 Muthen & Muthen