Mplus VERSION 7
MUTHEN & MUTHEN
09/22/2012  10:03 PM

INPUT INSTRUCTIONS

  title:
  	this is an example of a LCA with three-
  	category latent class indicators using
  	user-specified starting values without
  	random starts

  montecarlo:
  	names are u1-u4;
  	generate = u1-u4(2);
  	categorical = u1-u4;
  	genclasses = c(2);
  	classes = c(2);
  	nobs = 5000;
  	seed = 3454367;
  	nrep = 1;
  	save = ex7.6.dat;

  analysis:
  	type = mixture;

  model population:

  	%overall%

  	[c#1*0];
  	
  	%c#1%
  	[u1$1*.5 u2$1*.5 u3$1*-.5 u4$1*-.5];
  	[u1$2*1 u2$2*1 u3$2*0 u4$2*0];

  	%c#2%
  	[u1$1*-.5 u2$1*-.5 u3$1*.5 u4$1*.5];
  	[u1$2*0 u2$2*0 u3$2*1 u4$2*1];

  model:

  	%overall%

  	[c#1*0];
  	
  	%c#1%
  	[u1$1*.5 u2$1*.5 u3$1*-.5 u4$1*-.5];
  	[u1$2*1 u2$2*1 u3$2*0 u4$2*0];

  	%c#2%
  	[u1$1*-.5 u2$1*-.5 u3$1*.5 u4$1*.5];
  	[u1$2*0 u2$2*0 u3$2*1 u4$2*1];


  output:
  	tech8 tech9;
  	
  	
  	

  	
  	



INPUT READING TERMINATED NORMALLY




this is an example of a LCA with three-
category latent class indicators using
user-specified starting values without
random starts

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        5000

Number of replications
    Requested                                                    1
    Completed                                                    1
Value of seed                                              3454367

Number of dependent variables                                    4
Number of independent variables                                  0
Number of continuous latent variables                            0
Number of categorical latent variables                           1

Observed dependent variables

  Binary and ordered categorical (ordinal)
   U1          U2          U3          U4

Categorical latent variables
   C


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-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
Optimization algorithm                                         EMA
Link                                                         LOGIT





MODEL FIT INFORMATION

Number of Free Parameters                       17

Loglikelihood

    H0 Value

        Mean                            -19214.480
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000       -19214.480     -19214.480
           0.980       0.000       -19214.480     -19214.480
           0.950       0.000       -19214.480     -19214.480
           0.900       0.000       -19214.480     -19214.480
           0.800       0.000       -19214.480     -19214.480
           0.700       0.000       -19214.480     -19214.480
           0.500       0.000       -19214.480     -19214.480
           0.300       0.000       -19214.480     -19214.480
           0.200       0.000       -19214.480     -19214.480
           0.100       0.000       -19214.480     -19214.480
           0.050       0.000       -19214.480     -19214.480
           0.020       0.000       -19214.480     -19214.480
           0.010       0.000       -19214.480     -19214.480

Information Criteria

    Akaike (AIC)

        Mean                             38462.961
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000        38462.961      38462.961
           0.980       0.000        38462.961      38462.961
           0.950       0.000        38462.961      38462.961
           0.900       0.000        38462.961      38462.961
           0.800       0.000        38462.961      38462.961
           0.700       0.000        38462.961      38462.961
           0.500       0.000        38462.961      38462.961
           0.300       0.000        38462.961      38462.961
           0.200       0.000        38462.961      38462.961
           0.100       0.000        38462.961      38462.961
           0.050       0.000        38462.961      38462.961
           0.020       0.000        38462.961      38462.961
           0.010       0.000        38462.961      38462.961

    Bayesian (BIC)

        Mean                             38573.753
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000        38573.753      38573.753
           0.980       0.000        38573.753      38573.753
           0.950       0.000        38573.753      38573.753
           0.900       0.000        38573.753      38573.753
           0.800       0.000        38573.753      38573.753
           0.700       0.000        38573.753      38573.753
           0.500       0.000        38573.753      38573.753
           0.300       0.000        38573.753      38573.753
           0.200       0.000        38573.753      38573.753
           0.100       0.000        38573.753      38573.753
           0.050       0.000        38573.753      38573.753
           0.020       0.000        38573.753      38573.753
           0.010       0.000        38573.753      38573.753

    Sample-Size Adjusted BIC (n* = (n + 2) / 24)

        Mean                             38519.733
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000        38519.733      38519.733
           0.980       0.000        38519.733      38519.733
           0.950       0.000        38519.733      38519.733
           0.900       0.000        38519.733      38519.733
           0.800       0.000        38519.733      38519.733
           0.700       0.000        38519.733      38519.733
           0.500       0.000        38519.733      38519.733
           0.300       0.000        38519.733      38519.733
           0.200       0.000        38519.733      38519.733
           0.100       0.000        38519.733      38519.733
           0.050       0.000        38519.733      38519.733
           0.020       0.000        38519.733      38519.733
           0.010       0.000        38519.733      38519.733

Chi-Square Test of Model Fit for the Binary and Ordered Categorical
(Ordinal) Outcomes

    Pearson Chi-Square

        Mean                                65.388
        Std Dev                              0.000
        Degrees of freedom                      63
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       1.000           39.855         65.388
           0.980       1.000           42.143         65.388
           0.950       1.000           45.741         65.388
           0.900       1.000           49.111         65.388
           0.800       1.000           53.412         65.388
           0.700       1.000           56.666         65.388
           0.500       1.000           62.335         65.388
           0.300       0.000           68.369         65.388
           0.200       0.000           72.201         65.388
           0.100       0.000           77.745         65.388
           0.050       0.000           82.529         65.388
           0.020       0.000           88.137         65.388
           0.010       0.000           92.010         65.388

    Likelihood Ratio Chi-Square

        Mean                                62.447
        Std Dev                              0.000
        Degrees of freedom                      63
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       1.000           39.855         62.447
           0.980       1.000           42.143         62.447
           0.950       1.000           45.741         62.447
           0.900       1.000           49.111         62.447
           0.800       1.000           53.412         62.447
           0.700       1.000           56.666         62.447
           0.500       1.000           62.335         62.447
           0.300       0.000           68.369         62.447
           0.200       0.000           72.201         62.447
           0.100       0.000           77.745         62.447
           0.050       0.000           82.529         62.447
           0.020       0.000           88.137         62.447
           0.010       0.000           92.010         62.447



FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL

    Latent
   Classes

       1       2072.13560          0.41443
       2       2927.86440          0.58557


FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES

    Latent
   Classes

       1       2072.13560          0.41443
       2       2927.86440          0.58557


CLASSIFICATION QUALITY

     Entropy                         0.205


CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

Class Counts and Proportions

    Latent
   Classes

       1             1889          0.37780
       2             3111          0.62220


Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)

           1        2

    1   0.680    0.320
    2   0.253    0.747


MODEL RESULTS

                           ESTIMATES              S. E.     M. S. E.  95%  % Sig
              Population   Average   Std. Dev.   Average             Cover Coeff
Latent Class 1

 Thresholds
  U1$1             0.500     0.6155     0.0000     0.1922     0.0133 1.000 1.000
  U1$2             1.000     1.1468     0.0000     0.2238     0.0215 1.000 1.000
  U2$1             0.500     0.6035     0.0000     0.1786     0.0107 1.000 1.000
  U2$2             1.000     1.1436     0.0000     0.2142     0.0206 1.000 1.000
  U3$1            -0.500    -0.7133     0.0000     0.1820     0.0455 1.000 1.000
  U3$2             0.000    -0.1972     0.0000     0.1535     0.0389 1.000 0.000
  U4$1            -0.500    -0.5559     0.0000     0.1735     0.0031 1.000 1.000
  U4$2             0.000    -0.0520     0.0000     0.1592     0.0027 1.000 0.000

Latent Class 2

 Thresholds
  U1$1            -0.500    -0.5227     0.0000     0.1412     0.0005 1.000 1.000
  U1$2             0.000    -0.0021     0.0000     0.1282     0.0000 1.000 0.000
  U2$1            -0.500    -0.3639     0.0000     0.1136     0.0185 1.000 1.000
  U2$2             0.000     0.1021     0.0000     0.1092     0.0104 1.000 0.000
  U3$1             0.500     0.4252     0.0000     0.1472     0.0056 1.000 1.000
  U3$2             1.000     0.8937     0.0000     0.1625     0.0113 1.000 1.000
  U4$1             0.500     0.3827     0.0000     0.1093     0.0138 1.000 1.000
  U4$2             1.000     0.8724     0.0000     0.1162     0.0163 1.000 1.000

Categorical Latent Variables

 Means
  C#1              0.000    -0.3457     0.0000     0.4738     0.1195 1.000 0.000


QUALITY OF NUMERICAL RESULTS

     Average Condition Number for the Information Matrix      0.788E-03
       (ratio of smallest to largest eigenvalue)


TECHNICAL 1 OUTPUT


     PARAMETER SPECIFICATION FOR LATENT CLASS 1


     PARAMETER SPECIFICATION FOR LATENT CLASS 2


     PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR MODEL PART


           TAU(U) FOR LATENT CLASS 1
              U1$1          U1$2          U2$1          U2$2          U3$1
              ________      ________      ________      ________      ________
 1                  1             2             3             4             5


           TAU(U) FOR LATENT CLASS 1
              U3$2          U4$1          U4$2
              ________      ________      ________
 1                  6             7             8


           TAU(U) FOR LATENT CLASS 2
              U1$1          U1$2          U2$1          U2$2          U3$1
              ________      ________      ________      ________      ________
 1                  9            10            11            12            13


           TAU(U) FOR LATENT CLASS 2
              U3$2          U4$1          U4$2
              ________      ________      ________
 1                 14            15            16


     PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1           C#2
              ________      ________
 1                 17             0


     STARTING VALUES FOR LATENT CLASS 1


     STARTING VALUES FOR LATENT CLASS 2


     STARTING VALUES FOR LATENT CLASS INDICATOR MODEL PART


           TAU(U) FOR LATENT CLASS 1
              U1$1          U1$2          U2$1          U2$2          U3$1
              ________      ________      ________      ________      ________
 1              0.500         1.000         0.500         1.000        -0.500


           TAU(U) FOR LATENT CLASS 1
              U3$2          U4$1          U4$2
              ________      ________      ________
 1              0.000        -0.500         0.000


           TAU(U) FOR LATENT CLASS 2
              U1$1          U1$2          U2$1          U2$2          U3$1
              ________      ________      ________      ________      ________
 1             -0.500         0.000        -0.500         0.000         0.500


           TAU(U) FOR LATENT CLASS 2
              U3$2          U4$1          U4$2
              ________      ________      ________
 1              1.000         0.500         1.000


     STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1           C#2
              ________      ________
 1              0.000         0.000


     POPULATION VALUES FOR LATENT CLASS 1


     POPULATION VALUES FOR LATENT CLASS 2


     POPULATION VALUES FOR LATENT CLASS INDICATOR MODEL PART


           TAU(U) FOR LATENT CLASS 1
              U1$1          U1$2          U2$1          U2$2          U3$1
              ________      ________      ________      ________      ________
 1              0.500         1.000         0.500         1.000        -0.500


           TAU(U) FOR LATENT CLASS 1
              U3$2          U4$1          U4$2
              ________      ________      ________
 1              0.000        -0.500         0.000


           TAU(U) FOR LATENT CLASS 2
              U1$1          U1$2          U2$1          U2$2          U3$1
              ________      ________      ________      ________      ________
 1             -0.500         0.000        -0.500         0.000         0.500


           TAU(U) FOR LATENT CLASS 2
              U3$2          U4$1          U4$2
              ________      ________      ________
 1              1.000         0.500         1.000


     POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1           C#2
              ________      ________
 1              0.000         0.000


TECHNICAL 8 OUTPUT


  TECHNICAL 8 OUTPUT FOR REPLICATION 1


  ITER  LOGLIKELIHOOD    ABS CHANGE   REL CHANGE      CLASS COUNTS    ALGORITHM
     1 -0.19220264D+05    0.0000000    0.0000000   2507.984  2492.016    EM
     2 -0.19215521D+05    4.7428188    0.0002468   2506.584  2493.416    EM
     3 -0.19215311D+05    0.2101882    0.0000109   2505.184  2494.816    EM
     4 -0.19215164D+05    0.1468223    0.0000076   2503.787  2496.213    EM
     5 -0.19215060D+05    0.1041606    0.0000054   2502.394  2497.606    EM
     6 -0.19214985D+05    0.0749645    0.0000039   2501.008  2498.992    EM
     7 -0.19214930D+05    0.0546586    0.0000028   2499.628  2500.372    EM
     8 -0.19214890D+05    0.0403263    0.0000021   2498.256  2501.744    EM
     9 -0.19214860D+05    0.0300803    0.0000016   2496.892  2503.108    EM
    10 -0.19214837D+05    0.0226767    0.0000012   2495.536  2504.464    EM
    11 -0.19214820D+05    0.0172794    0.0000009   2494.187  2505.813    EM
    12 -0.19214807D+05    0.0133160    0.0000007   2492.847  2507.153    EM
    13 -0.19214796D+05    0.0103883    0.0000005   2491.514  2508.486    EM
    14 -0.19214788D+05    0.0082147    0.0000004   2490.188  2509.812    EM
    15 -0.19214781D+05    0.0065943    0.0000003   2488.870  2511.130    EM
    16 -0.19214776D+05    0.0053817    0.0000003   2487.559  2512.441    EM
    17 -0.19214772D+05    0.0044714    0.0000002   2486.254  2513.746    EM
    18 -0.19214768D+05    0.0037858    0.0000002   2484.956  2515.044    EM
    19 -0.19214764D+05    0.0032679    0.0000002   2483.663  2516.337    EM
    20 -0.19214762D+05    0.0028755    0.0000001   2482.377  2517.623    EM
    21 -0.19214759D+05    0.0025771    0.0000001   2481.096  2518.904    EM
    22 -0.19214757D+05    0.0023495    0.0000001   2479.821  2520.179    EM
    23 -0.19214755D+05    0.0021750    0.0000001   2478.551  2521.449    EM
    24 -0.19214752D+05    0.0020406    0.0000001   2477.286  2522.714    EM
    25 -0.19214751D+05    0.0019365    0.0000001   2476.026  2523.974    EM
    26 -0.19214749D+05    0.0018553    0.0000001   2474.771  2525.229    EM
    27 -0.19214747D+05    0.0017913    0.0000001   2473.520  2526.480    EM
    28 -0.19214745D+05    0.0017406    0.0000001   2472.275  2527.725    EM
    29 -0.19214743D+05    0.0016997    0.0000001   2471.033  2528.967    EM
    30 -0.19214742D+05    0.0016664    0.0000001   2469.796  2530.204    EM
    31 -0.19214740D+05    0.0016389    0.0000001   2468.564  2531.436    EM
    32 -0.19214739D+05    0.0016157    0.0000001   2467.335  2532.665    EM
    33 -0.19214737D+05    0.0015959    0.0000001   2466.111  2533.889    EM
    34 -0.19214735D+05    0.0015786    0.0000001   2464.890  2535.110    EM
    35 -0.19214525D+05    0.2104860    0.0000110   2112.963  2887.037    FS
    36 -0.19214481D+05    0.0441631    0.0000023   2082.494  2917.506    FS
    37 -0.19214480D+05    0.0003261    0.0000000   2073.984  2926.016    FS
    38 -0.19214480D+05    0.0000124    0.0000000   2072.576  2927.424    FS
    39 -0.19214480D+05    0.0000006    0.0000000   2072.192  2927.808    FS
    40 -0.19214480D+05    0.0000000    0.0000000   2072.136  2927.864    FS


TECHNICAL 9 OUTPUT

  Error messages for each replication (if any)



SAVEDATA INFORMATION

  Order of variables

    U1
    U2
    U3
    U4
    C

  Save file
    ex7.6.dat

  Save file format           Free
  Save file record length    10000


     Beginning Time:  22:03:27
        Ending Time:  22:03:27
       Elapsed Time:  00:00:00



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