Mplus VERSION 7
MUTHEN & MUTHEN
09/22/2012  10:04 PM

INPUT INSTRUCTIONS

  title: this is an example of a LCGA for a binary
              outcome
  montecarlo:
  	names are u1-u4;
  	generate = u1-u4(1);
  	categorical = u1-u4;
  	genclasses = c(2);
  	classes = c(2);
  	nobs = 500;
  	seed = 3454367;
  	nrep = 1;
  	save = ex8.9.dat;

  ANALYSIS:
  	TYPE = MIXTURE;

  model population:
  	%overall%
  	i s | u1@0 u2@1 u3@2 u4@3;
  	[i*1 s*1];
  	[u1$1-u4$1*1] (1);

  	%c#2%

  	[i@0 s*0];

  MODEL:
       %overall%
  	i s | u1@0 u2@1 u3@2 u4@3;
  	[i*1 s*1];
  	[u1$1-u4$1*1] (1);

  	%c#2%

  	[i@0 s*0];


  OUTPUT:
  	tech8 tech9;



INPUT READING TERMINATED NORMALLY



this is an example of a LCGA for a binary
outcome

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

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                            2
Number of categorical latent variables                           1

Observed dependent variables

  Binary and ordered categorical (ordinal)
   U1          U2          U3          U4

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                                 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                        5

Loglikelihood

    H0 Value

        Mean                             -1272.307
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000        -1272.307      -1272.307
           0.980       0.000        -1272.307      -1272.307
           0.950       0.000        -1272.307      -1272.307
           0.900       0.000        -1272.307      -1272.307
           0.800       0.000        -1272.307      -1272.307
           0.700       0.000        -1272.307      -1272.307
           0.500       0.000        -1272.307      -1272.307
           0.300       0.000        -1272.307      -1272.307
           0.200       0.000        -1272.307      -1272.307
           0.100       0.000        -1272.307      -1272.307
           0.050       0.000        -1272.307      -1272.307
           0.020       0.000        -1272.307      -1272.307
           0.010       0.000        -1272.307      -1272.307

Information Criteria

    Akaike (AIC)

        Mean                              2554.614
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000         2554.614       2554.614
           0.980       0.000         2554.614       2554.614
           0.950       0.000         2554.614       2554.614
           0.900       0.000         2554.614       2554.614
           0.800       0.000         2554.614       2554.614
           0.700       0.000         2554.614       2554.614
           0.500       0.000         2554.614       2554.614
           0.300       0.000         2554.614       2554.614
           0.200       0.000         2554.614       2554.614
           0.100       0.000         2554.614       2554.614
           0.050       0.000         2554.614       2554.614
           0.020       0.000         2554.614       2554.614
           0.010       0.000         2554.614       2554.614

    Bayesian (BIC)

        Mean                              2575.687
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000         2575.687       2575.687
           0.980       0.000         2575.687       2575.687
           0.950       0.000         2575.687       2575.687
           0.900       0.000         2575.687       2575.687
           0.800       0.000         2575.687       2575.687
           0.700       0.000         2575.687       2575.687
           0.500       0.000         2575.687       2575.687
           0.300       0.000         2575.687       2575.687
           0.200       0.000         2575.687       2575.687
           0.100       0.000         2575.687       2575.687
           0.050       0.000         2575.687       2575.687
           0.020       0.000         2575.687       2575.687
           0.010       0.000         2575.687       2575.687

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

        Mean                              2559.817
        Std Dev                              0.000
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       0.000         2559.817       2559.817
           0.980       0.000         2559.817       2559.817
           0.950       0.000         2559.817       2559.817
           0.900       0.000         2559.817       2559.817
           0.800       0.000         2559.817       2559.817
           0.700       0.000         2559.817       2559.817
           0.500       0.000         2559.817       2559.817
           0.300       0.000         2559.817       2559.817
           0.200       0.000         2559.817       2559.817
           0.100       0.000         2559.817       2559.817
           0.050       0.000         2559.817       2559.817
           0.020       0.000         2559.817       2559.817
           0.010       0.000         2559.817       2559.817

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

    Pearson Chi-Square

        Mean                                 6.013
        Std Dev                              0.000
        Degrees of freedom                      10
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       1.000            2.558          6.013
           0.980       1.000            3.059          6.013
           0.950       1.000            3.940          6.013
           0.900       1.000            4.865          6.013
           0.800       0.000            6.179          6.013
           0.700       0.000            7.267          6.013
           0.500       0.000            9.342          6.013
           0.300       0.000           11.781          6.013
           0.200       0.000           13.442          6.013
           0.100       0.000           15.987          6.013
           0.050       0.000           18.307          6.013
           0.020       0.000           21.161          6.013
           0.010       0.000           23.209          6.013

    Likelihood Ratio Chi-Square

        Mean                                 6.044
        Std Dev                              0.000
        Degrees of freedom                      10
        Number of successful computations        1

             Proportions                   Percentiles
        Expected    Observed         Expected       Observed
           0.990       1.000            2.558          6.044
           0.980       1.000            3.059          6.044
           0.950       1.000            3.940          6.044
           0.900       1.000            4.865          6.044
           0.800       0.000            6.179          6.044
           0.700       0.000            7.267          6.044
           0.500       0.000            9.342          6.044
           0.300       0.000           11.781          6.044
           0.200       0.000           13.442          6.044
           0.100       0.000           15.987          6.044
           0.050       0.000           18.307          6.044
           0.020       0.000           21.161          6.044
           0.010       0.000           23.209          6.044



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

    Latent
   Classes

       1        230.84750          0.46169
       2        269.15250          0.53831


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

    Latent
   Classes

       1        230.84750          0.46169
       2        269.15250          0.53831


CLASSIFICATION QUALITY

     Entropy                         0.648


CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

Class Counts and Proportions

    Latent
   Classes

       1              234          0.46800
       2              266          0.53200


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

           1        2

    1   0.889    0.111
    2   0.086    0.914


MODEL RESULTS

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

 I        |
  U1               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  U2               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  U3               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  U4               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000

 S        |
  U1               0.000     0.0000     0.0000     0.0000     0.0000 1.000 0.000
  U2               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  U3               2.000     2.0000     0.0000     0.0000     0.0000 1.000 0.000
  U4               3.000     3.0000     0.0000     0.0000     0.0000 1.000 0.000

 Means
  I                1.000     0.7993     0.0000     0.2003     0.0403 1.000 1.000
  S                1.000     1.0968     0.0000     0.2002     0.0094 1.000 1.000

 Thresholds
  U1$1             1.000     0.7761     0.0000     0.1237     0.0501 1.000 1.000
  U2$1             1.000     0.7761     0.0000     0.1237     0.0501 1.000 1.000
  U3$1             1.000     0.7761     0.0000     0.1237     0.0501 1.000 1.000
  U4$1             1.000     0.7761     0.0000     0.1237     0.0501 1.000 1.000

Latent Class 2

 I        |
  U1               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  U2               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  U3               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  U4               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000

 S        |
  U1               0.000     0.0000     0.0000     0.0000     0.0000 1.000 0.000
  U2               1.000     1.0000     0.0000     0.0000     0.0000 1.000 0.000
  U3               2.000     2.0000     0.0000     0.0000     0.0000 1.000 0.000
  U4               3.000     3.0000     0.0000     0.0000     0.0000 1.000 0.000

 Means
  I                0.000     0.0000     0.0000     0.0000     0.0000 1.000 0.000
  S                0.000    -0.0682     0.0000     0.0860     0.0047 1.000 0.000

 Thresholds
  U1$1             1.000     0.7761     0.0000     0.1237     0.0501 1.000 1.000
  U2$1             1.000     0.7761     0.0000     0.1237     0.0501 1.000 1.000
  U3$1             1.000     0.7761     0.0000     0.1237     0.0501 1.000 1.000
  U4$1             1.000     0.7761     0.0000     0.1237     0.0501 1.000 1.000

Categorical Latent Variables

 Means
  C#1              0.000    -0.1535     0.0000     0.1985     0.0236 1.000 0.000


QUALITY OF NUMERICAL RESULTS

     Average Condition Number for the Information Matrix      0.247E-01
       (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          U2$1          U3$1          U4$1
              ________      ________      ________      ________
 1                  1             1             1             1


           TAU(U) FOR LATENT CLASS 2
              U1$1          U2$1          U3$1          U4$1
              ________      ________      ________      ________
 1                  1             1             1             1


     PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1           C#2
              ________      ________
 1                  5             0


     PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR GROWTH MODEL PART


           LAMBDA(F) FOR LATENT CLASS 1
              I             S
              ________      ________
 U1                 0             0
 U2                 0             0
 U3                 0             0
 U4                 0             0


           ALPHA(F) FOR LATENT CLASS 1
              I             S
              ________      ________
 1                  2             3


           LAMBDA(F) FOR LATENT CLASS 2
              I             S
              ________      ________
 U1                 0             0
 U2                 0             0
 U3                 0             0
 U4                 0             0


           ALPHA(F) FOR LATENT CLASS 2
              I             S
              ________      ________
 1                  0             4


     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          U2$1          U3$1          U4$1
              ________      ________      ________      ________
 1              1.000         1.000         1.000         1.000


           TAU(U) FOR LATENT CLASS 2
              U1$1          U2$1          U3$1          U4$1
              ________      ________      ________      ________
 1              1.000         1.000         1.000         1.000


     STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART


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


     STARTING VALUES FOR LATENT CLASS INDICATOR GROWTH MODEL PART


           LAMBDA(F) FOR CLASS LATENT CLASS 1
              I             S
              ________      ________
 U1             1.000         0.000
 U2             1.000         1.000
 U3             1.000         2.000
 U4             1.000         3.000


           ALPHA(F) FOR LATENT CLASS 1
              I             S
              ________      ________
 1              1.000         1.000


           LAMBDA(F) FOR CLASS LATENT CLASS 2
              I             S
              ________      ________
 U1             1.000         0.000
 U2             1.000         1.000
 U3             1.000         2.000
 U4             1.000         3.000


           ALPHA(F) FOR LATENT CLASS 2
              I             S
              ________      ________
 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          U2$1          U3$1          U4$1
              ________      ________      ________      ________
 1              1.000         1.000         1.000         1.000


           TAU(U) FOR LATENT CLASS 2
              U1$1          U2$1          U3$1          U4$1
              ________      ________      ________      ________
 1              1.000         1.000         1.000         1.000


     POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART


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


     POPULATION VALUES FOR LATENT CLASS INDICATOR GROWTH MODEL PART


           LAMBDA(F) FOR LATENT CLASS 1
              I             S
              ________      ________
 U1             1.000         0.000
 U2             1.000         1.000
 U3             1.000         2.000
 U4             1.000         3.000


           ALPHA(F) FOR LATENT CLASS 1
              I             S
              ________      ________
 1              1.000         1.000


           LAMBDA(F) FOR LATENT CLASS 2
              I             S
              ________      ________
 U1             1.000         0.000
 U2             1.000         1.000
 U3             1.000         2.000
 U4             1.000         3.000


           ALPHA(F) FOR LATENT CLASS 2
              I             S
              ________      ________
 1              0.000         0.000


TECHNICAL 8 OUTPUT


  TECHNICAL 8 OUTPUT FOR REPLICATION 1


  ITER  LOGLIKELIHOOD    ABS CHANGE   REL CHANGE      CLASS COUNTS    ALGORITHM
     1 -0.12740621D+04    0.0000000    0.0000000    245.338   254.662    EM
     2 -0.12725843D+04    1.4777716    0.0011599    244.216   255.784    EM
     3 -0.12725080D+04    0.0762856    0.0000599    243.224   256.776    EM
     4 -0.12724721D+04    0.0359594    0.0000283    242.272   257.728    EM
     5 -0.12724459D+04    0.0261224    0.0000205    241.374   258.626    EM
     6 -0.12724245D+04    0.0214066    0.0000168    240.537   259.463    EM
     7 -0.12724065D+04    0.0180166    0.0000142    239.763   260.237    EM
     8 -0.12723913D+04    0.0152468    0.0000120    239.048   260.952    EM
     9 -0.12723783D+04    0.0129155    0.0000102    238.390   261.610    EM
    10 -0.12723674D+04    0.0109412    0.0000086    237.784   262.216    EM
    11 -0.12723581D+04    0.0092674    0.0000073    237.226   262.774    EM
    12 -0.12723503D+04    0.0078483    0.0000062    236.713   263.287    EM
    13 -0.12723436D+04    0.0066456    0.0000052    236.242   263.758    EM
    14 -0.12723380D+04    0.0056264    0.0000044    235.808   264.192    EM
    15 -0.12723333D+04    0.0047629    0.0000037    235.409   264.591    EM
    16 -0.12723292D+04    0.0040314    0.0000032    235.042   264.958    EM
    17 -0.12723258D+04    0.0034120    0.0000027    234.704   265.296    EM
    18 -0.12723229D+04    0.0028874    0.0000023    234.394   265.606    EM
    19 -0.12723205D+04    0.0024434    0.0000019    234.109   265.891    EM
    20 -0.12723184D+04    0.0020674    0.0000016    233.846   266.154    EM
    21 -0.12723167D+04    0.0017492    0.0000014    233.605   266.395    EM
    22 -0.12723152D+04    0.0014799    0.0000012    233.383   266.617    EM
    23 -0.12723139D+04    0.0012519    0.0000010    233.179   266.821    EM
    24 -0.12723129D+04    0.0010591    0.0000008    232.991   267.009    EM
    25 -0.12723120D+04    0.0008959    0.0000007    232.819   267.181    EM
    26 -0.12723112D+04    0.0007578    0.0000006    232.660   267.340    EM
    27 -0.12723106D+04    0.0006409    0.0000005    232.514   267.486    EM
    28 -0.12723100D+04    0.0005421    0.0000004    232.380   267.620    EM
    29 -0.12723096D+04    0.0004585    0.0000004    232.257   267.743    EM
    30 -0.12723092D+04    0.0003878    0.0000003    232.143   267.857    EM
    31 -0.12723089D+04    0.0003280    0.0000003    232.039   267.961    EM
    32 -0.12723086D+04    0.0002774    0.0000002    231.943   268.057    EM
    33 -0.12723083D+04    0.0002346    0.0000002    231.855   268.145    EM
    34 -0.12723082D+04    0.0001984    0.0000002    231.774   268.226    EM
    35 -0.12723080D+04    0.0001677    0.0000001    231.699   268.301    EM
    36 -0.12723071D+04    0.0009052    0.0000007    230.946   269.054    FS
    37 -0.12723071D+04    0.0000133    0.0000000    230.860   269.140    FS
    38 -0.12723071D+04    0.0000002    0.0000000    230.849   269.151    FS
    39 -0.12723071D+04    0.0000000    0.0000000    230.847   269.153    FS


TECHNICAL 9 OUTPUT

  Error messages for each replication (if any)



SAVEDATA INFORMATION

  Order of variables

    U1
    U2
    U3
    U4
    C

  Save file
    ex8.9.dat

  Save file format           Free
  Save file record length    10000


     Beginning Time:  22:04:08
        Ending Time:  22:04:08
       Elapsed Time:  00:00:00



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