```Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022  10:24 PM

INPUT INSTRUCTIONS

TITLE:	this is an example of a LCA with binary,
censored, unordered, and count latent
class indicators using user-specified
starting values without random starts

montecarlo:
names are u1 y1 u2 u3;
genclasses = c(2);
classes = c(2);
generate =  u1(1) y1(cb 0) u2(n 2) u3(ci);
categorical = u1;
censored = y1(b);
nominal = u2;
count = u3(i);
nobs = 1000;
seed = 3454367;
nrep = 1;
save = ex7.11.dat;

ANALYSIS:	TYPE = MIXTURE;

MODEL population:

%OVERALL%
%c#1%
[u1\$1*-1 y1*3 u2#1*0 u2#2*1 u3*.5 u3#1*1.5];
y1*2;
%c#2%
[u1\$1*0 y1*1 u2#1*-1 u2#2*0 u3*1 u3#1*1];
y1*1;

MODEL:

%OVERALL%
%c#1%
[u1\$1*-1 y1*3 u2#1*0 u2#2*1 u3*.5 u3#1*1.5];
y1*2;
%c#2%
[u1\$1*0 y1*1 u2#1*-1 u2#2*0 u3*1 u3#1*1];
y1*1;

OUTPUT:	tech8 tech9;

this is an example of a LCA with binary,
censored, unordered, and count latent
class indicators using user-specified
starting values without random starts

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        1000

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

Censored
Y1

Binary and ordered categorical (ordinal)
U1

Unordered categorical (nominal)
U2

Count
U3

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

SUMMARY OF CENSORED LIMITS

Y1                 0.000

MODEL FIT INFORMATION

Number of Free Parameters                       15

Loglikelihood

H0 Value

Mean                             -4348.434
Std Dev                              0.000
Number of successful computations        1

Proportions                   Percentiles
Expected    Observed         Expected       Observed
0.990       0.000        -4348.434      -4348.434
0.980       0.000        -4348.434      -4348.434
0.950       0.000        -4348.434      -4348.434
0.900       0.000        -4348.434      -4348.434
0.800       0.000        -4348.434      -4348.434
0.700       0.000        -4348.434      -4348.434
0.500       0.000        -4348.434      -4348.434
0.300       0.000        -4348.434      -4348.434
0.200       0.000        -4348.434      -4348.434
0.100       0.000        -4348.434      -4348.434
0.050       0.000        -4348.434      -4348.434
0.020       0.000        -4348.434      -4348.434
0.010       0.000        -4348.434      -4348.434

Information Criteria

Akaike (AIC)

Mean                              8726.869
Std Dev                              0.000
Number of successful computations        1

Proportions                   Percentiles
Expected    Observed         Expected       Observed
0.990       0.000         8726.869       8726.869
0.980       0.000         8726.869       8726.869
0.950       0.000         8726.869       8726.869
0.900       0.000         8726.869       8726.869
0.800       0.000         8726.869       8726.869
0.700       0.000         8726.869       8726.869
0.500       0.000         8726.869       8726.869
0.300       0.000         8726.869       8726.869
0.200       0.000         8726.869       8726.869
0.100       0.000         8726.869       8726.869
0.050       0.000         8726.869       8726.869
0.020       0.000         8726.869       8726.869
0.010       0.000         8726.869       8726.869

Bayesian (BIC)

Mean                              8800.485
Std Dev                              0.000
Number of successful computations        1

Proportions                   Percentiles
Expected    Observed         Expected       Observed
0.990       0.000         8800.485       8800.485
0.980       0.000         8800.485       8800.485
0.950       0.000         8800.485       8800.485
0.900       0.000         8800.485       8800.485
0.800       0.000         8800.485       8800.485
0.700       0.000         8800.485       8800.485
0.500       0.000         8800.485       8800.485
0.300       0.000         8800.485       8800.485
0.200       0.000         8800.485       8800.485
0.100       0.000         8800.485       8800.485
0.050       0.000         8800.485       8800.485
0.020       0.000         8800.485       8800.485
0.010       0.000         8800.485       8800.485

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

Mean                              8752.844
Std Dev                              0.000
Number of successful computations        1

Proportions                   Percentiles
Expected    Observed         Expected       Observed
0.990       0.000         8752.844       8752.844
0.980       0.000         8752.844       8752.844
0.950       0.000         8752.844       8752.844
0.900       0.000         8752.844       8752.844
0.800       0.000         8752.844       8752.844
0.700       0.000         8752.844       8752.844
0.500       0.000         8752.844       8752.844
0.300       0.000         8752.844       8752.844
0.200       0.000         8752.844       8752.844
0.100       0.000         8752.844       8752.844
0.050       0.000         8752.844       8752.844
0.020       0.000         8752.844       8752.844
0.010       0.000         8752.844       8752.844

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

Pearson Chi-Square

Mean                                 0.000
Std Dev                              0.000
Degrees of freedom                       0
Number of successful computations        1

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

Likelihood Ratio Chi-Square

Mean                                 0.000
Std Dev                              0.000
Degrees of freedom                       0
Number of successful computations        1

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

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

Latent
Classes

1        621.01529          0.62102
2        378.98471          0.37898

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

Latent
Classes

1        621.01529          0.62102
2        378.98471          0.37898

FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

Class Counts and Proportions

Latent
Classes

1              582          0.58200
2              418          0.41800

CLASSIFICATION QUALITY

Entropy                         0.369

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

1        2

1   0.843    0.157
2   0.312    0.688

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

1        2

1   0.790    0.210
2   0.241    0.759

Logits for the Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)

1        2

1      1.327    0.000
2     -1.149    0.000

MODEL RESULTS

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

Latent Class 1

Means
U3#1                1.500     1.5052     0.0000     0.1628     0.0000 1.000 1.000
U3                  0.500     0.7256     0.0000     0.1027     0.0509 0.000 1.000
Y1                  3.000     2.7354     0.0000     0.2066     0.0700 1.000 1.000
U2#1                0.000    -0.0060     0.0000     0.1627     0.0000 1.000 0.000
U2#2                1.000     0.9596     0.0000     0.1445     0.0016 1.000 1.000

Thresholds
U1\$1               -1.000    -0.7555     0.0000     0.1362     0.0598 1.000 1.000

Variances
Y1                  2.000     2.3324     0.0000     0.3174     0.1105 1.000 1.000

Latent Class 2

Means
U3#1                1.000     0.8256     0.0000     0.1801     0.0304 1.000 1.000
U3                  1.000     0.8966     0.0000     0.0749     0.0107 1.000 1.000
Y1                  1.000     0.9388     0.0000     0.1407     0.0038 1.000 1.000
U2#1               -1.000    -0.7716     0.0000     0.2413     0.0521 1.000 1.000
U2#2                0.000     0.0761     0.0000     0.2082     0.0058 1.000 0.000

Thresholds
U1\$1                0.000    -0.0526     0.0000     0.1797     0.0028 1.000 0.000

Variances
Y1                  1.000     0.9906     0.0000     0.2130     0.0001 1.000 1.000

Categorical Latent Variables

Means
C#1                 0.000     0.4939     0.0000     0.3441     0.2439 1.000 0.000

QUALITY OF NUMERICAL RESULTS

Average Condition Number for the Information Matrix      0.481E-02
(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
________
1

TAU(U) FOR LATENT CLASS 2
U1\$1
________
2

PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART

ALPHA(C)
C#1           C#2
________      ________
3             0

PARAMETER SPECIFICATION FOR THE CENSORED/NOMINAL/COUNT MODEL PART

NU(P) FOR LATENT CLASS 1
U3#1          U3            Y1#1          Y1            U2#1
________      ________      ________      ________      ________
4             5             0             6             7

NU(P) FOR LATENT CLASS 1
U2#2
________
8

THETA(C) FOR CLASS LATENT CLASS 1
Y1
________
9

NU(P) FOR LATENT CLASS 2
U3#1          U3            Y1#1          Y1            U2#1
________      ________      ________      ________      ________
10            11             0            12            13

NU(P) FOR LATENT CLASS 2
U2#2
________
14

THETA(C) FOR CLASS LATENT CLASS 2
Y1
________
15

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
________
-1.000

TAU(U) FOR LATENT CLASS 2
U1\$1
________
0.000

STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART

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

STARTING VALUES FOR THE CENSORED/NOMINAL/COUNT MODEL PART

NU(P) FOR LATENT CLASS 1
U3#1          U3            Y1#1          Y1            U2#1
________      ________      ________      ________      ________
1.500         0.500       -20.000         3.000         0.000

NU(P) FOR LATENT CLASS 1
U2#2
________
1.000

THETA(C) FOR LATENT CLASS 1
Y1
________
2.000

NU(P) FOR LATENT CLASS 2
U3#1          U3            Y1#1          Y1            U2#1
________      ________      ________      ________      ________
1.000         1.000       -20.000         1.000        -1.000

NU(P) FOR LATENT CLASS 2
U2#2
________
0.000

THETA(C) FOR LATENT CLASS 2
Y1
________
1.000

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
________
-1.000

TAU(U) FOR LATENT CLASS 2
U1\$1
________
0.000

POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART

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

POPULATION VALUES FOR THE CENSORED/NOMINAL/COUNT MODEL PART

NU(P) FOR LATENT CLASS 1
U3#1          U3            Y1#1          Y1            U2#1
________      ________      ________      ________      ________
1.500         0.500       -20.000         3.000         0.000

NU(P) FOR LATENT CLASS 1
U2#2
________
1.000

THETA(C) FOR LATENT CLASS 1
Y1
________
2.000

NU(P) FOR LATENT CLASS 2
U3#1          U3            Y1#1          Y1            U2#1
________      ________      ________      ________      ________
1.000         1.000       -20.000         1.000        -1.000

NU(P) FOR LATENT CLASS 2
U2#2
________
0.000

THETA(C) FOR LATENT CLASS 2
Y1
________
1.000

TECHNICAL 8 OUTPUT

TECHNICAL 8 OUTPUT FOR REPLICATION 1

E STEP  ITER  LOGLIKELIHOOD    ABS CHANGE   REL CHANGE  ALGORITHM
1 -0.43579548D+04    0.0000000    0.0000000  EM
2 -0.43501136D+04    7.8411565    0.0017993  EM
3 -0.43491950D+04    0.9186628    0.0002112  EM
4 -0.43489540D+04    0.2409893    0.0000554  EM
5 -0.43488802D+04    0.0737856    0.0000170  EM
6 -0.43488517D+04    0.0284651    0.0000065  EM
7 -0.43488364D+04    0.0153532    0.0000035  EM
8 -0.43488251D+04    0.0112674    0.0000026  EM
9 -0.43488153D+04    0.0098209    0.0000023  EM
10 -0.43488061D+04    0.0091792    0.0000021  EM
11 -0.43487973D+04    0.0087980    0.0000020  EM
12 -0.43487888D+04    0.0085104    0.0000020  EM
13 -0.43487805D+04    0.0082631    0.0000019  EM
14 -0.43487725D+04    0.0080376    0.0000018  EM
15 -0.43487647D+04    0.0078270    0.0000018  EM
16 -0.43487570D+04    0.0076277    0.0000018  EM
17 -0.43487496D+04    0.0074381    0.0000017  EM
18 -0.43487423D+04    0.0072566    0.0000017  EM
19 -0.43487352D+04    0.0070825    0.0000016  EM
20 -0.43487283D+04    0.0069148    0.0000016  EM
21 -0.43487216D+04    0.0067529    0.0000016  EM
22 -0.43487150D+04    0.0065963    0.0000015  EM
23 -0.43487085D+04    0.0064445    0.0000015  EM
24 -0.43487022D+04    0.0062972    0.0000014  EM
25 -0.43486961D+04    0.0061541    0.0000014  EM
26 -0.43486901D+04    0.0060148    0.0000014  EM
27 -0.43486842D+04    0.0058792    0.0000014  EM
28 -0.43486784D+04    0.0057471    0.0000013  EM
29 -0.43486728D+04    0.0056182    0.0000013  EM
30 -0.43486673D+04    0.0054925    0.0000013  EM
31 -0.43486620D+04    0.0053697    0.0000012  EM
32 -0.43486567D+04    0.0052499    0.0000012  EM
33 -0.43486516D+04    0.0051328    0.0000012  EM
34 -0.43486466D+04    0.0050184    0.0000012  EM
35 -0.43486417D+04    0.0049065    0.0000011  EM
36 -0.43486369D+04    0.0047972    0.0000011  EM
37 -0.43486322D+04    0.0046903    0.0000011  EM
38 -0.43486276D+04    0.0045858    0.0000011  EM
39 -0.43486231D+04    0.0044836    0.0000010  EM
40 -0.43486187D+04    0.0043836    0.0000010  EM
41 -0.43486144D+04    0.0042858    0.0000010  EM
42 -0.43486102D+04    0.0041902    0.0000010  EM
43 -0.43486061D+04    0.0040966    0.0000009  EM
44 -0.43486021D+04    0.0040051    0.0000009  EM
45 -0.43485982D+04    0.0039155    0.0000009  EM
46 -0.43485944D+04    0.0038279    0.0000009  EM
47 -0.43485907D+04    0.0037422    0.0000009  EM
48 -0.43485870D+04    0.0036583    0.0000008  EM
49 -0.43485834D+04    0.0035763    0.0000008  EM
50 -0.43485799D+04    0.0034960    0.0000008  EM
51 -0.43485765D+04    0.0034174    0.0000008  EM
52 -0.43485732D+04    0.0033406    0.0000008  EM
53 -0.43485699D+04    0.0032654    0.0000008  EM
54 -0.43485667D+04    0.0031918    0.0000007  EM
55 -0.43485636D+04    0.0031198    0.0000007  EM
56 -0.43485605D+04    0.0030494    0.0000007  EM
57 -0.43485576D+04    0.0029805    0.0000007  EM
58 -0.43485546D+04    0.0029131    0.0000007  EM
59 -0.43485518D+04    0.0028471    0.0000007  EM
60 -0.43484345D+04    0.1173271    0.0000270  QN
61 -0.43484345D+04    0.0000000    0.0000000  EM

TECHNICAL 9 OUTPUT

Error messages for each replication (if any)

SAVEDATA INFORMATION

Order of variables

U3
Y1
U2
U1
C

Save file
ex7.11.dat

Save file format           Free
Save file record length    10000

Beginning Time:  22:24:27
Ending Time:  22:24:28
Elapsed Time:  00:00:01

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Los Angeles, CA  90066

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Fax: (310) 391-8971
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Copyright (c) 1998-2022 Muthen & Muthen
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