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;
INPUT READING TERMINATED NORMALLY
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
Link LOGIT
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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