Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 10:24 PM
INPUT INSTRUCTIONS
title:
this is an example of a LCA with binary
latent class indicators using automatic
starting values with random starts
!same as ex7.3 and ex7.4
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 = ex7.5.dat;
analysis:
type = mixture;
model population:
%overall%
[c#1*0];
%c#1%
[u1$1*1 u2$1*1 u3$1*-1 u4$1*-1];
%c#2%
[u1$1*-1 u2$1*-1 u3$1*1 u4$1*1];
model:
%overall%
[c#1*0];
%c#1%
[u1$1*1 u2$1*1 u3$1*-1 u4$1*-1];
%c#2%
[u1$1*-1 u2$1*-1 u3$1*1 u4$1*1];
output:
tech8 tech9;
INPUT READING TERMINATED NORMALLY
this is an example of a LCA with binary
latent class indicators using automatic
starting values with random starts
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 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 9
Loglikelihood
H0 Value
Mean -1325.213
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 -1325.213 -1325.213
0.980 0.000 -1325.213 -1325.213
0.950 0.000 -1325.213 -1325.213
0.900 0.000 -1325.213 -1325.213
0.800 0.000 -1325.213 -1325.213
0.700 0.000 -1325.213 -1325.213
0.500 0.000 -1325.213 -1325.213
0.300 0.000 -1325.213 -1325.213
0.200 0.000 -1325.213 -1325.213
0.100 0.000 -1325.213 -1325.213
0.050 0.000 -1325.213 -1325.213
0.020 0.000 -1325.213 -1325.213
0.010 0.000 -1325.213 -1325.213
Information Criteria
Akaike (AIC)
Mean 2668.425
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 2668.425 2668.425
0.980 0.000 2668.425 2668.425
0.950 0.000 2668.425 2668.425
0.900 0.000 2668.425 2668.425
0.800 0.000 2668.425 2668.425
0.700 0.000 2668.425 2668.425
0.500 0.000 2668.425 2668.425
0.300 0.000 2668.425 2668.425
0.200 0.000 2668.425 2668.425
0.100 0.000 2668.425 2668.425
0.050 0.000 2668.425 2668.425
0.020 0.000 2668.425 2668.425
0.010 0.000 2668.425 2668.425
Bayesian (BIC)
Mean 2706.357
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 2706.357 2706.357
0.980 0.000 2706.357 2706.357
0.950 0.000 2706.357 2706.357
0.900 0.000 2706.357 2706.357
0.800 0.000 2706.357 2706.357
0.700 0.000 2706.357 2706.357
0.500 0.000 2706.357 2706.357
0.300 0.000 2706.357 2706.357
0.200 0.000 2706.357 2706.357
0.100 0.000 2706.357 2706.357
0.050 0.000 2706.357 2706.357
0.020 0.000 2706.357 2706.357
0.010 0.000 2706.357 2706.357
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 2677.790
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 2677.790 2677.790
0.980 0.000 2677.790 2677.790
0.950 0.000 2677.790 2677.790
0.900 0.000 2677.790 2677.790
0.800 0.000 2677.790 2677.790
0.700 0.000 2677.790 2677.790
0.500 0.000 2677.790 2677.790
0.300 0.000 2677.790 2677.790
0.200 0.000 2677.790 2677.790
0.100 0.000 2677.790 2677.790
0.050 0.000 2677.790 2677.790
0.020 0.000 2677.790 2677.790
0.010 0.000 2677.790 2677.790
Chi-Square Test of Model Fit for the Binary and Ordered Categorical
(Ordinal) Outcomes
Pearson Chi-Square
Mean 12.611
Std Dev 0.000
Degrees of freedom 6
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 1.000 0.872 12.611
0.980 1.000 1.134 12.611
0.950 1.000 1.635 12.611
0.900 1.000 2.204 12.611
0.800 1.000 3.070 12.611
0.700 1.000 3.828 12.611
0.500 1.000 5.348 12.611
0.300 1.000 7.231 12.611
0.200 1.000 8.558 12.611
0.100 1.000 10.645 12.611
0.050 1.000 12.592 12.611
0.020 0.000 15.033 12.611
0.010 0.000 16.812 12.611
Likelihood Ratio Chi-Square
Mean 12.742
Std Dev 0.000
Degrees of freedom 6
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 1.000 0.872 12.742
0.980 1.000 1.134 12.742
0.950 1.000 1.635 12.742
0.900 1.000 2.204 12.742
0.800 1.000 3.070 12.742
0.700 1.000 3.828 12.742
0.500 1.000 5.348 12.742
0.300 1.000 7.231 12.742
0.200 1.000 8.558 12.742
0.100 1.000 10.645 12.742
0.050 1.000 12.592 12.742
0.020 0.000 15.033 12.742
0.010 0.000 16.812 12.742
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 255.11204 0.51022
2 244.88796 0.48978
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 255.11204 0.51022
2 244.88796 0.48978
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 272 0.54400
2 228 0.45600
CLASSIFICATION QUALITY
Entropy 0.504
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.832 0.168
2 0.126 0.874
Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 0.888 0.112
2 0.186 0.814
Logits for the Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 2.066 0.000
2 -1.475 0.000
MODEL RESULTS
ESTIMATES S. E. M. S. E. 95% % Sig
Population Average Std. Dev. Average Cover Coeff
Latent Class 1
Thresholds
U1$1 1.000 1.2208 0.0000 0.2428 0.0487 1.000 1.000
U2$1 1.000 1.0856 0.0000 0.2730 0.0073 1.000 1.000
U3$1 -1.000 -0.9059 0.0000 0.1897 0.0089 1.000 1.000
U4$1 -1.000 -0.5107 0.0000 0.2238 0.2394 0.000 1.000
Latent Class 2
Thresholds
U1$1 -1.000 -1.2865 0.0000 0.3444 0.0821 1.000 1.000
U2$1 -1.000 -1.1190 0.0000 0.2394 0.0142 1.000 1.000
U3$1 1.000 0.9905 0.0000 0.2597 0.0001 1.000 1.000
U4$1 1.000 1.0483 0.0000 0.1793 0.0023 1.000 1.000
Categorical Latent Variables
Means
C#1 0.000 0.0409 0.0000 0.2536 0.0017 1.000 0.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.421E-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 2 3 4
TAU(U) FOR LATENT CLASS 2
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
5 6 7 8
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
9 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 U2$1 U3$1 U4$1
________ ________ ________ ________
1.000 1.000 -1.000 -1.000
TAU(U) FOR LATENT CLASS 2
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
-1.000 -1.000 1.000 1.000
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
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
________ ________ ________ ________
1.000 1.000 -1.000 -1.000
TAU(U) FOR LATENT CLASS 2
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
-1.000 -1.000 1.000 1.000
POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
0.000 0.000
TECHNICAL 8 OUTPUT
TECHNICAL 8 OUTPUT FOR REPLICATION 1
E STEP ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE ALGORITHM
1 -0.13308427D+04 0.0000000 0.0000000 EM
2 -0.13261044D+04 4.7383081 0.0035604 EM
3 -0.13255717D+04 0.5326802 0.0004017 EM
4 -0.13253885D+04 0.1831222 0.0001381 EM
5 -0.13253222D+04 0.0663120 0.0000500 EM
6 -0.13252942D+04 0.0280563 0.0000212 EM
7 -0.13252792D+04 0.0149917 0.0000113 EM
8 -0.13252692D+04 0.0099642 0.0000075 EM
9 -0.13252616D+04 0.0076148 0.0000057 EM
10 -0.13252554D+04 0.0062499 0.0000047 EM
11 -0.13252501D+04 0.0053068 0.0000040 EM
12 -0.13252455D+04 0.0045809 0.0000035 EM
13 -0.13252415D+04 0.0039878 0.0000030 EM
14 -0.13252380D+04 0.0034873 0.0000026 EM
15 -0.13252349D+04 0.0030574 0.0000023 EM
16 -0.13252323D+04 0.0026844 0.0000020 EM
17 -0.13252299D+04 0.0023588 0.0000018 EM
18 -0.13252278D+04 0.0020738 0.0000016 EM
19 -0.13252260D+04 0.0018236 0.0000014 EM
20 -0.13252244D+04 0.0016038 0.0000012 EM
21 -0.13252230D+04 0.0014106 0.0000011 EM
22 -0.13252217D+04 0.0012407 0.0000009 EM
23 -0.13252207D+04 0.0010912 0.0000008 EM
24 -0.13252197D+04 0.0009598 0.0000007 EM
25 -0.13252188D+04 0.0008441 0.0000006 EM
26 -0.13252181D+04 0.0007424 0.0000006 EM
27 -0.13252175D+04 0.0006529 0.0000005 EM
28 -0.13252169D+04 0.0005741 0.0000004 EM
29 -0.13252164D+04 0.0005049 0.0000004 EM
30 -0.13252159D+04 0.0004439 0.0000003 EM
31 -0.13252155D+04 0.0003904 0.0000003 EM
32 -0.13252152D+04 0.0003432 0.0000003 EM
33 -0.13252149D+04 0.0003018 0.0000002 EM
34 -0.13252146D+04 0.0002653 0.0000002 EM
35 -0.13252144D+04 0.0002333 0.0000002 EM
36 -0.13252142D+04 0.0002051 0.0000002 EM
37 -0.13252140D+04 0.0001803 0.0000001 EM
38 -0.13252128D+04 0.0011615 0.0000009 FS
39 -0.13252127D+04 0.0001294 0.0000001 FS
40 -0.13252127D+04 0.0000172 0.0000000 FS
41 -0.13252127D+04 0.0000024 0.0000000 FS
42 -0.13252127D+04 0.0000003 0.0000000 FS
43 -0.13252127D+04 0.0000000 0.0000000 FS
TECHNICAL 9 OUTPUT
Error messages for each replication (if any)
SAVEDATA INFORMATION
Order of variables
U1
U2
U3
U4
C
Save file
ex7.5.dat
Save file format Free
Save file record length 10000
Beginning Time: 22:24:38
Ending Time: 22:24:39
Elapsed Time: 00:00:01
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) 1998-2022 Muthen & Muthen
Back to examples