Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:13 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a LCA with binary
latent class indicators using user-
specified starting values without random
starts
DATA: FILE IS ex7.4.dat;
VARIABLE: NAMES ARE u1-u4 c;
USEVARIABLES ARE u1-u4;
CLASSES = c (2);
CATEGORICAL = u1-u4;
ANALYSIS: TYPE = MIXTURE;
STARTS = 0;
MODEL:
%OVERALL%
%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: TECH1 TECH8;
INPUT READING TERMINATED NORMALLY
this is an example of a LCA with binary
latent class indicators using user-
specified starting values without random
starts
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
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
Input data file(s)
ex7.4.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U1
Category 1 0.500 250.000
Category 2 0.500 250.000
U2
Category 1 0.502 251.000
Category 2 0.498 249.000
U3
Category 1 0.504 252.000
Category 2 0.496 248.000
U4
Category 1 0.554 277.000
Category 2 0.446 223.000
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 9
Loglikelihood
H0 Value -1325.213
H0 Scaling Correction Factor 1.0142
for MLR
Information Criteria
Akaike (AIC) 2668.425
Bayesian (BIC) 2706.357
Sample-Size Adjusted BIC 2677.790
(n* = (n + 2) / 24)
Chi-Square Test of Model Fit for the Binary and Ordered Categorical
(Ordinal) Outcomes
Pearson Chi-Square
Value 12.611
Degrees of Freedom 6
P-Value 0.0496
Likelihood Ratio Chi-Square
Value 12.742
Degrees of Freedom 6
P-Value 0.0473
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
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
Thresholds
U1$1 1.221 0.243 5.028 0.000
U2$1 1.086 0.273 3.977 0.000
U3$1 -0.906 0.190 -4.775 0.000
U4$1 -0.511 0.224 -2.282 0.022
Latent Class 2
Thresholds
U1$1 -1.287 0.344 -3.735 0.000
U2$1 -1.119 0.239 -4.674 0.000
U3$1 0.990 0.260 3.813 0.000
U4$1 1.048 0.179 5.847 0.000
Categorical Latent Variables
Means
C#1 0.041 0.254 0.161 0.872
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.421E-01
(ratio of smallest to largest eigenvalue)
RESULTS IN PROBABILITY SCALE
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
U1
Category 1 0.772 0.043 18.081 0.000
Category 2 0.228 0.043 5.334 0.000
U2
Category 1 0.748 0.052 14.513 0.000
Category 2 0.252 0.052 4.901 0.000
U3
Category 1 0.288 0.039 7.402 0.000
Category 2 0.712 0.039 18.312 0.000
U4
Category 1 0.375 0.052 7.149 0.000
Category 2 0.625 0.052 11.914 0.000
Latent Class 2
U1
Category 1 0.216 0.058 3.705 0.000
Category 2 0.784 0.058 13.413 0.000
U2
Category 1 0.246 0.044 5.541 0.000
Category 2 0.754 0.044 16.966 0.000
U3
Category 1 0.729 0.051 14.216 0.000
Category 2 0.271 0.051 5.280 0.000
U4
Category 1 0.740 0.034 21.491 0.000
Category 2 0.260 0.034 7.533 0.000
LATENT CLASS INDICATOR ODDS RATIOS FOR THE LATENT CLASSES
95% C.I.
Estimate S.E. Lower 2.5% Upper 2.5%
Latent Class 1 Compared to Latent Class 2
U1
Category > 1 0.081 0.033 0.037 0.179
U2
Category > 1 0.110 0.038 0.056 0.218
U3
Category > 1 6.662 2.094 3.598 12.335
U4
Category > 1 4.754 1.458 2.606 8.672
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
PARAMETER SPECIFICATION FOR LATENT CLASS 2
PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR MODEL PART
LAMBDA(U)
C#1 C#2
________ ________
U1 1 2
U2 3 4
U3 5 6
U4 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
LAMBDA(U)
C#1 C#2
________ ________
U1 -1.000 1.000
U2 -1.000 1.000
U3 1.000 -1.000
U4 1.000 -1.000
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
0.000 0.000
TECHNICAL 8 OUTPUT
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
Beginning Time: 23:13:23
Ending Time: 23:13:23
Elapsed Time: 00:00:00
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