Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 10:24 PM
INPUT INSTRUCTIONS
title:
this is an example of a LCA with
continuous latent class indicators using
user-specified starting values without
random starts
! this is the same as ex7.9, except
! estimating class-specific variances
montecarlo:
names are y1-y4;
genclasses = c(2);
classes = c(2);
nobs = 500;
seed = 3454367;
nrep = 1;
save = ex7.10.dat;
analysis:
type = mixture;
model population:
%overall%
[c#1*0];
%c#1%
[y1-y4*1];
y1-y4*1;
%c#2%
[y1-y4*-1];
y1-y4*1;
model:
%overall%
[c#1*0];
%c#1%
[y1-y4*1];
y1-y4*1;
%c#2%
[y1-y4*-1];
y1-y4*1;
output:
tech8 tech9;
*** WARNING in MODEL command
All variables are uncorrelated with all other variables within class.
Check that this is what is intended.
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
this is an example of a LCA with
continuous latent class indicators using
user-specified starting values without
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
Continuous
Y1 Y2 Y3 Y4
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
SAMPLE STATISTICS FOR THE FIRST REPLICATION
SAMPLE STATISTICS
Means
Y1 Y2 Y3 Y4
________ ________ ________ ________
0.035 0.002 -0.001 -0.012
Covariances
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 2.228
Y2 1.100 2.066
Y3 0.979 0.938 1.808
Y4 1.033 1.052 0.918 2.080
Correlations
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 1.000
Y2 0.513 1.000
Y3 0.488 0.485 1.000
Y4 0.480 0.507 0.474 1.000
MODEL FIT INFORMATION
Number of Free Parameters 17
Loglikelihood
H0 Value
Mean -3174.564
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 -3174.564 -3174.564
0.980 0.000 -3174.564 -3174.564
0.950 0.000 -3174.564 -3174.564
0.900 0.000 -3174.564 -3174.564
0.800 0.000 -3174.564 -3174.564
0.700 0.000 -3174.564 -3174.564
0.500 0.000 -3174.564 -3174.564
0.300 0.000 -3174.564 -3174.564
0.200 0.000 -3174.564 -3174.564
0.100 0.000 -3174.564 -3174.564
0.050 0.000 -3174.564 -3174.564
0.020 0.000 -3174.564 -3174.564
0.010 0.000 -3174.564 -3174.564
Information Criteria
Akaike (AIC)
Mean 6383.127
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 6383.127 6383.127
0.980 0.000 6383.127 6383.127
0.950 0.000 6383.127 6383.127
0.900 0.000 6383.127 6383.127
0.800 0.000 6383.127 6383.127
0.700 0.000 6383.127 6383.127
0.500 0.000 6383.127 6383.127
0.300 0.000 6383.127 6383.127
0.200 0.000 6383.127 6383.127
0.100 0.000 6383.127 6383.127
0.050 0.000 6383.127 6383.127
0.020 0.000 6383.127 6383.127
0.010 0.000 6383.127 6383.127
Bayesian (BIC)
Mean 6454.776
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 6454.776 6454.776
0.980 0.000 6454.776 6454.776
0.950 0.000 6454.776 6454.776
0.900 0.000 6454.776 6454.776
0.800 0.000 6454.776 6454.776
0.700 0.000 6454.776 6454.776
0.500 0.000 6454.776 6454.776
0.300 0.000 6454.776 6454.776
0.200 0.000 6454.776 6454.776
0.100 0.000 6454.776 6454.776
0.050 0.000 6454.776 6454.776
0.020 0.000 6454.776 6454.776
0.010 0.000 6454.776 6454.776
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 6400.817
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 6400.817 6400.817
0.980 0.000 6400.817 6400.817
0.950 0.000 6400.817 6400.817
0.900 0.000 6400.817 6400.817
0.800 0.000 6400.817 6400.817
0.700 0.000 6400.817 6400.817
0.500 0.000 6400.817 6400.817
0.300 0.000 6400.817 6400.817
0.200 0.000 6400.817 6400.817
0.100 0.000 6400.817 6400.817
0.050 0.000 6400.817 6400.817
0.020 0.000 6400.817 6400.817
0.010 0.000 6400.817 6400.817
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 260.17581 0.52035
2 239.82419 0.47965
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 260.17581 0.52035
2 239.82419 0.47965
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 261 0.52200
2 239 0.47800
CLASSIFICATION QUALITY
Entropy 0.909
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.975 0.025
2 0.024 0.976
Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 0.978 0.022
2 0.027 0.973
Logits for the Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 3.796 0.000
2 -3.574 0.000
MODEL RESULTS
ESTIMATES S. E. M. S. E. 95% % Sig
Population Average Std. Dev. Average Cover Coeff
Latent Class 1
Means
Y1 1.000 1.0398 0.0000 0.0705 0.0016 1.000 1.000
Y2 1.000 1.0064 0.0000 0.0638 0.0000 1.000 1.000
Y3 1.000 0.8672 0.0000 0.0679 0.0176 1.000 1.000
Y4 1.000 0.9798 0.0000 0.0605 0.0004 1.000 1.000
Variances
Y1 1.000 1.1541 0.0000 0.1002 0.0237 1.000 1.000
Y2 1.000 0.9788 0.0000 0.0790 0.0005 1.000 1.000
Y3 1.000 1.0847 0.0000 0.0869 0.0072 1.000 1.000
Y4 1.000 0.9094 0.0000 0.0775 0.0082 1.000 1.000
Latent Class 2
Means
Y1 -1.000 -1.0550 0.0000 0.0702 0.0030 1.000 1.000
Y2 -1.000 -1.0868 0.0000 0.0673 0.0075 1.000 1.000
Y3 -1.000 -0.9423 0.0000 0.0626 0.0033 1.000 1.000
Y4 -1.000 -1.0886 0.0000 0.0745 0.0079 1.000 1.000
Variances
Y1 1.000 1.1101 0.0000 0.1012 0.0121 1.000 1.000
Y2 1.000 0.9655 0.0000 0.0977 0.0012 1.000 1.000
Y3 1.000 0.8880 0.0000 0.0899 0.0125 1.000 1.000
Y4 1.000 1.1239 0.0000 0.1031 0.0153 1.000 1.000
Categorical Latent Variables
Means
C#1 0.000 0.0815 0.0000 0.0954 0.0066 1.000 0.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.179E+00
(ratio of smallest to largest eigenvalue)
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
1 2 3 4
THETA
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 5
Y2 0 6
Y3 0 0 7
Y4 0 0 0 8
PARAMETER SPECIFICATION FOR LATENT CLASS 2
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
9 10 11 12
THETA
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 13
Y2 0 14
Y3 0 0 15
Y4 0 0 0 16
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
17 0
STARTING VALUES FOR LATENT CLASS 1
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
1.000 1.000 1.000 1.000
THETA
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 1.000
Y2 0.000 1.000
Y3 0.000 0.000 1.000
Y4 0.000 0.000 0.000 1.000
STARTING VALUES FOR LATENT CLASS 2
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
-1.000 -1.000 -1.000 -1.000
THETA
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 1.000
Y2 0.000 1.000
Y3 0.000 0.000 1.000
Y4 0.000 0.000 0.000 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
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
1.000 1.000 1.000 1.000
THETA
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 1.000
Y2 0.000 1.000
Y3 0.000 0.000 1.000
Y4 0.000 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS 2
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
-1.000 -1.000 -1.000 -1.000
THETA
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 1.000
Y2 0.000 1.000
Y3 0.000 0.000 1.000
Y4 0.000 0.000 0.000 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 -0.31839188D+04 0.0000000 0.0000000 EM
2 -0.31747636D+04 9.1552394 0.0028755 EM
3 -0.31746020D+04 0.1615661 0.0000509 EM
4 -0.31745724D+04 0.0295914 0.0000093 EM
5 -0.31745658D+04 0.0066560 0.0000021 EM
6 -0.31745642D+04 0.0015574 0.0000005 EM
7 -0.31745639D+04 0.0003679 0.0000001 EM
8 -0.31745638D+04 0.0000870 0.0000000 EM
9 -0.31745637D+04 0.0000208 0.0000000 EM
10 -0.31745637D+04 0.0000050 0.0000000 EM
11 -0.31745637D+04 0.0000012 0.0000000 EM
12 -0.31745637D+04 0.0000003 0.0000000 EM
13 -0.31745637D+04 0.0000001 0.0000000 EM
14 -0.31745637D+04 0.0000000 0.0000000 EM
TECHNICAL 9 OUTPUT
Error messages for each replication (if any)
SAVEDATA INFORMATION
Order of variables
Y1
Y2
Y3
Y4
C
Save file
ex7.10.dat
Save file format Free
Save file record length 10000
Beginning Time: 22:24:27
Ending Time: 22:24:27
Elapsed Time: 00:00:00
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