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
automatic starting values with random
starts
montecarlo:
names are y1-y4;
genclasses = c(2);
classes = c(2);
nobs = 500;
seed = 3454367;
nrep = 1;
save = ex7.9.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];
y1-y4*1;
%c#1%
[y1-y4*1];
%c#2%
[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
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
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 13
Loglikelihood
H0 Value
Mean -3177.162
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 -3177.162 -3177.162
0.980 0.000 -3177.162 -3177.162
0.950 0.000 -3177.162 -3177.162
0.900 0.000 -3177.162 -3177.162
0.800 0.000 -3177.162 -3177.162
0.700 0.000 -3177.162 -3177.162
0.500 0.000 -3177.162 -3177.162
0.300 0.000 -3177.162 -3177.162
0.200 0.000 -3177.162 -3177.162
0.100 0.000 -3177.162 -3177.162
0.050 0.000 -3177.162 -3177.162
0.020 0.000 -3177.162 -3177.162
0.010 0.000 -3177.162 -3177.162
Information Criteria
Akaike (AIC)
Mean 6380.324
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 6380.324 6380.324
0.980 0.000 6380.324 6380.324
0.950 0.000 6380.324 6380.324
0.900 0.000 6380.324 6380.324
0.800 0.000 6380.324 6380.324
0.700 0.000 6380.324 6380.324
0.500 0.000 6380.324 6380.324
0.300 0.000 6380.324 6380.324
0.200 0.000 6380.324 6380.324
0.100 0.000 6380.324 6380.324
0.050 0.000 6380.324 6380.324
0.020 0.000 6380.324 6380.324
0.010 0.000 6380.324 6380.324
Bayesian (BIC)
Mean 6435.114
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 6435.114 6435.114
0.980 0.000 6435.114 6435.114
0.950 0.000 6435.114 6435.114
0.900 0.000 6435.114 6435.114
0.800 0.000 6435.114 6435.114
0.700 0.000 6435.114 6435.114
0.500 0.000 6435.114 6435.114
0.300 0.000 6435.114 6435.114
0.200 0.000 6435.114 6435.114
0.100 0.000 6435.114 6435.114
0.050 0.000 6435.114 6435.114
0.020 0.000 6435.114 6435.114
0.010 0.000 6435.114 6435.114
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 6393.851
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 6393.851 6393.851
0.980 0.000 6393.851 6393.851
0.950 0.000 6393.851 6393.851
0.900 0.000 6393.851 6393.851
0.800 0.000 6393.851 6393.851
0.700 0.000 6393.851 6393.851
0.500 0.000 6393.851 6393.851
0.300 0.000 6393.851 6393.851
0.200 0.000 6393.851 6393.851
0.100 0.000 6393.851 6393.851
0.050 0.000 6393.851 6393.851
0.020 0.000 6393.851 6393.851
0.010 0.000 6393.851 6393.851
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 260.61742 0.52123
2 239.38258 0.47877
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 260.61742 0.52123
2 239.38258 0.47877
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 262 0.52400
2 238 0.47600
CLASSIFICATION QUALITY
Entropy 0.909
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.974 0.026
2 0.023 0.977
Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 0.979 0.021
2 0.028 0.972
Logits for the Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 3.853 0.000
2 -3.533 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 1.0373 0.0000 0.0703 0.0014 1.000 1.000
Y2 1.000 1.0036 0.0000 0.0640 0.0000 1.000 1.000
Y3 1.000 0.8649 0.0000 0.0678 0.0183 0.000 1.000
Y4 1.000 0.9805 0.0000 0.0601 0.0004 1.000 1.000
Variances
Y1 1.000 1.1345 0.0000 0.0727 0.0181 1.000 1.000
Y2 1.000 0.9746 0.0000 0.0625 0.0006 1.000 1.000
Y3 1.000 0.9918 0.0000 0.0641 0.0001 1.000 1.000
Y4 1.000 1.0072 0.0000 0.0642 0.0001 1.000 1.000
Latent Class 2
Means
Y1 -1.000 -1.0562 0.0000 0.0703 0.0032 1.000 1.000
Y2 -1.000 -1.0877 0.0000 0.0669 0.0077 1.000 1.000
Y3 -1.000 -0.9431 0.0000 0.0627 0.0032 1.000 1.000
Y4 -1.000 -1.0931 0.0000 0.0744 0.0087 1.000 1.000
Variances
Y1 1.000 1.1345 0.0000 0.0727 0.0181 1.000 1.000
Y2 1.000 0.9746 0.0000 0.0625 0.0006 1.000 1.000
Y3 1.000 0.9918 0.0000 0.0641 0.0001 1.000 1.000
Y4 1.000 1.0072 0.0000 0.0642 0.0001 1.000 1.000
Categorical Latent Variables
Means
C#1 0.000 0.0850 0.0000 0.0956 0.0072 1.000 0.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.328E+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 5
Y2 0 6
Y3 0 0 7
Y4 0 0 0 8
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
13 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 -0.31774131D+04 6.5057125 0.0020433 EM
3 -0.31772025D+04 0.2105896 0.0000663 EM
4 -0.31771691D+04 0.0334158 0.0000105 EM
5 -0.31771632D+04 0.0058718 0.0000018 EM
6 -0.31771622D+04 0.0010572 0.0000003 EM
7 -0.31771620D+04 0.0001920 0.0000001 EM
8 -0.31771620D+04 0.0000350 0.0000000 EM
9 -0.31771619D+04 0.0000064 0.0000000 EM
10 -0.31771619D+04 0.0000012 0.0000000 EM
11 -0.31771619D+04 0.0000002 0.0000000 EM
12 -0.31771619D+04 0.0000000 0.0000000 EM
13 -0.31771619D+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
ex7.9.dat
Save file format Free
Save file record length 10000
Beginning Time: 22:24:42
Ending Time: 22:24:42
Elapsed Time: 00:00:00
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