Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 10:25 PM
INPUT INSTRUCTIONS
title: this is an example of GMM with known
classes (multiple group analysis)
montecarlo:
names are y1-y4 g x;
generate = g(1);
categorical = g;
genclasses = cg(2) c(2);
classes = cg(2) c(2);
nobs = 1000;
seed = 3454367;
nrep = 1;
save = ex8.8.dat;
analysis:
type = mixture;
model population:
%overall%
[x@0]; x@1;
i s | y1@0 y2@1 y3@2 y4@3;
y1-y4*.5;
i*1; s*.2;
c#1 on cg#1*1 x*1;
i on x*.5;
s on x*.2;
%cg#1.c#1%
[g$1@15];
[i*2 s*1];
%cg#1.c#2%
[g$1@15];
[i*0 s*0];
%cg#2.c#1%
[g$1@-15];
[i*3 s*1.5];
%cg#2.c#2%
[g$1@-15];
[i*1 s*.5];
model:
%overall%
i s | y1@0 y2@1 y3@2 y4@3;
y1-y4*.5;
i*1; s*.2;
c#1 on cg#1*1 x*1;
i on x*.5;
s on x*.2;
%cg#1.c#1%
[g$1@15];
[i*2 s*1];
%cg#1.c#2%
[g$1@15];
[i*0 s*0];
%cg#2.c#1%
[g$1@-15];
[i*3 s*1.5];
%cg#2.c#2%
[g$1@-15];
[i*1 s*.5];
output:
tech8 tech9;
INPUT READING TERMINATED NORMALLY
this is an example of GMM with known
classes (multiple group analysis)
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 5
Number of independent variables 1
Number of continuous latent variables 2
Number of categorical latent variables 2
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4
Binary and ordered categorical (ordinal)
G
Observed independent variables
X
Continuous latent variables
I S
Categorical latent variables
CG 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
Parameterization LOGIT
Link LOGIT
SAMPLE STATISTICS FOR THE FIRST REPLICATION
SAMPLE STATISTICS
Means
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
1.615 2.434 3.241 4.068 -0.040
Covariances
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 2.928
Y2 3.186 5.072
Y3 3.994 5.952 8.387
Y4 4.646 7.145 9.688 12.519
X 0.881 1.336 1.758 2.167 1.023
Correlations
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 1.000
Y2 0.827 1.000
Y3 0.806 0.913 1.000
Y4 0.767 0.897 0.945 1.000
X 0.509 0.586 0.600 0.605 1.000
MODEL FIT INFORMATION
Number of Free Parameters 21
Loglikelihood
H0 Value
Mean -7051.578
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 -7051.578 -7051.578
0.980 0.000 -7051.578 -7051.578
0.950 0.000 -7051.578 -7051.578
0.900 0.000 -7051.578 -7051.578
0.800 0.000 -7051.578 -7051.578
0.700 0.000 -7051.578 -7051.578
0.500 0.000 -7051.578 -7051.578
0.300 0.000 -7051.578 -7051.578
0.200 0.000 -7051.578 -7051.578
0.100 0.000 -7051.578 -7051.578
0.050 0.000 -7051.578 -7051.578
0.020 0.000 -7051.578 -7051.578
0.010 0.000 -7051.578 -7051.578
Information Criteria
Akaike (AIC)
Mean 14145.157
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 14145.157 14145.157
0.980 0.000 14145.157 14145.157
0.950 0.000 14145.157 14145.157
0.900 0.000 14145.157 14145.157
0.800 0.000 14145.157 14145.157
0.700 0.000 14145.157 14145.157
0.500 0.000 14145.157 14145.157
0.300 0.000 14145.157 14145.157
0.200 0.000 14145.157 14145.157
0.100 0.000 14145.157 14145.157
0.050 0.000 14145.157 14145.157
0.020 0.000 14145.157 14145.157
0.010 0.000 14145.157 14145.157
Bayesian (BIC)
Mean 14248.219
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 14248.219 14248.219
0.980 0.000 14248.219 14248.219
0.950 0.000 14248.219 14248.219
0.900 0.000 14248.219 14248.219
0.800 0.000 14248.219 14248.219
0.700 0.000 14248.219 14248.219
0.500 0.000 14248.219 14248.219
0.300 0.000 14248.219 14248.219
0.200 0.000 14248.219 14248.219
0.100 0.000 14248.219 14248.219
0.050 0.000 14248.219 14248.219
0.020 0.000 14248.219 14248.219
0.010 0.000 14248.219 14248.219
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 14181.522
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 14181.522 14181.522
0.980 0.000 14181.522 14181.522
0.950 0.000 14181.522 14181.522
0.900 0.000 14181.522 14181.522
0.800 0.000 14181.522 14181.522
0.700 0.000 14181.522 14181.522
0.500 0.000 14181.522 14181.522
0.300 0.000 14181.522 14181.522
0.200 0.000 14181.522 14181.522
0.100 0.000 14181.522 14181.522
0.050 0.000 14181.522 14181.522
0.020 0.000 14181.522 14181.522
0.010 0.000 14181.522 14181.522
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
MODEL RESULTS USE THE LATENT CLASS VARIABLE ORDER
CG C
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON THE ESTIMATED MODEL
Latent Class
Pattern
1 1 354.52759 0.35453
1 2 158.47242 0.15847
2 1 243.50309 0.24350
2 2 243.49691 0.24350
FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE
BASED ON THE ESTIMATED MODEL
Latent Class
Variable Class
CG 1 513.00000 0.51300
2 487.00000 0.48700
C 1 598.03070 0.59803
2 401.96933 0.40197
LATENT TRANSITION PROBABILITIES BASED ON THE ESTIMATED MODEL
CG Classes (Rows) by C Classes (Columns)
1 2
1 0.691 0.309
2 0.500 0.500
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent Class
Pattern
1 1 359.34776 0.35935
1 2 153.65224 0.15365
2 1 238.79358 0.23879
2 2 248.20642 0.24821
FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent Class
Variable Class
CG 1 513.00000 0.51300
2 487.00000 0.48700
C 1 598.14136 0.59814
2 401.85864 0.40186
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN
Class Counts and Proportions
Latent Class
Pattern
1 1 368 0.36800
1 2 145 0.14500
2 1 239 0.23900
2 2 248 0.24800
FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE
BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN
Latent Class
Variable Class
CG 1 513 0.51300
2 487 0.48700
C 1 607 0.60700
2 393 0.39300
CLASSIFICATION QUALITY
Entropy 0.886
MODEL RESULTS
ESTIMATES S. E. M. S. E. 95% % Sig
Population Average Std. Dev. Average Cover Coeff
Parameters in the Overall Part of the Model (Parameters Equal in All of the Classes)
I |
Y1 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
Y2 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
Y3 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
Y4 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
S |
Y1 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Y2 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
Y3 2.000 2.0000 0.0000 0.0000 0.0000 1.000 0.000
Y4 3.000 3.0000 0.0000 0.0000 0.0000 1.000 0.000
I ON
X 0.500 0.4689 0.0000 0.0465 0.0010 1.000 1.000
S ON
X 0.200 0.1969 0.0000 0.0244 0.0000 1.000 1.000
S WITH
I 0.000 0.0200 0.0000 0.0257 0.0004 1.000 0.000
Intercepts
Y1 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Y2 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Y3 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Y4 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Residual Variances
Y1 0.500 0.5108 0.0000 0.0451 0.0001 1.000 1.000
Y2 0.500 0.5186 0.0000 0.0293 0.0003 1.000 1.000
Y3 0.500 0.4520 0.0000 0.0325 0.0023 1.000 1.000
Y4 0.500 0.5760 0.0000 0.0602 0.0058 1.000 1.000
I 1.000 1.0168 0.0000 0.0738 0.0003 1.000 1.000
S 0.200 0.1810 0.0000 0.0172 0.0004 1.000 1.000
Parameters for Class-specific Model Parts
Latent Class Pattern 1 1
Intercepts
I 2.000 1.9566 0.0000 0.0682 0.0019 1.000 1.000
S 1.000 1.0483 0.0000 0.0371 0.0023 1.000 1.000
Thresholds
G$1 15.000 15.0000 0.0000 0.0000 0.0000 1.000 0.000
Latent Class Pattern 1 2
Intercepts
I 0.000 0.1105 0.0000 0.1357 0.0122 1.000 0.000
S 0.000 -0.1172 0.0000 0.0517 0.0137 0.000 1.000
Thresholds
G$1 15.000 15.0000 0.0000 0.0000 0.0000 1.000 0.000
Latent Class Pattern 2 1
Intercepts
I 3.000 2.8051 0.0000 0.0932 0.0380 0.000 1.000
S 1.500 1.4053 0.0000 0.0367 0.0090 0.000 1.000
Thresholds
G$1 -15.000 -15.0000 0.0000 0.0000 0.0000 1.000 0.000
Latent Class Pattern 2 2
Intercepts
I 1.000 0.9807 0.0000 0.0851 0.0004 1.000 1.000
S 0.500 0.5235 0.0000 0.0454 0.0006 1.000 1.000
Thresholds
G$1 -15.000 -15.0000 0.0000 0.0000 0.0000 1.000 0.000
Categorical Latent Variables
C#1 ON
CG#1 1.000 1.0749 0.0000 0.2169 0.0056 1.000 1.000
C#1 ON
X 1.000 1.3286 0.0000 0.1364 0.1080 0.000 1.000
Means
CG#1 0.000 0.0520 0.0000 0.0633 0.0027 1.000 0.000
C#1 0.000 0.0604 0.0000 0.1457 0.0037 1.000 0.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.350E-02
(ratio of smallest to largest eigenvalue)
C-SPECIFIC CLASSIFICATION RESULTS
Classification Quality for CG
Entropy 1.000
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 1.000 0.000
2 0.000 1.000
Classification Quality for C
Entropy 0.772
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.942 0.058
2 0.068 0.932
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS PATTERN 1 1
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0 0 0 0 0
LAMBDA
I S X
________ ________ ________
Y1 0 0 0
Y2 0 0 0
Y3 0 0 0
Y4 0 0 0
X 0 0 0
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 1
Y2 0 2
Y3 0 0 3
Y4 0 0 0 4
X 0 0 0 0 0
ALPHA
I S X
________ ________ ________
5 6 0
BETA
I S X
________ ________ ________
I 0 0 7
S 0 0 8
X 0 0 0
PSI
I S X
________ ________ ________
I 9
S 10 11
X 0 0 0
PARAMETER SPECIFICATION FOR LATENT CLASS PATTERN 1 2
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0 0 0 0 0
LAMBDA
I S X
________ ________ ________
Y1 0 0 0
Y2 0 0 0
Y3 0 0 0
Y4 0 0 0
X 0 0 0
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 1
Y2 0 2
Y3 0 0 3
Y4 0 0 0 4
X 0 0 0 0 0
ALPHA
I S X
________ ________ ________
12 13 0
BETA
I S X
________ ________ ________
I 0 0 7
S 0 0 8
X 0 0 0
PSI
I S X
________ ________ ________
I 9
S 10 11
X 0 0 0
PARAMETER SPECIFICATION FOR LATENT CLASS PATTERN 2 1
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0 0 0 0 0
LAMBDA
I S X
________ ________ ________
Y1 0 0 0
Y2 0 0 0
Y3 0 0 0
Y4 0 0 0
X 0 0 0
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 1
Y2 0 2
Y3 0 0 3
Y4 0 0 0 4
X 0 0 0 0 0
ALPHA
I S X
________ ________ ________
14 15 0
BETA
I S X
________ ________ ________
I 0 0 7
S 0 0 8
X 0 0 0
PSI
I S X
________ ________ ________
I 9
S 10 11
X 0 0 0
PARAMETER SPECIFICATION FOR LATENT CLASS PATTERN 2 2
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0 0 0 0 0
LAMBDA
I S X
________ ________ ________
Y1 0 0 0
Y2 0 0 0
Y3 0 0 0
Y4 0 0 0
X 0 0 0
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 1
Y2 0 2
Y3 0 0 3
Y4 0 0 0 4
X 0 0 0 0 0
ALPHA
I S X
________ ________ ________
16 17 0
BETA
I S X
________ ________ ________
I 0 0 7
S 0 0 8
X 0 0 0
PSI
I S X
________ ________ ________
I 9
S 10 11
X 0 0 0
PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS PATTERN 1 1
G$1
________
0
TAU(U) FOR LATENT CLASS PATTERN 1 2
G$1
________
0
TAU(U) FOR LATENT CLASS PATTERN 2 1
G$1
________
0
TAU(U) FOR LATENT CLASS PATTERN 2 2
G$1
________
0
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
CG#1 CG#2 C#1 C#2
________ ________ ________ ________
18 0 19 0
GAMMA(C)
X
________
CG#1 0
CG#2 0
C#1 20
C#2 0
BETA(C)
CG#1 CG#2
________ ________
C#1 21 0
C#2 0 0
STARTING VALUES FOR LATENT CLASS PATTERN 1 1
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.500
Y2 0.000 0.500
Y3 0.000 0.000 0.500
Y4 0.000 0.000 0.000 0.500
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
2.000 1.000 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.500
S 0.000 0.000 0.200
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.200
X 0.000 0.000 0.500
STARTING VALUES FOR LATENT CLASS PATTERN 1 2
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.500
Y2 0.000 0.500
Y3 0.000 0.000 0.500
Y4 0.000 0.000 0.000 0.500
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
0.000 0.000 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.500
S 0.000 0.000 0.200
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.200
X 0.000 0.000 0.500
STARTING VALUES FOR LATENT CLASS PATTERN 2 1
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.500
Y2 0.000 0.500
Y3 0.000 0.000 0.500
Y4 0.000 0.000 0.000 0.500
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
3.000 1.500 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.500
S 0.000 0.000 0.200
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.200
X 0.000 0.000 0.500
STARTING VALUES FOR LATENT CLASS PATTERN 2 2
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.500
Y2 0.000 0.500
Y3 0.000 0.000 0.500
Y4 0.000 0.000 0.000 0.500
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
1.000 0.500 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.500
S 0.000 0.000 0.200
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.200
X 0.000 0.000 0.500
STARTING VALUES FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS PATTERN 1 1
G$1
________
15.000
TAU(U) FOR LATENT CLASS PATTERN 1 2
G$1
________
15.000
TAU(U) FOR LATENT CLASS PATTERN 2 1
G$1
________
-15.000
TAU(U) FOR LATENT CLASS PATTERN 2 2
G$1
________
-15.000
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
CG#1 CG#2 C#1 C#2
________ ________ ________ ________
0.000 0.000 0.000 0.000
GAMMA(C)
X
________
CG#1 0.000
CG#2 0.000
C#1 1.000
C#2 0.000
BETA(C)
CG#1 CG#2
________ ________
C#1 1.000 0.000
C#2 0.000 0.000
POPULATION VALUES FOR LATENT CLASS PATTERN 1 1
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.500
Y2 0.000 0.500
Y3 0.000 0.000 0.500
Y4 0.000 0.000 0.000 0.500
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
2.000 1.000 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.500
S 0.000 0.000 0.200
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.200
X 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS PATTERN 1 2
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.500
Y2 0.000 0.500
Y3 0.000 0.000 0.500
Y4 0.000 0.000 0.000 0.500
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
0.000 0.000 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.500
S 0.000 0.000 0.200
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.200
X 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS PATTERN 2 1
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.500
Y2 0.000 0.500
Y3 0.000 0.000 0.500
Y4 0.000 0.000 0.000 0.500
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
3.000 1.500 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.500
S 0.000 0.000 0.200
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.200
X 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS PATTERN 2 2
NU
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 Y4 X
________ ________ ________ ________ ________
Y1 0.500
Y2 0.000 0.500
Y3 0.000 0.000 0.500
Y4 0.000 0.000 0.000 0.500
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
1.000 0.500 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.500
S 0.000 0.000 0.200
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.200
X 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS PATTERN 1 1
G$1
________
15.000
TAU(U) FOR LATENT CLASS PATTERN 1 2
G$1
________
15.000
TAU(U) FOR LATENT CLASS PATTERN 2 1
G$1
________
-15.000
TAU(U) FOR LATENT CLASS PATTERN 2 2
G$1
________
-15.000
POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
CG#1 CG#2 C#1 C#2
________ ________ ________ ________
0.000 0.000 0.000 0.000
GAMMA(C)
X
________
CG#1 0.000
CG#2 0.000
C#1 1.000
C#2 0.000
BETA(C)
CG#1 CG#2
________ ________
C#1 1.000 0.000
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 -0.70680535D+04 0.0000000 0.0000000 EM
2 -0.70532491D+04 14.8043631 0.0020945 EM
3 -0.70522822D+04 0.9669145 0.0001371 EM
4 -0.70519031D+04 0.3790780 0.0000538 EM
5 -0.70517298D+04 0.1733365 0.0000246 EM
6 -0.70516494D+04 0.0803697 0.0000114 EM
7 -0.70516119D+04 0.0375309 0.0000053 EM
8 -0.70515942D+04 0.0176456 0.0000025 EM
9 -0.70515859D+04 0.0083467 0.0000012 EM
10 -0.70515819D+04 0.0039683 0.0000006 EM
11 -0.70515800D+04 0.0018943 0.0000003 EM
12 -0.70515791D+04 0.0009070 0.0000001 EM
13 -0.70515787D+04 0.0004353 0.0000001 EM
14 -0.70515785D+04 0.0002094 0.0000000 EM
15 -0.70515784D+04 0.0001007 0.0000000 EM
16 -0.70515783D+04 0.0000485 0.0000000 EM
17 -0.70515783D+04 0.0000234 0.0000000 EM
18 -0.70515783D+04 0.0000113 0.0000000 EM
19 -0.70515783D+04 0.0000054 0.0000000 EM
20 -0.70515783D+04 0.0000026 0.0000000 EM
21 -0.70515783D+04 0.0000013 0.0000000 EM
22 -0.70515783D+04 0.0000006 0.0000000 EM
23 -0.70515783D+04 0.0000003 0.0000000 EM
24 -0.70515783D+04 0.0000001 0.0000000 EM
25 -0.70515783D+04 0.0000001 0.0000000 EM
26 -0.70515783D+04 0.0000000 0.0000000 EM
27 -0.70515783D+04 0.0000000 0.0000000 EM
28 -0.70515783D+04 0.0000000 0.0000000 EM
TECHNICAL 9 OUTPUT
Error messages for each replication (if any)
SAVEDATA INFORMATION
Order of variables
G
Y1
Y2
Y3
Y4
X
CG
C
Save file
ex8.8.dat
Save file format Free
Save file record length 10000
Beginning Time: 22:25:06
Ending Time: 22:25:07
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