Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 10:24 PM
INPUT INSTRUCTIONS
title:
this is an example of a confirmatory LCA
with two categorical latent variables
montecarlo:
names are u1-u4 y1-y4;
genclasses = cu(2) cy(3);
classes = cu(2) cy(3);
generate = u1-u4(1);
categorical = u1-u4;
nobs = 1000;
seed = 3454367;
nrep = 1;
save = ex7.14.dat;
analysis:
type = mixture;
parameterization = loglinear;
model population:
%overall%
y1-y4*1;
[y1-y4*0];
[cu#1*0 cy#1*0 cy#2*0];
cu#1 with cy#1*.5;
cu#1 with cy#2*.75;
model population-cu:
%cu#1%
[u1$1-u4$1*-1];
%cu#2%
[u1$1-u4$1*1];
model population-cy:
%cy#1%
[y1-y4*-1];
%cy#2%
[y1-y4*1];
%cy#3%
[y1-y4*2];
model:
%overall%
y1-y4*1;
[y1-y4*0];
[cu#1*0 cy#1*0 cy#2*0];
cu#1 with cy#1*.5;
cu#1 with cy#2*.75;
model cu:
%cu#1%
[u1$1-u4$1*-1];
%cu#2%
[u1$1-u4$1*1];
model cy:
%cy#1%
[y1-y4*-1];
%cy#2%
[y1-y4*1];
%cy#3%
[y1-y4*2];
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 confirmatory LCA
with two categorical latent variables
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 8
Number of independent variables 0
Number of continuous latent variables 0
Number of categorical latent variables 2
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4
Binary and ordered categorical (ordinal)
U1 U2 U3 U4
Categorical latent variables
CU CY
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 LOGLINEAR
Link LOGIT
SAMPLE STATISTICS FOR THE FIRST REPLICATION
SAMPLE STATISTICS
Means
Y1 Y2 Y3 Y4
________ ________ ________ ________
0.602 0.581 0.579 0.583
Covariances
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 2.380
Y2 1.413 2.468
Y3 1.404 1.515 2.529
Y4 1.307 1.491 1.450 2.379
Correlations
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 1.000
Y2 0.583 1.000
Y3 0.572 0.606 1.000
Y4 0.549 0.615 0.591 1.000
MODEL FIT INFORMATION
Number of Free Parameters 29
Loglikelihood
H0 Value
Mean -9141.129
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 -9141.129 -9141.129
0.980 0.000 -9141.129 -9141.129
0.950 0.000 -9141.129 -9141.129
0.900 0.000 -9141.129 -9141.129
0.800 0.000 -9141.129 -9141.129
0.700 0.000 -9141.129 -9141.129
0.500 0.000 -9141.129 -9141.129
0.300 0.000 -9141.129 -9141.129
0.200 0.000 -9141.129 -9141.129
0.100 0.000 -9141.129 -9141.129
0.050 0.000 -9141.129 -9141.129
0.020 0.000 -9141.129 -9141.129
0.010 0.000 -9141.129 -9141.129
Information Criteria
Akaike (AIC)
Mean 18340.257
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 18340.257 18340.257
0.980 0.000 18340.257 18340.257
0.950 0.000 18340.257 18340.257
0.900 0.000 18340.257 18340.257
0.800 0.000 18340.257 18340.257
0.700 0.000 18340.257 18340.257
0.500 0.000 18340.257 18340.257
0.300 0.000 18340.257 18340.257
0.200 0.000 18340.257 18340.257
0.100 0.000 18340.257 18340.257
0.050 0.000 18340.257 18340.257
0.020 0.000 18340.257 18340.257
0.010 0.000 18340.257 18340.257
Bayesian (BIC)
Mean 18482.582
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 18482.582 18482.582
0.980 0.000 18482.582 18482.582
0.950 0.000 18482.582 18482.582
0.900 0.000 18482.582 18482.582
0.800 0.000 18482.582 18482.582
0.700 0.000 18482.582 18482.582
0.500 0.000 18482.582 18482.582
0.300 0.000 18482.582 18482.582
0.200 0.000 18482.582 18482.582
0.100 0.000 18482.582 18482.582
0.050 0.000 18482.582 18482.582
0.020 0.000 18482.582 18482.582
0.010 0.000 18482.582 18482.582
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 18390.477
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 18390.477 18390.477
0.980 0.000 18390.477 18390.477
0.950 0.000 18390.477 18390.477
0.900 0.000 18390.477 18390.477
0.800 0.000 18390.477 18390.477
0.700 0.000 18390.477 18390.477
0.500 0.000 18390.477 18390.477
0.300 0.000 18390.477 18390.477
0.200 0.000 18390.477 18390.477
0.100 0.000 18390.477 18390.477
0.050 0.000 18390.477 18390.477
0.020 0.000 18390.477 18390.477
0.010 0.000 18390.477 18390.477
Chi-Square Test of Model Fit for the Binary and Ordered Categorical
(Ordinal) Outcomes
Pearson Chi-Square
Mean 10.624
Std Dev 0.000
Degrees of freedom 2
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 1.000 0.020 10.624
0.980 1.000 0.040 10.624
0.950 1.000 0.103 10.624
0.900 1.000 0.211 10.624
0.800 1.000 0.446 10.624
0.700 1.000 0.713 10.624
0.500 1.000 1.386 10.624
0.300 1.000 2.408 10.624
0.200 1.000 3.219 10.624
0.100 1.000 4.605 10.624
0.050 1.000 5.991 10.624
0.020 1.000 7.824 10.624
0.010 1.000 9.210 10.624
Likelihood Ratio Chi-Square
Mean 10.270
Std Dev 0.000
Degrees of freedom 2
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 1.000 0.020 10.270
0.980 1.000 0.040 10.270
0.950 1.000 0.103 10.270
0.900 1.000 0.211 10.270
0.800 1.000 0.446 10.270
0.700 1.000 0.713 10.270
0.500 1.000 1.386 10.270
0.300 1.000 2.408 10.270
0.200 1.000 3.219 10.270
0.100 1.000 4.605 10.270
0.050 1.000 5.991 10.270
0.020 1.000 7.824 10.270
0.010 1.000 9.210 10.270
MODEL RESULTS USE THE LATENT CLASS VARIABLE ORDER
CU CY
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON THE ESTIMATED MODEL
Latent Class
Pattern
1 1 227.91451 0.22791
1 2 307.96586 0.30797
1 3 104.85384 0.10485
2 1 108.04281 0.10804
2 2 125.56608 0.12557
2 3 125.65690 0.12566
FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE
BASED ON THE ESTIMATED MODEL
Latent Class
Variable Class
CU 1 640.73419 0.64073
2 359.26581 0.35927
CY 1 335.95731 0.33596
2 433.53192 0.43353
3 230.51074 0.23051
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent Class
Pattern
1 1 227.91449 0.22791
1 2 307.96570 0.30797
1 3 104.85406 0.10485
2 1 108.04282 0.10804
2 2 125.56606 0.12557
2 3 125.65686 0.12566
FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent Class
Variable Class
CU 1 640.73425 0.64073
2 359.26575 0.35927
CY 1 335.95731 0.33596
2 433.53177 0.43353
3 230.51093 0.23051
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 1 251 0.25100
1 2 330 0.33000
1 3 108 0.10800
2 1 84 0.08400
2 2 113 0.11300
2 3 114 0.11400
FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE
BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN
Latent Class
Variable Class
CU 1 689 0.68900
2 311 0.31100
CY 1 335 0.33500
2 443 0.44300
3 222 0.22200
CLASSIFICATION QUALITY
Entropy 0.651
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)
Variances
Y1 1.000 1.1372 0.0000 0.0549 0.0188 0.000 1.000
Y2 1.000 0.9629 0.0000 0.0511 0.0014 1.000 1.000
Y3 1.000 1.0222 0.0000 0.0526 0.0005 1.000 1.000
Y4 1.000 0.9286 0.0000 0.0536 0.0051 1.000 1.000
Parameters for Class-specific Model Parts of CU
Latent Class CU#1
Thresholds
U1$1 -1.000 -0.9204 0.0000 0.2059 0.0063 1.000 1.000
U2$1 -1.000 -1.0759 0.0000 0.1840 0.0058 1.000 1.000
U3$1 -1.000 -0.8999 0.0000 0.1389 0.0100 1.000 1.000
U4$1 -1.000 -0.7621 0.0000 0.1760 0.0566 1.000 1.000
Latent Class CU#2
Thresholds
U1$1 1.000 0.9695 0.0000 0.2390 0.0009 1.000 1.000
U2$1 1.000 1.0204 0.0000 0.3366 0.0004 1.000 1.000
U3$1 1.000 0.7856 0.0000 0.2952 0.0459 1.000 1.000
U4$1 1.000 1.1387 0.0000 0.2879 0.0192 1.000 1.000
Parameters for Class-specific Model Parts of CY
Latent Class CY#1
Means
Y1 -1.000 -0.9249 0.0000 0.0607 0.0056 1.000 1.000
Y2 -1.000 -1.0521 0.0000 0.0578 0.0027 1.000 1.000
Y3 -1.000 -1.0687 0.0000 0.0581 0.0047 1.000 1.000
Y4 -1.000 -0.9989 0.0000 0.0542 0.0000 1.000 1.000
Latent Class CY#2
Means
Y1 1.000 1.1486 0.0000 0.0849 0.0221 1.000 1.000
Y2 1.000 1.0521 0.0000 0.0827 0.0027 1.000 1.000
Y3 1.000 1.0863 0.0000 0.0768 0.0074 1.000 1.000
Y4 1.000 0.9980 0.0000 0.0691 0.0000 1.000 1.000
Latent Class CY#3
Means
Y1 2.000 1.7976 0.0000 0.0811 0.0410 0.000 1.000
Y2 2.000 2.0732 0.0000 0.1043 0.0054 1.000 1.000
Y3 2.000 2.0276 0.0000 0.1141 0.0008 1.000 1.000
Y4 2.000 2.1060 0.0000 0.1477 0.0112 1.000 1.000
Categorical Latent Variables
CU#1 WITH
CY#1 0.500 0.9274 0.0000 0.3334 0.1827 1.000 1.000
CY#2 0.750 1.0781 0.0000 0.3627 0.1077 1.000 1.000
Means
CU#1 0.000 -0.1810 0.0000 0.4158 0.0328 1.000 0.000
CY#1 0.000 -0.1510 0.0000 0.2609 0.0228 1.000 0.000
CY#2 0.000 -0.0007 0.0000 0.3392 0.0000 1.000 0.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.513E-04
(ratio of smallest to largest eigenvalue)
C-SPECIFIC CLASSIFICATION RESULTS
Classification Quality for CU
Entropy 0.479
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.850 0.150
2 0.176 0.824
Classification Quality for CY
Entropy 0.755
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2 3
1 0.981 0.019 0.000
2 0.017 0.856 0.127
3 0.000 0.215 0.785
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS PATTERN 1 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 PATTERN 1 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 PATTERN 1 3
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
13 14 15 16
THETA
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 5
Y2 0 6
Y3 0 0 7
Y4 0 0 0 8
PARAMETER SPECIFICATION FOR LATENT CLASS PATTERN 2 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 PATTERN 2 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 PATTERN 2 3
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
13 14 15 16
THETA
Y1 Y2 Y3 Y4
________ ________ ________ ________
Y1 5
Y2 0 6
Y3 0 0 7
Y4 0 0 0 8
PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS PATTERN 1 1
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
17 18 19 20
TAU(U) FOR LATENT CLASS PATTERN 1 2
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
17 18 19 20
TAU(U) FOR LATENT CLASS PATTERN 1 3
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
17 18 19 20
TAU(U) FOR LATENT CLASS PATTERN 2 1
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
21 22 23 24
TAU(U) FOR LATENT CLASS PATTERN 2 2
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
21 22 23 24
TAU(U) FOR LATENT CLASS PATTERN 2 3
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
21 22 23 24
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
CU#1 CU#2 CY#1 CY#2 CY#3
________ ________ ________ ________ ________
25 0 26 27 0
PSI(C)
CU#1 CU#2
________ ________
CY#1 28 0
CY#2 29 0
CY#3 0 0
STARTING VALUES FOR LATENT CLASS PATTERN 1 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 PATTERN 1 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 PATTERN 1 3
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
2.000 2.000 2.000 2.000
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 PATTERN 2 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 PATTERN 2 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 PATTERN 2 3
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
2.000 2.000 2.000 2.000
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 INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS PATTERN 1 1
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
-1.000 -1.000 -1.000 -1.000
TAU(U) FOR LATENT CLASS PATTERN 1 2
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
-1.000 -1.000 -1.000 -1.000
TAU(U) FOR LATENT CLASS PATTERN 1 3
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
-1.000 -1.000 -1.000 -1.000
TAU(U) FOR LATENT CLASS PATTERN 2 1
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
1.000 1.000 1.000 1.000
TAU(U) FOR LATENT CLASS PATTERN 2 2
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
1.000 1.000 1.000 1.000
TAU(U) FOR LATENT CLASS PATTERN 2 3
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)
CU#1 CU#2 CY#1 CY#2 CY#3
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
PSI(C)
CU#1 CU#2
________ ________
CY#1 0.500 0.000
CY#2 0.750 0.000
CY#3 0.000 0.000
POPULATION VALUES FOR LATENT CLASS PATTERN 1 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 PATTERN 1 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 PATTERN 1 3
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
2.000 2.000 2.000 2.000
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 PATTERN 2 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 PATTERN 2 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 PATTERN 2 3
NU
Y1 Y2 Y3 Y4
________ ________ ________ ________
2.000 2.000 2.000 2.000
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 INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS PATTERN 1 1
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
-1.000 -1.000 -1.000 -1.000
TAU(U) FOR LATENT CLASS PATTERN 1 2
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
-1.000 -1.000 -1.000 -1.000
TAU(U) FOR LATENT CLASS PATTERN 1 3
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
-1.000 -1.000 -1.000 -1.000
TAU(U) FOR LATENT CLASS PATTERN 2 1
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
1.000 1.000 1.000 1.000
TAU(U) FOR LATENT CLASS PATTERN 2 2
U1$1 U2$1 U3$1 U4$1
________ ________ ________ ________
1.000 1.000 1.000 1.000
TAU(U) FOR LATENT CLASS PATTERN 2 3
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)
CU#1 CU#2 CY#1 CY#2 CY#3
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
PSI(C)
CU#1 CU#2
________ ________
CY#1 0.500 0.000
CY#2 0.750 0.000
CY#3 0.000 0.000
TECHNICAL 8 OUTPUT
TECHNICAL 8 OUTPUT FOR REPLICATION 1
E STEP ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE ALGORITHM
1 -0.91566152D+04 0.0000000 0.0000000 EM
2 -0.91442688D+04 12.3464067 0.0013484 EM
3 -0.91426155D+04 1.6533281 0.0001808 EM
4 -0.91419904D+04 0.6250504 0.0000684 EM
5 -0.91417098D+04 0.2806811 0.0000307 EM
6 -0.91415645D+04 0.1452728 0.0000159 EM
7 -0.91414781D+04 0.0864293 0.0000095 EM
8 -0.91414201D+04 0.0579964 0.0000063 EM
9 -0.91413774D+04 0.0426183 0.0000047 EM
10 -0.91413441D+04 0.0333602 0.0000036 EM
11 -0.91413168D+04 0.0272419 0.0000030 EM
12 -0.91412940D+04 0.0228772 0.0000025 EM
13 -0.91412744D+04 0.0195702 0.0000021 EM
14 -0.91412574D+04 0.0169469 0.0000019 EM
15 -0.91412427D+04 0.0147942 0.0000016 EM
16 -0.91412297D+04 0.0129839 0.0000014 EM
17 -0.91412182D+04 0.0114353 0.0000013 EM
18 -0.91412081D+04 0.0100948 0.0000011 EM
19 -0.91411992D+04 0.0089250 0.0000010 EM
20 -0.91411913D+04 0.0078988 0.0000009 EM
21 -0.91411843D+04 0.0069953 0.0000008 EM
22 -0.91411781D+04 0.0061980 0.0000007 EM
23 -0.91411726D+04 0.0054932 0.0000006 EM
24 -0.91411678D+04 0.0048696 0.0000005 EM
25 -0.91411634D+04 0.0043176 0.0000005 EM
26 -0.91411596D+04 0.0038287 0.0000004 EM
27 -0.91411562D+04 0.0033957 0.0000004 EM
28 -0.91411532D+04 0.0030121 0.0000003 EM
29 -0.91411505D+04 0.0026722 0.0000003 EM
30 -0.91411482D+04 0.0023711 0.0000003 EM
31 -0.91411461D+04 0.0021044 0.0000002 EM
32 -0.91411442D+04 0.0018681 0.0000002 EM
33 -0.91411425D+04 0.0016587 0.0000002 EM
34 -0.91411411D+04 0.0014732 0.0000002 EM
35 -0.91411398D+04 0.0013089 0.0000001 EM
36 -0.91411386D+04 0.0011632 0.0000001 EM
37 -0.91411299D+04 0.0087058 0.0000010 FS
38 -0.91411289D+04 0.0009550 0.0000001 FS
39 -0.91411288D+04 0.0001532 0.0000000 FS
40 -0.91411287D+04 0.0000290 0.0000000 FS
41 -0.91411287D+04 0.0000065 0.0000000 FS
42 -0.91411287D+04 0.0000017 0.0000000 FS
43 -0.91411287D+04 0.0000005 0.0000000 FS
44 -0.91411287D+04 0.0000002 0.0000000 FS
45 -0.91411287D+04 0.0000001 0.0000000 FS
46 -0.91411287D+04 0.0000000 0.0000000 EM
TECHNICAL 9 OUTPUT
Error messages for each replication (if any)
SAVEDATA INFORMATION
Order of variables
U1
U2
U3
U4
Y1
Y2
Y3
Y4
CU
CY
Save file
ex7.14.dat
Save file format Free
Save file record length 10000
Beginning Time: 22:24:28
Ending Time: 22:24:29
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