Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 10:24 PM
INPUT INSTRUCTIONS
title:
this is an example of a GMM for a
categorical outcome using automatic
starting values and random starts
montecarlo:
names are u1-u4 x;
generate = u1-u4(4);
categorical = u1-u4;
genclasses = c(2);
classes = c(2);
nobs = 1000;
seed = 3454367;
nrep = 1;
save = ex8.4.dat;
analysis:
type = mixture;
algorithm = integration;
model population:
%overall%
[x@0]; x@1;
i s | u1@0 u2@1 u3@2 u4@3;
[u1$1-u4$1*-.5] (1);
[u1$2-u4$2*0] (2);
[u1$3-u4$3*.5] (3);
[u1$4-u4$4*1] (4);
i*1; s*.5;
c#1 on x*1;
i on x*.4;
s on x*.3;
%c#1%
[i*3 s*1];
%c#2%
[i@0 s*.5];
model:
%overall%
i s | u1@0 u2@1 u3@2 u4@3;
[u1$1-u4$1*-.5] (1);
[u1$2-u4$2*0] (2);
[u1$3-u4$3*.5] (3);
[u1$4-u4$4*1] (4);
i*1; s*.5;
c#1 on x*1;
i on x*.4;
s on x*.3;
%c#1%
[i*3 s*1];
%c#2%
[i@0 s*.5];
output:
tech8 tech9;
INPUT READING TERMINATED NORMALLY
this is an example of a GMM for a
categorical outcome using automatic
starting values and random starts
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 4
Number of independent variables 1
Number of continuous latent variables 2
Number of categorical latent variables 1
Observed dependent variables
Binary and ordered categorical (ordinal)
U1 U2 U3 U4
Observed independent variables
X
Continuous latent variables
I S
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-02
Relative loglikelihood change 0.100D-05
Derivative 0.100D-02
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-02
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-02
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
Integration Specifications
Type STANDARD
Number of integration points 15
Dimensions of numerical integration 2
Adaptive quadrature ON
Link LOGIT
Cholesky ON
SAMPLE STATISTICS FOR THE FIRST REPLICATION
SAMPLE STATISTICS
Means
X
________
0.003
Covariances
X
________
X 1.015
Correlations
X
________
X 1.000
MODEL FIT INFORMATION
Number of Free Parameters 14
Loglikelihood
H0 Value
Mean -3463.892
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 -3463.892 -3463.892
0.980 0.000 -3463.892 -3463.892
0.950 0.000 -3463.892 -3463.892
0.900 0.000 -3463.892 -3463.892
0.800 0.000 -3463.892 -3463.892
0.700 0.000 -3463.892 -3463.892
0.500 0.000 -3463.892 -3463.892
0.300 0.000 -3463.892 -3463.892
0.200 0.000 -3463.892 -3463.892
0.100 0.000 -3463.892 -3463.892
0.050 0.000 -3463.892 -3463.892
0.020 0.000 -3463.892 -3463.892
0.010 0.000 -3463.892 -3463.892
Information Criteria
Akaike (AIC)
Mean 6955.783
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 6955.783 6955.783
0.980 0.000 6955.783 6955.783
0.950 0.000 6955.783 6955.783
0.900 0.000 6955.783 6955.783
0.800 0.000 6955.783 6955.783
0.700 0.000 6955.783 6955.783
0.500 0.000 6955.783 6955.783
0.300 0.000 6955.783 6955.783
0.200 0.000 6955.783 6955.783
0.100 0.000 6955.783 6955.783
0.050 0.000 6955.783 6955.783
0.020 0.000 6955.783 6955.783
0.010 0.000 6955.783 6955.783
Bayesian (BIC)
Mean 7024.492
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 7024.492 7024.492
0.980 0.000 7024.492 7024.492
0.950 0.000 7024.492 7024.492
0.900 0.000 7024.492 7024.492
0.800 0.000 7024.492 7024.492
0.700 0.000 7024.492 7024.492
0.500 0.000 7024.492 7024.492
0.300 0.000 7024.492 7024.492
0.200 0.000 7024.492 7024.492
0.100 0.000 7024.492 7024.492
0.050 0.000 7024.492 7024.492
0.020 0.000 7024.492 7024.492
0.010 0.000 7024.492 7024.492
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 6980.027
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 6980.027 6980.027
0.980 0.000 6980.027 6980.027
0.950 0.000 6980.027 6980.027
0.900 0.000 6980.027 6980.027
0.800 0.000 6980.027 6980.027
0.700 0.000 6980.027 6980.027
0.500 0.000 6980.027 6980.027
0.300 0.000 6980.027 6980.027
0.200 0.000 6980.027 6980.027
0.100 0.000 6980.027 6980.027
0.050 0.000 6980.027 6980.027
0.020 0.000 6980.027 6980.027
0.010 0.000 6980.027 6980.027
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 502.70047 0.50270
2 497.29953 0.49730
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 502.70047 0.50270
2 497.29953 0.49730
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 519 0.51900
2 481 0.48100
CLASSIFICATION QUALITY
Entropy 0.593
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.872 0.128
2 0.104 0.896
Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 0.901 0.099
2 0.133 0.867
Logits for the Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 2.206 0.000
2 -1.874 0.000
MODEL RESULTS
ESTIMATES S. E. M. S. E. 95% % Sig
Population Average Std. Dev. Average Cover Coeff
Latent Class 1
I |
U1 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
U2 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
U3 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
U4 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
S |
U1 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
U2 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
U3 2.000 2.0000 0.0000 0.0000 0.0000 1.000 0.000
U4 3.000 3.0000 0.0000 0.0000 0.0000 1.000 0.000
I ON
X 0.400 0.1276 0.0000 0.1656 0.0742 1.000 0.000
S ON
X 0.300 0.4244 0.0000 0.0835 0.0155 1.000 1.000
S WITH
I 0.000 0.2608 0.0000 0.1216 0.0680 0.000 1.000
Intercepts
I 3.000 3.0454 0.0000 0.2960 0.0021 1.000 1.000
S 1.000 0.9845 0.0000 0.1793 0.0002 1.000 1.000
Thresholds
U1$1 -0.500 -0.4181 0.0000 0.1731 0.0067 1.000 1.000
U1$2 0.000 0.0942 0.0000 0.1718 0.0089 1.000 0.000
U1$3 0.500 0.5259 0.0000 0.1733 0.0007 1.000 1.000
U1$4 1.000 1.0799 0.0000 0.1785 0.0064 1.000 1.000
U2$1 -0.500 -0.4181 0.0000 0.1731 0.0067 1.000 1.000
U2$2 0.000 0.0942 0.0000 0.1718 0.0089 1.000 0.000
U2$3 0.500 0.5259 0.0000 0.1733 0.0007 1.000 1.000
U2$4 1.000 1.0799 0.0000 0.1785 0.0064 1.000 1.000
U3$1 -0.500 -0.4181 0.0000 0.1731 0.0067 1.000 1.000
U3$2 0.000 0.0942 0.0000 0.1718 0.0089 1.000 0.000
U3$3 0.500 0.5259 0.0000 0.1733 0.0007 1.000 1.000
U3$4 1.000 1.0799 0.0000 0.1785 0.0064 1.000 1.000
U4$1 -0.500 -0.4181 0.0000 0.1731 0.0067 1.000 1.000
U4$2 0.000 0.0942 0.0000 0.1718 0.0089 1.000 0.000
U4$3 0.500 0.5259 0.0000 0.1733 0.0007 1.000 1.000
U4$4 1.000 1.0799 0.0000 0.1785 0.0064 1.000 1.000
Residual Variances
I 1.000 0.2417 0.0000 0.3223 0.5749 0.000 0.000
S 0.500 0.4309 0.0000 0.1231 0.0048 1.000 1.000
Latent Class 2
I |
U1 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
U2 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
U3 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
U4 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
S |
U1 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
U2 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
U3 2.000 2.0000 0.0000 0.0000 0.0000 1.000 0.000
U4 3.000 3.0000 0.0000 0.0000 0.0000 1.000 0.000
I ON
X 0.400 0.1276 0.0000 0.1656 0.0742 1.000 0.000
S ON
X 0.300 0.4244 0.0000 0.0835 0.0155 1.000 1.000
S WITH
I 0.000 0.2608 0.0000 0.1216 0.0680 0.000 1.000
Intercepts
I 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
S 0.500 0.5028 0.0000 0.0987 0.0000 1.000 1.000
Thresholds
U1$1 -0.500 -0.4181 0.0000 0.1731 0.0067 1.000 1.000
U1$2 0.000 0.0942 0.0000 0.1718 0.0089 1.000 0.000
U1$3 0.500 0.5259 0.0000 0.1733 0.0007 1.000 1.000
U1$4 1.000 1.0799 0.0000 0.1785 0.0064 1.000 1.000
U2$1 -0.500 -0.4181 0.0000 0.1731 0.0067 1.000 1.000
U2$2 0.000 0.0942 0.0000 0.1718 0.0089 1.000 0.000
U2$3 0.500 0.5259 0.0000 0.1733 0.0007 1.000 1.000
U2$4 1.000 1.0799 0.0000 0.1785 0.0064 1.000 1.000
U3$1 -0.500 -0.4181 0.0000 0.1731 0.0067 1.000 1.000
U3$2 0.000 0.0942 0.0000 0.1718 0.0089 1.000 0.000
U3$3 0.500 0.5259 0.0000 0.1733 0.0007 1.000 1.000
U3$4 1.000 1.0799 0.0000 0.1785 0.0064 1.000 1.000
U4$1 -0.500 -0.4181 0.0000 0.1731 0.0067 1.000 1.000
U4$2 0.000 0.0942 0.0000 0.1718 0.0089 1.000 0.000
U4$3 0.500 0.5259 0.0000 0.1733 0.0007 1.000 1.000
U4$4 1.000 1.0799 0.0000 0.1785 0.0064 1.000 1.000
Residual Variances
I 1.000 0.2417 0.0000 0.3223 0.5749 0.000 0.000
S 0.500 0.4309 0.0000 0.1231 0.0048 1.000 1.000
Categorical Latent Variables
C#1 ON
X 1.000 1.2103 0.0000 0.3032 0.0442 1.000 1.000
Intercepts
C#1 0.000 0.0076 0.0000 0.2072 0.0001 1.000 0.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.241E-03
(ratio of smallest to largest eigenvalue)
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
NU
U1 U2 U3 U4 X
________ ________ ________ ________ ________
0 0 0 0 0
LAMBDA
I S X
________ ________ ________
U1 0 0 0
U2 0 0 0
U3 0 0 0
U4 0 0 0
X 0 0 0
THETA
U1 U2 U3 U4 X
________ ________ ________ ________ ________
U1 0
U2 0 0
U3 0 0 0
U4 0 0 0 0
X 0 0 0 0 0
ALPHA
I S X
________ ________ ________
1 2 0
BETA
I S X
________ ________ ________
I 0 0 3
S 0 0 4
X 0 0 0
PSI
I S X
________ ________ ________
I 5
S 6 7
X 0 0 0
PARAMETER SPECIFICATION FOR LATENT CLASS 2
NU
U1 U2 U3 U4 X
________ ________ ________ ________ ________
0 0 0 0 0
LAMBDA
I S X
________ ________ ________
U1 0 0 0
U2 0 0 0
U3 0 0 0
U4 0 0 0
X 0 0 0
THETA
U1 U2 U3 U4 X
________ ________ ________ ________ ________
U1 0
U2 0 0
U3 0 0 0
U4 0 0 0 0
X 0 0 0 0 0
ALPHA
I S X
________ ________ ________
0 8 0
BETA
I S X
________ ________ ________
I 0 0 3
S 0 0 4
X 0 0 0
PSI
I S X
________ ________ ________
I 5
S 6 7
X 0 0 0
PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS 1
U1$1 U1$2 U1$3 U1$4 U2$1
________ ________ ________ ________ ________
9 10 11 12 9
TAU(U) FOR LATENT CLASS 1
U2$2 U2$3 U2$4 U3$1 U3$2
________ ________ ________ ________ ________
10 11 12 9 10
TAU(U) FOR LATENT CLASS 1
U3$3 U3$4 U4$1 U4$2 U4$3
________ ________ ________ ________ ________
11 12 9 10 11
TAU(U) FOR LATENT CLASS 1
U4$4
________
12
TAU(U) FOR LATENT CLASS 2
U1$1 U1$2 U1$3 U1$4 U2$1
________ ________ ________ ________ ________
9 10 11 12 9
TAU(U) FOR LATENT CLASS 2
U2$2 U2$3 U2$4 U3$1 U3$2
________ ________ ________ ________ ________
10 11 12 9 10
TAU(U) FOR LATENT CLASS 2
U3$3 U3$4 U4$1 U4$2 U4$3
________ ________ ________ ________ ________
11 12 9 10 11
TAU(U) FOR LATENT CLASS 2
U4$4
________
12
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
13 0
GAMMA(C)
I S X
________ ________ ________
C#1 0 0 14
C#2 0 0 0
STARTING VALUES FOR LATENT CLASS 1
NU
U1 U2 U3 U4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
U1 1.000 0.000 0.000
U2 1.000 1.000 0.000
U3 1.000 2.000 0.000
U4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
U1 U2 U3 U4 X
________ ________ ________ ________ ________
U1 1.000
U2 0.000 1.000
U3 0.000 0.000 1.000
U4 0.000 0.000 0.000 1.000
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
3.000 1.000 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.400
S 0.000 0.000 0.300
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.500
X 0.000 0.000 0.500
STARTING VALUES FOR LATENT CLASS 2
NU
U1 U2 U3 U4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
U1 1.000 0.000 0.000
U2 1.000 1.000 0.000
U3 1.000 2.000 0.000
U4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
U1 U2 U3 U4 X
________ ________ ________ ________ ________
U1 1.000
U2 0.000 1.000
U3 0.000 0.000 1.000
U4 0.000 0.000 0.000 1.000
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
0.000 0.500 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.400
S 0.000 0.000 0.300
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.500
X 0.000 0.000 0.500
STARTING VALUES FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS 1
U1$1 U1$2 U1$3 U1$4 U2$1
________ ________ ________ ________ ________
-0.500 0.000 0.500 1.000 -0.500
TAU(U) FOR LATENT CLASS 1
U2$2 U2$3 U2$4 U3$1 U3$2
________ ________ ________ ________ ________
0.000 0.500 1.000 -0.500 0.000
TAU(U) FOR LATENT CLASS 1
U3$3 U3$4 U4$1 U4$2 U4$3
________ ________ ________ ________ ________
0.500 1.000 -0.500 0.000 0.500
TAU(U) FOR LATENT CLASS 1
U4$4
________
1.000
TAU(U) FOR LATENT CLASS 2
U1$1 U1$2 U1$3 U1$4 U2$1
________ ________ ________ ________ ________
-0.500 0.000 0.500 1.000 -0.500
TAU(U) FOR LATENT CLASS 2
U2$2 U2$3 U2$4 U3$1 U3$2
________ ________ ________ ________ ________
0.000 0.500 1.000 -0.500 0.000
TAU(U) FOR LATENT CLASS 2
U3$3 U3$4 U4$1 U4$2 U4$3
________ ________ ________ ________ ________
0.500 1.000 -0.500 0.000 0.500
TAU(U) FOR LATENT CLASS 2
U4$4
________
1.000
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
0.000 0.000
GAMMA(C)
I S X
________ ________ ________
C#1 0.000 0.000 1.000
C#2 0.000 0.000 0.000
POPULATION VALUES FOR LATENT CLASS 1
NU
U1 U2 U3 U4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
U1 1.000 0.000 0.000
U2 1.000 1.000 0.000
U3 1.000 2.000 0.000
U4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
U1 U2 U3 U4 X
________ ________ ________ ________ ________
U1 0.000
U2 0.000 0.000
U3 0.000 0.000 0.000
U4 0.000 0.000 0.000 0.000
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
3.000 1.000 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.400
S 0.000 0.000 0.300
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.500
X 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS 2
NU
U1 U2 U3 U4 X
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 0.000
LAMBDA
I S X
________ ________ ________
U1 1.000 0.000 0.000
U2 1.000 1.000 0.000
U3 1.000 2.000 0.000
U4 1.000 3.000 0.000
X 0.000 0.000 1.000
THETA
U1 U2 U3 U4 X
________ ________ ________ ________ ________
U1 0.000
U2 0.000 0.000
U3 0.000 0.000 0.000
U4 0.000 0.000 0.000 0.000
X 0.000 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
0.000 0.500 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.400
S 0.000 0.000 0.300
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.000 0.500
X 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS 1
U1$1 U1$2 U1$3 U1$4 U2$1
________ ________ ________ ________ ________
-0.500 0.000 0.500 1.000 -0.500
TAU(U) FOR LATENT CLASS 1
U2$2 U2$3 U2$4 U3$1 U3$2
________ ________ ________ ________ ________
0.000 0.500 1.000 -0.500 0.000
TAU(U) FOR LATENT CLASS 1
U3$3 U3$4 U4$1 U4$2 U4$3
________ ________ ________ ________ ________
0.500 1.000 -0.500 0.000 0.500
TAU(U) FOR LATENT CLASS 1
U4$4
________
1.000
TAU(U) FOR LATENT CLASS 2
U1$1 U1$2 U1$3 U1$4 U2$1
________ ________ ________ ________ ________
-0.500 0.000 0.500 1.000 -0.500
TAU(U) FOR LATENT CLASS 2
U2$2 U2$3 U2$4 U3$1 U3$2
________ ________ ________ ________ ________
0.000 0.500 1.000 -0.500 0.000
TAU(U) FOR LATENT CLASS 2
U3$3 U3$4 U4$1 U4$2 U4$3
________ ________ ________ ________ ________
0.500 1.000 -0.500 0.000 0.500
TAU(U) FOR LATENT CLASS 2
U4$4
________
1.000
POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
0.000 0.000
GAMMA(C)
I S X
________ ________ ________
C#1 0.000 0.000 1.000
C#2 0.000 0.000 0.000
TECHNICAL 8 OUTPUT
TECHNICAL 8 OUTPUT FOR REPLICATION 1
E STEP ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE ALGORITHM
1 -0.34733695D+04 0.0000000 0.0000000 EM
2 -0.34648739D+04 8.4956180 0.0024459 FS
3 -0.34641196D+04 0.7542556 0.0002177 FS
4 -0.34638995D+04 0.2201640 0.0000636 FS
5 -0.34638954D+04 0.0041095 0.0000012 FS
6 -0.34638922D+04 0.0031690 0.0000009 FS
7 -0.34638917D+04 0.0004716 0.0000001 FS
TECHNICAL 9 OUTPUT
Error messages for each replication (if any)
SAVEDATA INFORMATION
Order of variables
U1
U2
U3
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