Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 11:23 PM
INPUT INSTRUCTIONS
TITLE: mc2b.inp
Montecarlo:
names are y1-y5;
nobs = 500;
nreps=500;
seed=53487;
classes = c(1);
genclasses = c(2);
save = mc2b.sav;
analysis: type=mixture;
estimator=mlr;
model montecarlo:
%overall%
i by y1-y5@1;
s by y1@0 y2@1 y3@2 y4@3 y5@4;
[i*1.5 s*1.6 y1-y5@0];
i*1; s*.2; i with s*.11;
y1*1.0 y2*1.42 y3*2.24 y4*3.46 y5*5.08;
[c#1@-2];
%c#1%
[i*15 s*1.6];
i*5; s*.2; i with s*.11;
y1*1.0 y2*1.42 y3*2.24 y4*3.46 y5*5.08;
%c#2%
[i*0 s*0];
i*1; s*.2; i with s*.11;
y1*1.0 y2*1.42 y3*2.24 y4*3.46 y5*5.08;
model:
%overall%
i by y1-y5@1;
s by y1@0 y2@1 y3@2 y4@3 y5@4;
[i s y1-y5@0];
i*25.3642; s*0.4757; i with s*2.6744;
y1*1.0098 y2*1.4256 y3*2.2380
y4*3.4435 y5*5.0170;
%c#1%
[i*1.8161 s*0.1930];
output:
tech9;
INPUT READING TERMINATED NORMALLY
mc2b.inp
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of replications
Requested 500
Completed 500
Value of seed 53487
Number of dependent variables 5
Number of independent variables 0
Number of continuous latent variables 2
Number of categorical latent variables 1
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4 Y5
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-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 Y5
________ ________ ________ ________ ________
1 1.633 1.843 2.046 2.147 2.244
Covariances
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 23.263
Y2 24.410 28.322
Y3 27.436 30.386 36.988
Y4 30.113 33.626 38.675 46.259
Y5 31.815 35.757 41.228 46.427 54.855
Correlations
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1.000
Y2 0.951 1.000
Y3 0.935 0.939 1.000
Y4 0.918 0.929 0.935 1.000
Y5 0.891 0.907 0.915 0.922 1.000
TESTS OF MODEL FIT
Number of Free Parameters 10
Loglikelihood
H0 Value
Mean -5790.074
Std Dev 39.843
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.986 -5882.762 -5892.519
0.980 0.976 -5871.901 -5878.378
0.950 0.952 -5855.613 -5855.469
0.900 0.898 -5841.138 -5842.333
0.800 0.830 -5823.607 -5821.604
0.700 0.708 -5810.968 -5810.246
0.500 0.498 -5790.074 -5790.653
0.300 0.290 -5769.181 -5771.286
0.200 0.192 -5756.542 -5757.700
0.100 0.110 -5739.011 -5737.881
0.050 0.048 -5724.536 -5727.760
0.020 0.022 -5708.248 -5706.969
0.010 0.016 -5697.387 -5692.447
Information Criteria
Akaike (AIC)
Mean 11600.149
Std Dev 79.687
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.984 11414.773 11401.355
0.980 0.978 11436.496 11431.705
0.950 0.952 11469.072 11471.618
0.900 0.890 11498.022 11494.042
0.800 0.808 11533.084 11534.252
0.700 0.710 11558.361 11561.640
0.500 0.502 11600.149 11600.505
0.300 0.292 11641.937 11639.976
0.200 0.170 11667.213 11662.666
0.100 0.102 11702.275 11702.615
0.050 0.048 11731.226 11729.335
0.020 0.024 11763.802 11770.805
0.010 0.014 11785.524 11797.000
Bayesian (BIC)
Mean 11642.295
Std Dev 79.687
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.984 11456.919 11443.501
0.980 0.978 11478.642 11473.851
0.950 0.952 11511.218 11513.764
0.900 0.890 11540.168 11536.189
0.800 0.808 11575.231 11576.398
0.700 0.710 11600.507 11603.786
0.500 0.502 11642.295 11642.651
0.300 0.292 11684.083 11682.122
0.200 0.170 11709.359 11704.812
0.100 0.102 11744.422 11744.761
0.050 0.048 11773.372 11771.481
0.020 0.024 11805.948 11812.951
0.010 0.014 11827.670 11839.146
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 11610.554
Std Dev 79.687
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.984 11425.179 11411.761
0.980 0.978 11446.902 11442.111
0.950 0.952 11479.477 11482.023
0.900 0.890 11508.428 11504.448
0.800 0.808 11543.490 11544.658
0.700 0.710 11568.767 11572.045
0.500 0.502 11610.554 11610.911
0.300 0.292 11652.342 11650.382
0.200 0.170 11677.619 11673.071
0.100 0.102 11712.681 11713.021
0.050 0.048 11741.631 11739.740
0.020 0.024 11774.207 11781.211
0.010 0.014 11795.930 11807.405
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 500.00000 1.00000
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 500.00000 1.00000
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 500 1.00000
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1
1 1.000
MODEL RESULTS
ESTIMATES S. E. M. S. E. 95% % Sig
Population Average Std. Dev. Average Cover Coeff
Latent Class 1
I BY
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
Y5 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
S BY
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
Y5 4.000 4.0000 0.0000 0.0000 0.0000 1.000 0.000
I WITH
S 2.674 2.6441 0.2917 0.3062 0.0858 0.966 1.000
Means
I 1.816 1.8101 0.2226 0.2279 0.0495 0.944 1.000
S 0.193 0.1934 0.0370 0.0375 0.0014 0.958 1.000
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
Y5 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Variances
I 25.364 25.2987 2.6449 2.7278 6.9856 0.966 1.000
S 0.476 0.4667 0.0506 0.0520 0.0026 0.942 1.000
Residual Variances
Y1 1.010 1.0034 0.1444 0.1432 0.0209 0.952 1.000
Y2 1.426 1.4199 0.1227 0.1255 0.0150 0.940 1.000
Y3 2.238 2.2434 0.1834 0.1795 0.0336 0.948 1.000
Y4 3.444 3.4674 0.2709 0.2831 0.0738 0.956 1.000
Y5 5.017 5.0958 0.4491 0.4429 0.2075 0.948 1.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.156E-02
(ratio of smallest to largest eigenvalue)
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
NU
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
1 0 0 0 0 0
LAMBDA
I S
________ ________
Y1 0 0
Y2 0 0
Y3 0 0
Y4 0 0
Y5 0 0
THETA
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1
Y2 0 2
Y3 0 0 3
Y4 0 0 0 4
Y5 0 0 0 0 5
ALPHA
I S
________ ________
1 6 7
BETA
I S
________ ________
I 0 0
S 0 0
PSI
I S
________ ________
I 8
S 9 10
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1
________
1 0
STARTING VALUES FOR LATENT CLASS 1
NU
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
LAMBDA
I S
________ ________
Y1 1.000 0.000
Y2 1.000 1.000
Y3 1.000 2.000
Y4 1.000 3.000
Y5 1.000 4.000
THETA
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1.010
Y2 0.000 1.426
Y3 0.000 0.000 2.238
Y4 0.000 0.000 0.000 3.444
Y5 0.000 0.000 0.000 0.000 5.017
ALPHA
I S
________ ________
1 1.816 0.193
BETA
I S
________ ________
I 0.000 0.000
S 0.000 0.000
PSI
I S
________ ________
I 25.364
S 2.674 0.476
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1
________
1 0.000
POPULATION VALUES FOR LATENT CLASS 1
NU
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
LAMBDA
I S
________ ________
Y1 1.000 0.000
Y2 1.000 1.000
Y3 1.000 2.000
Y4 1.000 3.000
Y5 1.000 4.000
THETA
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1.000
Y2 0.000 1.420
Y3 0.000 0.000 2.240
Y4 0.000 0.000 0.000 3.460
Y5 0.000 0.000 0.000 0.000 5.080
ALPHA
I S
________ ________
1 15.000 1.600
BETA
I S
________ ________
I 0.000 0.000
S 0.000 0.000
PSI
I S
________ ________
I 5.000
S 0.110 0.200
POPULATION VALUES FOR LATENT CLASS 2
NU
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
LAMBDA
I S
________ ________
Y1 1.000 0.000
Y2 1.000 1.000
Y3 1.000 2.000
Y4 1.000 3.000
Y5 1.000 4.000
THETA
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1.000
Y2 0.000 1.420
Y3 0.000 0.000 2.240
Y4 0.000 0.000 0.000 3.460
Y5 0.000 0.000 0.000 0.000 5.080
ALPHA
I S
________ ________
1 0.000 0.000
BETA
I S
________ ________
I 0.000 0.000
S 0.000 0.000
PSI
I S
________ ________
I 1.000
S 0.110 0.200
POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
1 -2.000 0.000
TECHNICAL 9 OUTPUT
Error messages for each replication (if any)
SAVEDATA INFORMATION
Order of variables
Y1
Y2
Y3
Y4
Y5
C
Save file
mc2b.sav
Save file format Free
Save file record length 5000
Beginning Time: 23:23:59
Ending Time: 23:24:02
Elapsed Time: 00:00:03
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