Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 11:07 PM
INPUT INSTRUCTIONS
TITLE: cfa3.inp non-normal, no missing
MONTECARLO: NAMES ARE y1-y10;
NOBSERVATIONS = 265;
NREPS = 10000;
SEED = 53487;
CLASSES = C(1);
GENCLASSES = C(2);
SAVE = cfa3.sav;
ANALYSIS: TYPE = MIXTURE;
ESTIMATOR = MLR;
MODEL MONTECARLO:
%OVERALL%
f1 BY y1-y5*.8;
f2 BY y6-y10*.8;
f1@1 f2@1;
y1-y5*.36 y6-y10*9;
f1 WITH f2*.95;
[C#1@-2];
%C#1%
[f1@0 f2@15];
f1@1 f2@5;
%C#2%
[f1@0 f2@0];
f1@1 f2@1;
MODEL:
%OVERALL%
f1 BY y1-y5*.8;
f2 BY y6-y10*4;
f1@1 f2@1;
y1-y5*.36 y6-y10*9;
f1 WITH f2*.20;
[y6-y10*1.42];
OUTPUT: TECH9;
INPUT READING TERMINATED NORMALLY
cfa3.inp non-normal, no missing
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 265
Number of replications
Requested 10000
Completed 10000
Value of seed 53487
Number of dependent variables 10
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 Y6
Y7 Y8 Y9 Y10
Continuous latent variables
F1 F2
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 0.001 0.000 0.007 -0.045 -0.044
Means
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
1 1.170 1.437 1.231 1.165 1.109
Covariances
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1.026
Y2 0.701 1.014
Y3 0.634 0.614 1.014
Y4 0.616 0.639 0.643 0.962
Y5 0.568 0.599 0.565 0.619 0.918
Y6 -0.095 -0.262 0.360 -0.153 0.032
Y7 0.210 0.052 0.313 0.335 0.120
Y8 0.345 -0.026 0.649 0.435 0.278
Y9 -0.081 -0.336 0.482 0.044 0.026
Y10 0.346 -0.034 0.346 0.123 0.080
Covariances
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 20.712
Y7 11.656 20.941
Y8 12.110 11.905 20.960
Y9 12.900 12.353 13.561 22.666
Y10 11.612 11.176 12.129 12.924 20.333
Correlations
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1.000
Y2 0.687 1.000
Y3 0.621 0.605 1.000
Y4 0.620 0.647 0.651 1.000
Y5 0.585 0.621 0.586 0.658 1.000
Y6 -0.021 -0.057 0.079 -0.034 0.007
Y7 0.045 0.011 0.068 0.075 0.027
Y8 0.074 -0.006 0.141 0.097 0.063
Y9 -0.017 -0.070 0.100 0.009 0.006
Y10 0.076 -0.007 0.076 0.028 0.019
Correlations
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 1.000
Y7 0.560 1.000
Y8 0.581 0.568 1.000
Y9 0.595 0.567 0.622 1.000
Y10 0.566 0.542 0.588 0.602 1.000
TESTS OF MODEL FIT
Number of Free Parameters 31
Loglikelihood
H0 Value
Mean -5126.647
Std Dev 39.581
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.990 -5218.724 -5219.493
0.980 0.980 -5207.934 -5208.018
0.950 0.953 -5191.754 -5190.715
0.900 0.904 -5177.374 -5176.595
0.800 0.800 -5159.958 -5159.991
0.700 0.694 -5147.403 -5147.971
0.500 0.499 -5126.647 -5126.758
0.300 0.298 -5105.891 -5106.081
0.200 0.198 -5093.336 -5093.745
0.100 0.098 -5075.920 -5076.406
0.050 0.049 -5061.540 -5061.857
0.020 0.020 -5045.360 -5045.394
0.010 0.011 -5034.570 -5033.568
Information Criteria
Akaike (AIC)
Mean 10315.294
Std Dev 79.162
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 10131.140 10129.105
0.980 0.980 10152.719 10152.668
0.950 0.951 10185.081 10185.651
0.900 0.902 10213.840 10214.634
0.800 0.802 10248.671 10249.440
0.700 0.702 10273.782 10274.160
0.500 0.501 10315.294 10315.512
0.300 0.306 10356.807 10357.932
0.200 0.200 10381.917 10381.876
0.100 0.096 10416.748 10415.120
0.050 0.047 10445.508 10443.421
0.020 0.020 10477.869 10478.034
0.010 0.010 10499.448 10499.943
Bayesian (BIC)
Mean 10426.266
Std Dev 79.162
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 10242.111 10240.077
0.980 0.980 10263.691 10263.639
0.950 0.951 10296.052 10296.622
0.900 0.902 10324.812 10325.605
0.800 0.802 10359.643 10360.412
0.700 0.702 10384.753 10385.131
0.500 0.501 10426.266 10426.483
0.300 0.306 10467.778 10468.904
0.200 0.200 10492.888 10492.848
0.100 0.096 10527.720 10526.092
0.050 0.047 10556.479 10554.393
0.020 0.020 10588.841 10589.006
0.010 0.010 10610.420 10610.915
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 10327.979
Std Dev 79.162
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 10143.825 10141.790
0.980 0.980 10165.404 10165.353
0.950 0.951 10197.766 10198.336
0.900 0.902 10226.525 10227.319
0.800 0.802 10261.356 10262.125
0.700 0.702 10286.467 10286.845
0.500 0.501 10327.979 10328.197
0.300 0.306 10369.492 10370.617
0.200 0.200 10394.602 10394.561
0.100 0.096 10429.433 10427.806
0.050 0.047 10458.193 10456.106
0.020 0.020 10490.554 10490.719
0.010 0.010 10512.134 10512.628
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 265.00000 1.00000
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 265.00000 1.00000
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 265 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
F1 BY
Y1 0.800 0.7981 0.0536 0.0523 0.0029 0.938 1.000
Y2 0.800 0.7980 0.0531 0.0524 0.0028 0.942 1.000
Y3 0.800 0.7976 0.0535 0.0523 0.0029 0.942 1.000
Y4 0.800 0.7987 0.0534 0.0524 0.0029 0.945 1.000
Y5 0.800 0.7984 0.0527 0.0524 0.0028 0.947 1.000
F2 BY
Y6 4.000 3.9880 0.3596 0.3552 0.1294 0.944 1.000
Y7 4.000 3.9908 0.3600 0.3555 0.1297 0.945 1.000
Y8 4.000 3.9857 0.3607 0.3552 0.1303 0.944 1.000
Y9 4.000 3.9890 0.3614 0.3551 0.1307 0.941 1.000
Y10 4.000 3.9886 0.3596 0.3555 0.1294 0.943 1.000
F1 WITH
F2 0.200 0.1915 0.0684 0.0664 0.0048 0.938 0.797
Means
F1 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
F2 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Intercepts
Y1 0.000 0.0008 0.0613 0.0613 0.0038 0.948 0.052
Y2 0.000 0.0004 0.0626 0.0613 0.0039 0.943 0.057
Y3 0.000 0.0013 0.0618 0.0612 0.0038 0.946 0.054
Y4 0.000 0.0006 0.0617 0.0613 0.0038 0.947 0.052
Y5 0.000 0.0010 0.0621 0.0613 0.0039 0.947 0.053
Y6 1.420 1.4296 0.3093 0.3063 0.0958 0.945 0.999
Y7 1.420 1.4306 0.3096 0.3065 0.0959 0.946 0.999
Y8 1.420 1.4288 0.3078 0.3063 0.0948 0.947 0.999
Y9 1.420 1.4298 0.3072 0.3064 0.0945 0.948 0.998
Y10 1.420 1.4306 0.3098 0.3065 0.0961 0.947 0.999
Variances
F1 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
F2 1.000 1.0000 0.0000 0.0000 0.0000 1.000 0.000
Residual Variances
Y1 0.360 0.3569 0.0389 0.0388 0.0015 0.940 1.000
Y2 0.360 0.3577 0.0395 0.0389 0.0016 0.935 1.000
Y3 0.360 0.3570 0.0394 0.0388 0.0016 0.936 1.000
Y4 0.360 0.3572 0.0397 0.0388 0.0016 0.934 1.000
Y5 0.360 0.3571 0.0396 0.0388 0.0016 0.934 1.000
Y6 9.000 8.9203 0.9848 0.9685 0.9760 0.935 1.000
Y7 9.000 8.9304 0.9941 0.9692 0.9929 0.937 1.000
Y8 9.000 8.9348 0.9771 0.9699 0.9590 0.938 1.000
Y9 9.000 8.9165 0.9927 0.9679 0.9923 0.935 1.000
Y10 9.000 8.9384 0.9839 0.9709 0.9718 0.936 1.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.409E-03
(ratio of smallest to largest eigenvalue)
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
NU
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
1 1 2 3 4 5
NU
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
1 6 7 8 9 10
LAMBDA
F1 F2
________ ________
Y1 11 0
Y2 12 0
Y3 13 0
Y4 14 0
Y5 15 0
Y6 0 16
Y7 0 17
Y8 0 18
Y9 0 19
Y10 0 20
THETA
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 21
Y2 0 22
Y3 0 0 23
Y4 0 0 0 24
Y5 0 0 0 0 25
Y6 0 0 0 0 0
Y7 0 0 0 0 0
Y8 0 0 0 0 0
Y9 0 0 0 0 0
Y10 0 0 0 0 0
THETA
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 26
Y7 0 27
Y8 0 0 28
Y9 0 0 0 29
Y10 0 0 0 0 30
ALPHA
F1 F2
________ ________
1 0 0
BETA
F1 F2
________ ________
F1 0 0
F2 0 0
PSI
F1 F2
________ ________
F1 0
F2 31 0
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
NU
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
1 1.420 1.420 1.420 1.420 1.420
LAMBDA
F1 F2
________ ________
Y1 0.800 0.000
Y2 0.800 0.000
Y3 0.800 0.000
Y4 0.800 0.000
Y5 0.800 0.000
Y6 0.000 4.000
Y7 0.000 4.000
Y8 0.000 4.000
Y9 0.000 4.000
Y10 0.000 4.000
THETA
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 0.360
Y2 0.000 0.360
Y3 0.000 0.000 0.360
Y4 0.000 0.000 0.000 0.360
Y5 0.000 0.000 0.000 0.000 0.360
Y6 0.000 0.000 0.000 0.000 0.000
Y7 0.000 0.000 0.000 0.000 0.000
Y8 0.000 0.000 0.000 0.000 0.000
Y9 0.000 0.000 0.000 0.000 0.000
Y10 0.000 0.000 0.000 0.000 0.000
THETA
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 9.000
Y7 0.000 9.000
Y8 0.000 0.000 9.000
Y9 0.000 0.000 0.000 9.000
Y10 0.000 0.000 0.000 0.000 9.000
ALPHA
F1 F2
________ ________
1 0.000 0.000
BETA
F1 F2
________ ________
F1 0.000 0.000
F2 0.000 0.000
PSI
F1 F2
________ ________
F1 1.000
F2 0.200 1.000
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
NU
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
LAMBDA
F1 F2
________ ________
Y1 0.800 0.000
Y2 0.800 0.000
Y3 0.800 0.000
Y4 0.800 0.000
Y5 0.800 0.000
Y6 0.000 0.800
Y7 0.000 0.800
Y8 0.000 0.800
Y9 0.000 0.800
Y10 0.000 0.800
THETA
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 0.360
Y2 0.000 0.360
Y3 0.000 0.000 0.360
Y4 0.000 0.000 0.000 0.360
Y5 0.000 0.000 0.000 0.000 0.360
Y6 0.000 0.000 0.000 0.000 0.000
Y7 0.000 0.000 0.000 0.000 0.000
Y8 0.000 0.000 0.000 0.000 0.000
Y9 0.000 0.000 0.000 0.000 0.000
Y10 0.000 0.000 0.000 0.000 0.000
THETA
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 9.000
Y7 0.000 9.000
Y8 0.000 0.000 9.000
Y9 0.000 0.000 0.000 9.000
Y10 0.000 0.000 0.000 0.000 9.000
ALPHA
F1 F2
________ ________
1 0.000 15.000
BETA
F1 F2
________ ________
F1 0.000 0.000
F2 0.000 0.000
PSI
F1 F2
________ ________
F1 1.000
F2 0.950 5.000
POPULATION VALUES FOR LATENT CLASS 2
NU
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
NU
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
LAMBDA
F1 F2
________ ________
Y1 0.800 0.000
Y2 0.800 0.000
Y3 0.800 0.000
Y4 0.800 0.000
Y5 0.800 0.000
Y6 0.000 0.800
Y7 0.000 0.800
Y8 0.000 0.800
Y9 0.000 0.800
Y10 0.000 0.800
THETA
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 0.360
Y2 0.000 0.360
Y3 0.000 0.000 0.360
Y4 0.000 0.000 0.000 0.360
Y5 0.000 0.000 0.000 0.000 0.360
Y6 0.000 0.000 0.000 0.000 0.000
Y7 0.000 0.000 0.000 0.000 0.000
Y8 0.000 0.000 0.000 0.000 0.000
Y9 0.000 0.000 0.000 0.000 0.000
Y10 0.000 0.000 0.000 0.000 0.000
THETA
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 9.000
Y7 0.000 9.000
Y8 0.000 0.000 9.000
Y9 0.000 0.000 0.000 9.000
Y10 0.000 0.000 0.000 0.000 9.000
ALPHA
F1 F2
________ ________
1 0.000 0.000
BETA
F1 F2
________ ________
F1 0.000 0.000
F2 0.000 0.000
PSI
F1 F2
________ ________
F1 1.000
F2 0.950 1.000
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
Y6
Y7
Y8
Y9
Y10
C
Save file
cfa3.sav
Save file format Free
Save file record length 5000
Beginning Time: 23:07:43
Ending Time: 23:09:54
Elapsed Time: 00:02:11
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