Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 11:03 PM
INPUT INSTRUCTIONS
TITLE: cfa2.inp normal, missing
MONTECARLO: NAMES ARE y1-y10;
NOBSERVATIONS = 175;
NREPS = 10000;
SEED = 53487;
CLASSES = C(1);
GENCLASSES = C(1);
PATMISS = y6 (.5) y7 (.5) y8 (.5) y9 (.5) y10 (.5);
PATPROB = 1;
SAVE = cfa2.sav;
ANALYSIS: TYPE = MIXTURE MISSING;
ESTIMATOR = ML;
MODEL MONTECARLO:
%OVERALL%
f1 BY y1-y5*.8;
f2 BY y6-y10*.8;
f1@1 f2@1;
y1-y10*.36;
f1 WITH f2*.25;
MODEL:
%OVERALL%
f1 BY y1-y5*.8;
f2 BY y6-y10*.8;
f1@1 f2@1;
y1-y10*.36;
f1 WITH f2*.25;
OUTPUT: PATTERNS TECH9;
*** WARNING in ANALYSIS command
Starting with Version 5, TYPE=MISSING is the default for all analyses.
To obtain listwise deletion, use LISTWISE=ON in the DATA command.
*** WARNING in OUTPUT command
PATTERNS option is the default for MONTECARLO.
2 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
cfa2.inp normal, missing
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 175
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 ML
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
Maximum number of iterations for H1 2000
Convergence criterion for H1 0.100D-03
Optimization algorithm EMA
SUMMARY OF DATA FOR THE FIRST REPLICATION
Number of missing data patterns 31
Number of y missing data patterns 31
Number of u missing data patterns 0
SUMMARY OF MISSING DATA PATTERNS FOR THE FIRST REPLICATION
MISSING DATA PATTERNS FOR Y (x = not missing)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Y1 x x x x x x x x x x x x x x x x x x x x
Y2 x x x x x x x x x x x x x x x x x x x x
Y3 x x x x x x x x x x x x x x x x x x x x
Y4 x x x x x x x x x x x x x x x x x x x x
Y5 x x x x x x x x x x x x x x x x x x x x
Y6 x x x x x x x x x x x
Y7 x x x x x x x x x x
Y8 x x x x x x x x x x
Y9 x x x x x x x x x x
Y10 x x x x x x x x x x x x
21 22 23 24 25 26 27 28 29 30 31
Y1 x x x x x x x x x x x
Y2 x x x x x x x x x x x
Y3 x x x x x x x x x x x
Y4 x x x x x x x x x x x
Y5 x x x x x x x x x x x
Y6 x x x x x
Y7 x x x x x
Y8 x x x x x
Y9 x x x x x x
Y10 x x x x
MISSING DATA PATTERN FREQUENCIES FOR Y
Pattern Frequency Pattern Frequency Pattern Frequency
1 4 12 5 23 7
2 5 13 5 24 11
3 4 14 4 25 5
4 6 15 5 26 7
5 6 16 5 27 5
6 9 17 4 28 2
7 5 18 5 29 10
8 5 19 7 30 5
9 8 20 8 31 2
10 5 21 6
11 4 22 6
COVARIANCE COVERAGE OF DATA FOR THE FIRST REPLICATION
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT FOR Y
Covariance Coverage
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1.000
Y2 1.000 1.000
Y3 1.000 1.000 1.000
Y4 1.000 1.000 1.000 1.000
Y5 1.000 1.000 1.000 1.000 1.000
Y6 0.566 0.566 0.566 0.566 0.566
Y7 0.497 0.497 0.497 0.497 0.497
Y8 0.486 0.486 0.486 0.486 0.486
Y9 0.469 0.469 0.469 0.469 0.469
Y10 0.486 0.486 0.486 0.486 0.486
Covariance Coverage
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 0.566
Y7 0.269 0.497
Y8 0.280 0.234 0.486
Y9 0.246 0.257 0.246 0.469
Y10 0.240 0.251 0.211 0.229 0.486
SAMPLE STATISTICS FOR THE FIRST REPLICATION
ESTIMATED SAMPLE STATISTICS
Means
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
1 0.080 0.018 0.040 -0.018 -0.040
Means
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
1 -0.111 0.123 -0.029 -0.015 -0.034
Covariances
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 0.951
Y2 0.695 1.023
Y3 0.589 0.566 0.967
Y4 0.577 0.590 0.567 0.908
Y5 0.517 0.530 0.490 0.556 0.879
Y6 0.090 0.195 0.065 0.028 0.099
Y7 0.252 0.238 0.192 0.279 0.191
Y8 0.141 0.173 0.068 0.127 0.059
Y9 0.226 0.214 0.241 0.190 0.164
Y10 0.257 0.274 0.120 0.222 0.210
Covariances
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 0.920
Y7 0.748 1.257
Y8 0.667 0.881 1.067
Y9 0.555 0.793 0.791 0.997
Y10 0.639 0.870 0.921 0.789 1.170
Correlations
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1.000
Y2 0.704 1.000
Y3 0.614 0.569 1.000
Y4 0.621 0.612 0.605 1.000
Y5 0.566 0.559 0.531 0.623 1.000
Y6 0.096 0.201 0.068 0.030 0.110
Y7 0.230 0.210 0.174 0.261 0.182
Y8 0.140 0.166 0.067 0.129 0.061
Y9 0.232 0.212 0.246 0.200 0.175
Y10 0.244 0.250 0.113 0.216 0.207
Correlations
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 1.000
Y7 0.696 1.000
Y8 0.673 0.760 1.000
Y9 0.579 0.708 0.767 1.000
Y10 0.616 0.717 0.825 0.730 1.000
MAXIMUM LOG-LIKELIHOOD VALUE FOR THE UNRESTRICTED (H1) MODEL IS -1503.825
TESTS OF MODEL FIT
Number of Free Parameters 31
Loglikelihood
H0 Value
Mean -1514.213
Std Dev 30.726
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.991 -1585.691 -1584.523
0.980 0.981 -1577.315 -1576.506
0.950 0.951 -1564.754 -1564.351
0.900 0.901 -1553.592 -1553.280
0.800 0.802 -1540.072 -1539.977
0.700 0.697 -1530.326 -1530.572
0.500 0.497 -1514.213 -1514.453
0.300 0.298 -1498.101 -1498.250
0.200 0.198 -1488.355 -1488.651
0.100 0.099 -1474.835 -1474.963
0.050 0.053 -1463.673 -1462.812
0.020 0.022 -1451.112 -1449.210
0.010 0.011 -1442.736 -1441.616
Information Criteria
Akaike (AIC)
Mean 3090.427
Std Dev 61.452
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 2947.472 2945.004
0.980 0.978 2964.224 2960.355
0.950 0.947 2989.345 2987.619
0.900 0.901 3011.670 3011.887
0.800 0.802 3038.709 3039.218
0.700 0.702 3058.202 3058.486
0.500 0.503 3090.427 3090.893
0.300 0.303 3122.652 3123.101
0.200 0.198 3142.145 3141.921
0.100 0.099 3169.184 3168.543
0.050 0.049 3191.509 3190.630
0.020 0.019 3216.630 3214.976
0.010 0.009 3233.382 3230.558
Bayesian (BIC)
Mean 3188.535
Std Dev 61.452
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 3045.580 3043.113
0.980 0.978 3062.332 3058.463
0.950 0.947 3087.453 3085.727
0.900 0.901 3109.779 3109.995
0.800 0.802 3136.818 3137.326
0.700 0.702 3156.310 3156.594
0.500 0.503 3188.535 3189.002
0.300 0.303 3220.761 3221.210
0.200 0.198 3240.253 3240.030
0.100 0.099 3267.292 3266.652
0.050 0.049 3289.617 3288.738
0.020 0.019 3314.739 3313.085
0.010 0.009 3331.490 3328.666
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 3090.368
Std Dev 61.452
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 2947.413 2944.945
0.980 0.978 2964.165 2960.296
0.950 0.947 2989.286 2987.560
0.900 0.901 3011.611 3011.828
0.800 0.802 3038.650 3039.159
0.700 0.702 3058.143 3058.427
0.500 0.503 3090.368 3090.834
0.300 0.303 3122.593 3123.042
0.200 0.198 3142.086 3141.862
0.100 0.099 3169.124 3168.484
0.050 0.049 3191.450 3190.571
0.020 0.019 3216.571 3214.917
0.010 0.009 3233.323 3230.499
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 175.00000 1.00000
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 175.00000 1.00000
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 175 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 0.7985 0.0654 0.0647 0.0043 0.946 1.000
Y2 0.800 0.7982 0.0653 0.0647 0.0043 0.945 1.000
Y3 0.800 0.7970 0.0658 0.0647 0.0043 0.945 1.000
Y4 0.800 0.7982 0.0651 0.0647 0.0042 0.949 1.000
Y5 0.800 0.7980 0.0649 0.0647 0.0042 0.951 1.000
F2 BY
Y6 0.800 0.7973 0.0934 0.0923 0.0087 0.944 1.000
Y7 0.800 0.7976 0.0953 0.0923 0.0091 0.942 1.000
Y8 0.800 0.7959 0.0962 0.0922 0.0093 0.938 1.000
Y9 0.800 0.7963 0.0941 0.0923 0.0089 0.947 1.000
Y10 0.800 0.7961 0.0952 0.0924 0.0091 0.942 1.000
F1 WITH
F2 0.250 0.2504 0.0876 0.0855 0.0077 0.941 0.812
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 0.0004 0.0751 0.0754 0.0056 0.950 0.050
Y2 0.000 0.0003 0.0769 0.0754 0.0059 0.942 0.058
Y3 0.000 0.0015 0.0760 0.0753 0.0058 0.948 0.052
Y4 0.000 -0.0001 0.0759 0.0754 0.0058 0.945 0.055
Y5 0.000 0.0008 0.0752 0.0753 0.0057 0.947 0.053
Y6 0.000 0.0009 0.0937 0.0929 0.0088 0.946 0.054
Y7 0.000 -0.0002 0.0942 0.0930 0.0089 0.946 0.054
Y8 0.000 -0.0002 0.0939 0.0929 0.0088 0.946 0.054
Y9 0.000 0.0008 0.0933 0.0929 0.0087 0.948 0.052
Y10 0.000 0.0002 0.0933 0.0929 0.0087 0.950 0.051
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 0.3556 0.0482 0.0479 0.0023 0.937 1.000
Y2 0.360 0.3559 0.0490 0.0479 0.0024 0.934 1.000
Y3 0.360 0.3553 0.0480 0.0478 0.0023 0.938 1.000
Y4 0.360 0.3559 0.0489 0.0479 0.0024 0.936 1.000
Y5 0.360 0.3551 0.0486 0.0478 0.0024 0.934 1.000
Y6 0.360 0.3472 0.0868 0.0842 0.0077 0.919 0.993
Y7 0.360 0.3478 0.0881 0.0843 0.0079 0.922 0.993
Y8 0.360 0.3485 0.0870 0.0843 0.0077 0.925 0.993
Y9 0.360 0.3490 0.0885 0.0844 0.0080 0.924 0.994
Y10 0.360 0.3488 0.0872 0.0844 0.0077 0.921 0.993
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.115E-01
(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 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 0.360
Y7 0.000 0.360
Y8 0.000 0.000 0.360
Y9 0.000 0.000 0.000 0.360
Y10 0.000 0.000 0.000 0.000 0.360
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 0.250 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 0.360
Y7 0.000 0.360
Y8 0.000 0.000 0.360
Y9 0.000 0.000 0.000 0.360
Y10 0.000 0.000 0.000 0.000 0.360
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 0.250 1.000
POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1
________
1 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
PATTERN
Save file
cfa2.sav
Save file format Free
Save file record length 5000
Missing designated by 999
Beginning Time: 23:03:08
Ending Time: 23:07:43
Elapsed Time: 00:04:35
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Los Angeles, CA 90066
Tel: (310) 391-9971
Fax: (310) 391-8971
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