Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 11:09 PM
INPUT INSTRUCTIONS
TITLE: cfa4.inp non-normal, missing
MONTECARLO: NAMES ARE y1-y10;
NOBSERVATIONS = 315;
NREPS = 10000;
SEED = 53487;
CLASSES = C(1);
GENCLASSES = C(2);
PATMISS = y6 (.5) y7 (.5) y8 (.5) y9(.5) y10 (.5);
PATPROB = 1;
SAVE = cfa4.sav;
ANALYSIS: TYPE = MIXTURE MISSING;
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: 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
cfa4.inp non-normal, missing
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 315
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
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 32
Number of y missing data patterns 32
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 32
Y1 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
Y3 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
Y5 x x x x x x x x x x x x
Y6 x x x x x
Y7 x x x x x x
Y8 x 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 7 12 13 23 11
2 9 13 8 24 14
3 8 14 8 25 11
4 11 15 12 26 10
5 11 16 8 27 11
6 15 17 8 28 6
7 11 18 8 29 20
8 10 19 12 30 11
9 11 20 10 31 5
10 10 21 9 32 4
11 6 22 7
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 0.533 0.533 0.533 0.533 0.533
Y7 0.492 0.492 0.492 0.492 0.492
Y8 0.476 0.476 0.476 0.476 0.476
Y9 0.486 0.486 0.486 0.486 0.486
Y10 0.486 0.486 0.486 0.486 0.486
Covariance Coverage
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 0.533
Y7 0.257 0.492
Y8 0.241 0.222 0.476
Y9 0.251 0.263 0.238 0.486
Y10 0.238 0.257 0.213 0.241 0.486
SAMPLE STATISTICS FOR THE FIRST REPLICATION
ESTIMATED SAMPLE STATISTICS
Means
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
1 -0.012 -0.017 -0.018 -0.057 -0.067
Means
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
1 0.795 1.503 1.219 1.364 1.473
Covariances
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1.053
Y2 0.695 0.990
Y3 0.640 0.615 1.000
Y4 0.632 0.651 0.652 0.990
Y5 0.585 0.615 0.569 0.629 0.937
Y6 0.433 0.849 0.414 0.196 0.559
Y7 1.357 1.339 1.097 1.217 1.226
Y8 0.938 0.910 0.901 0.765 0.489
Y9 0.995 1.030 1.222 0.762 0.777
Y10 0.689 1.199 0.465 0.776 1.064
Covariances
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 18.561
Y7 12.822 24.035
Y8 11.322 14.044 19.979
Y9 8.968 13.124 12.389 21.281
Y10 12.688 15.375 14.273 14.497 24.535
Correlations
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1.000
Y2 0.680 1.000
Y3 0.624 0.618 1.000
Y4 0.619 0.657 0.655 1.000
Y5 0.589 0.639 0.587 0.653 1.000
Y6 0.098 0.198 0.096 0.046 0.134
Y7 0.270 0.275 0.224 0.250 0.258
Y8 0.204 0.205 0.202 0.172 0.113
Y9 0.210 0.224 0.265 0.166 0.174
Y10 0.135 0.243 0.094 0.158 0.222
Correlations
Y6 Y7 Y8 Y9 Y10
________ ________ ________ ________ ________
Y6 1.000
Y7 0.607 1.000
Y8 0.588 0.641 1.000
Y9 0.451 0.580 0.601 1.000
Y10 0.595 0.633 0.645 0.634 1.000
MAXIMUM LOG-LIKELIHOOD VALUE FOR THE UNRESTRICTED (H1) MODEL IS -3938.692
TESTS OF MODEL FIT
Number of Free Parameters 31
Loglikelihood
H0 Value
Mean -4007.745
Std Dev 65.368
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 -4159.811 -4162.466
0.980 0.981 -4141.991 -4140.704
0.950 0.950 -4115.269 -4115.187
0.900 0.901 -4091.521 -4091.287
0.800 0.805 -4062.759 -4061.741
0.700 0.701 -4042.024 -4041.687
0.500 0.503 -4007.745 -4007.356
0.300 0.298 -3973.465 -3973.795
0.200 0.195 -3952.731 -3953.853
0.100 0.099 -3923.969 -3924.199
0.050 0.051 -3900.220 -3899.821
0.020 0.021 -3873.498 -3871.645
0.010 0.011 -3855.678 -3853.595
Information Criteria
Akaike (AIC)
Mean 8077.489
Std Dev 130.737
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 7773.356 7769.133
0.980 0.979 7808.995 7805.269
0.950 0.949 7862.440 7861.410
0.900 0.901 7909.937 7910.324
0.800 0.805 7967.461 7969.576
0.700 0.702 8008.931 8009.588
0.500 0.497 8077.489 8076.698
0.300 0.299 8146.047 8145.372
0.200 0.195 8187.517 8185.442
0.100 0.099 8245.041 8244.314
0.050 0.050 8292.538 8292.321
0.020 0.019 8345.983 8343.074
0.010 0.011 8381.622 8386.894
Bayesian (BIC)
Mean 8193.819
Std Dev 130.737
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 7889.686 7885.463
0.980 0.979 7925.325 7921.599
0.950 0.949 7978.770 7977.740
0.900 0.901 8026.267 8026.654
0.800 0.805 8083.791 8085.906
0.700 0.702 8125.261 8125.918
0.500 0.497 8193.819 8193.027
0.300 0.299 8262.377 8261.701
0.200 0.195 8303.847 8301.771
0.100 0.099 8361.371 8360.644
0.050 0.050 8408.868 8408.651
0.020 0.019 8462.313 8459.403
0.010 0.011 8497.951 8503.224
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 8095.495
Std Dev 130.737
Number of successful computations 10000
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.989 7791.363 7787.139
0.980 0.979 7827.002 7823.276
0.950 0.949 7880.447 7879.417
0.900 0.901 7927.943 7928.331
0.800 0.805 7985.467 7987.582
0.700 0.702 8026.937 8027.595
0.500 0.497 8095.495 8094.704
0.300 0.299 8164.054 8163.378
0.200 0.195 8205.523 8203.448
0.100 0.099 8263.047 8262.321
0.050 0.050 8310.544 8310.327
0.020 0.019 8363.989 8361.080
0.010 0.011 8399.628 8404.900
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 315.00000 1.00000
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 315.00000 1.00000
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 315 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 0.7982 0.0493 0.0481 0.0024 0.942 1.000
Y2 0.800 0.7985 0.0487 0.0481 0.0024 0.947 1.000
Y3 0.800 0.7978 0.0485 0.0481 0.0024 0.946 1.000
Y4 0.800 0.7989 0.0488 0.0481 0.0024 0.945 1.000
Y5 0.800 0.7983 0.0483 0.0481 0.0023 0.946 1.000
F2 BY
Y6 4.000 3.9776 0.4345 0.4251 0.1893 0.944 1.000
Y7 4.000 3.9837 0.4352 0.4259 0.1896 0.943 1.000
Y8 4.000 3.9778 0.4350 0.4246 0.1897 0.945 1.000
Y9 4.000 3.9793 0.4323 0.4251 0.1873 0.944 1.000
Y10 4.000 3.9793 0.4357 0.4255 0.1903 0.942 1.000
F1 WITH
F2 0.200 0.1915 0.0676 0.0663 0.0046 0.940 0.803
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 0.0006 0.0563 0.0562 0.0032 0.947 0.053
Y2 0.000 0.0001 0.0572 0.0562 0.0033 0.944 0.056
Y3 0.000 0.0007 0.0567 0.0562 0.0032 0.947 0.053
Y4 0.000 0.0007 0.0564 0.0562 0.0032 0.947 0.053
Y5 0.000 0.0006 0.0567 0.0562 0.0032 0.949 0.051
Y6 1.420 1.4294 0.3467 0.3474 0.1203 0.952 0.987
Y7 1.420 1.4252 0.3507 0.3481 0.1230 0.947 0.988
Y8 1.420 1.4279 0.3481 0.3478 0.1212 0.952 0.988
Y9 1.420 1.4269 0.3468 0.3476 0.1203 0.949 0.988
Y10 1.420 1.4265 0.3454 0.3478 0.1193 0.952 0.990
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 0.3574 0.0360 0.0356 0.0013 0.941 1.000
Y2 0.360 0.3580 0.0362 0.0357 0.0013 0.939 1.000
Y3 0.360 0.3572 0.0355 0.0356 0.0013 0.943 1.000
Y4 0.360 0.3576 0.0363 0.0357 0.0013 0.935 1.000
Y5 0.360 0.3575 0.0361 0.0357 0.0013 0.943 1.000
Y6 9.000 8.8219 1.6070 1.5789 2.6139 0.929 1.000
Y7 9.000 8.8435 1.6334 1.5860 2.6923 0.929 1.000
Y8 9.000 8.8614 1.6134 1.5841 2.6221 0.932 1.000
Y9 9.000 8.8314 1.6438 1.5810 2.7304 0.927 1.000
Y10 9.000 8.8509 1.6256 1.5877 2.6646 0.936 1.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.563E-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
PATTERN
Save file
cfa4.sav
Save file format Free
Save file record length 5000
Missing designated by 999
Beginning Time: 23:09:54
Ending Time: 23:15:39
Elapsed Time: 00:05:45
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) 1998-2010 Muthen & Muthen
Back to table of examples