Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 11:24 PM
INPUT INSTRUCTIONS
TITLE: mc2c.inp
Montecarlo:
names are y1-y5 x;
nobs = 500;
nreps=500;
seed=53487;
classes = c(1);
genclasses = c(2);
save = mc2c.sav;
missing=y1-y5;
analysis: type=mixture missing;
estimator=ml;
model montecarlo:
%overall%
[x@0];
x@1;
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;
i on x*.7;
s on x*.2;
[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;
i on x*.7;
s on x*.2;
%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;
i on x*.7;
s on x*.2;
model missing:
%Overall%
[y1-y5@-1];
y2-y5 on x@1;
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;
i on x*0.6926;
s on x*0.1988;
%c#1%
[i*1.8161 s*0.1930];
output:
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.
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
mc2c.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 1
Number of continuous latent variables 2
Number of categorical latent variables 1
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4 Y5
Observed independent variables
X
Continuous latent variables
I S
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
Y2 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 x
Y5 x x x x x x x x x x x
X x x x x x x x x 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
Y2 x x x x x x x x
Y3 x x x x
Y4 x x x
Y5 x x x x x
X x x x x x x x x x x x
MISSING DATA PATTERN FREQUENCIES FOR Y
Pattern Frequency Pattern Frequency Pattern Frequency
1 98 12 7 23 3
2 11 13 2 24 12
3 13 14 20 25 14
4 29 15 8 26 11
5 36 16 3 27 4
6 13 17 21 28 2
7 6 18 38 29 3
8 30 19 15 30 5
9 15 20 16 31 1
10 24 21 12
11 17 22 11
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 0.732
Y2 0.452 0.630
Y3 0.486 0.468 0.692
Y4 0.488 0.486 0.506 0.684
Y5 0.466 0.454 0.484 0.490 0.666
X 0.732 0.630 0.692 0.684 0.666
Covariance Coverage
X
________
X 1.000
SAMPLE STATISTICS FOR THE FIRST REPLICATION
ESTIMATED SAMPLE STATISTICS
Means
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
1 1.482 1.703 1.809 1.958 2.335
Means
X
________
1 0.026
Covariances
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 24.704
Y2 25.861 29.801
Y3 28.593 31.694 38.124
Y4 30.735 34.352 39.052 45.434
Y5 34.223 38.266 43.184 47.220 56.533
X 0.741 0.871 1.112 1.104 1.526
Covariances
X
________
X 1.105
Correlations
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
Y1 1.000
Y2 0.953 1.000
Y3 0.932 0.940 1.000
Y4 0.917 0.934 0.938 1.000
Y5 0.916 0.932 0.930 0.932 1.000
X 0.142 0.152 0.171 0.156 0.193
Correlations
X
________
X 1.000
MAXIMUM LOG-LIKELIHOOD VALUE FOR THE UNRESTRICTED (H1) MODEL IS -4854.797
TESTS OF MODEL FIT
Number of Free Parameters 12
Loglikelihood
H0 Value
Mean -4315.888
Std Dev 64.770
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.990 -4466.562 -4467.982
0.980 0.978 -4448.906 -4455.817
0.950 0.966 -4422.428 -4414.653
0.900 0.914 -4398.897 -4397.028
0.800 0.796 -4370.398 -4373.679
0.700 0.688 -4349.853 -4351.847
0.500 0.500 -4315.888 -4316.199
0.300 0.306 -4281.923 -4280.316
0.200 0.188 -4261.378 -4264.584
0.100 0.100 -4232.879 -4233.939
0.050 0.058 -4209.348 -4206.464
0.020 0.022 -4182.870 -4181.143
0.010 0.010 -4165.214 -4169.296
Information Criteria
Akaike (AIC)
Mean 8655.776
Std Dev 129.540
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.990 8354.427 8352.719
0.980 0.978 8389.740 8383.302
0.950 0.942 8442.696 8428.512
0.900 0.900 8489.758 8486.155
0.800 0.812 8546.755 8551.468
0.700 0.694 8587.845 8584.211
0.500 0.500 8655.776 8655.187
0.300 0.312 8723.706 8727.539
0.200 0.204 8764.796 8766.189
0.100 0.086 8821.794 8815.757
0.050 0.034 8868.856 8851.222
0.020 0.022 8921.812 8928.000
0.010 0.010 8957.124 8948.495
Bayesian (BIC)
Mean 8706.351
Std Dev 129.540
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.990 8405.003 8403.295
0.980 0.978 8440.315 8433.877
0.950 0.942 8493.271 8479.087
0.900 0.900 8540.333 8536.730
0.800 0.812 8597.330 8602.043
0.700 0.694 8638.420 8634.786
0.500 0.500 8706.351 8705.763
0.300 0.312 8774.282 8778.114
0.200 0.204 8815.372 8816.765
0.100 0.086 8872.369 8866.332
0.050 0.034 8919.431 8901.798
0.020 0.022 8972.387 8978.576
0.010 0.010 9007.699 8999.070
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 8668.262
Std Dev 129.540
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.990 8366.914 8365.206
0.980 0.978 8402.227 8395.788
0.950 0.942 8455.182 8440.998
0.900 0.900 8502.244 8498.641
0.800 0.812 8559.242 8563.954
0.700 0.694 8600.332 8596.697
0.500 0.500 8668.262 8667.674
0.300 0.312 8736.193 8740.025
0.200 0.204 8777.283 8778.676
0.100 0.086 8834.280 8828.243
0.050 0.034 8881.342 8863.709
0.020 0.022 8934.298 8940.487
0.010 0.010 8969.611 8960.981
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 ON
X 0.693 0.7036 0.2361 0.2304 0.0558 0.958 0.848
S ON
X 0.199 0.2028 0.0453 0.0439 0.0021 0.948 0.992
I WITH
S 2.674 2.6095 0.3234 0.2372 0.1086 0.840 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
I 1.816 1.7740 0.2296 0.2284 0.0544 0.934 1.000
S 0.193 0.1912 0.0406 0.0423 0.0016 0.964 0.998
Residual Variances
Y1 1.010 1.0025 0.2122 0.2013 0.0450 0.938 1.000
Y2 1.426 1.4271 0.1764 0.1704 0.0311 0.934 1.000
Y3 2.238 2.2354 0.2455 0.2365 0.0601 0.940 1.000
Y4 3.444 3.4392 0.3740 0.3658 0.1396 0.956 1.000
Y5 5.017 5.0355 0.5753 0.5758 0.3307 0.940 1.000
I 25.364 24.7939 2.6771 1.6546 7.4777 0.754 1.000
S 0.476 0.4666 0.0628 0.0589 0.0040 0.918 1.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.246E-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
NU
X
________
1 0
LAMBDA
I S X
________ ________ ________
Y1 0 0 0
Y2 0 0 0
Y3 0 0 0
Y4 0 0 0
Y5 0 0 0
X 0 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
X 0 0 0 0 0
THETA
X
________
X 0
ALPHA
I S X
________ ________ ________
1 6 7 0
BETA
I S X
________ ________ ________
I 0 0 8
S 0 0 9
X 0 0 0
PSI
I S X
________ ________ ________
I 10
S 11 12
X 0 0 0
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1
________
1 0
GAMMA(C)
X
________
C#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
X
________
1 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
Y5 1.000 4.000 0.000
X 0.000 0.000 1.000
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
X 0.000 0.000 0.000 0.000 0.000
THETA
X
________
X 0.000
ALPHA
I S X
________ ________ ________
1 1.816 0.193 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.693
S 0.000 0.000 0.199
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 25.364
S 2.674 0.476
X 0.000 0.000 0.500
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1
________
1 0.000
GAMMA(C)
X
________
C#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
X
________
1 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
Y5 1.000 4.000 0.000
X 0.000 0.000 1.000
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
X 0.000 0.000 0.000 0.000 0.000
THETA
X
________
X 0.000
ALPHA
I S X
________ ________ ________
1 15.000 1.600 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.700
S 0.000 0.000 0.200
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 5.000
S 0.110 0.200
X 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS 2
NU
Y1 Y2 Y3 Y4 Y5
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
NU
X
________
1 0.000
LAMBDA
I S X
________ ________ ________
Y1 1.000 0.000 0.000
Y2 1.000 1.000 0.000
Y3 1.000 2.000 0.000
Y4 1.000 3.000 0.000
Y5 1.000 4.000 0.000
X 0.000 0.000 1.000
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
X 0.000 0.000 0.000 0.000 0.000
THETA
X
________
X 0.000
ALPHA
I S X
________ ________ ________
1 0.000 0.000 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.700
S 0.000 0.000 0.200
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 1.000
S 0.110 0.200
X 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
1 -2.000 0.000
GAMMA(C)
X
________
C#1 0.000
C#2 0.000
TECHNICAL 9 OUTPUT
Error messages for each replication (if any)
SAVEDATA INFORMATION
Order of variables
Y1
Y2
Y3
Y4
Y5
X
C
Save file
mc2c.sav
Save file format Free
Save file record length 5000
Missing designated by 999
Beginning Time: 23:24:02
Ending Time: 23:24:09
Elapsed Time: 00:00:07
MUTHEN & MUTHEN
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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
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