Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 11:24 PM
INPUT INSTRUCTIONS
title: jasaa.inp
montecarlo:
names are y1 y2 y3 x;
nobs = 2000;
nreps = 500;
seed = 578243;
classes = c(2);
genclasses = c(2);
cutpoints = x(0);
analysis:
type = mixture;
model montecarlo:
%overall%
[x@0]; x@1;
i by y1-y3@1;
s by y1@0 y2@1 y3@2;
[y1-y3@0 i*0 s*1];
i*6.25; ! SD = 2.5
s*1;
!total s variance is 1.25 (1/5 of i variance), SD = 1.12
i with s*.699; !this gives correlation 0.25
y1*2.083 y2*0.417 y3*.417; !this gives y1 and y2 r-square 0.75
! y1 variance = 8.333, SD = 2.89
! y2 variance = 9.135, SD = 3.052
! within-group y2 variance = 9.135 - 0.25 = 9.065, SD = 3.01
s on x*1;
!this gives ES = 0.33 in y2 within-group SD terms (for low class)
!r-squared for s is 20%
[c#1*0];
%c#1% ! high class
[i*2.5 s*2.25];
!high class grows at 3/4 SD per grade
s on x*.25;
! high class has 1/4 effect of low class, ES = 0.08
%c#2% !low class
[i*0.0 s*.75];
!low class grows at 1/4 SD per grade
!low class is lower by 1 SD for intercept,
!about 1.5 SD lower for slope
model:
%overall%
i by y1-y3@1;
s by y1@0 y2@1 y3@2;
[y1-y3@0 i*0 s*1];
i*6.25; ! SD = 2.5
s*1;
!total s variance is 1.25 (1/5 of i variance), SD = 1.12
i with s*.699;
y1*2.083 y2*0.417 y3*.417; !this gives y1 and y2 r-square 0.75
! y1 variance = 8.333, SD = 2.89
! y2 variance = 9.135, SD = 3.052
! within-group y2 variance = 9.135 - 0.25 = 9.065, SD = 3.01
s on x*1;
!this gives ES = 0.33 in y2 within-group SD terms (for low class)
!r-squared for s is 20%
[c#1*0];
%c#1% ! high class
[i*2.5 s*2.25];
!high class grows at 3/4 SD per grade
s on x*.25;
! high class has 1/4 effect of low class, ES = 0.08
%c#2% !low class
[i*0.0 s*.75];
!low class grows at 1/4 SD per grade
!low class is lower by 1 SD for intercept,
!about 1.5 SD lower for slope
output:
tech9;
INPUT READING TERMINATED NORMALLY
jasaa.inp
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 2000
Number of replications
Requested 500
Completed 500
Value of seed 578243
Number of dependent variables 3
Number of independent variables 1
Number of continuous latent variables 2
Number of categorical latent variables 1
Observed dependent variables
Continuous
Y1 Y2 Y3
Observed independent variables
X
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 X
________ ________ ________ ________
1 1.186 3.002 4.834 0.501
Covariances
Y1 Y2 Y3 X
________ ________ ________ ________
Y1 9.809
Y2 9.162 12.481
Y3 10.636 14.972 19.636
X 0.006 0.185 0.334 0.250
Correlations
Y1 Y2 Y3 X
________ ________ ________ ________
Y1 1.000
Y2 0.828 1.000
Y3 0.766 0.956 1.000
X 0.004 0.105 0.151 1.000
TESTS OF MODEL FIT
Number of Free Parameters 13
Loglikelihood
H0 Value
Mean -12660.705
Std Dev 52.289
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.990 -12782.346 -12782.752
0.980 0.980 -12768.092 -12769.627
0.950 0.958 -12746.716 -12746.087
0.900 0.902 -12727.719 -12728.162
0.800 0.802 -12704.712 -12704.870
0.700 0.704 -12688.126 -12687.859
0.500 0.498 -12660.705 -12661.844
0.300 0.298 -12633.285 -12634.190
0.200 0.202 -12616.699 -12616.557
0.100 0.104 -12593.691 -12590.140
0.050 0.054 -12574.695 -12573.730
0.020 0.022 -12553.319 -12551.106
0.010 0.010 -12539.065 -12539.334
Information Criteria
Akaike (AIC)
Mean 25347.411
Std Dev 104.578
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.990 25104.130 25101.419
0.980 0.978 25132.638 25125.896
0.950 0.946 25175.390 25170.306
0.900 0.896 25213.383 25203.243
0.800 0.798 25259.397 25258.045
0.700 0.702 25292.570 25293.265
0.500 0.502 25347.411 25347.990
0.300 0.296 25402.251 25401.680
0.200 0.198 25435.424 25435.226
0.100 0.098 25481.438 25478.177
0.050 0.042 25519.432 25517.553
0.020 0.020 25562.183 25562.120
0.010 0.010 25590.691 25575.430
Bayesian (BIC)
Mean 25420.222
Std Dev 104.578
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.990 25176.942 25174.231
0.980 0.978 25205.450 25198.708
0.950 0.946 25248.201 25243.118
0.900 0.896 25286.195 25276.054
0.800 0.798 25332.209 25330.857
0.700 0.702 25365.381 25366.077
0.500 0.502 25420.222 25420.802
0.300 0.296 25475.063 25474.492
0.200 0.198 25508.235 25508.038
0.100 0.098 25554.250 25550.989
0.050 0.042 25592.243 25590.365
0.020 0.020 25634.995 25634.932
0.010 0.010 25663.503 25648.241
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 25378.921
Std Dev 104.578
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.990 25135.640 25132.929
0.980 0.978 25164.148 25157.406
0.950 0.946 25206.900 25201.816
0.900 0.896 25244.893 25234.753
0.800 0.798 25290.907 25289.555
0.700 0.702 25324.080 25324.775
0.500 0.502 25378.921 25379.500
0.300 0.296 25433.761 25433.190
0.200 0.198 25466.934 25466.736
0.100 0.098 25512.948 25509.687
0.050 0.042 25550.942 25549.063
0.020 0.020 25593.693 25593.630
0.010 0.010 25622.201 25606.940
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 1004.79656 0.50240
2 995.20344 0.49760
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 1004.79659 0.50240
2 995.20341 0.49760
CLASSIFICATION QUALITY
Entropy 0.296
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 1008 0.50379
2 992 0.49621
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.752 0.248
2 0.249 0.751
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
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
S ON
X 0.250 0.2013 0.2459 0.1847 0.0627 0.928 0.488
I WITH
S 0.699 0.7507 0.2444 0.2353 0.0623 0.884 0.910
Means
I 2.500 2.4366 0.3762 0.3542 0.1452 0.884 0.970
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
S 2.250 2.2624 0.1577 0.1601 0.0250 0.924 1.000
Variances
I 6.250 6.3139 0.6974 0.6875 0.4895 0.852 0.998
Residual Variances
Y1 2.083 2.0734 0.1515 0.1505 0.0230 0.944 1.000
Y2 0.417 0.4222 0.0794 0.0805 0.0063 0.954 1.000
Y3 0.417 0.4090 0.1947 0.1993 0.0379 0.964 0.532
S 1.000 0.9828 0.1440 0.1647 0.0210 0.940 0.992
Latent Class 2
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
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
S ON
X 1.000 1.0605 0.2436 0.1923 0.0629 0.932 0.982
I WITH
S 0.699 0.7507 0.2444 0.2353 0.0623 0.884 0.910
Means
I 0.000 0.0521 0.3542 0.3528 0.1279 0.898 0.102
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
S 0.750 0.7236 0.1742 0.1670 0.0310 0.936 0.936
Variances
I 6.250 6.3139 0.6974 0.6875 0.4895 0.852 0.998
Residual Variances
Y1 2.083 2.0734 0.1515 0.1505 0.0230 0.944 1.000
Y2 0.417 0.4222 0.0794 0.0805 0.0063 0.954 1.000
Y3 0.417 0.4090 0.1947 0.1993 0.0379 0.964 0.532
S 1.000 0.9828 0.1440 0.1647 0.0210 0.940 0.992
Categorical Latent Variables
Means
C#1 0.000 0.0135 0.3599 0.3305 0.1295 0.950 0.050
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.330E-02
(ratio of smallest to largest eigenvalue)
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
NU
Y1 Y2 Y3 X
________ ________ ________ ________
1 0 0 0 0
LAMBDA
I S X
________ ________ ________
Y1 0 0 0
Y2 0 0 0
Y3 0 0 0
X 0 0 0
THETA
Y1 Y2 Y3 X
________ ________ ________ ________
Y1 1
Y2 0 2
Y3 0 0 3
X 0 0 0 0
ALPHA
I S X
________ ________ ________
1 4 5 0
BETA
I S X
________ ________ ________
I 0 0 0
S 0 0 6
X 0 0 0
PSI
I S X
________ ________ ________
I 7
S 8 9
X 0 0 0
PARAMETER SPECIFICATION FOR LATENT CLASS 2
NU
Y1 Y2 Y3 X
________ ________ ________ ________
1 0 0 0 0
LAMBDA
I S X
________ ________ ________
Y1 0 0 0
Y2 0 0 0
Y3 0 0 0
X 0 0 0
THETA
Y1 Y2 Y3 X
________ ________ ________ ________
Y1 1
Y2 0 2
Y3 0 0 3
X 0 0 0 0
ALPHA
I S X
________ ________ ________
1 10 11 0
BETA
I S X
________ ________ ________
I 0 0 0
S 0 0 12
X 0 0 0
PSI
I S X
________ ________ ________
I 7
S 8 9
X 0 0 0
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
1 13 0
GAMMA(C)
X
________
C#1 0
C#2 0
STARTING VALUES FOR LATENT CLASS 1
NU
Y1 Y2 Y3 X
________ ________ ________ ________
1 0.000 0.000 0.000 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
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 X
________ ________ ________ ________
Y1 2.083
Y2 0.000 0.417
Y3 0.000 0.000 0.417
X 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
1 2.500 2.250 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.000
S 0.000 0.000 0.250
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 6.250
S 0.699 1.000
X 0.000 0.000 0.500
STARTING VALUES FOR LATENT CLASS 2
NU
Y1 Y2 Y3 X
________ ________ ________ ________
1 0.000 0.000 0.000 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
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 X
________ ________ ________ ________
Y1 2.083
Y2 0.000 0.417
Y3 0.000 0.000 0.417
X 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
1 0.000 0.750 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.000
S 0.000 0.000 1.000
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 6.250
S 0.699 1.000
X 0.000 0.000 0.500
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
1 0.000 0.000
GAMMA(C)
X
________
C#1 0.000
C#2 0.000
POPULATION VALUES FOR LATENT CLASS 1
NU
Y1 Y2 Y3 X
________ ________ ________ ________
1 0.000 0.000 0.000 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
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 X
________ ________ ________ ________
Y1 2.083
Y2 0.000 0.417
Y3 0.000 0.000 0.417
X 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
1 2.500 2.250 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.000
S 0.000 0.000 0.250
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 6.250
S 0.699 1.000
X 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS 2
NU
Y1 Y2 Y3 X
________ ________ ________ ________
1 0.000 0.000 0.000 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
X 0.000 0.000 1.000
THETA
Y1 Y2 Y3 X
________ ________ ________ ________
Y1 2.083
Y2 0.000 0.417
Y3 0.000 0.000 0.417
X 0.000 0.000 0.000 0.000
ALPHA
I S X
________ ________ ________
1 0.000 0.750 0.000
BETA
I S X
________ ________ ________
I 0.000 0.000 0.000
S 0.000 0.000 1.000
X 0.000 0.000 0.000
PSI
I S X
________ ________ ________
I 6.250
S 0.699 1.000
X 0.000 0.000 1.000
POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
1 0.000 0.000
GAMMA(C)
X
________
C#1 0.000
C#2 0.000
TECHNICAL 9 OUTPUT
Error messages for each replication (if any)
REPLICATION 31:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 1 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 31:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 2 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 86:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 1 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 86:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 2 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 128:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 1 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 128:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 2 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 140:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 1 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 140:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 2 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 213:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 1 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 213:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 2 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 271:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 1 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 271:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 2 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 358:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 1 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 358:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 2 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 360:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 1 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 360:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 2 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 439:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 1 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 439:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 2 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 471:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 1 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
REPLICATION 471:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 2 IS NOT
POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL
VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE
BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO
OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION.
PROBLEM INVOLVING VARIABLE Y3.
Beginning Time: 23:24:25
Ending Time: 23:25:58
Elapsed Time: 00:01:33
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