Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 11:25 PM
INPUT INSTRUCTIONS
title: jasab.inp
montecarlo:
names are y1 y2 y3 x;
nobs = 2000;
nreps = 500;
seed = 578243;
classes = c(3);
genclasses = c(3);
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 medium class)
!r-squared for s is 20%
[c#1*-1.9459 c#2*-1.2528];
%c#1% !low class (10%)
[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
s on x*0.25;
%c#2% ! high class (20%)
[i*5.0 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#3% !medium class (70%)
[i*2.5 s*2.25];
!medium class grows at 3/4 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; !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 medium class)
!r-squared for s is 20%
[c#1*-1.9459 c#2*-1.2528];
%c#1% !low class (10%)
[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
s on x*0.25;
%c#2% ! high class (20%)
[i*5.0 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#3% !medium class (70%)
[i*2.5 s*2.25];
!medium class grows at 3/4 SD per grade
!low class is lower by 1 SD for intercept,
!about 1.5 SD lower for slope
output:
sampstat tech9;
*** WARNING in OUTPUT command
SAMPSTAT option is the default for MONTECARLO.
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
jasab.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 2.716 5.221 7.741 0.501
Covariances
Y1 Y2 Y3 X
________ ________ ________ ________
Y1 10.374
Y2 9.373 12.330
Y3 10.588 14.595 19.110
X 0.017 0.235 0.422 0.250
Correlations
Y1 Y2 Y3 X
________ ________ ________ ________
Y1 1.000
Y2 0.829 1.000
Y3 0.752 0.951 1.000
X 0.011 0.134 0.193 1.000
TESTS OF MODEL FIT
Number of Free Parameters 17
Loglikelihood
H0 Value
Mean -12694.911
Std Dev 54.494
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.992 -12821.681 -12819.615
0.980 0.988 -12806.826 -12800.511
0.950 0.956 -12784.549 -12779.514
0.900 0.908 -12764.751 -12763.184
0.800 0.784 -12740.774 -12744.027
0.700 0.692 -12723.488 -12724.559
0.500 0.478 -12694.911 -12697.354
0.300 0.286 -12666.335 -12667.846
0.200 0.200 -12649.049 -12650.044
0.100 0.108 -12625.072 -12620.331
0.050 0.054 -12605.274 -12602.827
0.020 0.030 -12582.997 -12574.438
0.010 0.016 -12568.142 -12563.020
Information Criteria
Akaike (AIC)
Mean 25423.823
Std Dev 108.988
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.984 25170.284 25152.296
0.980 0.970 25199.994 25181.820
0.950 0.946 25244.549 25235.684
0.900 0.892 25284.144 25271.737
0.800 0.800 25332.099 25330.633
0.700 0.714 25366.670 25369.586
0.500 0.522 25423.823 25428.672
0.300 0.308 25480.976 25483.092
0.200 0.216 25515.547 25521.021
0.100 0.092 25563.502 25559.132
0.050 0.044 25603.097 25585.040
0.020 0.012 25647.651 25624.358
0.010 0.008 25677.361 25664.597
Bayesian (BIC)
Mean 25519.038
Std Dev 108.988
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.984 25265.500 25247.512
0.980 0.970 25295.210 25277.035
0.950 0.946 25339.764 25330.900
0.900 0.892 25379.359 25366.952
0.800 0.800 25427.314 25425.848
0.700 0.714 25461.885 25464.801
0.500 0.522 25519.038 25523.888
0.300 0.308 25576.191 25578.308
0.200 0.216 25610.762 25616.236
0.100 0.092 25658.717 25654.348
0.050 0.044 25698.312 25680.255
0.020 0.012 25742.867 25719.573
0.010 0.008 25772.577 25759.812
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 25465.028
Std Dev 108.988
Number of successful computations 500
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.984 25211.490 25193.502
0.980 0.970 25241.200 25223.025
0.950 0.946 25285.754 25276.890
0.900 0.892 25325.349 25312.942
0.800 0.800 25373.304 25371.838
0.700 0.714 25407.875 25410.791
0.500 0.522 25465.028 25469.878
0.300 0.308 25522.182 25524.298
0.200 0.216 25556.752 25562.226
0.100 0.092 25604.707 25600.338
0.050 0.044 25644.302 25626.245
0.020 0.012 25688.857 25665.563
0.010 0.008 25718.567 25705.803
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 202.95151 0.10148
2 495.83800 0.24792
3 1301.21049 0.65061
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 202.95152 0.10148
2 495.83799 0.24792
3 1301.21049 0.65061
CLASSIFICATION QUALITY
Entropy 0.441
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 133 0.06675
2 396 0.19791
3 1471 0.73535
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2 3
1 0.704 0.075 0.221
2 0.037 0.665 0.298
3 0.063 0.163 0.774
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 0.2261 0.3942 0.3395 0.1557 0.932 0.190
I WITH
S 0.699 0.6900 0.2466 0.2305 0.0608 0.862 0.850
Means
I 0.000 -0.1154 0.5855 0.5771 0.3554 0.908 0.092
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 0.7364 0.3653 0.3215 0.1334 0.940 0.676
Variances
I 6.250 6.0463 0.8096 0.7358 0.6957 0.824 0.994
Residual Variances
Y1 2.083 2.0738 0.1484 0.1517 0.0221 0.948 1.000
Y2 0.417 0.4215 0.0748 0.0783 0.0056 0.962 1.000
Y3 0.417 0.4116 0.1808 0.1908 0.0327 0.958 0.566
S 1.000 0.9430 0.1512 0.1555 0.0261 0.928 0.994
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 0.250 0.1234 0.7024 0.4750 0.5084 0.772 0.394
I WITH
S 0.699 0.6900 0.2466 0.2305 0.0608 0.862 0.850
Means
I 5.000 5.1837 1.2638 0.8943 1.6278 0.826 0.976
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 2.3161 0.5347 0.3808 0.2897 0.810 0.972
Variances
I 6.250 6.0463 0.8096 0.7358 0.6957 0.824 0.994
Residual Variances
Y1 2.083 2.0738 0.1484 0.1517 0.0221 0.948 1.000
Y2 0.417 0.4215 0.0748 0.0783 0.0056 0.962 1.000
Y3 0.417 0.4116 0.1808 0.1908 0.0327 0.958 0.566
S 1.000 0.9430 0.1512 0.1555 0.0261 0.928 0.994
Latent Class 3
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 1.0613 0.2997 0.1815 0.0934 0.896 0.978
I WITH
S 0.699 0.6900 0.2466 0.2305 0.0608 0.862 0.850
Means
I 2.500 2.4099 0.4233 0.3782 0.1870 0.786 0.962
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 2.2487 0.2412 0.1750 0.0581 0.854 0.992
Variances
I 6.250 6.0463 0.8096 0.7358 0.6957 0.824 0.994
Residual Variances
Y1 2.083 2.0738 0.1484 0.1517 0.0221 0.948 1.000
Y2 0.417 0.4215 0.0748 0.0783 0.0056 0.962 1.000
Y3 0.417 0.4116 0.1808 0.1908 0.0327 0.958 0.566
S 1.000 0.9430 0.1512 0.1555 0.0261 0.928 0.994
Categorical Latent Variables
Means
C#1 -1.946 -1.8771 0.4755 0.4753 0.2304 0.934 0.928
C#2 -1.253 -1.2269 1.1996 0.8853 1.4369 0.768 0.382
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.827E-03
(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 3
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 13 14 0
BETA
I S X
________ ________ ________
I 0 0 0
S 0 0 15
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 C#3
________ ________ ________
1 16 17 0
GAMMA(C)
X
________
C#1 0
C#2 0
C#3 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 0.000 0.750 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 5.000 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 3
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 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 C#3
________ ________ ________
1 -1.946 -1.253 0.000
GAMMA(C)
X
________
C#1 0.000
C#2 0.000
C#3 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 0.000 0.750 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 5.000 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 3
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 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 C#3
________ ________ ________
1 -1.946 -1.253 0.000
GAMMA(C)
X
________
C#1 0.000
C#2 0.000
C#3 0.000
TECHNICAL 9 OUTPUT
Error messages for each replication (if any)
REPLICATION 48:
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 48:
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 48:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 3 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 186:
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 186:
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 186:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 3 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 236:
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 236:
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 236:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 3 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 248:
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 248:
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 248:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 3 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 271:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 3 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 358:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 3 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 361:
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 361:
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 361:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IN CLASS 3 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: 23:25:58
Ending Time: 23:28:16
Elapsed Time: 00:02:18
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