Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 11:01 PM
INPUT INSTRUCTIONS
Title: app17
GS3cVaruxSAND.inp
- internal and external controls
- adding x's
- 3-class linear growth grade 1-2 (note females are included)
- different residual and intercept variance in the low/flat class 3
- adding u information, grades 3-7
- sandwich s.e.'s
Data:
File is ggmmfull.dat;
Variable:
Names are id u1-u7 y1-y9
g1fc intgroup male white lunch cavlunch cavtoc1f t1 t2;
!intgroup = (1 = STD/GBG, 3 = STD/STD, 4 = GBG/GBG)
!u1-u7 = school removal indicator for grades 1-7
!lunch = {0 = self-paid, 1 = (partially) free}
!y1-y9 = TOCA-R score for school terms 1f,...,7s
!cavtoca = 1st grade fall classroom avg. TOCA-R (excluding case)
!cavlunch = 1st grade fall classroom % lunch (partially) free (excluding case)
!male = {0 = female, 1 = male}
!white = {0 = non-white, 1 = white}
!g1fc = first grade fall classroom cluster (1-24)
missing = all (999);
useobs = intgroup ne 4;
usevar = u3-u7 y1-y4
intgroup male white lunch cavlunch cavtoc1f;
Cluster = g1fc;
Categorical are u3-u7;
Missing are all (999);
Classes = c(3);
Define:
if(intgroup eq 1)then intgroup = 0;
if(intgroup eq 3)then intgroup = 1;
Analysis:
Type = Mixture Missing Complex;
! Removing the CLUSTER= option in VARIABLE and the COMPLEX
! setting above will produce the same parameter estimates but
! different standard errors.
Model:
%overall%
i by y1-y4@1;
s by y1@0 y2@1 y3@2 y4@3;
[y1-y4@0];
[i* s*];
f by u3-u7@1;
i s on intgroup male white lunch cavlunch cavtoc1f;
f on intgroup male white lunch cavlunch cavtoc1f;
c#1 on intgroup male white lunch cavlunch cavtoc1f;
c#2 on intgroup male white lunch cavlunch cavtoc1f;
%c#1%
[i*2.6 s*-0.2];
[f*3];
[u3$1*3] (1);
[u4$1*3] (2);
[u5$1*2] (3);
[u6$1*2] (4);
[u7$1*1] (5);
%c#2%
[i*1.9 s*0.4];
[f*2];
[u3$1*3] (1);
[u4$1*3] (2);
[u5$1*2] (3);
[u6$1*2] (4);
[u7$1*1] (5);
%c#3%
[i*1.3 s*0];
i s;
s with i;
y1-y4;
[f@0];
[u3$1*3] (1);
[u4$1*3] (2);
[u5$1*2] (3);
[u6$1*2] (4);
[u7$1*1] (5);
Output:
patterns standardized residual tech8;
*** 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 VARIABLE command
When a subpopulation is analyzed with TYPE=COMPLEX, standard errors
may be incorrect. Use the SUBPOPULATION option instead of the
USEOBSERVATIONS option to obtain correct standard errors.
2 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
app17
GS3cVaruxSAND.inp
- internal and external controls
- adding x's
- 3-class linear growth grade 1-2 (note females are included)
- different residual and intercept variance in the low/flat class 3
- adding u information, grades 3-7
- sandwich s.e.'s
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 403
Number of dependent variables 9
Number of independent variables 6
Number of continuous latent variables 3
Number of categorical latent variables 1
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4
Binary and ordered categorical (ordinal)
U3 U4 U5 U6 U7
Observed independent variables
INTGROUP MALE WHITE LUNCH CAVLUNCH CAVTOC1F
Continuous latent variables
I S F
Categorical latent variables
C
Variables with special functions
Cluster variable G1FC
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
Random Starts Specifications
Number of initial stage random starts 10
Number of final stage optimizations 2
Number of initial stage iterations 10
Initial stage convergence criterion 0.100D+01
Random starts scale 0.500D+01
Random seed for generating random starts 0
Link LOGIT
Input data file(s)
ggmmfull.dat
Input data format FREE
SUMMARY OF DATA
Number of missing data patterns 32
Number of y missing data patterns 8
Number of u missing data patterns 6
Number of clusters 16
SUMMARY OF MISSING DATA PATTERNS
MISSING DATA PATTERNS (x = not missing)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
U3 x x x x x x x x x x x x x x x x x x
U4 x x x x x x x x x x x x x x x
U5 x x x x x x x x x x x x
U6 x x x x x x x x
U7 x x x x x
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
Y3 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
INTGROUP x x x x x x x x x x x x x x x x x x x x
MALE x x x x x x x x x x x x x x x x x x x x
WHITE x x x x x x x x x x x x x x x x x x x x
LUNCH x x x x x x x x x x x x x x x x x x x x
CAVLUNCH x x x x x x x x x x x x x x x x x x x x
CAVTOC1F 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 32
U3 x x x x x x x x x
U4 x x x x x x x
U5 x x x x x x
U6 x x x x x
U7 x x x
Y1 x x x x x x x x x x x x
Y2 x x x x
Y3 x
Y4 x x x x x x
INTGROUP x x x x x x x x x x x x
MALE x x x x x x x x x x x x
WHITE x x x x x x x x x x x x
LUNCH x x x x x x x x x x x x
CAVLUNCH x x x x x x x x x x x x
CAVTOC1F x x x x x x x x x x x x
MISSING DATA PATTERN FREQUENCIES
Pattern Frequency Pattern Frequency Pattern Frequency
1 13 12 2 23 33
2 185 13 3 24 1
3 8 14 1 25 1
4 4 15 4 26 1
5 4 16 1 27 3
6 6 17 1 28 2
7 1 18 1 29 1
8 23 19 11 30 2
9 2 20 1 31 11
10 1 21 1 32 1
11 68 22 6
MISSING DATA PATTERNS FOR U (x = not missing)
1 2 3 4 5 6
U3 x x x x x
U4 x x x x
U5 x x x
U6 x x
U7 x
MISSING DATA PATTERN FREQUENCIES FOR U
Pattern Frequency Pattern Frequency Pattern Frequency
1 23 3 15 5 8
2 338 4 9 6 10
MISSING DATA PATTERNS FOR Y (x = not missing)
1 2 3 4 5 6 7 8
Y1 x x x x x x x x
Y2 x x x x
Y3 x x x x
Y4 x x x x
INTGROUP x x x x x x x x
MALE x x x x x x x x
WHITE x x x x x x x x
LUNCH x x x x x x x x
CAVLUNCH x x x x x x x x
CAVTOC1F x x x x x x x x
MISSING DATA PATTERN FREQUENCIES FOR Y
Pattern Frequency Pattern Frequency Pattern Frequency
1 220 4 6 7 6
2 27 5 14 8 14
3 74 6 42
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT
Covariance Coverage
U3 U4 U5 U6 U7
________ ________ ________ ________ ________
U3 0.975
U4 0.955 0.955
U5 0.933 0.933 0.933
U6 0.896 0.896 0.896 0.896
U7 0.839 0.839 0.839 0.839 0.839
Y1 0.975 0.955 0.933 0.896 0.839
Y2 0.779 0.767 0.749 0.722 0.682
Y3 0.645 0.630 0.618 0.591 0.553
Y4 0.645 0.630 0.620 0.591 0.551
INTGROUP 0.975 0.955 0.933 0.896 0.839
MALE 0.975 0.955 0.933 0.896 0.839
WHITE 0.975 0.955 0.933 0.896 0.839
LUNCH 0.975 0.955 0.933 0.896 0.839
CAVLUNCH 0.975 0.955 0.933 0.896 0.839
CAVTOC1F 0.975 0.955 0.933 0.896 0.839
Covariance Coverage
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
Y1 1.000
Y2 0.799 0.799
Y3 0.663 0.581 0.663
Y4 0.663 0.581 0.613 0.663
INTGROUP 1.000 0.799 0.663 0.663 1.000
MALE 1.000 0.799 0.663 0.663 1.000
WHITE 1.000 0.799 0.663 0.663 1.000
LUNCH 1.000 0.799 0.663 0.663 1.000
CAVLUNCH 1.000 0.799 0.663 0.663 1.000
CAVTOC1F 1.000 0.799 0.663 0.663 1.000
Covariance Coverage
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
MALE 1.000
WHITE 1.000 1.000
LUNCH 1.000 1.000 1.000
CAVLUNCH 1.000 1.000 1.000 1.000
CAVTOC1F 1.000 1.000 1.000 1.000 1.000
PROPORTION OF DATA PRESENT FOR U
Covariance Coverage
U3 U4 U5 U6 U7
________ ________ ________ ________ ________
U3 0.975
U4 0.955 0.955
U5 0.933 0.933 0.933
U6 0.896 0.896 0.896 0.896
U7 0.839 0.839 0.839 0.839 0.839
PROPORTION OF DATA PRESENT FOR Y
Covariance Coverage
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
Y1 1.000
Y2 0.799 0.799
Y3 0.663 0.581 0.663
Y4 0.663 0.581 0.613 0.663
INTGROUP 1.000 0.799 0.663 0.663 1.000
MALE 1.000 0.799 0.663 0.663 1.000
WHITE 1.000 0.799 0.663 0.663 1.000
LUNCH 1.000 0.799 0.663 0.663 1.000
CAVLUNCH 1.000 0.799 0.663 0.663 1.000
CAVTOC1F 1.000 0.799 0.663 0.663 1.000
Covariance Coverage
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
MALE 1.000
WHITE 1.000 1.000
LUNCH 1.000 1.000 1.000
CAVLUNCH 1.000 1.000 1.000 1.000
CAVTOC1F 1.000 1.000 1.000 1.000 1.000
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U3
Category 1 0.980 385.000
Category 2 0.020 8.000
U4
Category 1 0.977 376.000
Category 2 0.023 9.000
U5
Category 1 0.960 361.000
Category 2 0.040 15.000
U6
Category 1 0.936 338.000
Category 2 0.064 23.000
U7
Category 1 0.825 279.000
Category 2 0.175 59.000
RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES
Final stage loglikelihood values at local maxima, seeds, and initial stage start numbers:
-1432.492 unperturbed 0
-1461.971 608496 4
WARNING: WHEN ESTIMATING A MODEL WITH MORE THAN TWO CLASSES, IT MAY BE
NECESSARY TO INCREASE THE NUMBER OF RANDOM STARTS USING THE STARTS OPTION
TO AVOID LOCAL MAXIMA.
WARNING: THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED. THE
SOLUTION MAY NOT BE TRUSTWORTHY DUE TO LOCAL MAXIMA. INCREASE THE
NUMBER OF RANDOM STARTS.
THE MODEL ESTIMATION TERMINATED NORMALLY
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 Y1.
TESTS OF MODEL FIT
Loglikelihood
H0 Value -1432.492
H0 Scaling Correction Factor 1.797
for MLR
Information Criteria
Number of Free Parameters 59
Akaike (AIC) 2982.983
Bayesian (BIC) 3218.921
Sample-Size Adjusted BIC 3031.708
(n* = (n + 2) / 24)
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 39.61778 0.09831
2 191.80492 0.47594
3 171.57729 0.42575
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 39.61778 0.09831
2 191.80493 0.47594
3 171.57729 0.42575
CLASSIFICATION QUALITY
Entropy 0.831
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 39 0.09677
2 185 0.45906
3 179 0.44417
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2 3
1 0.896 0.104 0.000
2 0.025 0.936 0.039
3 0.000 0.082 0.918
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
I BY
Y1 1.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 1.000 0.000 999.000 999.000
Y4 1.000 0.000 999.000 999.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 2.000 0.000 999.000 999.000
Y4 3.000 0.000 999.000 999.000
F BY
U3 1.000 0.000 999.000 999.000
U4 1.000 0.000 999.000 999.000
U5 1.000 0.000 999.000 999.000
U6 1.000 0.000 999.000 999.000
U7 1.000 0.000 999.000 999.000
I ON
INTGROUP -0.283 0.052 -5.451 0.000
MALE 0.064 0.053 1.203 0.229
WHITE -0.132 0.069 -1.912 0.056
LUNCH -0.012 0.043 -0.276 0.783
CAVLUNCH -0.218 0.089 -2.448 0.014
CAVTOC1F 0.127 0.083 1.524 0.127
S ON
INTGROUP -0.012 0.079 -0.148 0.882
MALE 0.032 0.037 0.883 0.377
WHITE 0.032 0.073 0.442 0.658
LUNCH -0.019 0.028 -0.665 0.506
CAVLUNCH 0.190 0.105 1.820 0.069
CAVTOC1F -0.194 0.118 -1.640 0.101
F ON
INTGROUP 0.046 0.265 0.172 0.863
MALE 0.675 0.227 2.974 0.003
WHITE -0.477 0.343 -1.392 0.164
LUNCH -0.276 0.267 -1.034 0.301
CAVLUNCH 1.366 0.407 3.356 0.001
CAVTOC1F -0.938 0.255 -3.671 0.000
S WITH
I -0.120 0.028 -4.325 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 3.885 0.414 9.395 0.000
S 0.172 0.300 0.575 0.565
Intercepts
F 2.407 0.435 5.529 0.000
Thresholds
U3$1 3.859 0.658 5.867 0.000
U4$1 3.681 0.647 5.687 0.000
U5$1 3.017 0.712 4.239 0.000
U6$1 2.439 0.665 3.668 0.000
U7$1 1.102 0.549 2.008 0.045
Residual Variances
Y1 0.171 0.075 2.285 0.022
Y2 0.359 0.073 4.890 0.000
Y3 0.519 0.100 5.204 0.000
Y4 0.292 0.124 2.352 0.019
I 0.290 0.069 4.214 0.000
S 0.084 0.017 4.859 0.000
Latent Class 2
I BY
Y1 1.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 1.000 0.000 999.000 999.000
Y4 1.000 0.000 999.000 999.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 2.000 0.000 999.000 999.000
Y4 3.000 0.000 999.000 999.000
F BY
U3 1.000 0.000 999.000 999.000
U4 1.000 0.000 999.000 999.000
U5 1.000 0.000 999.000 999.000
U6 1.000 0.000 999.000 999.000
U7 1.000 0.000 999.000 999.000
I ON
INTGROUP -0.283 0.052 -5.451 0.000
MALE 0.064 0.053 1.203 0.229
WHITE -0.132 0.069 -1.912 0.056
LUNCH -0.012 0.043 -0.276 0.783
CAVLUNCH -0.218 0.089 -2.448 0.014
CAVTOC1F 0.127 0.083 1.524 0.127
S ON
INTGROUP -0.012 0.079 -0.148 0.882
MALE 0.032 0.037 0.883 0.377
WHITE 0.032 0.073 0.442 0.658
LUNCH -0.019 0.028 -0.665 0.506
CAVLUNCH 0.190 0.105 1.820 0.069
CAVTOC1F -0.194 0.118 -1.640 0.101
F ON
INTGROUP 0.046 0.265 0.172 0.863
MALE 0.675 0.227 2.974 0.003
WHITE -0.477 0.343 -1.392 0.164
LUNCH -0.276 0.267 -1.034 0.301
CAVLUNCH 1.366 0.407 3.356 0.001
CAVTOC1F -0.938 0.255 -3.671 0.000
S WITH
I -0.120 0.028 -4.325 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 2.131 0.256 8.333 0.000
S 0.345 0.260 1.329 0.184
Intercepts
F 0.792 0.274 2.886 0.004
Thresholds
U3$1 3.859 0.658 5.867 0.000
U4$1 3.681 0.647 5.687 0.000
U5$1 3.017 0.712 4.239 0.000
U6$1 2.439 0.665 3.668 0.000
U7$1 1.102 0.549 2.008 0.045
Residual Variances
Y1 0.171 0.075 2.285 0.022
Y2 0.359 0.073 4.890 0.000
Y3 0.519 0.100 5.204 0.000
Y4 0.292 0.124 2.352 0.019
I 0.290 0.069 4.214 0.000
S 0.084 0.017 4.859 0.000
Latent Class 3
I BY
Y1 1.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 1.000 0.000 999.000 999.000
Y4 1.000 0.000 999.000 999.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 2.000 0.000 999.000 999.000
Y4 3.000 0.000 999.000 999.000
F BY
U3 1.000 0.000 999.000 999.000
U4 1.000 0.000 999.000 999.000
U5 1.000 0.000 999.000 999.000
U6 1.000 0.000 999.000 999.000
U7 1.000 0.000 999.000 999.000
I ON
INTGROUP -0.283 0.052 -5.451 0.000
MALE 0.064 0.053 1.203 0.229
WHITE -0.132 0.069 -1.912 0.056
LUNCH -0.012 0.043 -0.276 0.783
CAVLUNCH -0.218 0.089 -2.448 0.014
CAVTOC1F 0.127 0.083 1.524 0.127
S ON
INTGROUP -0.012 0.079 -0.148 0.882
MALE 0.032 0.037 0.883 0.377
WHITE 0.032 0.073 0.442 0.658
LUNCH -0.019 0.028 -0.665 0.506
CAVLUNCH 0.190 0.105 1.820 0.069
CAVTOC1F -0.194 0.118 -1.640 0.101
F ON
INTGROUP 0.046 0.265 0.172 0.863
MALE 0.675 0.227 2.974 0.003
WHITE -0.477 0.343 -1.392 0.164
LUNCH -0.276 0.267 -1.034 0.301
CAVLUNCH 1.366 0.407 3.356 0.001
CAVTOC1F -0.938 0.255 -3.671 0.000
S WITH
I -0.029 0.009 -3.419 0.001
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 1.344 0.146 9.208 0.000
S 0.276 0.218 1.271 0.204
Intercepts
F 0.000 0.000 999.000 999.000
Thresholds
U3$1 3.859 0.658 5.867 0.000
U4$1 3.681 0.647 5.687 0.000
U5$1 3.017 0.712 4.239 0.000
U6$1 2.439 0.665 3.668 0.000
U7$1 1.102 0.549 2.008 0.045
Residual Variances
Y1 -0.010 0.015 -0.681 0.496
Y2 0.068 0.024 2.806 0.005
Y3 0.058 0.020 2.902 0.004
Y4 0.036 0.030 1.192 0.233
I 0.066 0.028 2.385 0.017
S 0.026 0.007 3.525 0.000
Categorical Latent Variables
C#1 ON
INTGROUP 1.686 0.455 3.707 0.000
MALE 1.707 0.621 2.749 0.006
WHITE -0.144 0.450 -0.320 0.749
LUNCH 0.777 0.812 0.957 0.339
CAVLUNCH 0.551 0.944 0.583 0.560
CAVTOC1F 3.410 0.425 8.021 0.000
C#2 ON
INTGROUP 0.680 0.341 1.993 0.046
MALE 0.531 0.435 1.219 0.223
WHITE -0.312 0.377 -0.828 0.408
LUNCH 0.767 0.274 2.797 0.005
CAVLUNCH -0.884 0.665 -1.329 0.184
CAVTOC1F 1.904 0.470 4.049 0.000
Intercepts
C#1 -10.973 1.192 -9.201 0.000
C#2 -3.990 0.810 -4.929 0.000
LOGISTIC REGRESSION ODDS RATIO RESULTS
Categorical Latent Variables
C#1 ON
INTGROUP 5.398
MALE 5.512
WHITE 0.866
LUNCH 2.175
CAVLUNCH 1.734
CAVTOC1F 30.251
C#2 ON
INTGROUP 1.973
MALE 1.700
WHITE 0.732
LUNCH 2.153
CAVLUNCH 0.413
CAVTOC1F 6.715
ALTERNATIVE PARAMETERIZATIONS FOR THE CATEGORICAL LATENT VARIABLE REGRESSION
Parameterization using Reference Class 1
C#2 ON
INTGROUP -1.006 0.466 -2.159 0.031
MALE -1.176 0.560 -2.101 0.036
WHITE -0.168 0.219 -0.769 0.442
LUNCH -0.010 0.792 -0.013 0.990
CAVLUNCH -1.435 0.922 -1.557 0.120
CAVTOC1F -1.505 0.465 -3.234 0.001
C#3 ON
INTGROUP -1.686 0.455 -3.707 0.000
MALE -1.707 0.621 -2.749 0.006
WHITE 0.144 0.450 0.320 0.749
LUNCH -0.777 0.812 -0.957 0.339
CAVLUNCH -0.551 0.944 -0.583 0.560
CAVTOC1F -3.410 0.425 -8.021 0.000
Intercepts
C#2 6.983 1.394 5.008 0.000
C#3 10.973 1.192 9.201 0.000
Parameterization using Reference Class 2
C#1 ON
INTGROUP 1.006 0.466 2.159 0.031
MALE 1.176 0.560 2.101 0.036
WHITE 0.168 0.219 0.769 0.442
LUNCH 0.010 0.792 0.013 0.990
CAVLUNCH 1.435 0.922 1.557 0.120
CAVTOC1F 1.505 0.465 3.234 0.001
C#3 ON
INTGROUP -0.680 0.341 -1.993 0.046
MALE -0.531 0.435 -1.219 0.223
WHITE 0.312 0.377 0.828 0.408
LUNCH -0.767 0.274 -2.797 0.005
CAVLUNCH 0.884 0.665 1.329 0.184
CAVTOC1F -1.904 0.470 -4.049 0.000
Intercepts
C#1 -6.983 1.394 -5.008 0.000
C#3 3.990 0.810 4.929 0.000
STANDARDIZED MODEL RESULTS
STDYX Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
I BY
Y1 0.806 0.080 10.105 0.000
Y2 0.792 0.103 7.723 0.000
Y3 0.683 0.096 7.079 0.000
Y4 0.696 0.107 6.526 0.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 0.428 0.064 6.739 0.000
Y3 0.739 0.085 8.662 0.000
Y4 1.130 0.106 10.684 0.000
I ON
INTGROUP -0.233 0.052 -4.518 0.000
MALE 0.049 0.039 1.236 0.216
WHITE -0.106 0.058 -1.821 0.069
LUNCH -0.010 0.038 -0.273 0.785
CAVLUNCH -0.127 0.053 -2.405 0.016
CAVTOC1F 0.104 0.068 1.522 0.128
S ON
INTGROUP -0.018 0.121 -0.148 0.882
MALE 0.046 0.052 0.874 0.382
WHITE 0.048 0.108 0.446 0.655
LUNCH -0.030 0.044 -0.669 0.503
CAVLUNCH 0.204 0.112 1.820 0.069
CAVTOC1F -0.294 0.175 -1.678 0.093
S WITH
I -0.771 0.094 -8.186 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 6.895 1.090 6.328 0.000
S 0.566 0.967 0.585 0.559
Residual Variances
Y1 0.350 0.129 2.725 0.006
Y2 0.710 0.059 12.082 0.000
Y3 0.762 0.078 9.715 0.000
Y4 0.447 0.167 2.671 0.008
I 0.915 0.037 24.832 0.000
S 0.904 0.094 9.593 0.000
Latent Class 2
I BY
Y1 0.804 0.080 10.085 0.000
Y2 0.781 0.100 7.808 0.000
Y3 0.670 0.094 7.122 0.000
Y4 0.679 0.105 6.470 0.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 0.422 0.062 6.856 0.000
Y3 0.725 0.083 8.695 0.000
Y4 1.101 0.107 10.330 0.000
I ON
INTGROUP -0.236 0.052 -4.571 0.000
MALE 0.057 0.046 1.234 0.217
WHITE -0.109 0.060 -1.825 0.068
LUNCH -0.011 0.039 -0.273 0.785
CAVLUNCH -0.140 0.058 -2.398 0.016
CAVTOC1F 0.092 0.061 1.504 0.132
S ON
INTGROUP -0.018 0.122 -0.148 0.882
MALE 0.053 0.061 0.877 0.380
WHITE 0.049 0.110 0.447 0.655
LUNCH -0.031 0.045 -0.673 0.501
CAVLUNCH 0.225 0.122 1.848 0.065
CAVTOC1F -0.261 0.155 -1.688 0.091
S WITH
I -0.771 0.094 -8.186 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 3.802 0.648 5.866 0.000
S 1.138 0.836 1.361 0.174
Residual Variances
Y1 0.353 0.128 2.749 0.006
Y2 0.698 0.059 11.790 0.000
Y3 0.742 0.083 8.938 0.000
Y4 0.429 0.163 2.627 0.009
I 0.924 0.031 30.070 0.000
S 0.914 0.099 9.217 0.000
Latent Class 3
I BY
Y1 1.060 0.083 12.801 0.000
Y2 0.866 0.085 10.173 0.000
Y3 0.800 0.142 5.620 0.000
Y4 0.650 0.155 4.201 0.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 0.514 0.081 6.358 0.000
Y3 0.950 0.099 9.587 0.000
Y4 1.158 0.104 11.083 0.000
I ON
INTGROUP -0.454 0.079 -5.758 0.000
MALE 0.101 0.082 1.230 0.219
WHITE -0.200 0.115 -1.743 0.081
LUNCH -0.019 0.067 -0.282 0.778
CAVLUNCH -0.263 0.129 -2.043 0.041
CAVTOC1F 0.132 0.086 1.530 0.126
S ON
INTGROUP -0.032 0.211 -0.150 0.880
MALE 0.087 0.099 0.876 0.381
WHITE 0.082 0.185 0.445 0.657
LUNCH -0.049 0.077 -0.641 0.522
CAVLUNCH 0.387 0.167 2.311 0.021
CAVTOC1F -0.341 0.160 -2.134 0.033
S WITH
I -0.701 0.095 -7.347 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 4.353 0.811 5.369 0.000
S 1.508 0.973 1.550 0.121
Residual Variances
Y1 -0.124 999.000 999.000 999.000
Y2 0.531 0.086 6.188 0.000
Y3 0.387 0.115 3.369 0.001
Y4 0.159 0.162 0.978 0.328
I 0.694 0.105 6.626 0.000
S 0.773 0.205 3.779 0.000
STDY Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
I BY
Y1 0.806 0.080 10.105 0.000
Y2 0.792 0.103 7.723 0.000
Y3 0.683 0.096 7.079 0.000
Y4 0.696 0.107 6.526 0.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 0.428 0.064 6.739 0.000
Y3 0.739 0.085 8.662 0.000
Y4 1.130 0.106 10.684 0.000
I ON
INTGROUP -0.502 0.110 -4.568 0.000
MALE 0.113 0.091 1.237 0.216
WHITE -0.233 0.128 -1.824 0.068
LUNCH -0.021 0.078 -0.273 0.785
CAVLUNCH -0.388 0.161 -2.413 0.016
CAVTOC1F 0.225 0.147 1.524 0.128
S ON
INTGROUP -0.039 0.261 -0.148 0.882
MALE 0.106 0.121 0.875 0.382
WHITE 0.105 0.236 0.446 0.655
LUNCH -0.061 0.091 -0.670 0.503
CAVLUNCH 0.625 0.343 1.823 0.068
CAVTOC1F -0.636 0.379 -1.681 0.093
S WITH
I -0.771 0.094 -8.186 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 6.895 1.090 6.328 0.000
S 0.566 0.967 0.585 0.559
Residual Variances
Y1 0.350 0.129 2.725 0.006
Y2 0.710 0.059 12.082 0.000
Y3 0.762 0.078 9.715 0.000
Y4 0.447 0.167 2.671 0.008
I 0.915 0.037 24.832 0.000
S 0.904 0.094 9.593 0.000
Latent Class 2
I BY
Y1 0.804 0.080 10.085 0.000
Y2 0.781 0.100 7.808 0.000
Y3 0.670 0.094 7.122 0.000
Y4 0.679 0.105 6.470 0.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 0.422 0.062 6.856 0.000
Y3 0.725 0.083 8.695 0.000
Y4 1.101 0.107 10.330 0.000
I ON
INTGROUP -0.505 0.109 -4.625 0.000
MALE 0.114 0.092 1.235 0.217
WHITE -0.235 0.128 -1.829 0.067
LUNCH -0.021 0.078 -0.273 0.785
CAVLUNCH -0.390 0.162 -2.406 0.016
CAVTOC1F 0.226 0.150 1.507 0.132
S ON
INTGROUP -0.039 0.262 -0.148 0.882
MALE 0.107 0.122 0.878 0.380
WHITE 0.106 0.237 0.447 0.655
LUNCH -0.061 0.091 -0.673 0.501
CAVLUNCH 0.628 0.339 1.852 0.064
CAVTOC1F -0.640 0.378 -1.690 0.091
S WITH
I -0.771 0.094 -8.186 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 3.802 0.648 5.866 0.000
S 1.138 0.836 1.361 0.174
Residual Variances
Y1 0.353 0.128 2.749 0.006
Y2 0.698 0.059 11.790 0.000
Y3 0.742 0.083 8.938 0.000
Y4 0.429 0.163 2.627 0.009
I 0.924 0.031 30.070 0.000
S 0.914 0.099 9.217 0.000
Latent Class 3
I BY
Y1 1.060 0.083 12.801 0.000
Y2 0.866 0.085 10.173 0.000
Y3 0.800 0.142 5.620 0.000
Y4 0.650 0.155 4.201 0.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 0.514 0.081 6.358 0.000
Y3 0.950 0.099 9.587 0.000
Y4 1.158 0.104 11.083 0.000
I ON
INTGROUP -0.916 0.157 -5.825 0.000
MALE 0.206 0.167 1.232 0.218
WHITE -0.426 0.244 -1.747 0.081
LUNCH -0.039 0.137 -0.282 0.778
CAVLUNCH -0.708 0.346 -2.048 0.041
CAVTOC1F 0.410 0.268 1.532 0.125
S ON
INTGROUP -0.064 0.426 -0.150 0.880
MALE 0.177 0.201 0.876 0.381
WHITE 0.175 0.394 0.445 0.657
LUNCH -0.101 0.158 -0.641 0.521
CAVLUNCH 1.039 0.448 2.318 0.020
CAVTOC1F -1.058 0.495 -2.138 0.033
S WITH
I -0.701 0.095 -7.347 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 4.353 0.811 5.369 0.000
S 1.508 0.973 1.550 0.121
Residual Variances
Y1 999.000 999.000 999.000 999.000
Y2 0.531 0.086 6.188 0.000
Y3 0.387 0.115 3.369 0.001
Y4 0.159 0.162 0.978 0.328
I 0.694 0.105 6.626 0.000
S 0.773 0.205 3.779 0.000
STD Standardization
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
I BY
Y1 0.563 0.061 9.179 0.000
Y2 0.563 0.061 9.179 0.000
Y3 0.563 0.061 9.179 0.000
Y4 0.563 0.061 9.179 0.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 0.305 0.027 11.081 0.000
Y3 0.609 0.055 11.081 0.000
Y4 0.914 0.082 11.081 0.000
I ON
INTGROUP -0.502 0.110 -4.568 0.000
MALE 0.113 0.091 1.237 0.216
WHITE -0.233 0.128 -1.824 0.068
LUNCH -0.021 0.078 -0.273 0.785
CAVLUNCH -0.388 0.161 -2.413 0.016
CAVTOC1F 0.225 0.147 1.524 0.128
S ON
INTGROUP -0.039 0.261 -0.148 0.882
MALE 0.106 0.121 0.875 0.382
WHITE 0.105 0.236 0.446 0.655
LUNCH -0.061 0.091 -0.670 0.503
CAVLUNCH 0.625 0.343 1.823 0.068
CAVTOC1F -0.636 0.379 -1.681 0.093
S WITH
I -0.771 0.094 -8.186 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 6.895 1.090 6.328 0.000
S 0.566 0.967 0.585 0.559
Residual Variances
Y1 0.171 0.075 2.285 0.022
Y2 0.359 0.073 4.890 0.000
Y3 0.519 0.100 5.204 0.000
Y4 0.292 0.124 2.352 0.019
I 0.915 0.037 24.832 0.000
S 0.904 0.094 9.593 0.000
Latent Class 2
I BY
Y1 0.560 0.061 9.210 0.000
Y2 0.560 0.061 9.210 0.000
Y3 0.560 0.061 9.210 0.000
Y4 0.560 0.061 9.210 0.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 0.303 0.027 11.124 0.000
Y3 0.606 0.054 11.124 0.000
Y4 0.909 0.082 11.124 0.000
I ON
INTGROUP -0.505 0.109 -4.625 0.000
MALE 0.114 0.092 1.235 0.217
WHITE -0.235 0.128 -1.829 0.067
LUNCH -0.021 0.078 -0.273 0.785
CAVLUNCH -0.390 0.162 -2.406 0.016
CAVTOC1F 0.226 0.150 1.507 0.132
S ON
INTGROUP -0.039 0.262 -0.148 0.882
MALE 0.107 0.122 0.878 0.380
WHITE 0.106 0.237 0.447 0.655
LUNCH -0.061 0.091 -0.673 0.501
CAVLUNCH 0.628 0.339 1.852 0.064
CAVTOC1F -0.640 0.378 -1.690 0.091
S WITH
I -0.771 0.094 -8.186 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 3.802 0.648 5.866 0.000
S 1.138 0.836 1.361 0.174
Residual Variances
Y1 0.171 0.075 2.285 0.022
Y2 0.359 0.073 4.890 0.000
Y3 0.519 0.100 5.204 0.000
Y4 0.292 0.124 2.352 0.019
I 0.924 0.031 30.070 0.000
S 0.914 0.099 9.217 0.000
Latent Class 3
I BY
Y1 0.309 0.049 6.259 0.000
Y2 0.309 0.049 6.259 0.000
Y3 0.309 0.049 6.259 0.000
Y4 0.309 0.049 6.259 0.000
S BY
Y1 0.000 0.000 999.000 999.000
Y2 0.183 0.035 5.217 0.000
Y3 0.367 0.070 5.217 0.000
Y4 0.550 0.105 5.217 0.000
I ON
INTGROUP -0.916 0.157 -5.825 0.000
MALE 0.206 0.167 1.232 0.218
WHITE -0.426 0.244 -1.747 0.081
LUNCH -0.039 0.137 -0.282 0.778
CAVLUNCH -0.708 0.346 -2.048 0.041
CAVTOC1F 0.410 0.268 1.532 0.125
S ON
INTGROUP -0.064 0.426 -0.150 0.880
MALE 0.177 0.201 0.876 0.381
WHITE 0.175 0.394 0.445 0.657
LUNCH -0.101 0.158 -0.641 0.521
CAVLUNCH 1.039 0.448 2.318 0.020
CAVTOC1F -1.058 0.495 -2.138 0.033
S WITH
I -0.701 0.095 -7.347 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
I 4.353 0.811 5.369 0.000
S 1.508 0.973 1.550 0.121
Residual Variances
Y1 -0.010 999.000 999.000 999.000
Y2 0.068 0.024 2.806 0.005
Y3 0.058 0.020 2.902 0.004
Y4 0.036 0.030 1.192 0.233
I 0.694 0.105 6.626 0.000
S 0.773 0.205 3.779 0.000
R-SQUARE
Class 1
Observed Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
Y1 0.650 0.129 5.052 0.000
Y2 0.290 0.059 4.938 0.000
Y3 0.238 0.078 3.028 0.002
Y4 0.553 0.167 3.309 0.001
Latent Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
I 0.085 0.037 2.321 0.020
S 0.096 0.094 1.014 0.311
Class 2
Observed Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
Y1 0.647 0.128 5.042 0.000
Y2 0.302 0.059 5.099 0.000
Y3 0.258 0.083 3.105 0.002
Y4 0.571 0.163 3.493 0.000
Latent Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
I 0.076 0.031 2.460 0.014
S 0.086 0.099 0.868 0.385
Class 3
Observed Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
Y1 Undefined 0.11236E+01
Y2 0.469 0.086 5.461 0.000
Y3 0.613 0.115 5.329 0.000
Y4 0.841 0.162 5.192 0.000
Latent Two-Tailed
Variable Estimate S.E. Est./S.E. P-Value
I 0.306 0.105 2.917 0.004
S 0.227 0.205 1.110 0.267
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.186E-05
(ratio of smallest to largest eigenvalue)
RESIDUAL OUTPUT
ESTIMATED MODEL AND RESIDUALS (OBSERVED - ESTIMATED) FOR CLASS 1
Model Estimated Means
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
1 3.847 3.717 3.588 3.458 0.684
Model Estimated Means
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
1 0.756 0.294 0.609 0.574 2.196
Residuals for Means
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
1 0.034 -0.091 -0.401 0.157 0.000
Residuals for Means
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
Model Estimated Covariances
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
Y1 0.489
Y2 0.186 0.506
Y3 0.054 0.108 0.681
Y4 -0.078 0.069 0.216 0.655
INTGROUP -0.069 -0.047 -0.024 -0.001 0.216
MALE 0.002 0.013 0.023 0.034 0.008
WHITE -0.004 -0.017 -0.030 -0.044 0.003
LUNCH 0.021 0.027 0.033 0.039 -0.095
CAVLUNCH 0.007 0.019 0.032 0.045 -0.061
CAVTOC1F 0.067 0.032 -0.004 -0.040 -0.180
Model Estimated Covariances
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
MALE 0.184
WHITE 0.024 0.208
LUNCH 0.044 -0.067 0.238
CAVLUNCH 0.003 -0.082 0.089 0.107
CAVTOC1F -0.021 0.033 0.036 0.021 0.214
Residuals for Covariances
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
Y1 0.374
Y2 -0.317 0.273
Y3 0.114 -0.171 0.276
Y4 -0.245 -0.669 0.085 0.177
INTGROUP -0.019 0.467 -0.191 -0.241 0.000
MALE 0.037 0.025 0.078 -0.005 0.000
WHITE 0.029 -0.158 -0.182 -0.205 0.000
LUNCH -0.006 -0.214 0.213 0.249 0.000
CAVLUNCH -0.004 -0.106 0.135 0.122 0.000
CAVTOC1F 0.034 -0.435 0.155 0.192 0.000
Residuals for Covariances
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
MALE 0.000
WHITE 0.000 0.000
LUNCH 0.000 0.000 0.000
CAVLUNCH 0.000 0.000 0.000 0.000
CAVTOC1F 0.000 0.000 0.000 0.000 0.000
ESTIMATED MODEL AND RESIDUALS (OBSERVED - ESTIMATED) FOR CLASS 2
Model Estimated Means
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
1 2.082 2.133 2.184 2.235 0.678
Model Estimated Means
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
1 0.520 0.313 0.487 0.438 1.998
Residuals for Means
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
1 -0.013 0.085 -0.215 0.051 0.000
Residuals for Means
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
Model Estimated Covariances
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
Y1 0.485
Y2 0.189 0.515
Y3 0.064 0.122 0.699
Y4 -0.062 0.088 0.239 0.681
INTGROUP -0.061 -0.061 -0.061 -0.062 0.218
MALE 0.009 0.020 0.030 0.040 0.010
WHITE -0.015 -0.023 -0.031 -0.039 0.034
LUNCH -0.005 0.007 0.020 0.032 -0.039
CAVLUNCH -0.002 0.012 0.027 0.041 -0.044
CAVTOC1F 0.027 -0.001 -0.028 -0.055 -0.045
Model Estimated Covariances
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
MALE 0.250
WHITE 0.029 0.215
LUNCH -0.012 -0.096 0.250
CAVLUNCH -0.007 -0.092 0.123 0.128
CAVTOC1F -0.014 -0.001 0.019 0.027 0.167
Residuals for Covariances
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
Y1 -0.069
Y2 -0.049 -0.086
Y3 -0.174 -0.271 -0.053
Y4 -0.027 -0.172 0.057 -0.016
INTGROUP 0.007 0.080 -0.073 -0.054 0.000
MALE 0.043 0.000 -0.050 -0.024 0.000
WHITE 0.012 0.056 -0.043 -0.045 0.000
LUNCH 0.014 -0.034 0.011 0.050 0.000
CAVLUNCH -0.006 -0.042 0.033 0.022 0.000
CAVTOC1F 0.015 0.040 -0.089 -0.052 0.000
Residuals for Covariances
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
MALE 0.000
WHITE 0.000 0.000
LUNCH 0.000 0.000 0.000
CAVLUNCH 0.000 0.000 0.000 0.000
CAVTOC1F 0.000 0.000 0.000 0.000 0.000
ESTIMATED MODEL AND RESIDUALS (OBSERVED - ESTIMATED) FOR CLASS 3
Model Estimated Means
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
1 1.292 1.318 1.344 1.371 0.565
Model Estimated Means
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
1 0.410 0.328 0.393 0.434 1.767
Residuals for Means
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
1 0.001 0.024 -0.046 0.034 0.000
Residuals for Means
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
Model Estimated Covariances
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
Y1 0.085
Y2 0.061 0.127
Y3 0.026 0.059 0.149
Y4 -0.009 0.058 0.124 0.226
INTGROUP -0.072 -0.073 -0.074 -0.075 0.246
MALE 0.015 0.019 0.024 0.028 0.006
WHITE -0.006 -0.016 -0.027 -0.038 0.011
LUNCH -0.024 -0.004 0.015 0.035 0.008
CAVLUNCH -0.023 -0.002 0.020 0.041 0.003
CAVTOC1F 0.014 -0.007 -0.028 -0.049 -0.004
Model Estimated Covariances
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
MALE 0.242
WHITE 0.043 0.220
LUNCH -0.017 -0.090 0.239
CAVLUNCH -0.033 -0.097 0.125 0.139
CAVTOC1F -0.007 0.012 -0.020 -0.008 0.104
Residuals for Covariances
Y1 Y2 Y3 Y4 INTGROUP
________ ________ ________ ________ ________
Y1 0.001
Y2 -0.001 -0.003
Y3 -0.031 -0.059 -0.007
Y4 -0.017 -0.051 0.057 0.008
INTGROUP -0.001 0.043 -0.022 -0.021 0.000
MALE -0.006 0.000 -0.073 -0.061 0.000
WHITE -0.002 -0.011 -0.013 -0.002 0.000
LUNCH -0.004 0.001 0.087 0.059 0.000
CAVLUNCH -0.001 0.003 0.056 0.040 0.000
CAVTOC1F -0.002 0.022 -0.010 -0.040 0.000
Residuals for Covariances
MALE WHITE LUNCH CAVLUNCH CAVTOC1F
________ ________ ________ ________ ________
MALE 0.000
WHITE 0.000 0.000
LUNCH 0.000 0.000 0.000
CAVLUNCH 0.000 0.000 0.000 0.000
CAVTOC1F 0.000 0.000 0.000 0.000 0.000
RESIDUALS FOR DEPENDENT VARIABLES ARE NOT AVAILABLE FOR MODELS WITH
COVARIATES BECAUSE THE RESIDUAL VALUES VARY AS A FUNCTION OF THE
COVARIATE VALUES.
TECHNICAL 8 OUTPUT
INITIAL STAGE ITERATIONS
TECHNICAL 8 OUTPUT FOR UNPERTURBED STARTING VALUE SET
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.22656705D+04 0.0000000 0.0000000 45.548 79.507 EM
277.945
2 -0.15515754D+04 714.0951063 0.3151805 47.133 69.210 EM
286.657
3 -0.15083175D+04 43.2579775 0.0278800 49.322 73.348 EM
280.331
4 -0.14981538D+04 10.1636525 0.0067384 51.846 79.101 EM
272.053
5 -0.14926752D+04 5.4785795 0.0036569 53.631 85.807 EM
263.562
6 -0.14881081D+04 4.5671191 0.0030597 54.447 93.383 EM
255.170
7 -0.14836709D+04 4.4372216 0.0029818 54.232 101.830 EM
246.938
8 -0.14791312D+04 4.5396363 0.0030597 52.978 111.014 EM
239.008
9 -0.14745539D+04 4.5773759 0.0030946 50.757 120.714 EM
231.529
10 -0.14699009D+04 4.6529883 0.0031555 47.727 130.759 EM
224.514
TECHNICAL 8 OUTPUT FOR STARTING VALUE SET 1
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.60365389D+05 0.0000000 0.0000000 25.217 95.064 EM
282.719
2 -0.16441339D+04 ************ 0.9727636 26.720 90.411 EM
285.869
3 -0.15929281D+04 51.2057492 0.0311445 29.852 85.830 EM
287.317
4 -0.15465495D+04 46.3785790 0.0291153 33.022 85.945 EM
284.033
5 -0.15161867D+04 30.3628087 0.0196326 36.741 88.856 EM
277.403
6 -0.15019483D+04 14.2383926 0.0093909 41.037 91.636 EM
270.327
7 -0.14963003D+04 5.6480161 0.0037605 45.548 94.209 EM
263.244
8 -0.14921758D+04 4.1245371 0.0027565 50.163 96.597 EM
256.241
9 -0.14885038D+04 3.6720079 0.0024608 54.704 98.842 EM
249.454
10 -0.14851978D+04 3.3059378 0.0022210 58.928 101.018 EM
243.053
TECHNICAL 8 OUTPUT FOR STARTING VALUE SET 2
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.24188443D+05 0.0000000 0.0000000 35.379 23.965 EM
343.657
2 -0.16636542D+04 ************ 0.9312211 35.752 24.761 EM
342.487
3 -0.16505020D+04 13.1521716 0.0079056 35.560 26.061 EM
341.380
4 -0.16400499D+04 10.4521514 0.0063327 34.804 28.008 EM
340.188
5 -0.16302518D+04 9.7980627 0.0059742 33.217 30.234 EM
339.550
6 -0.16231479D+04 7.1039335 0.0043576 31.222 33.253 EM
338.525
7 -0.16162017D+04 6.9462117 0.0042795 29.439 37.091 EM
336.470
8 -0.16077725D+04 8.4292087 0.0052154 28.582 41.537 EM
332.881
9 -0.15959998D+04 11.7726159 0.0073223 29.640 46.422 EM
326.938
10 -0.15649417D+04 31.0581611 0.0194600 33.137 52.013 EM
317.850
TECHNICAL 8 OUTPUT FOR STARTING VALUE SET 3
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.71006226D+05 0.0000000 0.0000000 41.819 313.647 EM
47.534
2 -0.16893853D+04 ************ 0.9762079 40.909 312.388 EM
49.703
3 -0.16805651D+04 8.8202737 0.0052210 39.896 310.141 EM
52.963
4 -0.16581728D+04 22.3922733 0.0133243 39.762 305.593 EM
57.645
5 -0.16203056D+04 37.8672107 0.0228367 40.585 295.928 EM
66.487
6 -0.15765731D+04 43.7324383 0.0269902 41.464 284.431 EM
77.105
7 -0.15486282D+04 27.9449822 0.0177251 42.126 273.165 EM
87.710
8 -0.15272969D+04 21.3312766 0.0137743 42.277 261.725 EM
98.998
9 -0.15095031D+04 17.7937598 0.0116505 41.859 252.027 EM
109.114
10 -0.14996962D+04 9.8068846 0.0064968 41.084 244.232 EM
117.684
TECHNICAL 8 OUTPUT FOR STARTING VALUE SET 4
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.62106542D+05 0.0000000 0.0000000 50.403 64.522 EM
288.076
2 -0.16892147D+04 ************ 0.9728013 50.969 66.072 EM
285.959
3 -0.16703366D+04 18.8780658 0.0111756 54.588 72.665 EM
275.748
4 -0.16281920D+04 42.1445942 0.0252312 64.023 87.913 EM
251.065
5 -0.15611819D+04 67.0101486 0.0411562 76.650 105.864 EM
220.485
6 -0.15042566D+04 56.9252486 0.0364629 85.128 115.401 EM
202.471
7 -0.14858404D+04 18.4162666 0.0122428 87.954 117.814 EM
197.232
8 -0.14806127D+04 5.2277100 0.0035184 88.298 117.642 EM
197.060
9 -0.14783574D+04 2.2552108 0.0015232 87.783 116.648 EM
198.569
10 -0.14772563D+04 1.1011656 0.0007449 87.050 115.460 EM
200.490
TECHNICAL 8 OUTPUT FOR STARTING VALUE SET 5
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.92098021D+05 0.0000000 0.0000000 118.428 199.500 EM
85.072
2 -0.16895883D+04 ************ 0.9816545 116.995 199.556 EM
86.449
3 -0.16659173D+04 23.6710594 0.0140100 113.633 199.608 EM
89.759
4 -0.16133636D+04 52.5536176 0.0315464 110.961 200.637 EM
91.402
5 -0.15692068D+04 44.1568620 0.0273694 107.706 198.865 EM
96.429
6 -0.15398490D+04 29.3577678 0.0187087 102.749 194.323 EM
105.927
7 -0.15177418D+04 22.1071748 0.0143567 98.353 188.944 EM
115.702
8 -0.15073843D+04 10.3575582 0.0068243 94.773 184.068 EM
124.159
9 -0.15023889D+04 4.9953761 0.0033139 91.490 179.680 EM
131.831
10 -0.14985262D+04 3.8627182 0.0025711 88.351 175.696 EM
138.953
TECHNICAL 8 OUTPUT FOR STARTING VALUE SET 6
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.81504807D+05 0.0000000 0.0000000 62.112 109.916 EM
230.972
2 -0.16797153D+04 ************ 0.9793912 63.467 111.831 EM
227.703
3 -0.16626540D+04 17.0612352 0.0101572 65.969 115.887 EM
221.145
4 -0.16445964D+04 18.0576669 0.0108607 69.915 122.599 EM
210.486
5 -0.16206780D+04 23.9183393 0.0145436 75.244 132.058 EM
195.699
6 -0.15881310D+04 32.5470150 0.0200823 80.589 141.836 EM
180.575
7 -0.15482943D+04 39.8367065 0.0250840 83.875 148.053 EM
171.072
8 -0.15160369D+04 32.2573880 0.0208341 84.685 150.045 EM
168.271
9 -0.15001261D+04 15.9107952 0.0104950 84.057 149.433 EM
169.511
10 -0.14925230D+04 7.6031566 0.0050683 82.704 147.246 EM
173.050
TECHNICAL 8 OUTPUT FOR STARTING VALUE SET 7
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.59097970D+05 0.0000000 0.0000000 161.399 207.399 EM
34.202
2 -0.16704914D+04 ************ 0.9717335 160.554 202.635 EM
39.811
3 -0.16224270D+04 48.0643726 0.0287726 157.598 197.202 EM
48.200
4 -0.15781550D+04 44.2719818 0.0272875 150.261 189.284 EM
63.455
5 -0.15484053D+04 29.7497367 0.0188510 144.919 182.956 EM
75.125
6 -0.15355486D+04 12.8566213 0.0083031 140.255 177.178 EM
85.567
7 -0.15266708D+04 8.8778756 0.0057816 135.760 171.574 EM
95.667
8 -0.15189986D+04 7.6721342 0.0050254 131.454 166.093 EM
105.453
9 -0.15124904D+04 6.5082705 0.0042846 127.442 160.759 EM
114.799
10 -0.15070492D+04 5.4411283 0.0035975 123.735 155.620 EM
123.644
TECHNICAL 8 OUTPUT FOR STARTING VALUE SET 8
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.31721494D+05 0.0000000 0.0000000 3.584 121.011 EM
278.405
2 -0.16775903D+04 ************ 0.9471150 4.721 121.119 EM
277.160
3 -0.16461941D+04 31.3961508 0.0187150 6.402 120.141 EM
276.457
4 -0.16093203D+04 36.8738080 0.0223994 8.243 117.317 EM
277.440
5 -0.15694224D+04 39.8978735 0.0247918 10.236 115.339 EM
277.425
6 -0.15335198D+04 35.9026357 0.0228763 10.916 117.047 EM
275.037
7 -0.15092779D+04 24.2418950 0.0158080 11.628 120.769 EM
270.602
8 -0.14980515D+04 11.2263740 0.0074382 12.114 124.943 EM
265.944
9 -0.14930897D+04 4.9618379 0.0033122 12.437 129.693 EM
260.870
10 -0.14897941D+04 3.2955620 0.0022072 12.650 134.659 EM
255.691
TECHNICAL 8 OUTPUT FOR STARTING VALUE SET 9
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.32450055D+05 0.0000000 0.0000000 315.786 70.983 EM
16.231
2 -0.16883142D+04 ************ 0.9479719 313.447 71.454 EM
18.100
3 -0.16746107D+04 13.7034435 0.0081166 310.587 70.751 EM
21.662
4 -0.16639024D+04 10.7083448 0.0063945 307.839 69.732 EM
25.429
5 -0.16571726D+04 6.7297591 0.0040446 305.897 68.700 EM
28.404
6 -0.16527244D+04 4.4482257 0.0026842 304.445 67.651 EM
30.904
7 -0.16475698D+04 5.1545605 0.0031188 303.193 66.306 EM
33.501
8 -0.16422492D+04 5.3206797 0.0032294 302.054 65.388 EM
35.558
9 -0.16378117D+04 4.4375003 0.0027021 300.987 64.615 EM
37.397
10 -0.16354301D+04 2.3815162 0.0014541 300.203 63.849 EM
38.948
TECHNICAL 8 OUTPUT FOR STARTING VALUE SET 10
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.92321623D+05 0.0000000 0.0000000 249.825 77.508 EM
75.667
2 -0.16906421D+04 ************ 0.9816875 249.016 76.822 EM
77.162
3 -0.16804675D+04 10.1745712 0.0060182 247.077 75.471 EM
80.452
4 -0.16614992D+04 18.9683263 0.0112875 244.428 74.991 EM
83.581
5 -0.16361432D+04 25.3559677 0.0152609 241.872 76.699 EM
84.429
6 -0.16077171D+04 28.4261250 0.0173739 237.698 79.650 EM
85.652
7 -0.15789514D+04 28.7656309 0.0178922 231.875 82.072 EM
89.053
8 -0.15574209D+04 21.5305163 0.0136360 223.943 82.906 EM
96.151
9 -0.15344767D+04 22.9442394 0.0147322 214.460 82.478 EM
106.062
10 -0.15153684D+04 19.1082808 0.0124526 205.605 81.373 EM
116.022
FINAL STAGE ITERATIONS
TECHNICAL 8 OUTPUT FOR UNPERTURBED STARTING VALUE SET
10 -0.14699009D+04 4.6529883 0.0031555 47.727 130.759 EM
224.514
11 -0.14647852D+04 5.1156549 0.0034803 44.132 141.031 EM
217.838
12 -0.14586860D+04 6.0992093 0.0041639 40.419 151.161 EM
211.420
13 -0.14517440D+04 6.9420150 0.0047591 37.319 160.247 EM
205.434
14 -0.14454890D+04 6.2550416 0.0043086 35.388 167.475 EM
200.137
15 -0.14411332D+04 4.3557946 0.0030134 34.568 172.889 EM
195.544
16 -0.14382658D+04 2.8673995 0.0019897 34.514 176.930 EM
191.557
17 -0.14363069D+04 1.9588576 0.0013620 34.904 179.970 EM
188.126
18 -0.14349843D+04 1.3225846 0.0009208 35.496 182.276 EM
185.228
19 -0.14341176D+04 0.8667144 0.0006040 36.131 184.045 EM
182.824
20 -0.14335598D+04 0.5577629 0.0003889 36.725 185.424 EM
180.851
21 -0.14332017D+04 0.3580956 0.0002498 37.241 186.517 EM
179.242
22 -0.14329698D+04 0.2319641 0.0001619 37.675 187.394 EM
177.930
23 -0.14328174D+04 0.1523656 0.0001063 38.033 188.107 EM
176.860
24 -0.14327159D+04 0.1015667 0.0000709 38.324 188.691 EM
175.984
25 -0.14326472D+04 0.0686251 0.0000479 38.560 189.175 EM
175.264
26 -0.14326003D+04 0.0469040 0.0000327 38.752 189.578 EM
174.670
27 -0.14325680D+04 0.0323609 0.0000226 38.907 189.917 EM
174.177
28 -0.14325455D+04 0.0224961 0.0000157 39.032 190.201 EM
173.767
29 -0.14325297D+04 0.0157329 0.0000110 39.134 190.442 EM
173.424
30 -0.14325187D+04 0.0110562 0.0000077 39.217 190.646 EM
173.137
31 -0.14325109D+04 0.0078002 0.0000054 39.285 190.819 EM
172.895
32 -0.14325054D+04 0.0055206 0.0000039 39.341 190.967 EM
172.692
33 -0.14325014D+04 0.0039175 0.0000027 39.387 191.092 EM
172.521
34 -0.14324987D+04 0.0027860 0.0000019 39.425 191.198 EM
172.377
35 -0.14324967D+04 0.0019849 0.0000014 39.456 191.289 EM
172.254
36 -0.14324948D+04 0.0018727 0.0000013 39.533 191.508 FS
171.959
37 -0.14324931D+04 0.0016795 0.0000012 39.544 191.538 EM
171.918
38 -0.14324927D+04 0.0004347 0.0000003 39.554 191.574 EM
171.872
39 -0.14324924D+04 0.0002873 0.0000002 39.563 191.607 EM
171.830
40 -0.14324922D+04 0.0002021 0.0000001 39.571 191.636 EM
171.793
41 -0.14324920D+04 0.0001441 0.0000001 39.578 191.661 EM
171.761
42 -0.14324919D+04 0.0001031 0.0000001 39.584 191.683 EM
171.733
43 -0.14324919D+04 0.0000740 0.0000001 39.589 191.701 EM
171.710
44 -0.14324918D+04 0.0000531 0.0000000 39.593 191.717 EM
171.690
45 -0.14324918D+04 0.0000382 0.0000000 39.597 191.730 EM
171.673
46 -0.14324918D+04 0.0000274 0.0000000 39.600 191.742 EM
171.658
47 -0.14324917D+04 0.0000197 0.0000000 39.603 191.751 EM
171.646
48 -0.14324917D+04 0.0000142 0.0000000 39.605 191.759 EM
171.636
49 -0.14324917D+04 0.0000102 0.0000000 39.607 191.766 EM
171.627
50 -0.14324917D+04 0.0000073 0.0000000 39.609 191.772 EM
171.619
51 -0.14324917D+04 0.0000053 0.0000000 39.610 191.777 EM
171.613
52 -0.14324917D+04 0.0000038 0.0000000 39.611 191.781 EM
171.608
53 -0.14324917D+04 0.0000027 0.0000000 39.612 191.785 EM
171.603
54 -0.14324917D+04 0.0000020 0.0000000 39.613 191.788 EM
171.599
55 -0.14324917D+04 0.0000014 0.0000000 39.614 191.790 EM
171.596
56 -0.14324917D+04 0.0000010 0.0000000 39.614 191.793 EM
171.593
57 -0.14324917D+04 0.0000007 0.0000000 39.615 191.795 EM
171.591
58 -0.14324917D+04 0.0000005 0.0000000 39.615 191.796 EM
171.589
59 -0.14324917D+04 0.0000004 0.0000000 39.616 191.797 EM
171.587
60 -0.14324917D+04 0.0000003 0.0000000 39.616 191.799 EM
171.585
61 -0.14324917D+04 0.0000002 0.0000000 39.616 191.800 EM
171.584
62 -0.14324917D+04 0.0000001 0.0000000 39.617 191.800 EM
171.583
63 -0.14324917D+04 0.0000001 0.0000000 39.617 191.801 EM
171.582
64 -0.14324917D+04 0.0000001 0.0000000 39.617 191.802 EM
171.581
65 -0.14324917D+04 0.0000001 0.0000000 39.617 191.802 EM
171.581
66 -0.14324917D+04 0.0000000 0.0000000 39.617 191.803 EM
171.580
67 -0.14324917D+04 0.0000000 0.0000000 39.617 191.803 EM
171.580
68 -0.14324917D+04 0.0000000 0.0000000 39.617 191.803 EM
171.579
69 -0.14324917D+04 0.0000000 0.0000000 39.617 191.804 EM
171.579
70 -0.14324917D+04 0.0000000 0.0000000 39.617 191.804 EM
171.579
71 -0.14324917D+04 0.0000000 0.0000000 39.617 191.804 EM
171.579
72 -0.14324917D+04 0.0000000 0.0000000 39.618 191.804 EM
171.578
73 -0.14324917D+04 0.0000000 0.0000000 39.618 191.804 EM
171.578
74 -0.14324917D+04 0.0000000 0.0000000 39.618 191.804 EM
171.578
75 -0.14324917D+04 0.0000000 0.0000000 39.618 191.804 EM
171.578
76 -0.14324917D+04 0.0000000 0.0000000 39.618 191.804 EM
171.578
77 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.578
78 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.578
79 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.578
80 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.578
81 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.578
82 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.577
83 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.577
84 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.577
85 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.577
86 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.577
87 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.577
88 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.577
89 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 FS
171.577
90 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.577
91 -0.14324917D+04 0.0000000 0.0000000 39.618 191.805 EM
171.577
TECHNICAL 8 OUTPUT FOR STARTING VALUE SET 4
10 -0.14772563D+04 1.1011656 0.0007449 87.050 115.460 EM
200.490
11 -0.14766152D+04 0.6410910 0.0004340 86.328 114.293 EM
202.379
12 -0.14761431D+04 0.4721167 0.0003197 85.678 113.210 EM
204.112
13 -0.14757473D+04 0.3957607 0.0002681 85.109 112.220 EM
205.671
14 -0.14753907D+04 0.3566000 0.0002416 84.618 111.315 EM
207.067
15 -0.14750515D+04 0.3392407 0.0002299 84.199 110.485 EM
208.316
16 -0.14747132D+04 0.3382574 0.0002293 83.850 109.716 EM
209.435
17 -0.14743619D+04 0.3512936 0.0002382 83.569 108.994 EM
210.436
18 -0.14739850D+04 0.3769457 0.0002557 83.357 108.309 EM
211.334
19 -0.14735715D+04 0.4134634 0.0002805 83.212 107.649 EM
212.139
20 -0.14731142D+04 0.4572717 0.0003103 83.130 107.009 EM
212.862
21 -0.14726129D+04 0.5013315 0.0003403 83.101 106.387 EM
213.512
22 -0.14720784D+04 0.5344695 0.0003629 83.106 105.792 EM
214.101
23 -0.14715347D+04 0.5437274 0.0003694 83.123 105.235 EM
214.642
24 -0.14710142D+04 0.5205413 0.0003537 83.124 104.730 EM
215.146
25 -0.14705470D+04 0.4672000 0.0003176 83.093 104.285 EM
215.622
26 -0.14701497D+04 0.3972615 0.0002701 83.021 103.904 EM
216.075
27 -0.14698213D+04 0.3283972 0.0002234 82.912 103.582 EM
216.506
28 -0.14695475D+04 0.2738338 0.0001863 82.774 103.313 EM
216.913
29 -0.14693086D+04 0.2388430 0.0001625 82.613 103.092 EM
217.295
30 -0.14690861D+04 0.2225637 0.0001515 82.435 102.914 EM
217.651
31 -0.14688646D+04 0.2215163 0.0001508 82.241 102.779 EM
217.980
32 -0.14686325D+04 0.2320408 0.0001580 82.028 102.689 EM
218.282
33 -0.14683814D+04 0.2511323 0.0001710 81.792 102.650 EM
218.559
34 -0.14681051D+04 0.2762637 0.0001881 81.525 102.664 EM
218.811
35 -0.14678002D+04 0.3048852 0.0002077 81.225 102.735 EM
219.040
36 -0.14674661D+04 0.3340946 0.0002276 80.888 102.860 EM
219.252
37 -0.14671053D+04 0.3607923 0.0002459 80.513 103.037 EM
219.450
38 -0.14667230D+04 0.3823309 0.0002606 80.099 103.256 EM
219.645
39 -0.14663258D+04 0.3971889 0.0002708 79.648 103.506 EM
219.847
40 -0.14659209D+04 0.4049099 0.0002761 79.161 103.772 EM
220.067
41 -0.14655158D+04 0.4050873 0.0002763 78.647 104.038 EM
220.315
42 -0.14651196D+04 0.3962178 0.0002704 78.117 104.285 EM
220.598
43 -0.14647439D+04 0.3756971 0.0002564 77.586 104.494 EM
220.920
44 -0.14644025D+04 0.3414146 0.0002331 77.073 104.652 EM
221.275
45 -0.14641084D+04 0.2940744 0.0002008 76.592 104.752 EM
221.656
46 -0.14638700D+04 0.2384309 0.0001629 76.152 104.798 EM
222.050
47 -0.14636879D+04 0.1820812 0.0001244 75.755 104.803 EM
222.443
48 -0.14635556D+04 0.1323255 0.0000904 75.398 104.783 EM
222.819
49 -0.14634622D+04 0.0934259 0.0000638 75.075 104.756 EM
223.169
50 -0.14633962D+04 0.0660126 0.0000451 74.777 104.736 EM
223.487
51 -0.14633478D+04 0.0483273 0.0000330 74.498 104.734 EM
223.768
52 -0.14633100D+04 0.0378511 0.0000259 74.230 104.756 EM
224.014
53 -0.14632776D+04 0.0323459 0.0000221 73.968 104.805 EM
224.227
54 -0.14632474D+04 0.0302102 0.0000206 73.707 104.883 EM
224.410
55 -0.14632170D+04 0.0304394 0.0000208 73.443 104.989 EM
224.568
56 -0.14631845D+04 0.0324520 0.0000222 73.174 105.122 EM
224.704
57 -0.14631486D+04 0.0359081 0.0000245 72.897 105.280 EM
224.824
58 -0.14631081D+04 0.0405502 0.0000277 72.609 105.460 EM
224.930
59 -0.14630620D+04 0.0460679 0.0000315 72.311 105.660 EM
225.029
60 -0.14630100D+04 0.0520012 0.0000355 72.003 105.873 EM
225.123
61 -0.14629523D+04 0.0577269 0.0000395 71.688 106.095 EM
225.218
62 -0.14628897D+04 0.0625781 0.0000428 71.367 106.317 EM
225.316
63 -0.14628236D+04 0.0660745 0.0000452 71.045 106.535 EM
225.421
64 -0.14627555D+04 0.0681151 0.0000466 70.724 106.741 EM
225.535
65 -0.14626865D+04 0.0689577 0.0000471 70.407 106.933 EM
225.660
66 -0.14626176D+04 0.0689769 0.0000472 70.096 107.109 EM
225.795
67 -0.14619706D+04 0.6469775 0.0004423 66.446 106.045 QN
230.509
68 -0.14619706D+04 0.0000000 0.0000000 66.446 106.045 EM
230.509
Beginning Time: 23:01:15
Ending Time: 23:01:19
Elapsed Time: 00:00:04
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