Mplus VERSION 5.2
MUTHEN & MUTHEN
12/02/2008 6:51 PM
INPUT INSTRUCTIONS
! SCRIPT NAME : ordVCut4d (cvb)
! GOAL : univariate Mx script for the analysis of one categorical phenotype
! DATA : ordinal
! INPUT : raw data
! UNI/BI/MULTI : uni
! DATA-GROUPS : MZM, DZM, MZF, DZF
! MEANS MODEL : assuming no differences in prevalences across twin1, twin2 and MZ,
! VARIANCE COVARIANCE MODEL(S) : 1. ADE/KLM (sex diffs in variance components) 2. A
!
! Evaluated models:
! 1. different prevalences for males and females, ADE for males, KLM for females
! 2. same prevalences for males and females, ADE for males, KLM for females
! 3. same prevalences for males and females, ADE= KLM for males and females
! 4. same prevalences for males and females, AE= KM for males and females
! 5. same prevalences for males and females, E= M for males and females
! 6. different prevalences for males and females, ADE= KLM for males and females
! 7. different prevalences for males and females, AE= KM for males and females
! 8. different prevalences for males and females, E= M for males and females
! assuming no differences in prevalences across twin1, twin2 and MZ, DZ
data: file is ordraw1.dat;
variable: names are id y1 y2 zygot age;
categorical=y1 y2;
usevar are y1 y2;
grouping=zygot(1=MZM 2=DZM 3=MZF 4=DZF); ! specify the groups
missing=all(-9); ! specify missing data symbol
analysis: conv=1e-6;
model:
[y1$1] (mt);
[y2$1] (mt);
y1 with y2 (mzmc);
model dzm:
[y1$1] (mt);
[y2$1] (mt);
y1 with y2 (dzmc);
model mzf:
[y1$1] (ft);
[y2$1] (ft);
y1 with y2 (mzfc);
model dzf:
[y1$1] (ft);
[y2$1] (ft);
y1 with y2 (dzfc);
model constraint:
new(a d e x w z);
a=x*x;
d=w*w;
e=1-x*x-w*w;
z=sqrt(1-x*x-w*w);
mzmc=x*x+w*w;
dzmc=0.5*x*x+w*w;
new(k l m s t u);
k=s*s;
l=t*t;
m=1-s*s-t*t;
u=sqrt(1-s*s-t*t);
mzfc=s*s+t*t;
dzfc=0.5*s*s+0.25*t*t;
! Uncomment for same prevalences for males and females
! mt=ft;
! Uncomment for Model ADE=KLM
! a=k;
! d=l;
! Uncomment for Model AE=KM
! d=0;
! Uncomment for Model DE=LM
! a=0;
! Uncomment for Model E=M
! a=0;
! d=0;
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
! MEANS MODEL : assuming no differences in prevalences across twin1, twin2 and MZ, DZ, t
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
! VARIANCE COVARIANCE MODEL(S) : 1. ADE/KLM (sex diffs in variance components) 2. ADE (n
*** WARNING
Data set contains unknown or missing values for GROUPING,
PATTERN, COHORT and/or CLUSTER variables.
Number of cases with unknown or missing values: 711
3 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
SUMMARY OF ANALYSIS
Number of groups 4
Number of observations
Group MZM 399
Group DZM 273
Group MZF 891
Group DZF 577
Number of dependent variables 2
Number of independent variables 0
Number of continuous latent variables 0
Observed dependent variables
Binary and ordered categorical (ordinal)
Y1 Y2
Variables with special functions
Grouping variable ZYGOT
Estimator WLSMV
Maximum number of iterations 1000
Convergence criterion 0.100D-05
Maximum number of steepest descent iterations 20
Maximum number of iterations for H1 2000
Convergence criterion for H1 0.100D-03
Parameterization DELTA
Input data file(s)
ordraw1.dat
Input data format FREE
SUMMARY OF DATA
Group MZM
Number of missing data patterns 3
Group DZM
Number of missing data patterns 3
Group MZF
Number of missing data patterns 3
Group DZF
Number of missing data patterns 3
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT FOR MZM
Covariance Coverage
Y1 Y2
________ ________
Y1 0.827
Y2 0.609 0.782
PROPORTION OF DATA PRESENT FOR DZM
Covariance Coverage
Y1 Y2
________ ________
Y1 0.744
Y2 0.498 0.755
PROPORTION OF DATA PRESENT FOR MZF
Covariance Coverage
Y1 Y2
________ ________
Y1 0.856
Y2 0.694 0.837
PROPORTION OF DATA PRESENT FOR DZF
Covariance Coverage
Y1 Y2
________ ________
Y1 0.792
Y2 0.549 0.757
SUMMARY OF CATEGORICAL DATA PROPORTIONS
Group MZM
Y1
Category 1 0.952
Category 2 0.048
Y2
Category 1 0.933
Category 2 0.067
Group DZM
Y1
Category 1 0.946
Category 2 0.054
Y2
Category 1 0.937
Category 2 0.063
Group MZF
Y1
Category 1 0.814
Category 2 0.186
Y2
Category 1 0.822
Category 2 0.178
Group DZF
Y1
Category 1 0.838
Category 2 0.162
Y2
Category 1 0.812
Category 2 0.188
WARNING: THE BIVARIATE TABLE OF Y2 AND Y1 HAS AN EMPTY CELL.
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 3.026*
Degrees of Freedom 8**
P-Value 0.9327
Chi-Square Contributions From Each Group
MZM 1.099
DZM 0.440
MZF 0.248
DZF 1.240
* The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
for chi-square difference tests. MLM, MLR and WLSM chi-square difference
testing is described in the Mplus Technical Appendices at www.statmodel.com.
See chi-square difference testing in the index of the Mplus User's Guide.
** The degrees of freedom for MLMV, ULSMV and WLSMV are estimated according to
a formula given in the Mplus Technical Appendices at www.statmodel.com.
See degrees of freedom in the index of the Mplus User's Guide.
Chi-Square Test of Model Fit for the Baseline Model
Value 170.738
Degrees of Freedom 4
P-Value 0.0000
CFI/TLI
CFI 1.000
TLI 1.015
Number of Free Parameters 6
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
WRMR (Weighted Root Mean Square Residual)
Value 0.981
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Group MZM
Y1 WITH
Y2 0.642 0.136 4.704 0.000
Thresholds
Y1$1 1.569 0.066 23.702 0.000
Y2$1 1.569 0.066 23.702 0.000
Group DZM
Y1 WITH
Y2 0.321 0.068 4.704 0.000
Thresholds
Y1$1 1.569 0.066 23.702 0.000
Y2$1 1.569 0.066 23.702 0.000
Group MZF
Y1 WITH
Y2 0.651 0.053 12.178 0.000
Thresholds
Y1$1 0.917 0.033 27.554 0.000
Y2$1 0.917 0.033 27.554 0.000
Group DZF
Y1 WITH
Y2 0.326 0.027 12.178 0.000
Thresholds
Y1$1 0.917 0.033 27.554 0.000
Y2$1 0.917 0.033 27.554 0.000
New/Additional Parameters
A 0.642 0.136 4.704 0.000
D 0.000 0.000 0.001 1.000
E 0.358 0.136 2.626 0.009
X 0.801 0.085 9.408 0.000
W 0.000 0.048 0.001 0.999
Z 0.599 0.114 5.253 0.000
K 0.651 0.053 12.178 0.000
L 0.000 0.000 0.000 1.000
M 0.349 0.053 6.527 0.000
S 0.807 0.033 24.357 0.000
T 0.000 0.231 0.000 1.000
U 0.591 0.045 13.054 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.992E-06
(ratio of smallest to largest eigenvalue)
Beginning Time: 18:51:27
Ending Time: 18:51:28
Elapsed Time: 00:00:01
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Los Angeles, CA 90066
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Copyright (c) 1998-2008 Muthen & Muthen
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