Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:57 PM
INPUT INSTRUCTIONS
! SCRIPT NAME : ordVCut2c (cvb)
! GOAL : univariate Mx script for the analysis of one categorical phenotype
! DATA : ordinal
! INPUT : raw data
! UNI/BI/MULTI : uni
! DATA-GROUPS : MZ DZ
! MEANS MODEL : assuming no differences in prevalences across twin1, twin2, males,
! VARIANCE COVARIANCE MODEL(S) : 1. ACE 2. AE 3. CE 4. E
data: file is ordraw1.dat;
variable: names are id y1 y2 zygot age;
categorical=y1 y2;
usevar are y1 y2;
grouping=zygot(1=MZ 2=DZ); ! specify the two groups MZ and DZ
missing=all(-9); ! specify missing data symbol
model:
[y1$1 y2$1] (t);
y1 with y2 (mzc);
model dz:
y1 with y2 (dzc);
model constraint:
! Uncomment for Model ACE
new(a c e x y z);
a=x*x;
c=y*y;
e=1-x*x-y*y;
z=sqrt(1-x*x-y*y);
mzc=x*x+y*y;
dzc=0.5*x*x+y*y;
! Uncomment for Model AE
! c=0;
! Uncomment for Model CE
! a=0;
! Uncomment for Model E
! a=0; c=0;
*** WARNING
Data set contains unknown or missing values for GROUPING,
PATTERN, COHORT, CLUSTER and/or STRATIFICATION variables.
Number of cases with unknown or missing values: 2179
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
SUMMARY OF ANALYSIS
Number of groups 2
Number of observations
Group MZ 399
Group DZ 273
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 0.500D-04
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 MZ
Number of missing data patterns 3
Group DZ
Number of missing data patterns 3
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT FOR MZ
Covariance Coverage
Y1 Y2
________ ________
Y1 0.827
Y2 0.609 0.782
PROPORTION OF DATA PRESENT FOR DZ
Covariance Coverage
Y1 Y2
________ ________
Y1 0.744
Y2 0.498 0.755
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
Group MZ
Y1
Category 1 0.952 314.000
Category 2 0.048 16.000
Y2
Category 1 0.933 291.000
Category 2 0.067 21.000
Group DZ
Y1
Category 1 0.946 192.000
Category 2 0.054 11.000
Y2
Category 1 0.937 193.000
Category 2 0.063 13.000
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 0.879*
Degrees of Freedom 3
P-Value 0.8306
Chi-Square Contributions From Each Group
MZ 0.627
DZ 0.251
* The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
for chi-square difference testing in the regular way. MLM, MLR and WLSM
chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
and ULSMV difference testing is done using the DIFFTEST option.
Chi-Square Test of Model Fit for the Baseline Model
Value 22.404
Degrees of Freedom 2
P-Value 0.0000
CFI/TLI
CFI 1.000
TLI 1.069
Number of Free Parameters 3
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
WRMR (Weighted Root Mean Square Residual)
Value 0.699
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Group MZ
Y1 WITH
Y2 0.642 0.136 4.706 0.000
Thresholds
Y1$1 1.569 0.066 23.701 0.000
Y2$1 1.569 0.066 23.701 0.000
Group DZ
Y1 WITH
Y2 0.321 0.071 4.528 0.000
Thresholds
Y1$1 1.569 0.066 23.701 0.000
Y2$1 1.569 0.066 23.701 0.000
New/Additional Parameters
A 0.642 0.142 4.518 0.000
C 0.000 0.039 0.000 1.000
E 0.358 0.136 2.625 0.009
X 0.801 0.089 9.037 0.000
Y 0.001 13.268 0.000 1.000
Z 0.598 0.114 5.249 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.283E-06
(ratio of smallest to largest eigenvalue)
Beginning Time: 22:57:40
Ending Time: 22:57:40
Elapsed Time: 00:00:00
MUTHEN & MUTHEN
3463 Stoner Ave.
Los Angeles, CA 90066
Tel: (310) 391-9971
Fax: (310) 391-8971
Web: www.StatModel.com
Support: Support@StatModel.com
Copyright (c) 1998-2010 Muthen & Muthen
Back to the list of genetics examples