Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:57 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, test
*** 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 (no s
*** 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: 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
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
Group MZM
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 DZM
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
Group MZF
Y1
Category 1 0.814 621.000
Category 2 0.186 142.000
Y2
Category 1 0.822 613.000
Category 2 0.178 133.000
Group DZF
Y1
Category 1 0.838 383.000
Category 2 0.162 74.000
Y2
Category 1 0.812 355.000
Category 2 0.188 82.000
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 1.751*
Degrees of Freedom 6
P-Value 0.9411
Chi-Square Contributions From Each Group
MZM 0.636
DZM 0.254
MZF 0.144
DZF 0.717
* 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 170.738
Degrees of Freedom 4
P-Value 0.0000
CFI/TLI
CFI 1.000
TLI 1.017
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 12.179 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 12.179 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 0.007 0.001 0.999
Z 0.599 0.114 5.253 0.000
K 0.651 0.053 12.179 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 0.031 0.000 1.000
U 0.591 0.045 13.054 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.100E-05
(ratio of smallest to largest eigenvalue)
Beginning Time: 22:57:40
Ending Time: 22:57:40
Elapsed Time: 00:00:00
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