Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:57 PM
INPUT INSTRUCTIONS
! SCRIPT NAME : ordVCut6d (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, DOSMF, DOSFM
! MEANS MODEL : assuming no differences in prevalences across twin1, twin2 and MZ,
! VARIANCE COVARIANCE MODEL(S) :
! 1. ADE/KLM (sex diffs in variance components), rg-DOS free
! 2. same, but rg-DOS is fixed at 0.5
! 3. AdE (no sex diffs)
! 4. AE
! 5. E
!
! Evaluated models:
! 1. different prevalences for males and females, rg free for DOS, ADE for males, KLM for
! 2. same prevalences for males and females, rg free for DOS, ADE for males, KLM for femal
! 3. same prevalences for males and females, rg fixed at 0.5 for DOS, ADE for males, KLM f
! 4. same prevalences for males and females, rg fixed at 0.5 for DOS, ADE= KLM for males a
! 5. same prevalences for males and females, rg fixed at 0.5 for DOS, AE= KM for males and
! 6. same prevalences for males and females, rg fixed at 0.5 for DOS, E= M for males and f
! 7. different prevalences for males and females, rg fixed at 0.5 for DOS, ADE for males,
! 8. different prevalences for males and females, rg fixed at 0.5 for DOS, ADE= KLM for ma
! 9. different prevalences for males and females, rg fixed at 0.5 for DOS, AE= KM for male
! 10. different prevalences for males and females, rg fixed at 0.5 for DOS, E= M for males
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 5=DOSMF 6=DOSFM); ! specify the groups
missing=all(-9); ! specify missing data symbol
analysis: conv=1e-5;
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 dosmf:
[y1$1] (mt);
[y2$1] (ft);
y1 with y2 (dosfmc);
model dosfm:
[y1$1] (ft);
[y2$1] (mt);
y1 with y2 (dosfmc);
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;
x>0; w>0; z>0;
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;
s>0; t>0; u>0;
new(f*0.2); f>0; f<0.5;
dosfmc=f*x*s+0.25*w*t;
! Uncomment for same prevalences for males and females
! mt=ft;
! Uncomment to fix rg to 0.5
! f=0.5;
! 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.
! 1. different prevalences for males and females, rg free for DOS, ADE for males, KLM for f
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
! 2. same prevalences for males and females, rg free for DOS, ADE for males, KLM for female
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
! 3. same prevalences for males and females, rg fixed at 0.5 for DOS, ADE for males, KLM fo
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
! 4. same prevalences for males and females, rg fixed at 0.5 for DOS, ADE= KLM for males an
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
! 6. same prevalences for males and females, rg fixed at 0.5 for DOS, E= M for males and fe
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
! 7. different prevalences for males and females, rg fixed at 0.5 for DOS, ADE for males, K
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
! 8. different prevalences for males and females, rg fixed at 0.5 for DOS, ADE= KLM for mal
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
! 9. different prevalences for males and females, rg fixed at 0.5 for DOS, AE= KM for males
9 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
SUMMARY OF ANALYSIS
Number of groups 6
Number of observations
Group MZM 399
Group DZM 273
Group MZF 891
Group DZF 577
Group DOSMF 381
Group DOSFM 330
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-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 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
Group DOSMF
Number of missing data patterns 3
Group DOSFM
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
PROPORTION OF DATA PRESENT FOR DOSMF
Covariance Coverage
Y1 Y2
________ ________
Y1 0.633
Y2 0.454 0.822
PROPORTION OF DATA PRESENT FOR DOSFM
Covariance Coverage
Y1 Y2
________ ________
Y1 0.845
Y2 0.424 0.579
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
Group DOSMF
Y1
Category 1 0.909 219.000
Category 2 0.091 22.000
Y2
Category 1 0.812 254.000
Category 2 0.188 59.000
Group DOSFM
Y1
Category 1 0.857 239.000
Category 2 0.143 40.000
Y2
Category 1 0.916 175.000
Category 2 0.084 16.000
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 8.250*
Degrees of Freedom 11
P-Value 0.6907
Chi-Square Contributions From Each Group
MZM 1.681
DZM 0.786
MZF 0.358
DZF 0.914
DOSMF 1.956
DOSFM 2.557
* 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 176.183
Degrees of Freedom 6
P-Value 0.0000
CFI/TLI
CFI 1.000
TLI 1.009
Number of Free Parameters 7
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
WRMR (Weighted Root Mean Square Residual)
Value 1.804
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Group MZM
Y1 WITH
Y2 0.641 0.136 4.701 0.000
Thresholds
Y1$1 1.494 0.052 28.512 0.000
Y2$1 1.494 0.052 28.512 0.000
Group DZM
Y1 WITH
Y2 0.321 0.068 4.708 0.000
Thresholds
Y1$1 1.494 0.052 28.512 0.000
Y2$1 1.494 0.052 28.512 0.000
Group MZF
Y1 WITH
Y2 0.651 0.053 12.176 0.000
Thresholds
Y1$1 0.926 0.029 31.533 0.000
Y2$1 0.926 0.029 31.533 0.000
Group DZF
Y1 WITH
Y2 0.325 0.027 12.176 0.000
Thresholds
Y1$1 0.926 0.029 31.533 0.000
Y2$1 0.926 0.029 31.533 0.000
Group DOSMF
Y1 WITH
Y2 0.307 0.131 2.344 0.019
Thresholds
Y1$1 1.494 0.052 28.512 0.000
Y2$1 0.926 0.029 31.533 0.000
Group DOSFM
Y1 WITH
Y2 0.307 0.131 2.344 0.019
Thresholds
Y1$1 0.926 0.029 31.533 0.000
Y2$1 1.494 0.052 28.512 0.000
New/Additional Parameters
A 0.640 0.136 4.693 0.000
D 0.001 0.001 0.780 0.435
E 0.359 0.136 2.630 0.009
X 0.800 0.085 9.387 0.000
W 0.031 0.020 1.561 0.119
Z 0.599 0.114 5.260 0.000
K 0.651 0.053 12.176 0.000
L 0.000 0.000 12.180 0.000
M 0.349 0.053 6.529 0.000
S 0.807 0.033 24.352 0.000
T 0.005 0.000 24.361 0.000
U 0.591 0.045 13.058 0.000
F 0.475 0.210 2.264 0.024
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.924E-06
(ratio of smallest to largest eigenvalue)
Beginning Time: 22:57:41
Ending Time: 22:57:41
Elapsed Time: 00:00:00
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