Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:57 PM
INPUT INSTRUCTIONS
! SCRIPT NAME : ctVCut4c (cb)
! GOAL : To evaluate best model for variance components A C E
! DATA : ordinal
! INPUT : contingency tables
! UNI/BI/MULTI : uni
! DATA-GROUPS : MZM DZM MZF DZF
! MEANS MODEL : -
! VARIANCE COVARIANCE MODEL(S) :
! 1. different prevalences for males and females, ACE for males, KLM for females
! 2. same prevalences for males and females, ACE for males, KLM for females
! 3. same prevalences for males and females, ACE= 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, CE= LM for males and females
! 6. same prevalences for males and females, E= M for males and females
! 7. different prevalences for males and females, ACE= KLM for males and females
! 8. different prevalences for males and females, AE= KM for males and females
! 9. different prevalences for males and females, CE= LM for males and females
! 10. different prevalences for males and females, E= M for males and females
data: file is ct4.dat;
variable: names are g y1 y2 weight;
categorical=y1 y2;
grouping=g(1=MZM 2=DZM 3=MZF 4=DZF); ! specify the groups
freqweight=weight;
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 c e x y z);
a=x*x;
c=y*y;
e=1-x*x-y*y;
z=sqrt(1-x*x-y*y);
mzmc=x*x+y*y;
dzmc=0.5*x*x+y*y;
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+t*t;
! Uncomment for same prevalences for males and females
! mt=ft;
! Uncomment for Model ACE=KLM
! a=k;
! c=l;
! Uncomment for Model AE=KM
! c=0;
! Uncomment for Model CE=LM
! a=0;
! Uncomment for Model E=M
! a=0;
! c=0;
INPUT READING TERMINATED NORMALLY
SUMMARY OF ANALYSIS
Number of groups 4
Number of observations
Group MZM 243
Group DZM 137
Group MZF 620
Group DZF 317
Number of patterns
Group MZM 4
Group DZM 4
Group MZF 4
Group DZF 4
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 G
Weight variable WEIGHT
Estimator WLSMV
Maximum number of iterations 1000
Convergence criterion 0.100D-05
Maximum number of steepest descent iterations 20
Parameterization DELTA
Input data file(s)
ct4.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
Group MZM
Y1
Category 1 0.951 231.000
Category 2 0.049 12.000
Y2
Category 1 0.934 227.000
Category 2 0.066 16.000
Group DZM
Y1
Category 1 0.942 129.000
Category 2 0.058 8.000
Y2
Category 1 0.927 127.000
Category 2 0.073 10.000
Group MZF
Y1
Category 1 0.821 509.000
Category 2 0.179 111.000
Y2
Category 1 0.832 516.000
Category 2 0.168 104.000
Group DZF
Y1
Category 1 0.864 274.000
Category 2 0.136 43.000
Y2
Category 1 0.817 259.000
Category 2 0.183 58.000
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 4.397*
Degrees of Freedom 6
P-Value 0.6231
Chi-Square Contributions From Each Group
MZM 0.635
DZM 0.597
MZF 0.415
DZF 2.751
* 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 167.776
Degrees of Freedom 4
P-Value 0.0000
CFI/TLI
CFI 1.000
TLI 1.007
Number of Free Parameters 6
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
WRMR (Weighted Root Mean Square Residual)
Value 1.267
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Group MZM
Y1 WITH
Y2 0.633 0.134 4.710 0.000
Thresholds
Y1$1 1.548 0.080 19.412 0.000
Y2$1 1.548 0.080 19.412 0.000
Group DZM
Y1 WITH
Y2 0.316 0.067 4.710 0.000
Thresholds
Y1$1 1.548 0.080 19.412 0.000
Y2$1 1.548 0.080 19.412 0.000
Group MZF
Y1 WITH
Y2 0.646 0.055 11.682 0.000
Thresholds
Y1$1 0.958 0.040 24.226 0.000
Y2$1 0.958 0.040 24.226 0.000
Group DZF
Y1 WITH
Y2 0.333 0.112 2.970 0.003
Thresholds
Y1$1 0.958 0.040 24.226 0.000
Y2$1 0.958 0.040 24.226 0.000
New/Additional Parameters
A 0.633 0.134 4.710 0.000
C 0.000 0.000 0.000 1.000
E 0.367 0.134 2.734 0.006
X 0.795 0.084 9.420 0.000
Y 0.000 0.011 0.000 1.000
Z 0.606 0.111 5.469 0.000
K 0.627 0.250 2.508 0.012
L 0.019 0.231 0.084 0.933
M 0.354 0.055 6.403 0.000
S 0.792 0.158 5.016 0.000
T -0.139 0.830 -0.168 0.867
U 0.595 0.046 12.805 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.100E-05
(ratio of smallest to largest eigenvalue)
Beginning Time: 22:57:38
Ending Time: 22:57:38
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