Mplus VERSION 5.2
MUTHEN & MUTHEN
12/02/2008 2:01 PM
INPUT INSTRUCTIONS
! SCRIPT NAME : rawVC2b (dp)
! GOAL : To evaluate sex-differences in parameter estimates of variance com
! DATA : continuous
! INPUT : raw data
! UNI/BI/MULTI : uni
! DATA-GROUPS : MZM DZM MZF DZF
! MEANS MODEL : grand mean, age effect, sex effect
! VARIANCE COVARIANCE MODEL(S) : 1.ADE males + ADE females 2.ADE males = ADE femal
data: file is example.dat;
variable:
names are country famid zygos sex1 age1 height1 weight1 bmi1
sex2 age2 height2 weight2 bmi2;
usevar are bmi1 sex1 age1 bmi2 sex2 age2;
grouping=zygos(1=MZM 2=DZM 3=MZF 4=DZF); ! specify the groups
missing=all(-1); ! specify missing data symbol
analysis: conv=1e-5;
model :
bmi1 on sex1 (b1)
age1 (b2);
bmi2 on sex2 (b1)
age2 (b2);
[bmi1 bmi2] (m);
model MZM :
bmi1 bmi2 (mv);
bmi1 with bmi2 (mc1);
model DZM :
bmi1 bmi2 (mv);
bmi1 with bmi2 (mc2);
model MZF :
bmi1 bmi2 (fv);
bmi1 with bmi2 (fc1);
model DZF :
bmi1 bmi2 (fv);
bmi1 with bmi2 (fc2);
model constraint:
new(ma md me mx mw mz);
ma=mx*mx;
md=mw*mw;
me=mz*mz;
mv=ma+md+me;
mc1=ma+md;
mc2=0.5*ma+0.25*md;
new(fa fd fe fx fw fz);
fa=fx*fx;
fd=fw*fw;
fe=fz*fz;
fv=fa+fd+fe;
fc1=fa+fd;
fc2=0.5*fa+0.25*fd;
! Uncomment for Model ADE with A_male=A_female
! ma=fa;
! Uncomment for Model ADE with A_male=A_female and D_male=D_female
! ma=fa;
! md=fd;
! Uncomment for Model ADE with all components equal
! ma=fa;
! md=fd;
! me=fe;
*** WARNING
Data set contains unknown or missing values for GROUPING,
PATTERN, COHORT and/or CLUSTER variables.
Number of cases with unknown or missing values: 61
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
SUMMARY OF ANALYSIS
Number of groups 4
Number of observations
Group MZM 61
Group DZM 40
Group MZF 77
Group DZF 68
Number of dependent variables 2
Number of independent variables 4
Number of continuous latent variables 0
Observed dependent variables
Continuous
BMI1 BMI2
Observed independent variables
SEX1 AGE1 SEX2 AGE2
Variables with special functions
Grouping variable ZYGOS
Estimator ML
Information matrix OBSERVED
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
Input data file(s)
example.dat
Input data format FREE
SUMMARY OF DATA
Group MZM
Number of missing data patterns 3
Group DZM
Number of missing data patterns 4
Group MZF
Number of missing data patterns 3
Group DZF
Number of missing data patterns 4
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT FOR MZM
Covariance Coverage
BMI1 BMI2 SEX1 AGE1 SEX2
________ ________ ________ ________ ________
BMI1 0.902
BMI2 0.852 0.951
SEX1 0.902 0.951 1.000
AGE1 0.902 0.951 1.000 1.000
SEX2 0.902 0.951 1.000 1.000 1.000
AGE2 0.902 0.951 1.000 1.000 1.000
Covariance Coverage
AGE2
________
AGE2 1.000
PROPORTION OF DATA PRESENT FOR DZM
Covariance Coverage
BMI1 BMI2 SEX1 AGE1 SEX2
________ ________ ________ ________ ________
BMI1 0.875
BMI2 0.725 0.825
SEX1 0.875 0.825 1.000
AGE1 0.875 0.825 1.000 1.000
SEX2 0.875 0.825 1.000 1.000 1.000
AGE2 0.875 0.825 1.000 1.000 1.000
Covariance Coverage
AGE2
________
AGE2 1.000
PROPORTION OF DATA PRESENT FOR MZF
Covariance Coverage
BMI1 BMI2 SEX1 AGE1 SEX2
________ ________ ________ ________ ________
BMI1 0.961
BMI2 0.922 0.961
SEX1 0.961 0.961 1.000
AGE1 0.961 0.961 1.000 1.000
SEX2 0.961 0.961 1.000 1.000 1.000
AGE2 0.961 0.961 1.000 1.000 1.000
Covariance Coverage
AGE2
________
AGE2 1.000
PROPORTION OF DATA PRESENT FOR DZF
Covariance Coverage
BMI1 BMI2 SEX1 AGE1 SEX2
________ ________ ________ ________ ________
BMI1 0.956
BMI2 0.926 0.956
SEX1 0.956 0.956 1.000
AGE1 0.956 0.956 1.000 1.000
SEX2 0.956 0.956 1.000 1.000 1.000
AGE2 0.956 0.956 1.000 1.000 1.000
Covariance Coverage
AGE2
________
AGE2 1.000
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 32.654
Degrees of Freedom 43
P-Value 0.8742
Chi-Square Contributions From Each Group
MZM 13.985
DZM 5.350
MZF 3.696
DZF 9.624
Chi-Square Test of Model Fit for the Baseline Model
Value 177.746
Degrees of Freedom 36
P-Value 0.0000
CFI/TLI
CFI 1.000
TLI 1.061
Loglikelihood
H0 Value -3139.974
H1 Value -3123.647
Information Criteria
Number of Free Parameters 9
Akaike (AIC) 6297.949
Bayesian (BIC) 6329.497
Sample-Size Adjusted BIC 6300.967
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
90 Percent C.I. 0.000 0.045
SRMR (Standardized Root Mean Square Residual)
Value 0.226
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Group MZM
BMI1 ON
SEX1 0.975 0.408 2.390 0.017
AGE1 0.095 0.016 6.045 0.000
BMI2 ON
SEX2 0.975 0.408 2.390 0.017
AGE2 0.095 0.016 6.045 0.000
BMI2 WITH
BMI1 9.539 1.420 6.716 0.000
Intercepts
BMI1 20.152 0.661 30.504 0.000
BMI2 20.152 0.661 30.504 0.000
Residual Variances
BMI1 11.427 1.389 8.229 0.000
BMI2 11.427 1.389 8.229 0.000
Group DZM
BMI1 ON
SEX1 0.975 0.408 2.390 0.017
AGE1 0.095 0.016 6.045 0.000
BMI2 ON
SEX2 0.975 0.408 2.390 0.017
AGE2 0.095 0.016 6.045 0.000
BMI2 WITH
BMI1 2.422 1.814 1.335 0.182
Intercepts
BMI1 20.152 0.661 30.504 0.000
BMI2 20.152 0.661 30.504 0.000
Residual Variances
BMI1 11.427 1.389 8.229 0.000
BMI2 11.427 1.389 8.229 0.000
Group MZF
BMI1 ON
SEX1 0.975 0.408 2.390 0.017
AGE1 0.095 0.016 6.045 0.000
BMI2 ON
SEX2 0.975 0.408 2.390 0.017
AGE2 0.095 0.016 6.045 0.000
BMI2 WITH
BMI1 9.526 1.436 6.636 0.000
Intercepts
BMI1 20.152 0.661 30.504 0.000
BMI2 20.152 0.661 30.504 0.000
Residual Variances
BMI1 13.941 1.316 10.591 0.000
BMI2 13.941 1.316 10.591 0.000
Group DZF
BMI1 ON
SEX1 0.975 0.408 2.390 0.017
AGE1 0.095 0.016 6.045 0.000
BMI2 ON
SEX2 0.975 0.408 2.390 0.017
AGE2 0.095 0.016 6.045 0.000
BMI2 WITH
BMI1 3.772 1.597 2.363 0.018
Intercepts
BMI1 20.152 0.661 30.504 0.000
BMI2 20.152 0.661 30.504 0.000
Residual Variances
BMI1 13.941 1.316 10.591 0.000
BMI2 13.941 1.316 10.591 0.000
New/Additional Parameters
MA 0.149 7.014 0.021 0.983
MD 9.390 7.055 1.331 0.183
ME 1.888 0.373 5.060 0.000
MX -0.386 9.077 -0.043 0.966
MW 3.064 1.151 2.662 0.008
MZ 1.374 0.136 10.120 0.000
FA 5.563 6.058 0.918 0.358
FD 3.963 6.061 0.654 0.513
FE 4.415 0.740 5.965 0.000
FX 2.359 1.284 1.837 0.066
FW -1.991 1.522 -1.308 0.191
FZ 2.101 0.176 11.931 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.291E-06
(ratio of smallest to largest eigenvalue)
Beginning Time: 14:01:29
Ending Time: 14:01:30
Elapsed Time: 00:00:01
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) 1998-2008 Muthen & Muthen
Back to the list of genetics examples