Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:57 PM
INPUT INSTRUCTIONS
! SCRIPT NAME : rawVCQ2 (dp)
! GOAL : To evaluate significance of linkage - using weighted likelihood ap
! DATA : continuous
! INPUT : raw data
! UNI/BI/MULTI : uni
! DATA-GROUPS : MZ DZ
! MEANS MODEL : grand mean, age effect, sex effect
! VARIANCE COVARIANCE MODEL(S) :
! 1. AEQ
! 2. AE
data: file is example3.dat;
variable:
names are
famnr zygos
fata1 fata2 mota1 mota2 !fatherallele1 fatherallele2 motherallele1 mothereallele
pheno1 age1 sex1 tw1a1 tw1a2 !fenotypetwin1 agetwin1 sextwin1 twin1allele1 twin1
pheno2 age2 sex2 tw2a1 tw2a2 !fenotypetwin2 agetwin2 sextwin2 twin2allele1 twin2
z0_0 z1_0 z2_0 z0_1 z1_1 z2_1 z0_2 z1_2 z2_2 z0_3 z1_3 z2_3 z0_4 z1_4 z2_4
z0_5 z1_5 z2_5 z0_6 z1_6 z2_6 z0_7 z1_7 z2_7 z0_8 z1_8 z2_8 z0_9 z1_9 z2_9
z0_10 z1_10 z2_10 z0_11 z1_11 z2_11 z0_12 z1_12 z2_12 z0_13 z1_13 z2_13
z0_14 z1_14 z2_14 z0_15 z1_15 z2_15 z0_16 z1_16 z2_16 z0_17 z1_17 z2_17
z0_18 z1_18 z2_18 z0_19 z1_19 z2_19 z0_20 z1_20 z2_20 z0_21 z1_21 z2_21
z0_22 z1_22 z2_22 z0_23 z1_23 z2_23 z0_24 z1_24 z2_24 z0_25 z1_25 z2_25
z0_26 z1_26 z2_26 z0_27 z1_27 z2_27 z0_28 z1_28 z2_28 z0_29 z1_29 z2_29
z0_30 z1_30 z2_30 z0_31 z1_31 z2_31 z0_32 z1_32 z2_32 z0_33 z1_33 z2_33
z0_34 z1_34 z2_34 z0_35 z1_35 z2_35;
usevar are pheno1 pheno2 age1 age2 sex1 sex2 z0_20 z1_20 z2_20 z00;
training= z00 z0_20 z1_20 z2_20 (prior);
missing =all(-99.00);
classes=c(4);
define: if (zygos<3) then z00=1 else z00=0;
if (zygos<3) then z2_20=0;
analysis: type=mixture;
model:
%overall%
pheno1 on age1 (b1);
pheno2 on age2 (b1);
pheno1 on sex1 (b2);
pheno2 on sex2 (b2);
pheno1 pheno2 (v);
[pheno1 pheno2] (m);
%C#1%
pheno1 with pheno2 (c1);
%C#2%
pheno1 with pheno2 (c2);
%C#3%
pheno1 with pheno2 (c3);
%C#4%
pheno1 with pheno2 (c4);
model constraint:
new(a q e x d z);
a=x*x;
q=d*d;
e=z*z;
v=a+e+q;
c1=a+q; ! MZ group
c2=0.5*a; ! IBD 0
c3=0.5*a+0.5*q; ! IBD 1
c4=0.5*a+q; ! IBD 2
! Uncomment for AE model
! q=0;
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
fata1 fata2 mota1 mota2 !fatherallele1 fatherallele2 motherallele1 mothereallele2, allel
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
pheno1 age1 sex1 tw1a1 tw1a2 !fenotypetwin1 agetwin1 sextwin1 twin1allele1 twin1allele2,
*** WARNING
Input line exceeded 90 characters. Some input may be truncated.
pheno2 age2 sex2 tw2a1 tw2a2 !fenotypetwin2 agetwin2 sextwin2 twin2allele1 twin2allele2,
*** WARNING
Data set contains cases with missing on all variables except
x-variables. These cases were not included in the analysis.
Number of cases with missing on all variables except x-variables: 5
4 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 147
Number of dependent variables 2
Number of independent variables 4
Number of continuous latent variables 0
Number of categorical latent variables 1
Observed dependent variables
Continuous
PHENO1 PHENO2
Observed independent variables
AGE1 AGE2 SEX1 SEX2
Categorical latent variables
C
Variables with special functions
Training variables (priors)
Z00 Z0_20 Z1_20 Z2_20
Estimator MLR
Information matrix OBSERVED
Optimization Specifications for the Quasi-Newton Algorithm for
Continuous Outcomes
Maximum number of iterations 100
Convergence criterion 0.100D-05
Optimization Specifications for the EM Algorithm
Maximum number of iterations 500
Convergence criteria
Loglikelihood change 0.100D-06
Relative loglikelihood change 0.100D-06
Derivative 0.100D-05
Optimization Specifications for the M step of the EM Algorithm for
Categorical Latent variables
Number of M step iterations 1
M step convergence criterion 0.100D-05
Basis for M step termination ITERATION
Optimization Specifications for the M step of the EM Algorithm for
Censored, Binary or Ordered Categorical (Ordinal), Unordered
Categorical (Nominal) and Count Outcomes
Number of M step iterations 1
M step convergence criterion 0.100D-05
Basis for M step termination ITERATION
Maximum value for logit thresholds 15
Minimum value for logit thresholds -15
Minimum expected cell size for chi-square 0.100D-01
Maximum number of iterations for H1 2000
Convergence criterion for H1 0.100D-03
Optimization algorithm EMA
Random Starts Specifications
Number of initial stage random starts 10
Number of final stage optimizations 2
Number of initial stage iterations 10
Initial stage convergence criterion 0.100D+01
Random starts scale 0.500D+01
Random seed for generating random starts 0
Input data file(s)
example3.dat
Input data format FREE
SUMMARY OF DATA
Number of missing data patterns 1
Number of y missing data patterns 1
Number of u missing data patterns 0
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT FOR Y
Covariance Coverage
PHENO1 PHENO2 AGE1 AGE2 SEX1
________ ________ ________ ________ ________
PHENO1 1.000
PHENO2 1.000 1.000
AGE1 1.000 1.000 1.000
AGE2 1.000 1.000 1.000 1.000
SEX1 1.000 1.000 1.000 1.000 1.000
SEX2 1.000 1.000 1.000 1.000 1.000
Covariance Coverage
SEX2
________
SEX2 1.000
RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES
Final stage loglikelihood values at local maxima, seeds, and initial stage start numbers:
-605.842 195873 6
-605.842 93468 3
WARNING: WHEN ESTIMATING A MODEL WITH MORE THAN TWO CLASSES, IT MAY BE
NECESSARY TO INCREASE THE NUMBER OF RANDOM STARTS USING THE STARTS OPTION
TO AVOID LOCAL MAXIMA.
WARNING: THE SAMPLE CORRELATION OF SEX2 AND SEX1
IN CLASS 1 IS 1.000.
WARNING: THE SAMPLE CORRELATION OF AGE2 AND AGE1
IN CLASS 2 IS 1.000.
WARNING: THE SAMPLE CORRELATION OF AGE2 AND AGE1
IN CLASS 3 IS 1.000.
WARNING: THE SAMPLE CORRELATION OF AGE2 AND AGE1
IN CLASS 4 IS 1.000.
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Loglikelihood
H0 Value -605.842
H0 Scaling Correction Factor 1.155
for MLR
Information Criteria
Number of Free Parameters 6
Akaike (AIC) 1223.685
Bayesian (BIC) 1241.627
Sample-Size Adjusted BIC 1222.640
(n* = (n + 2) / 24)
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 65.00000 0.44218
2 18.15003 0.12347
3 41.85275 0.28471
4 21.99723 0.14964
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 65.00000 0.44218
2 18.29050 0.12443
3 41.87286 0.28485
4 21.83664 0.14855
CLASSIFICATION QUALITY
Entropy 0.799
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 65 0.44218
2 13 0.08844
3 45 0.30612
4 24 0.16327
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2 3 4
1 1.000 0.000 0.000 0.000
2 0.000 0.827 0.167 0.006
3 0.000 0.161 0.779 0.060
4 0.000 0.013 0.193 0.795
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
PHENO1 ON
AGE1 -0.234 0.071 -3.307 0.001
SEX1 1.473 0.303 4.860 0.000
PHENO2 ON
AGE2 -0.234 0.071 -3.307 0.001
SEX2 1.473 0.303 4.860 0.000
PHENO2 WITH
PHENO1 4.454 0.595 7.479 0.000
Intercepts
PHENO1 9.953 1.219 8.162 0.000
PHENO2 9.953 1.219 8.162 0.000
Residual Variances
PHENO1 5.166 0.613 8.434 0.000
PHENO2 5.166 0.613 8.434 0.000
Latent Class 2
PHENO1 ON
AGE1 -0.234 0.071 -3.307 0.001
SEX1 1.473 0.303 4.860 0.000
PHENO2 ON
AGE2 -0.234 0.071 -3.307 0.001
SEX2 1.473 0.303 4.860 0.000
PHENO2 WITH
PHENO1 2.013 1.310 1.537 0.124
Intercepts
PHENO1 9.953 1.219 8.162 0.000
PHENO2 9.953 1.219 8.162 0.000
Residual Variances
PHENO1 5.166 0.613 8.434 0.000
PHENO2 5.166 0.613 8.434 0.000
Latent Class 3
PHENO1 ON
AGE1 -0.234 0.071 -3.307 0.001
SEX1 1.473 0.303 4.860 0.000
PHENO2 ON
AGE2 -0.234 0.071 -3.307 0.001
SEX2 1.473 0.303 4.860 0.000
PHENO2 WITH
PHENO1 2.227 0.298 7.479 0.000
Intercepts
PHENO1 9.953 1.219 8.162 0.000
PHENO2 9.953 1.219 8.162 0.000
Residual Variances
PHENO1 5.166 0.613 8.434 0.000
PHENO2 5.166 0.613 8.434 0.000
Latent Class 4
PHENO1 ON
AGE1 -0.234 0.071 -3.307 0.001
SEX1 1.473 0.303 4.860 0.000
PHENO2 ON
AGE2 -0.234 0.071 -3.307 0.001
SEX2 1.473 0.303 4.860 0.000
PHENO2 WITH
PHENO1 2.441 1.242 1.965 0.049
Intercepts
PHENO1 9.953 1.219 8.162 0.000
PHENO2 9.953 1.219 8.162 0.000
Residual Variances
PHENO1 5.166 0.613 8.434 0.000
PHENO2 5.166 0.613 8.434 0.000
Categorical Latent Variables
New/Additional Parameters
A 4.026 2.620 1.537 0.124
Q 0.428 2.482 0.172 0.863
E 0.712 0.123 5.773 0.000
X 2.006 0.653 3.073 0.002
D -0.654 1.897 -0.345 0.730
Z -0.844 0.073 -11.547 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.394E-04
(ratio of smallest to largest eigenvalue)
Beginning Time: 22:57:53
Ending Time: 22:57:53
Elapsed Time: 00:00:00
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