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Equating means and variances across MZs, DZs and Siblings
Hi everyone!

Bivariate analysis: why best-fitting model's ACE estimates don't correspond well with the univariate estimates?
Hi all,
I have a question about bivariate cholesky ACE model. For model fitting, I compared the reduced models (selectively dropping a21, c21 and e21) to the full bivariate ACE model and the best-fitting model was selected based on lowest AIC and nonsignificant likelihood ratio chi-square test.
However, I noticed that the best-fitting model’s ACE estimates do not seem to match with what I obtained from fitting each of the two variables in univariate ACE models. For example, in univariate model fitting I got:
First trait: C = 60% (CI: 0.46-0.71) and E = 40% (CI: 0.29-0.54).

Question about univariate ACE model selection
Hi all,
I have a quick question to ask about model selection.
Let’s say I am interested to find out the ACE for years of music training, and for the full model I got the following estimates: A = 51% (CI: 0.18-0.89), C = 33.6% (CI: 0-0.65), E = 15.4% (CI: 0.10-0.24).
The fit statistics of the submodels are as follows:
base comparison ep minus2LL df AIC diffLL diffdf p
1 HomoAce
2 HomoAce AE 5 1024.610 194 636.6104 1.710576 1 1.909107e-01
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High MZ correlations exceed the Chronbach's alpha of the Questionnaire
Hi Everyone,
I have a more general question about MZ and DZ correlations and their relationship with the Chronbach's alpha of the questionnaire that is used.
I am running different twin models to estimates A,C and E contribution to variation in behaviours using a parent measured questionnaire in a sample of infants and toddlers.
When running the analyses I am consistently finding large MZ correlations (0.80-0.95) for the different traits, which exceed the chronbach's alpas of the scale.

Multivariate Covariance Estimate CI's
I'm currently runnning a three variable Cholesky ACE model and am able to grab CI's for my variance estimates and correlations but my model crashes and gives me an NLOPT fatal error -1 when I attempt to retrieve confidence intervals for covariance estimates. I'm wondering if anyone can tell me why and how to retrieve the problematic CI's. Here's my script:
# -----------------------------------------------------------------------------------------------------------
# Script: 2ACE_Chol.R
# Fits a MV Cholesky ACE Decomposition
# Data File: Covariate Regressed Cross-trait Data.csv
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ADE and submodels
Hi all,
I noticed in the ADE code (http://www.vipbg.vcu.edu/HGEN619_2014/twinAdeCon.R) that the only submodels considered are AE and E models. I would like to ask: why isn't the DE model a possible model to consider?
Thanks and best regards,
Yi Ting
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Univariate Heterogeneity model with covariates (including DZO)
Hi all,
I tried to modify the heterogeneity model for MZM, DZM, MZF, DZF and DZO groups to include covariates. I got the following error message when I tried to run my code:
Running QualACE
Error: Unknown reference 'expMeanGf' detected in the entity 'expMeanGfm' in model 'DZo'
May I know what changes should I make to my code to run it successfully? My code is as follows:
require(OpenMx)
require(psych)
# ------------------------------------------------------------------------------
# PREPARE DATA
# Select Variables for Analysis

Missing CIs - How to get them?
Hi all,
When I ran the univariate or bivariate scripts for some of my variables, I often get NA for some of the CIs requested, followed by this line: "Investigate missing CIs with the detail output in MxComputeConfidenceInterval "
I would like to know what causes the missing CIs and what can I do to get the missing CIs? I'm not sure if I understand what arguments to put in the MxComputeConfidenceInterval.
Thank you very much for the help!
Best Regards,
Yi Ting
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Using FIML to impute missing covariate data
I am running a simple ACE model with covariates (e.g., sex, age). Now I am trying to impute missing covariate data using full information maximum likelihood (FILM) (as described here: http://openmx.psyc.virginia.edu/docs/OpenMx/2.0.0-3838/FIML_RowFit.html).
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GMM with missing data
Hi all,
I have been working on applying growth mixture model to psychology data with missing data. I know I can use existing software to deal with the missing data. But I want to understand the math behind of it. There is one paper for the Growth mixture model detailing the math titled "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm". I want to know the methodology of the growth mixture model with missing data. Can anyone show me the math or which paper should I refer to?
Thanks a lot.
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