Behavioral Genetics Models

Hello,

How do I specify the intercept and slope means for a growth curve, using an ACE parameterization.

I can follow the RAM notation for growth curves in the documentation, but I'm not sure how to specify the intercept and slope means outside of the RAM framework.

Maybe another way to ask the question is as such: In the ACE factor model examples (common pathway model) the factor means are fixed at zero. How can I change the code to estimate the factor mean?

Thanks,

Scott

Hi All,

I'm having similar difficulties adding covariates as in the ACE thread - but I thought this deserved a separate thread as my problem is related to equating means in a saturated model.

I have the model broken down by zygosity - the following code is for MZF. Real values for MZF data are shown after the code, followed by expected values and then estimated (free) parameters after that.

Hello
I am trying to fit multigroup models with 15 groups. When I want to assess the fit of my models, I encounter problems :
(1) on one hand, the fit measures like Chi or RMSEA are never good for any model different from the saturated model

Hi ,

I am new to Twin data analysis and would like to get help on how to adjust for covariates ( Age and Gender) in TWIN ACE model to estimate the genetic and environmental variance from the total molecular phenotypic variance . What modifications are needed in the script UnivariateTwinAnalysis_MatrixRaw.R for age and gender adjusted estimation of A, C and E .

Any insight would be of great help.

Sanchita

I'm running a multivariate ACE model with raw data and matrices input, based on a version by Hermine Maes.
Changing the start value for the paths from .6 to .5 makes the c path estimates flip from positive to negative.
Am I right in assuming I can just ignore the sign, because the true estimate should be the (positive) square root of the squared path?

I'm trying to understand the analysis used in a paper that has recently created a splash in Science:
Taylor, J., Roehrig, A. D., Hensler, B. S., Connor, C. M., & Schatschneider, C. (2010). Teacher quality moderates the genetic effects on early reading. Science, 328, 512-514.
They have used a moderator variable analysis described by:Purcell, S. (2002). Variance components models for gene-environment interaction in twin analysis. Twin Research, 6, 377-382.
This is in old Mx.