Behavioral Genetics Models
How to add object to a model without re-run
I have fitted a very computationally demanding model (around one week) and now I need to add an mxAlgebra object into this model:
threG2 <- mxAlgebra( expression= threG[,1:7], name="threG2" )
I know I can do it in this way:
modelIPAE2 <- mxModel( fitIPAE,threG2, name="mulIPAE2c" )
fitIPAE2 <- mxTryHardOrdinal( modelIPAE2, intervals=F, extraTries=11 )
But I wonder if there is any way to modify the model without re-running it
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Van der Sluis in OpenMX?
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Fix mean latent variable
I am working on a CP model (using a reference script from Boulder). I would like to know if it is possible to fix (or freely estimate) the mean of the latent variables (here the latent factor) using a model specification like this one (see below). Would it be possible by fixing one of the manifest means? I know it is possible (and must be done) to fix the variance to one so that the model is identified but I am also curious if the mean of latent variables can be fixed (or freely estimated). If so how could I do it?
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Multivariate moderation
I'm an undergrad working on my dissertation, and I've managed to build my first models thanks to these forums, the Boulder Workshops, as well as [Hermine's wonderful stash of scripts](https://hermine-maes.squarespace.com/). I have several questions, however, just to make sure I'm doing things the right way, and if what I want to do is possible.
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when to standardize?
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impact of ignoring higher level clustering
I've read before that when cases are nested, ignoring the clustering generally affects standard errors but not the regression coefficients. Is this the case for ACE model parameters as well, e.g., when twin pairs are nested in schools or geographic regions?
Thanks!
George
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Question about covariates/definition variables
I have a question about covariates/definition variables. I have noticed that in most of the scripts (Boulder workshop) covariates are included as definition variables (also in GxE models). I just wondered if covariates could be included as manifest(measured) variables and a regression path with the studied phenotype. I know both methods should be equivalent but this would allow to include missing values. Is there any reason/advantage for including covariates always as definition variables?
Trivariate Cholesky decomposition model
I have a script from SGDP summer school, I can analyze using 2 continuous variables with this script. However, when I try using 3 variables (2 continuous 1 dummy coded), I get stuck while setting up the matrix. Before starting the analysis, I standardized my variables so that the SDs were 1 and their mean was 0.
Part of my code is as follows:
nv <- 3 # number of variables for a twin
ntv <- 2*nv # number of variables for a pair
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