Behavioral Genetics Models
JuanJMV
Joined: 07/20/2016
Intra Class Corelations
# Select Variables for Analysis
vars <- "AG_Ln" # list of variables names
nv <- 1 # number of variables
ntv <- nv*2 # number of total variables
selVars <- paste(vars,c(rep(1,nv),rep(2,nv)),sep="")- Read more about Intra Class Corelations
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deManzano
Joined: 04/09/2015
variable with inherently skewed distribution
I would like to perform classical twin modeling on a variable with an inherently very skewed distribution (positive). I asked around a bit and the suggestion I got was to cut my original continuous data into bins, treat the variable as ordinal, and use a threshold liability model. However, this approach still assumes an underlying normal distribution. Would there be any way of generalizing the modelling in OpenMx (as e.g. in generalized linear regression)? Do you have any other suggestions or recommendations?
Cheers,
Örjan
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JBosdriesz
Joined: 03/07/2017
Bivariate twin model (output)
I've done some univariate twin model analyses in OpenMx recently, but now I'm trying my hand at bivariate models.
I'm using a script by Hermine (http://ibg.colorado.edu/cdrom2016/maes/MultivariateAnalysis/mulACEc2.R).
So far I succeeded in running the model and obtaining indicators of fit and path loadings. But I've got a couple of questions:
1) However, the output I get does not produce viable 95%CI's (the model summary shows lbounds converging on zero and no ubounds). How can I obtain proper intervals around my path loadings?
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dacan
Joined: 06/04/2017
Selecting the best model for LGM Twin model
in my work, I am trying to capture longitudinal development of depression with a biometrical quadratic LGM model following model described in Reynolds, Finkel, McArdle, Gatz, Berg, & Pedersen (2005; see Figure 2). Here are the specifications:
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MichelNivard
Joined: 05/16/2017
Cholesky model WLS
I am running Hermine's Cholesky script (Boulder "cdrom" 2016) only change I make is that I Use mxDataWLS to precompute the polychoric correlations and WLS weights:
DZdata <- mxDataWLS(DZ)
This runs without errors.
I then proceed to adjust the mxExpectation to reflect the nature of the data:
expMZ <- mxExpectationNormal( covariance="expCovMZ", means="meanG",thresholds="Tr", dimnames=selVars )
expDZ <- mxExpectationNormal( covariance="expCovDZ", means="meanG",thresholds="Tr", dimnames=selVars )
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JuanJMV
Joined: 07/20/2016
Trivariate Model
I am trying to fit a trivariate model since I think is the best option instead two separately bivariate models but I am not sure.
I have two errors:
1)
Warning messages:
* thinG <- mxMatrix( type="Full", nrow=nth, ncol=ntvo, free=TRUE, values=svTh, lbound=lbTh, labels=labTh, name="thinG" )
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xixizhao
Joined: 04/11/2017
How to get the P value of heritability in ACE model
I am very new with ACE twin modeling as well as R, therefore I need some help.
jjmorosoli
Joined: 04/15/2015
Moderation of OSDZ intrapair correlation (ra, rc) in a sex-lim model
I want to study if the similarity (through A and C) between female twin and male twin, within pair, changes due to a moderator (as age) using the GxE approach of Purcell (2002) in a sex-limitation model.
I thought to put it in a very simple way such as:
rados <- mxMatrix(type="Full", nrow=1, ncol=1, free=F, values=0.5, label="rg", lbound=-1, ubound=1, name="ra")
rcdos <- mxMatrix(type="Full", nrow=1, ncol=1, free=T, values=.01, label="re", lbound=-1, ubound=1, name="rc")
JuanJMV
Joined: 07/20/2016
Doubt with confidence intervals
I am very new with open Mx and I need a little bit of help.
I am doing a bivariate model with the generic scripts of Boulder. I need to know how could I get the confidence intervals for the rg,rc,re.
Thank you everyone in advance.
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JBosdriesz
Joined: 03/07/2017
Multiple imputation
We are just beginning to work with OpenMx to study behavior in relation to genetic and environmental components in twins. In our dataset we have a fairly large number of missing values (checks showed they are missing at random). Normally we would opt for multiple imputation in SPSS and run our analyses on the separate imputed datasets, while also presenting a pooled analyses. But because of the behavioral genetics component, we want to use OpenMx for our analyses.
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