Can anyone help me with the function or syntex needed to calculate the 95% CI for A,C,E estimates in the existing
Thanks in advance.
Hi, I followed UnivariateTwinAnalysis_PathRaw.R instead of UnivariateTwinAnalysis_MatrixRaw.R.
I'm pretty new to OpenMx and R. Could anyone help me generate the 95%CI for standardized A, C, E? Thanks!
Do you want confidence intervals for the standardized path coefficients connecting the latent A, C, and E to the manifest variables? Or do you want confidence intervals for the standardized biometric variance components?
If you want them for the path coefficients, see my post in this other thread: http://openmx.psyc.virginia.edu/thread/2835 .
If you want them for the variance components, try adding something like the following to either your MZ-twin or DZ-twin submodel (it should not matter which):
mxAlgebra( (ace%^%2) %x% solve(t(ace)%*%ace), name="StdVarComp",dimnames=list(c("a2","c2","e2"),NULL) ),
What the code is doing is creating an mxMatrix to hold the path coefficients, because we're going to calculate something from them with an mxAlgebra, and algebras require matrices. What the algebra does is square each path coefficient, turning them into raw (unstandardized) variance components, and then divide them by the sum of their squares which is equal to the total phenotypic variance. The result is that raw variance components get divided by total variance, yielding standardized components--estimates of the phenotype's narrow-sense heritability, shared-environmentality, and unshared-environmentality.
What I want are the confidence intervals for the standardized biometric variance components (a2, c2, e2). My problem got solved after adding your script to the original file. The instruction is very helpful and clear. Thank you so much for your help.
You're welcome. Glad to be of help.
I'm would like to get bootstrapped CI for my standardized path coefficients connecting the latent A, C, and E to the manifest variables, and to my standardized biometric variance components in a ACE model, defined using matrixes (I modified Neale's script (http://openmx.psyc.virginia.edu/thread/554) to add a further covariant, I couldn't figure it out using path definitions). Conventional 95% intervals won't work because of empirical under-identification. Is there an easier way then to run the model then taking bootstrapped samples and defining the mean and min max of the bootstrap distribution of the standardized path coefficients and variance components?
See this part of the wiki: http://openmx.psyc.virginia.edu/wiki/mxCI-help
Thanks for your help. I was able to get the CI for A,C,E. I also would like to get the CI for standardized components ( e.g ACE.A/V,ACE.C/ACE.V,ACE.E/ACE.V). How do I compute it.
To add to mikes reply: You will need to create these standardizing algebras in your model, if they are not already present.
i.e., you can't just ask for mxCI("ACE.A/ACE.Vtot"), you have to create
On that note, wouldn't it be great if you could just include things in the mxCI statement and they would be automagically created!!
All the information is there.
You spelled my wish. Initially, I did try that way but it didn't work. Thanks a lot to both of you. It worked. Great help.
Same way, just use the names of the algebras instead of the names of the matrices in the list of elements for which you would like the CIs.