Third Party Software
gdash
Joined: 11/29/2021
Bivariate umxACE model data length error
Hello,
I am new to using umx, so I apologize for asking what is perhaps a very simple question. I am trying to run a bivariate ACE model using binary variables (they are structured as ordered factors). When I run the model, I receive the warning message copied below alongside model output:
In matrix(labels, nrow, ncol, byrow = byrow) :
data length [3] is not a sub-multiple or multiple of the number of rows [2]
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mirusem
Joined: 11/19/2018
Difference between running umxConfint then mxSE vs mxSE directly
I was wondering what the difference might be when you create a umx ACEv model and to get the standard estimates (for purposes outside of the likelihood based CIs using umxConfint) you use mxSE on the parameter of interest.
So essentially, doing:
ACE=umxACEv()
umxConfint(ACE, run=TRUE)
^Extract CIs from here (using summary).
Then doing:
mxSE(top.A_std,ACE,run=TRUE)
vs just:
ACE=umxACEv()
mxSE(top.A_std,ACE,run=TRUE)
mirusem
Joined: 11/19/2018
Error for testing assumptions of ACE model
One last question hopefully for now (I am using one of the scripts by Hermine Maes that uses umx to test the general assumptions of the twin design: [oneSATu.pdf](https://static1.squarespace.com/static/58b2481a9f7456906a3b9600/t/5a9cfe548165f5587482dec5/1520238164994/oneSATu.pdf)
For a given phenotype I've gotten this error:
> All fit attempts resulted in errors - check starting values or model specification
> Error in if (rfu == "r'Wr") { : argument is of length zero
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mirusem
Joined: 11/19/2018
tryHard option retry limit reached (valid attempts)?
I was kind of surprised to see (before I used the tryHard option) not having any issues per se, but when I did incorporate it, it looks like sometimes the output of a phenotype would be, for example: Retry limit reached; Best fit=-353.49615 (started at -352.57463) (11 attempt(s): 2 valid, 9 errors)
Does that mean it was a success and it just picks one of the valid solutions? It is certainly different from the output:
Solution found! Final fit=-332.97766 (started at -295.8754) (2 attempt(s): 2 valid, 0 errors).
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mirusem
Joined: 11/19/2018
mxTryHard attempts in umx
Is there a way to modify/control the amount of tries in the tryHard option for umx for any given function being called?
I appreciate it!
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mirusem
Joined: 11/19/2018
umxACEv vs umxSexLim
I was wondering, how similar or different is umxACEv as compared to umxSexLim (the closest of umxSexLim being the homogeneity choice)? Are they both using the direct variance approach (I know it says for umxSexLim it says it is based on a correlated factors model)? If they are in fact different in that sense, is there a tradeoff between them in that sense (correlated factors vs direct variance)? Definitely would be helpful to know!
Thanks as always!
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srinivas_b
Joined: 09/08/2021
Conducting a metaSEM on two-arm randomized controlled trial data
Hello Mike,
We are working on a meta-analysis of RCTs, looking at the efficacy of brain stimulation (active vs sham TMS) in reducing OCD severity as the primary outcome, along with secondary outcomes as reduction in anxiety and depression severity. We have 17 studies included in this analysis.
Dabiriyan.
Joined: 06/17/2020
Using three-level meta-analysis in OSMASEM
Dear all,
I am currently working on MASEM to assess the direct effect of LSC on ASB as well as the indirect effect of LSC on ASB via SB.
I have a question about how to use the three-level meta-analysis in OSMASEM for studies that did not provide the correlation coefficient of an aggregated measure between LSC, SB, and ASB. In this respect, some studies provide multiple effect sizes for a single relationship.
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a_researcher
Joined: 06/11/2021
umxACEv - twin variance component
Hi,
I was wondering if the umxACEv function allows the extension of the twin design to include the twin variance component, T? Thanks a lot!
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shaun.hkw
Joined: 07/04/2021
Heywood case when modelling second-order factor
Hi all,
First time poster here.
# Query 1 ---
I am running the metaSEM package and I am fitting a measurement model with the following specification:
- 10 indicators
- 4 first order factors
- 1 second/higher order factor
All input matrices are correlation matrices.