problems with simple ACE model
vadim
Joined: 08/03/2024
I am running a simple ACE model for twin analysis with age and gender as confounds (115 DZ pairs and 60 MZ pairs). I use the model from International Statistical Genetics Workshop:
[https://ibg.colorado.edu/cdrom2022/day2/00_ACEvc_contin.R](https://ibg.colorado.edu/cdrom2022/day2/00_ACEvc_contin.R "https://ibg.colorado.edu/cdrom2022/day2/00_ACEvc_contin.R")
When I run the model, I get the following results:
free parameters:
name matrix row col Estimate Std.Error A lbound ubound
1 interC intercept 1 1 1.5044971 0.64957699
2 betaS bS 1 1 -0.5795265 0.12804871
3 betaA bA 1 1 -0.0236737 0.01893570
4 VA11 VA 1 1 0.9179206 0.34811542
5 VC11 VC 1 1 -0.4279909 0.32400691
6 VE11 VE 1 1 0.4132866 0.05129183
confidence intervals:
lbound estimate ubound note
ACEvc.VarC[1,4] 0.3595914 1.0162799 1.7306621
ACEvc.VarC[1,5] -1.1498335 -0.4738520 0.1515639
ACEvc.VarC[1,6] 0.3540964 0.4575721 0.5850078
CI details:
parameter side value fit diagnostic statusCode method interC betaS betaA VA11 VC11 VE11
1 ACEvc.VarC[1,4] lower 0.3595914 921.3400 success OK neale-miller-1997 1.397043 -0.5711129 -0.02000247 0.3277627 0.1253068 0.4584169
2 ACEvc.VarC[1,4] upper 1.7306621 921.3660 success OK neale-miller-1997 1.643095 -0.5960471 -0.02823600 1.6357127 -1.0803400 0.3897642
3 ACEvc.VarC[1,5] lower -1.1498335 921.3463 success OK neale-miller-1997 1.655002 -0.5904675 -0.02897582 1.6119600 -1.0772610 0.4021853
4 ACEvc.VarC[1,5] upper 0.1515639 921.3503 success OK neale-miller-1997 1.382750 -0.5690776 -0.01958674 0.3453931 0.1397735 0.4370419
5 ACEvc.VarC[1,6] lower 0.3540964 921.3554 success OK neale-miller-1997 1.474451 -0.5694362 -0.02322113 1.1770877 -0.5488576 0.3444075
6 ACEvc.VarC[1,6] upper 0.5850078 921.3530 success OK neale-miller-1997 1.522639 -0.5798345 -0.02417389 0.6512583 -0.2910815 0.5077355
Critically, I also get the following error:
Running CE with 5 parameters
Error: The job for model 'CE' exited abnormally with the error message: fit is not finite (The continuous part of the model implied covariance (loc2) is not positive definite in data 'DZ.data' row 59. Detail:
covariance = matrix(c( # 2x2
-0.014704266851264, -0.427990888364092
, -0.427990888364092, -0.014704266851264), byrow=TRUE, nrow=2, ncol=2)
)
Following suggestion in this post: [https://openmx.ssri.psu.edu/node/4593](https://openmx.ssri.psu.edu/node/4593 "https://openmx.ssri.psu.edu/node/4593") I set the lbound=1e-4 to covC.
As a result, the error disappears, but the results become very different to those I got before. In addition, I am not sure how to treat the confidence interval of VA, given that it is constrained.
free parameters:
name matrix row col Estimate Std.Error A lbound ubound
1 interC intercept 1 1 1.41722921 0.67171698
2 betaS bS 1 1 -0.57166848 0.13325128
3 betaA bA 1 1 -0.02073634 0.01960500
4 VA11 VA 1 1 0.48480850 0.25577544
5 VC11 VC 1 1 0.00010000 0.24940786 ! 1e-04!
6 VE11 VE 1 1 0.42938467 0.05552459
confidence intervals:
lbound estimate ubound note
ACEvc.VarC[1,4] 0.227153 0.5302549731 NA !!!
ACEvc.VarC[1,5] NA 0.0001093741 0.2708948 !!!
ACEvc.VarC[1,6] NA 0.4696356528 NA !!!
CI details:
parameter side value fit diagnostic statusCode method interC betaS betaA VA11 VC11 VE11
1 ACEvc.VarC[1,4] lower 2.271530e-01 923.3903 success OK neale-miller-1997 1.383110 -0.5685890 -0.01955053 0.2076383 0.2297972 0.4766547
2 ACEvc.VarC[1,4] upper 6.390220e-01 923.3817 active box constraint OK neale-miller-1997 1.406398 -0.5723281 -0.02036513 0.6334840 0.0001000 0.3577496
3 ACEvc.VarC[1,5] lower 9.411526e-05 923.4075 active box constraint OK neale-miller-1997 1.417229 -0.5716684 -0.02073514 0.5589758 0.0001000 0.5034512
4 ACEvc.VarC[1,5] upper 2.708948e-01 923.3912 success OK neale-miller-1997 1.359038 -0.5664194 -0.01889902 0.2312665 0.2535373 0.4511215
5 ACEvc.VarC[1,6] lower 3.606429e-01 923.4018 active box constraint OK neale-miller-1997 1.412693 -0.5717267 -0.02061276 0.6350086 0.0001000 0.3582465
6 ACEvc.VarC[1,6] upper 6.013603e-01 923.3829 active box constraint OK neale-miller-1997 1.444580 -0.5744477 -0.02149105 0.3475080 0.0001000 0.5243773
How should I interpret these results? Which of the models is correct? According to Falconer’s formula, h-square is about 1, which is close to the first result. But in the first results, not only that I get an error, but there is also negative VC. I would very much appreciate your help .
Best wishes,
Vadim
Bad starting values?
When the model is rerun to fit CE, I suspect that it is doing so from the solution of the ACE model, and the C component happens to be negative, so the CE model has a likelihood that cannot be computed. To avoid this, modify the ACE model before it was run, instead of the result of running the ACE model.
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In reply to Bad starting values? by AdminNeale
Thank you very much for your
Thank you very much for your answer, but can you please elaborate a bit more. I will very appreciate it. The fitACE is indeed passed as the parameter to estimation of
So, what should I modify?
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In reply to Thank you very much for your by vadim
try this
fitCE <- omxSetParameters(modelACE, labels = "VA11", values = 0, free = F, name = "CE")
fitCE <- mxRun( fitCE, intervals=F )
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In reply to try this by AdminRobK
Thank you for your help.
When I run the original code (without lbound constraint) after I replaced my two lines with your code, the error disappeared. But the variance result is the same as before (with the negative variance VC):
name matrix row col Estimate Std.Error A
1 interC intercept 1 1 1.53784349 0.65261107
2 betaS bS 1 1 -0.58236697 0.12811632
3 betaA bA 1 1 -0.02468047 0.01903097
4 betaM bM 1 1 0.02468528 0.04961712
5 VA11 VA 1 1 0.92411484 0.35230753
6 VC11 VC 1 1 -0.43334699 0.32837219
7 VE11 VE 1 1 0.41226047 0.05112496
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