Error in Obtaining Cis
gary.marks@acu…
Joined: 07/14/2016
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No SEs are suspect and the return code is zero.
However when I try the command:
FitCholMod <- mxRun(CholMod, intervals =TRUE)
It runs as before but I receive this error message.
Error in runHelper(model, frontendStart, intervals, silent, suppressWarnings, :
NLOPT fatal error -1
So the confidence intervals are not produced.
I have tried the other optimizes. With CSOLNP a number of SEs are suspect. With NPSOL MX returns a code of 1 and more SEs are suspect.
I can obtain CIs with a worse fitting (non-preferred) model with the SLSQP optimizer. The non-preferred model has a significantly worse fit than the full-Cholesky, so I don't want to use that model.
With other models, just MZ vs DZ and a cohort model (young MZ, old MZ, young DZ, old DZ) I can obtain CIs.
I have several suspicions:
* I don't have enough male dizygotic twins !!!!
* I should rescale the dependent variables (years of education is left as is 8-18, income have been divided by $10,000, but occupational status still ranges from 0-100). I could divide occupational status by 10.
*It did seem to work before I downloaded the new MX version but I am at all not certain that I was analyzing the same model.
Any suggestions?
CIs
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In reply to CIs by jpritikin
CIs for Multivariate ACE
I tried to run the code from within R but got an error.
> source('https://github.com/OpenMx/OpenMx')
Error in source("https://github.com/OpenMx/OpenMx") :
https://github.com/OpenMx/OpenMx:5:1: unexpected '<'
4:
5: <
Should I try an earlier version of MX? I am not sure a how to do that.
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In reply to CIs for Multivariate ACE by gary.marks@acu…
installing from GitHub
You'll need, for instance, a working compiler for fortran and C++. You can learn more here:
http://openmx.psyc.virginia.edu/wiki/howto-build-openmx-source-repository
It's fun, but a bit geeky. Alternatively, if you can share the model (just
save(CholMod, file = "CholMod.RData"), a developer with the latest version could then check if you email the model file to them.Log in or register to post comments
Rescaling the variables might
mxTryHard()in place ofmxRun(); make sure you're passing argumentintervals=TRUEno matter which of the two you're using.Log in or register to post comments
Custom compute plan
fitCholAce = mxRun(modelChol, intervals=F, onlyFrontend=T)
fitCholAce$compute$steps$CI$engine = "NPSOL"
fitCholAce$compute$.persist = TRUE
fitCholAce = mxRun(fitCholAce)
Note that
fitCholAce, and any MxModel object derived from it, will thereafter always calculate confidence intervals, regardless of whether argumentintervalsisTRUEorFALSE. You can restore the usual behavior by running the MxModel after setting the compute plan's.persistslot toFALSE.Edit: Looks like R's main assignment operator is being parsed as an HTML tag(?) in forum posts right now...
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Thanks I feel I am making
It's a good idea but I get an error after the MxRun command:
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘assignId’ for signature ‘"list"’
I find the ML estimates for the SLSQP and CSOLNP optimizers are identical but they return status codes of zero and 6 respectively. When both produce CIs, for a straight MZ/DZ model they are also the same.
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In reply to Thanks I feel I am making by gary.marks@acu…
intervals=TRUE
Ah, I bet the first call to
mxRun()needs to haveintervals=T, as infitCholAce <- mxRun(modelChol, intervals=F, onlyFrontend=T)
If that doesn't help, I'd be curious to see the
traceback()for that error message.OK, that's encouraging.
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