# Error in Obtaining Cis

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Multi_ACE_Twin Nov 2016 By Gender Nov 10.R | 16.6 KB |

Table 1.pdf | 43.16 KB |

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 of`mxRun()`

; make sure you're passing argument`intervals=TRUE`

no 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 argument`intervals`

is`TRUE`

or`FALSE`

. You can restore the usual behavior by running the MxModel after setting the compute plan's`.persist`

slot to`FALSE`

.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 have`intervals=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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