Repeatability across platforms
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LDEUnivariateExample091116.R | 2.89 KB |
LDEUnivariateExample091116.txt | 2.88 KB |
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I have run into an interesting problem. A model (which is admittedly of low stability) that I used in class today ran on the Mac (with warnings, but reasonably close estimates) but not on the PC (non-invertible). Both are Intel processors, both running R 2.9.2 and OpenMx 0.2.2. The model is of simulated data with boundary constraints on residuals because variance explained is very high.
I attach the script and data as a test case for the limits of similarities between architectures.
This code requires
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from my (humble)
numerical optimization with constraints is one of those situations where slight differences in compiler settings can generate discrepant results within a platform, let alone across platforms. (most numerical guru's that i've talked to always recommend unconstrained optimization for likelihood problems). suggest trying a model without constraints and then comparing the results across platforms.
if you cannot get a model without constraints to converge, then there is probably something the matter with the model, given the data.
greg
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