Hi,
I am encountering memory problems when running Shapley Value Regressions and have tried a number of solutions. I am running SPSS v20 on Windows XP 32bit.
I have successfully been able to run a Shapley Regression using the STATS_RELIMP R plugin with <12 predictors and 82 cases, but when I increase the number of predictors I receive the message "Error: cannot allocate the vector size of n Mb."
I have tried a number of the suggested online solutions, including increasing the memory limit in R and adding the /3GB switch in the Windows boot.ini file, but I haven't been able to run my full model (65 predictors, 82 cases).
One option I may have is to install SPSS and R on a server to get more RAM, but I'd love to know if there are other ways around this problem. Does anyone have any suggestions?
If you need more information, I'd be happy to provide it.
Best,
Will
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 20130722T13:46:14Z by JohnDoews
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Pinned topic STATS_RELIMP, Shapley Regression, and Memory Restrictions?
20121012T21:41:32Z

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Updated on 20121016T17:19:06Z at 20121016T17:19:06Z by SystemAdmin

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Re: STATS_RELIMP, Shapley Regression, and Memory Restrictions?
20121016T17:19:06Z in response to WilliamWelchYou are running up against the limitations of 32bit addressing and the R Relimp implementation. Moving to a system with more RAM will not help.
The 64bit version of R can be used with Statistics V21 64bit and might solve this problem, but I haven't tried this myself.
Otherwise, I suggest that you look at subsets of variables and then perhaps force in ones that you are sure matter and look at the Shapley (just awarded the Nobel prize in Economics) value for others.
HTH,
Jon Peck
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Re: STATS_RELIMP, Shapley Regression, and Memory Restrictions?
20130722T13:46:14Z in response to SystemAdminI tried running on a 64bit system. It would work with up to 15 independent variables but not more for a database of about 800 cases.
I went and tried to run the analysis in R directly. It did not help. The limitation is not from SPSS but from R. So unless someone addresses the issue in R, I would not expect any change soon ...
