Hello dear R mailing list members. I have recently became curious of the possibility applying model selection algorithms (even as simple as AIC) to regressions of large datasets. I searched as best as I could, but couldn't find any reference or wrapper for using step or stepAIC to packages such as biglm. Any ideas or directions of how to implement such a concept ? Best, Tal -- ---------------------------------------------- My contact information: Tal Galili Phone number: 972-50-3373767 FaceBook: Tal Galili My Blogs: www.talgalili.com www.biostatistics.co.il
Charles C. Berry
2009-Feb-21 18:09 UTC
[R] variable/model selction (step/stepAIC) for biglm ?
On Sat, 21 Feb 2009, Tal Galili wrote:> Hello dear R mailing list members. > > I have recently became curious of the possibility applying model > selection algorithms (even as simple as AIC) to regressions of large > datasets.Large in the sense of many observations, one assumes. But how large in terms of the number of variables?? If not too many variables, then you can form the regression sums of squares for all 2^p combinations of regressors from a biglm() fit of all variables as biglm provides coef() and vcov() methods. If it is large, then you most likely will need to do subsampling to reduce the number to 'not too many' via lm() and friends then and apply the above strategy. I searched as best as I could, but couldn't find any> reference or wrapper for using step or stepAIC to packages such as > biglm.Surely any direct implementation of step() would be hopelessly long in execution time. HTH, Chuck> > Any ideas or directions of how to implement such a concept ? > > > Best, > Tal > > > > > > > > > > -- > ---------------------------------------------- > > > My contact information: > Tal Galili > Phone number: 972-50-3373767 > FaceBook: Tal Galili > My Blogs: > www.talgalili.com > www.biostatistics.co.il > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >Charles C. Berry (858) 534-2098 Dept of Family/Preventive Medicine E mailto:cberry at tajo.ucsd.edu UC San Diego http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
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